fix(missing method): adding missing add_microphones_recordings method in LeRobotDataset

benchmarks/audio/run_microphone_benchmark.py

@@ -0,0 +1,219 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+from soundfile import read
+
+from lerobot.microphones.configs import MicrophoneConfig
+from lerobot.microphones.portaudio import PortAudioMicrophone, PortAudioMicrophoneConfig
+from lerobot.microphones.utils import (
+    async_microphones_start_recording,
+    async_microphones_stop_recording,
+    make_microphones_from_configs,
+)
+from lerobot.utils.robot_utils import (
+    precise_sleep,
+)
+
+
+def main(
+    microphones_configs: dict[str, MicrophoneConfig],
+    audio_chunks_number: int,
+    audio_chunks_duration: float,
+    repetitions: int,
+    multiprocessing: bool = False,
+):
+    recording_dir = Path("outputs/audio_benchmark")
+    recording_dir.mkdir(parents=True, exist_ok=True)
+
+    # Create microphones
+    microphones = make_microphones_from_configs(microphones_configs)
+
+    # Connect microphones
+    for microphone in microphones.values():
+        microphone.connect()
+
+    all_audio_chunks = []
+    for i in range(repetitions):
+        print(f"Repetition {i + 1}/{repetitions}...")
+
+        # Create audio chunks
+        audio_chunks = {}
+        for microphone_key in microphones:
+            audio_chunks.update({microphone_key: []})
+
+        # Start recording
+        async_microphones_start_recording(
+            microphones,
+            output_files=[
+                recording_dir / f"{microphone_key}_recording_{i}.wav" for microphone_key in microphones
+            ],
+            multiprocessing=multiprocessing,
+        )
+
+        # Record audio chunks
+        for j in range(audio_chunks_number):
+            precise_sleep(audio_chunks_duration)
+
+            for microphone_key, microphone in microphones.items():
+                audio_chunk = microphone.read()
+                print(f"{microphone_key} - repetition {i} - chunk {j} - samples {audio_chunk.shape[0]}")
+                audio_chunks[microphone_key].append(audio_chunk)
+
+        # Stop recording
+        async_microphones_stop_recording(microphones)
+
+        for microphone_key in microphones:
+            audio_chunks[microphone_key] = np.concatenate(audio_chunks[microphone_key], axis=0)
+
+        all_audio_chunks.append(audio_chunks)
+
+    # Disconnect microphones
+    for microphone in microphones.values():
+        microphone.disconnect()
+
+    # Compute statistics
+    cmap = plt.get_cmap("tab10")
+    _, ax = plt.subplots(nrows=repetitions, ncols=len(microphones))
+    chunk_length = np.zeros((repetitions, len(microphones)))
+    record_length = np.zeros((repetitions, len(microphones)))
+    for i in range(repetitions):
+        for j, (microphone_key, microphone) in enumerate(microphones.items()):
+            # Get recorded audio chunks
+            recorded_audio_chunks = all_audio_chunks[i][microphone_key]
+
+            # Load recorded file
+            recorded_data, _ = read(recording_dir / f"{microphone_key}_recording_{i}.wav")
+            if recorded_data.ndim == 1:
+                recorded_data = np.expand_dims(recorded_data, axis=1)
+
+            record_length[i, j] = recorded_data.shape[0]
+            chunk_length[i, j] = recorded_audio_chunks.shape[0]
+
+            for k, (chunk_data, record_data) in enumerate(
+                zip(recorded_audio_chunks.T, recorded_data.T, strict=False)
+            ):
+                # Plot audio chunks and recorded data
+                ax[i, j].plot(
+                    np.arange(0, len(chunk_data)) / microphone.sample_rate,
+                    chunk_data,
+                    label=f"audio chunks - channel {k}",
+                    color=cmap(2 * k),
+                )
+                ax[i, j].plot(
+                    np.arange(0, len(record_data)) / microphone.sample_rate,
+                    record_data,
+                    label=f"recorded data - channel {k}",
+                    linestyle="dashed",
+                    color=cmap(2 * k + 1),
+                )
+
+                # Plot absolute difference (errors should be located at the end of the recordings)
+                if recorded_data.shape[0] - recorded_audio_chunks.shape[0] > 0:
+                    chunk_data = np.append(
+                        chunk_data, np.zeros(int(recorded_data.shape[0] - recorded_audio_chunks.shape[0]))
+                    )
+                else:
+                    record_data = np.append(
+                        record_data, np.zeros(int(-recorded_data.shape[0] + recorded_audio_chunks.shape[0]))
+                    )
+                ax[i, j].plot(
+                    np.arange(0, len(record_data)) / microphone.sample_rate,
+                    np.abs(chunk_data - record_data),
+                    label=f"differences - channel {k}",
+                    color="red",
+                    linestyle="dotted",
+                )
+                ax[i, j].set_title(f"{microphone_key} - repetition {i}")
+            ax[i, j].legend()
+
+    plt.show()
+
+    # Print statistics
+    differences = record_length - chunk_length
+    for i, (microphone_key, microphone) in enumerate(microphones.items()):
+        print(
+            f"Average recorded duration for {microphone_key} : {np.mean(record_length[:, i]) / microphone.sample_rate:.3f} seconds"
+        )
+        print(
+            f"Average chunk duration for {microphone_key} : {np.mean(chunk_length[:, i]) / microphone.sample_rate:.3f} seconds"
+        )
+        print(f"Average difference for {microphone_key} : {np.mean(differences[:, i]):.3f} samples")
+        print(
+            f"Average difference for {microphone_key} : {np.mean(differences[:, i]) / microphone.sample_rate:.3f} seconds"
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--microphones_indices",
+        type=int,
+        nargs="+",
+        default=[microphone["index"] for microphone in PortAudioMicrophone.find_microphones()],
+    )
+    parser.add_argument(
+        "--microphones_sample_rate",
+        type=float,
+        nargs="+",
+        default=[None] * len(PortAudioMicrophone.find_microphones()),
+    )
+    parser.add_argument(
+        "--microphones_channels",
+        type=int,
+        nargs="+",
+        default=[None] * len(PortAudioMicrophone.find_microphones()),
+    )
+    parser.add_argument("--audio_chunks_number", type=int, default=2)
+    parser.add_argument(
+        "--audio_chunks_duration",
+        type=float,
+        default=1.0,
+    )
+    parser.add_argument(
+        "--repetitions",
+        type=int,
+        default=2,
+    )
+    parser.add_argument(
+        "--multiprocessing",
+        action="store_true",
+    )
+
+    args = vars(parser.parse_args())
+
+    args["microphones_configs"] = {}
+    for index, sample_rate, channels in zip(
+        args["microphones_indices"],
+        args["microphones_sample_rate"],
+        args["microphones_channels"],
+        strict=False,
+    ):
+        microphone_config = PortAudioMicrophoneConfig(
+            microphone_index=index,
+            sample_rate=sample_rate,
+            channels=channels,
+        )
+        args["microphones_configs"].update({f"microphone_{index}": microphone_config})
+    args.pop("microphones_indices")
+    args.pop("microphones_sample_rate")
+    args.pop("microphones_channels")
+
+    main(**args)

benchmarks/audio/run_tactile_benchmark.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+
+from lerobot.microphones.configs import MicrophoneConfig
+from lerobot.microphones.touchlab import TouchLabSensorConfig
+from lerobot.microphones.utils import (
+    async_microphones_start_recording,
+    async_microphones_stop_recording,
+    make_microphones_from_configs,
+)
+from lerobot.utils.robot_utils import (
+    precise_sleep,
+)
+
+
+def main(
+    sensors_configs: dict[str, MicrophoneConfig],
+    multiprocessing: bool = False,
+):
+    recording_dir = Path("outputs/tactile_benchmark")
+    recording_dir.mkdir(parents=True, exist_ok=True)
+
+    # Create microphones
+    sensors = make_microphones_from_configs(sensors_configs)
+
+    # Connect microphones
+    for sensor in sensors.values():
+        sensor.connect()
+
+    # Create audio chunks
+    data_chunks = {}
+    for sensor_key in sensors:
+        data_chunks.update({sensor_key: []})
+
+    # Start recording
+    async_microphones_start_recording(
+        sensors,
+        output_files=[recording_dir / f"{sensor_key}_recording.wav" for sensor_key in sensors],
+        multiprocessing=multiprocessing,
+    )
+
+    # Record audio chunks
+    precise_sleep(10.0)
+
+    for sensor_key, sensor in sensors.items():
+        data_chunk = sensor.read()
+        print(f"{sensor_key} - samples {data_chunk.shape[0]}")
+        data_chunks[sensor_key].append(data_chunk)
+
+    # Stop recording
+    async_microphones_stop_recording(sensors)
+
+    for sensor_key in sensors:
+        data_chunks[sensor_key] = np.concatenate(data_chunks[sensor_key], axis=0)
+
+    # Disconnect microphones
+    for sensor in sensors.values():
+        sensor.disconnect()
+
+    for sensor_key in sensors:
+        data, sample_rate = sf.read(recording_dir / f"{sensor_key}_recording.wav")
+        print(f"{sensor_key} - samples {data.shape[0]}")
+        print(f"{sensor_key} - sample rate {sample_rate}")
+        print(f"{sensor_key} - data {data}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--sensors_ports",
+        type=str,
+        nargs="+",
+    )
+    parser.add_argument(
+        "--sensors_baud_rate",
+        type=int,
+        nargs="+",
+    )
+    parser.add_argument(
+        "--sensors_sample_rate",
+        type=int,
+        nargs="+",
+    )
+    parser.add_argument(
+        "--sensors_channels",
+        type=int,
+        nargs="+",
+    )
+    parser.add_argument(
+        "--multiprocessing",
+        action="store_true",
+    )
+
+    args = vars(parser.parse_args())
+
+    args["sensors_configs"] = {}
+    for port, baud_rate, sample_rate, channels in zip(
+        args["sensors_ports"],
+        args["sensors_baud_rate"],
+        args["sensors_sample_rate"],
+        args["sensors_channels"],
+        strict=False,
+    ):
+        if isinstance(channels, int):
+            channels = [channels]
+        sensor_config = TouchLabSensorConfig(
+            sensor_port=port,
+            baud_rate=baud_rate,
+            sample_rate=sample_rate,
+            channels=channels,
+        )
+        args["sensors_configs"].update({f"sensor_{port}": sensor_config})
+    args.pop("sensors_ports")
+    args.pop("sensors_baud_rate")
+    args.pop("sensors_sample_rate")
+    args.pop("sensors_channels")
+
+    main(**args)

examples/lekiwi/teleoperate.py

@@ -43,12 +43,13 @@ def main():
     keyboard.connect()
 
     # Init rerun viewer
-    init_rerun(session_name="lekiwi_teleop")
+    init_rerun(session_name="lekiwi_teleop", robot=robot, reset_time=True)
 
     if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
         raise ValueError("Robot or teleop is not connected!")
 
     print("Starting teleop loop...")
+    start = time.perf_counter()
     while True:
         t0 = time.perf_counter()
 
@@ -69,7 +70,7 @@ def main():
         _ = robot.send_action(action)
 
         # Visualize
-        log_rerun_data(observation=observation, action=action)
+        log_rerun_data(observation=observation, action=action, log_time=time.perf_counter() - start)
 
         precise_sleep(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
 

examples/phone_to_so100/teleoperate.py

@@ -90,12 +90,13 @@ def main():
     teleop_device.connect()
 
     # Init rerun viewer
-    init_rerun(session_name="phone_so100_teleop")
+    init_rerun(session_name="phone_so100_teleop", robot=robot, reset_time=True)
 
     if not robot.is_connected or not teleop_device.is_connected:
         raise ValueError("Robot or teleop is not connected!")
 
     print("Starting teleop loop. Move your phone to teleoperate the robot...")
+    start = time.perf_counter()
     while True:
         t0 = time.perf_counter()
 
@@ -112,7 +113,7 @@ def main():
         _ = robot.send_action(joint_action)
 
         # Visualize
-        log_rerun_data(observation=phone_obs, action=joint_action)
+        log_rerun_data(observation=phone_obs, action=joint_action, log_time=time.perf_counter() - start)
 
         precise_sleep(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
 

examples/so100_to_so100_EE/teleoperate.py

@@ -95,9 +95,10 @@ def main():
     leader.connect()
 
     # Init rerun viewer
-    init_rerun(session_name="so100_so100_EE_teleop")
+    init_rerun(session_name="so100_so100_EE_teleop", robot=follower, reset_time=True)
 
     print("Starting teleop loop...")
+    start = time.perf_counter()
     while True:
         t0 = time.perf_counter()
 
@@ -117,7 +118,9 @@ def main():
         _ = follower.send_action(follower_joints_act)
 
         # Visualize
-        log_rerun_data(observation=leader_ee_act, action=follower_joints_act)
+        log_rerun_data(
+            observation=leader_ee_act, action=follower_joints_act, log_time=time.perf_counter() - start
+        )
 
         precise_sleep(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
 

pyproject.toml

@@ -164,6 +164,7 @@ hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpci
 # Features
 async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
 peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
+audio = ["sounddevice>=0.5.1,<0.6.0", "soundfile>=0.13.1,<0.14.0", "librosa>=0.11.0,<0.12.0", "torchaudio>=2.6.0,<2.10.0"]
 
 # Development
 dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1"]
@@ -198,6 +199,7 @@ all = [
     "lerobot[xvla]",
     "lerobot[hilserl]",
     "lerobot[async]",
+    "lerobot[audio]",
     "lerobot[dev]",
     "lerobot[test]",
     "lerobot[video_benchmark]",

src/lerobot/__init__.py

@@ -29,6 +29,7 @@ Example:
         print(lerobot.available_policies_per_env)
         print(lerobot.available_robots)
         print(lerobot.available_cameras)
+        print(lerobot.available_microphones)
         print(lerobot.available_motors)
     ```
 
@@ -174,6 +175,12 @@ available_cameras = [
     "intelrealsense",
 ]
 
+# lists all available microphones from `lerobot/microphones`
+available_microphones = [
+    "portaudio",
+    "touchlab",
+]
+
 # lists all available motors from `lerobot/motors`
 available_motors = [
     "dynamixel",

src/lerobot/async_inference/robot_client.py

@@ -49,6 +49,8 @@ import torch
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.microphones.portaudio.configuration_portaudio import PortAudioMicrophoneConfig  # noqa: F401
+from lerobot.microphones.touchlab.configuration_touchlab import TouchLabSensorConfig  # noqa: F401
 from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,

src/lerobot/configs/policies.py

@@ -151,6 +151,12 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
             return {}
         return {key: ft for key, ft in self.input_features.items() if ft.type is FeatureType.VISUAL}
 
+    @property
+    def audio_features(self) -> dict[str, PolicyFeature]:
+        if not self.input_features:
+            return {}
+        return {key: ft for key, ft in self.input_features.items() if ft.type is FeatureType.AUDIO}
+
     @property
     def action_feature(self) -> PolicyFeature | None:
         if not self.output_features:

src/lerobot/configs/types.py

@@ -20,6 +20,7 @@ from enum import Enum
 class FeatureType(str, Enum):
     STATE = "STATE"
     VISUAL = "VISUAL"
+    AUDIO = "AUDIO"
     ENV = "ENV"
     ACTION = "ACTION"
     REWARD = "REWARD"

src/lerobot/datasets/aggregate.py

@@ -35,6 +35,8 @@ from lerobot.datasets.io_utils import (
     write_tasks,
 )
 from lerobot.datasets.utils import (
+    DEFAULT_AUDIO_FILE_SIZE_IN_MB,
+    DEFAULT_AUDIO_PATH,
     DEFAULT_CHUNK_SIZE,
     DEFAULT_DATA_FILE_SIZE_IN_MB,
     DEFAULT_DATA_PATH,
@@ -43,7 +45,7 @@ from lerobot.datasets.utils import (
     DEFAULT_VIDEO_PATH,
     update_chunk_file_indices,
 )
-from lerobot.datasets.video_utils import concatenate_video_files, get_video_duration_in_s
+from lerobot.datasets.video_utils import concatenate_media_files, get_media_duration_in_s
 
 
 def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]):
@@ -112,6 +114,7 @@ def update_meta_data(
     meta_idx,
     data_idx,
     videos_idx,
+    audios_idx,
 ):
     """Updates metadata DataFrame with new chunk, file, and timestamp indices.
 
@@ -127,7 +130,7 @@ def update_meta_data(
         meta_idx: Dictionary containing current metadata chunk and file indices.
         data_idx: Dictionary containing current data chunk and file indices.
         videos_idx: Dictionary containing current video indices and timestamps.
-
+        audios_idx: Dictionary containing current audio indices and timestamps.
     Returns:
         pd.DataFrame: Updated DataFrame with adjusted indices and timestamps.
     """
@@ -225,6 +228,36 @@ def update_meta_data(
         # Clean up temporary columns
         df = df.drop(columns=["_orig_chunk", "_orig_file"])
 
+    for key, audio_idx in audios_idx.items():
+        # Store original audio file indices before updating
+        orig_chunk_col = f"audio/{key}/chunk_index"
+        orig_file_col = f"audio/{key}/file_index"
+        df["_orig_chunk"] = df[orig_chunk_col].copy()
+        df["_orig_file"] = df[orig_file_col].copy()
+
+        # Update chunk and file indices to point to destination
+        df[orig_chunk_col] = audio_idx["chunk"]
+        df[orig_file_col] = audio_idx["file"]
+
+        # Apply per-source-file timestamp offsets
+        src_to_offset = audio_idx.get("src_to_offset", {})
+        if src_to_offset:
+            # Apply offset based on original source file
+            for idx in df.index:
+                src_key = (df.at[idx, "_orig_chunk"], df.at[idx, "_orig_file"])
+                offset = src_to_offset.get(src_key, 0)
+                df.at[idx, f"audio/{key}/from_timestamp"] += offset
+                df.at[idx, f"audio/{key}/to_timestamp"] += offset
+        else:
+            # Fallback to simple offset (for backward compatibility)
+            df[f"audio/{key}/from_timestamp"] = (
+                df[f"audio/{key}/from_timestamp"] + audio_idx["latest_duration"]
+            )
+            df[f"audio/{key}/to_timestamp"] = df[f"audio/{key}/to_timestamp"] + audio_idx["latest_duration"]
+
+        # Clean up temporary columns
+        df = df.drop(columns=["_orig_chunk", "_orig_file"])
+
     df["dataset_from_index"] = df["dataset_from_index"] + dst_meta.info["total_frames"]
     df["dataset_to_index"] = df["dataset_to_index"] + dst_meta.info["total_frames"]
     df["episode_index"] = df["episode_index"] + dst_meta.info["total_episodes"]
@@ -239,6 +272,7 @@ def aggregate_datasets(
     aggr_root: Path | None = None,
     data_files_size_in_mb: float | None = None,
     video_files_size_in_mb: float | None = None,
+    audio_files_size_in_mb: float | None = None,
     chunk_size: int | None = None,
 ):
     """Aggregates multiple LeRobot datasets into a single unified dataset.
@@ -256,6 +290,7 @@ def aggregate_datasets(
         aggr_root: Optional root path for the aggregated dataset.
         data_files_size_in_mb: Maximum size for data files in MB (defaults to DEFAULT_DATA_FILE_SIZE_IN_MB)
         video_files_size_in_mb: Maximum size for video files in MB (defaults to DEFAULT_VIDEO_FILE_SIZE_IN_MB)
+        audio_files_size_in_mb: Maximum size for audio files in MB (defaults to DEFAULT_AUDIO_FILE_SIZE_IN_MB)
         chunk_size: Maximum number of files per chunk (defaults to DEFAULT_CHUNK_SIZE)
     """
     logging.info("Start aggregate_datasets")
@@ -264,6 +299,8 @@ def aggregate_datasets(
         data_files_size_in_mb = DEFAULT_DATA_FILE_SIZE_IN_MB
     if video_files_size_in_mb is None:
         video_files_size_in_mb = DEFAULT_VIDEO_FILE_SIZE_IN_MB
+    if audio_files_size_in_mb is None:
+        audio_files_size_in_mb = DEFAULT_AUDIO_FILE_SIZE_IN_MB
     if chunk_size is None:
         chunk_size = DEFAULT_CHUNK_SIZE
 
@@ -276,6 +313,7 @@ def aggregate_datasets(
     )
     fps, robot_type, features = validate_all_metadata(all_metadata)
     video_keys = [key for key in features if features[key]["dtype"] == "video"]
+    audio_keys = [key for key in features if features[key]["dtype"] == "audio"]
 
     dst_meta = LeRobotDatasetMetadata.create(
         repo_id=aggr_repo_id,
@@ -287,6 +325,7 @@ def aggregate_datasets(
         chunks_size=chunk_size,
         data_files_size_in_mb=data_files_size_in_mb,
         video_files_size_in_mb=video_files_size_in_mb,
+        audio_files_size_in_mb=audio_files_size_in_mb,
     )
 
     logging.info("Find all tasks")
@@ -300,14 +339,18 @@ def aggregate_datasets(
     videos_idx = {
         key: {"chunk": 0, "file": 0, "latest_duration": 0, "episode_duration": 0} for key in video_keys
     }
+    audios_idx = {
+        key: {"chunk": 0, "file": 0, "latest_duration": 0, "episode_duration": 0} for key in audio_keys
+    }
 
     dst_meta.episodes = {}
 
     for src_meta in tqdm.tqdm(all_metadata, desc="Copy data and videos"):
         videos_idx = aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chunk_size)
+        audios_idx = aggregate_audio(src_meta, dst_meta, audios_idx, audio_files_size_in_mb, chunk_size)
         data_idx = aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_size)
 
-        meta_idx = aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx)
+        meta_idx = aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx, audios_idx)
 
         # Clear the src_to_dst mapping after processing each source dataset
         # to avoid interference between different source datasets
@@ -375,7 +418,7 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
                 file_index=file_idx,
             )
 
-            src_duration = get_video_duration_in_s(src_path)
+            src_duration = get_media_duration_in_s(src_path, media_type="video")
             dst_key = (chunk_idx, file_idx)
 
             if not dst_path.exists():
@@ -414,7 +457,7 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
                 current_dst_duration = dst_file_durations.get(dst_key, 0)
                 videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_dst_duration
                 videos_idx[key]["src_to_dst"][(src_chunk_idx, src_file_idx)] = dst_key
-                concatenate_video_files(
+                concatenate_media_files(
                     [dst_path, src_path],
                     dst_path,
                 )
@@ -429,6 +472,101 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
     return videos_idx
 
 
+def aggregate_audio(src_meta, dst_meta, audios_idx, audio_files_size_in_mb, chunk_size):
+    """Aggregates audio files from a source dataset into the destination dataset.
+
+    Handles audio file concatenation and rotation based on file size limits.
+    Creates new audio files when size limits are exceeded.
+
+    Args:
+        src_meta: Source dataset metadata.
+        dst_meta: Destination dataset metadata.
+        audio_idx: Dictionary tracking audio chunk and file indices.
+        audio_files_size_in_mb: Maximum size for audio files in MB (defaults to DEFAULT_AUDIO_FILE_SIZE_IN_MB)
+        chunk_size: Maximum number of files per chunk (defaults to DEFAULT_CHUNK_SIZE)
+
+    Returns:
+        dict: Updated audio_idx with current chunk and file indices.
+    """
+    for key in audios_idx:
+        audios_idx[key]["episode_duration"] = 0
+        # Track offset for each source (chunk, file) pair
+        audios_idx[key]["src_to_offset"] = {}
+
+    for key, audio_idx in audios_idx.items():
+        unique_chunk_file_pairs = {
+            (chunk, file)
+            for chunk, file in zip(
+                src_meta.episodes[f"audio/{key}/chunk_index"],
+                src_meta.episodes[f"audio/{key}/file_index"],
+                strict=False,
+            )
+        }
+        unique_chunk_file_pairs = sorted(unique_chunk_file_pairs)
+
+        chunk_idx = audio_idx["chunk"]
+        file_idx = audio_idx["file"]
+        current_offset = audio_idx["latest_duration"]
+
+        for src_chunk_idx, src_file_idx in unique_chunk_file_pairs:
+            src_path = src_meta.root / DEFAULT_AUDIO_PATH.format(
+                audio_key=key,
+                chunk_index=src_chunk_idx,
+                file_index=src_file_idx,
+            )
+
+            dst_path = dst_meta.root / DEFAULT_AUDIO_PATH.format(
+                audio_key=key,
+                chunk_index=chunk_idx,
+                file_index=file_idx,
+            )
+
+            src_duration = get_media_duration_in_s(src_path, media_type="audio")
+
+            if not dst_path.exists():
+                # Store offset before incrementing
+                audios_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_offset
+                dst_path.parent.mkdir(parents=True, exist_ok=True)
+                shutil.copy(str(src_path), str(dst_path))
+                audios_idx[key]["episode_duration"] += src_duration
+                current_offset += src_duration
+                continue
+
+            # Check file sizes before appending
+            src_size = get_file_size_in_mb(src_path)
+            dst_size = get_file_size_in_mb(dst_path)
+
+            if dst_size + src_size >= audio_files_size_in_mb:
+                # Rotate to a new file, this source becomes start of new destination
+                # So its offset should be 0
+                audios_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = 0
+                chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, chunk_size)
+                dst_path = dst_meta.root / DEFAULT_AUDIO_PATH.format(
+                    audio_key=key,
+                    chunk_index=chunk_idx,
+                    file_index=file_idx,
+                )
+                dst_path.parent.mkdir(parents=True, exist_ok=True)
+                shutil.copy(str(src_path), str(dst_path))
+                # Reset offset for next file
+                current_offset = src_duration
+            else:
+                # Append to existing video file - use current accumulated offset
+                audios_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_offset
+                concatenate_media_files(
+                    [dst_path, src_path],
+                    dst_path,
+                )
+                current_offset += src_duration
+
+            audios_idx[key]["episode_duration"] += src_duration
+
+        audios_idx[key]["chunk"] = chunk_idx
+        audios_idx[key]["file"] = file_idx
+
+    return audios_idx
+
+
 def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_size):
     """Aggregates data chunks from a source dataset into the destination dataset.
 
@@ -501,7 +639,7 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
     return data_idx
 
 
-def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
+def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx, audios_idx):
     """Aggregates metadata from a source dataset into the destination dataset.
 
     Reads source metadata files, updates all indices and timestamps,
@@ -513,6 +651,7 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
         meta_idx: Dictionary tracking metadata chunk and file indices.
         data_idx: Dictionary tracking data chunk and file indices.
         videos_idx: Dictionary tracking video indices and timestamps.
+        audios_idx: Dictionary tracking audio indices and timestamps.
 
     Returns:
         dict: Updated meta_idx with current chunk and file indices.
@@ -536,6 +675,7 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
             meta_idx,
             data_idx,
             videos_idx,
+            audios_idx,
         )
 
         meta_idx, _ = append_or_create_parquet_file(
@@ -552,7 +692,8 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
     # Increment latest_duration by the total duration added from this source dataset
     for k in videos_idx:
         videos_idx[k]["latest_duration"] += videos_idx[k]["episode_duration"]
-
+    for k in audios_idx:
+        audios_idx[k]["latest_duration"] += audios_idx[k]["episode_duration"]
     return meta_idx
 
 

src/lerobot/datasets/audio_utils.py

@@ -0,0 +1,275 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+from pathlib import Path
+
+import av
+import torch
+import torchaudio
+import torchcodec
+from numpy import ceil
+
+CHANNELS_LAYOUTS_MAPPING = {
+    1: "mono",
+    2: "stereo",
+    3: "2.1",
+    4: "3.1",
+    5: "4.1",
+    6: "5.1",
+    7: "6.1",
+    8: "7.1",
+    16: "hexadecagonal",
+    24: "22.2",
+}
+
+
+def decode_audio(
+    audio_path: Path | str,
+    timestamps: list[float],
+    duration: float,
+    start_time_s: float | None = 0.0,
+    backend: str | None = "torchcodec",
+) -> torch.Tensor:
+    """
+    Decodes audio using the specified backend.
+    Args:
+        audio_path (Path): Path to the audio file.
+        timestamps (list[float]): List of (starting) timestamps to extract audio chunks.
+        duration (float): Duration of the audio chunks in seconds.
+        backend (str, optional): Backend to use for decoding. Defaults to "torchcodec".
+
+    Returns:
+        torch.Tensor: Decoded audio chunks.
+
+    Currently supports torchaudio.
+    """
+    if backend == "torchcodec":
+        return decode_audio_torchcodec(audio_path, timestamps, duration, start_time_s)
+    elif backend == "torchaudio":
+        return decode_audio_torchaudio(audio_path, timestamps, duration, start_time_s)
+    else:
+        raise ValueError(f"Unsupported video backend: {backend}")
+
+
+def decode_audio_torchcodec(
+    audio_path: Path | str,
+    timestamps: list[float],
+    duration: float,
+    start_time_s: float | None = 0.0,
+    log_loaded_timestamps: bool = False,
+) -> torch.Tensor:
+    # TODO(CarolinePascal) : add channels selection
+    audio_decoder = torchcodec.decoders.AudioDecoder(audio_path)
+    audio_sample_rate = audio_decoder.metadata.sample_rate
+    audio_channels = audio_decoder.metadata.num_channels
+    # TODO(CarolinePascal) : assert ts < total record duration
+
+    audio_chunks = []
+    timestamps = [
+        timestamp + start_time_s for timestamp in timestamps
+    ]  # Add an offset of start_time_s to each timestamp
+    for ts in timestamps:
+        current_audio_chunk = audio_decoder.get_samples_played_in_range(
+            start_seconds=max(0.0, ts - duration), stop_seconds=ts
+        )
+
+        current_audio_chunk_data = current_audio_chunk.data
+
+        # Case where the requested audio chunk starts before the beginning of the audio stream
+        if ts - duration < 0:
+            # No useful audio sample has been recorded
+            if ts < 1 / audio_sample_rate:
+                # TODO(CarolinePascal) : add low level white noise instead of zeros ?
+                current_audio_chunk_data = torch.zeros(
+                    (audio_channels, int(ceil(duration * audio_sample_rate)))
+                )
+            # At least one useful audio sample has been recorded
+            else:
+                # Pad the beginning of the audio chunk with zeros
+                # TODO(CarolinePascal) : add low level white noise instead of zeros ?
+                current_audio_chunk_data = torch.nn.functional.pad(
+                    current_audio_chunk_data,
+                    (int(ceil((duration - ts) * audio_sample_rate)), 0, 0, 0),  # left, right, top, bottom
+                )
+
+        if log_loaded_timestamps:
+            logging.info(
+                f"audio chunk loaded at timestamp={current_audio_chunk.pts_seconds:.4f} with duration={current_audio_chunk.duration_seconds:.4f}"
+            )
+
+        audio_chunks.append(current_audio_chunk_data)
+
+    audio_chunks = torch.stack(audio_chunks)
+
+    assert len(timestamps) == len(audio_chunks)
+    return audio_chunks
+
+
+def decode_audio_torchaudio(
+    audio_path: Path | str,
+    timestamps: list[float],
+    duration: float,
+    start_time_s: float | None = 0.0,
+    log_loaded_timestamps: bool = False,
+) -> torch.Tensor:
+    # TODO(CarolinePascal) : add channels selection
+    audio_path = str(audio_path)
+
+    reader = torchaudio.io.StreamReader(src=audio_path)
+    audio_sample_rate = reader.get_src_stream_info(reader.default_audio_stream).sample_rate
+    audio_channels = reader.get_src_stream_info(reader.default_audio_stream).num_channels
+    # TODO(CarolinePascal) : assert ts < total record duration
+
+    # TODO(CarolinePascal) : sort timestamps ?
+
+    reader.add_basic_audio_stream(
+        frames_per_chunk=int(ceil(duration * audio_sample_rate)),  # Too much is better than not enough
+        buffer_chunk_size=-1,  # No dropping frames
+        format="fltp",  # Format as float32
+    )
+
+    audio_chunks = []
+    timestamps = [
+        timestamp + start_time_s for timestamp in timestamps
+    ]  # Add an offset of start_time_s to each timestamp
+    for ts in timestamps:
+        reader.seek(max(0.0, ts - duration))  # Default to closest audio sample. Needs to be non-negative !
+        status = reader.fill_buffer()
+        if status != 0:
+            # Should not happen, but just in case
+            logging.warning("Audio stream reached end of recording before decoding desired timestamps.")
+
+        current_audio_chunk = reader.pop_chunks()[0]
+        current_audio_chunk_data = current_audio_chunk.t()  # Channel first format
+
+        # Case where the requested audio chunk starts before the beginning of the audio stream
+        if ts - duration < 0:
+            # No useful audio sample has been recorded
+            if ts < 1 / audio_sample_rate:
+                current_audio_chunk_data = torch.zeros(
+                    (audio_channels, int(ceil(duration * audio_sample_rate)))
+                )
+            # At least one useful audio sample has been recorded
+            else:
+                # Remove the superfluous last samples of the audio chunk
+                current_audio_chunk_data = current_audio_chunk_data[:, : int(ceil(ts * audio_sample_rate))]
+                # Pad the beginning of the audio chunk with zeros
+                # TODO(CarolinePascal) : add low level white noise instead of zeros ?
+                current_audio_chunk_data = torch.nn.functional.pad(
+                    current_audio_chunk_data,
+                    (int(ceil((duration - ts) * audio_sample_rate)), 0, 0, 0),  # left, right, top, bottom
+                )
+
+        if log_loaded_timestamps:
+            logging.info(
+                f"audio chunk loaded at starting timestamp={current_audio_chunk['pts']:.4f} with duration={len(current_audio_chunk) / audio_sample_rate:.4f}"
+            )
+
+        audio_chunks.append(current_audio_chunk_data)
+
+    audio_chunks = torch.stack(audio_chunks)
+
+    assert len(timestamps) == len(audio_chunks)
+    return audio_chunks
+
+
+def encode_audio(
+    input_path: Path | str,
+    output_path: Path | str,
+    codec: str = "aac",  # TODO(CarolinePascal) : investigate Fraunhofer FDK AAC (libfdk_aac) codec and and constant (file size control) /variable (quality control) bitrate options
+    bit_rate: int | None = None,
+    sample_rate: int | None = None,
+    log_level: int | None = av.logging.ERROR,
+    overwrite: bool = False,
+) -> None:
+    """Encodes an audio file using ffmpeg."""
+    output_path = Path(output_path)
+    output_path.parent.mkdir(parents=True, exist_ok=overwrite)
+
+    # Set logging level
+    if log_level is not None:
+        # "While less efficient, it is generally preferable to modify logging with Python’s logging"
+        logging.getLogger("libav").setLevel(log_level)
+
+    # Open input file
+    with av.open(str(input_path), "r") as input:
+        input_stream = input.streams.audio[0]  # Assuming the first stream is the audio stream to be encoded
+
+        # Define sub-sampling options
+        if sample_rate is None:
+            sample_rate = input_stream.rate
+
+        # Create and open output file (overwrite by default)
+        with av.open(str(output_path), "w") as output:
+            output_stream = output.add_stream(
+                codec, rate=sample_rate, layout=CHANNELS_LAYOUTS_MAPPING[input_stream.channels]
+            )
+
+            if bit_rate is not None:
+                output_stream.bit_rate = bit_rate
+
+            # Loop through input WAV packets and encode them
+            for input_frame in input.decode(
+                input_stream
+            ):  # This step handles both demuxing and decoding under the hood
+                packet = output_stream.encode(input_frame)
+                if packet:
+                    output.mux(packet)
+
+            # Flush the encoder
+            packet = output_stream.encode()
+            if packet:
+                output.mux(packet)
+
+    # Reset logging level
+    if log_level is not None:
+        av.logging.restore_default_callback()
+
+    if not output_path.exists():
+        raise OSError(f"Audio encoding did not work. File not found: {output_path}.")
+
+
+def get_audio_info(video_path: Path | str) -> dict:
+    # Set logging level
+    logging.getLogger("libav").setLevel(av.logging.ERROR)
+
+    # Getting audio stream information
+    audio_info = {}
+    with av.open(str(video_path), "r") as audio_file:
+        try:
+            audio_stream = audio_file.streams.audio[0]
+        except IndexError:
+            # Reset logging level
+            av.logging.restore_default_callback()
+            return {"has_audio": False}
+
+        audio_info["audio.channels"] = audio_stream.channels
+        audio_info["audio.codec"] = audio_stream.codec.canonical_name
+        # In an ideal loseless case : bit depth x sample rate x channels = bit rate.
+        # In an actual compressed case, the bit rate is set according to the compression level : the lower the bit rate, the more compression is applied.
+        audio_info["audio.bit_rate"] = audio_stream.bit_rate
+        audio_info["audio.sample_rate"] = audio_stream.sample_rate  # Number of samples per second
+        # In an ideal loseless case : fixed number of bits per sample.
+        # In an actual compressed case : variable number of bits per sample (often reduced to match a given depth rate).
+        audio_info["audio.bit_depth"] = audio_stream.format.bits
+        audio_info["audio.channel_layout"] = audio_stream.layout.name
+        audio_info["has_audio"] = True
+
+    # Reset logging level
+    av.logging.restore_default_callback()
+
+    return audio_info

src/lerobot/datasets/compute_stats.py

@@ -19,8 +19,7 @@ import logging
 
 import numpy as np
 
-from lerobot.datasets.io_utils import load_image_as_numpy
-from lerobot.utils.constants import ACTION, OBS_STATE
+from lerobot.datasets.io_utils import load_audio_from_path, load_image_as_numpy
 
 DEFAULT_QUANTILES = [0.01, 0.10, 0.50, 0.90, 0.99]
 
@@ -250,6 +249,20 @@ def sample_images(image_paths: list[str]) -> np.ndarray:
     return images
 
 
+def sample_audio_from_path(audio_path: str) -> np.ndarray:
+    """Samples audio data from an audio recording stored in a WAV file."""
+    data = load_audio_from_path(audio_path)
+    sampled_indices = sample_indices(len(data))
+
+    return data[sampled_indices]
+
+
+def sample_audio_from_data(data: np.ndarray) -> np.ndarray:
+    """Samples audio data from an audio recording stored in a numpy array."""
+    sampled_indices = sample_indices(len(data))
+    return data[sampled_indices]
+
+
 def _reshape_stats_by_axis(
     stats: dict[str, np.ndarray],
     axis: int | tuple[int, ...] | None,
@@ -517,6 +530,13 @@ def compute_episode_stats(
             ep_ft_array = sample_images(data)
             axes_to_reduce = (0, 2, 3)
             keepdims = True
+        elif features[key]["dtype"] == "audio":
+            try:
+                ep_ft_array = sample_audio_from_path(data[0])
+            except TypeError:  # Should only be triggered for LeKiwi robot, for which audio is stored chunk by chunk in a visual frame-like manner
+                ep_ft_array = sample_audio_from_data(data)
+            axes_to_reduce = 0
+            keepdims = True
         else:
             ep_ft_array = data
             axes_to_reduce = 0

src/lerobot/datasets/dataset_metadata.py

@@ -23,6 +23,7 @@ import pyarrow as pa
 import pyarrow.parquet as pq
 from huggingface_hub import snapshot_download
 
+from lerobot.datasets.audio_utils import get_audio_info
 from lerobot.datasets.compute_stats import aggregate_stats
 from lerobot.datasets.feature_utils import _validate_feature_names, create_empty_dataset_info
 from lerobot.datasets.io_utils import (
@@ -40,6 +41,7 @@ from lerobot.datasets.io_utils import (
 from lerobot.datasets.utils import (
     DEFAULT_EPISODES_PATH,
     DEFAULT_FEATURES,
+    DEFAULT_INITIAL_AUDIO_BUFFER_DURATION,
     INFO_PATH,
     check_version_compatibility,
     flatten_dict,
@@ -269,6 +271,32 @@ class LeRobotDatasetMetadata:
         fpath = self.video_path.format(video_key=vid_key, chunk_index=chunk_idx, file_index=file_idx)
         return Path(fpath)
 
+    def get_audio_file_path(self, ep_index: int, audio_key: str) -> Path:
+        """Return the relative audio file path for the given episode and audio key.
+
+        Args:
+            ep_index: Zero-based episode index.
+            audio_key: Feature key identifying the audio stream
+                (e.g. ``'observation.audio.microphone'``).
+
+        Returns:
+            Path to the audio file containing this episode's audio.
+
+        Raises:
+            IndexError: If ``ep_index`` is out of range.
+        """
+        if self.episodes is None:
+            self.episodes = load_episodes(self.root)
+        if ep_index >= len(self.episodes):
+            raise IndexError(
+                f"Episode index {ep_index} out of range. Episodes: {len(self.episodes) if self.episodes else 0}"
+            )
+        ep = self.episodes[ep_index]
+        chunk_idx = ep[f"audio/{audio_key}/chunk_index"]
+        file_idx = ep[f"audio/{audio_key}/file_index"]
+        fpath = self.audio_path.format(audio_key=audio_key, chunk_index=chunk_idx, file_index=file_idx)
+        return Path(fpath)
+
     @property
     def data_path(self) -> str:
         """Formattable string for the parquet files."""
@@ -279,6 +307,11 @@ class LeRobotDatasetMetadata:
         """Formattable string for the video files."""
         return self.info["video_path"]
 
+    @property
+    def audio_path(self) -> str | None:
+        """Formattable string for the audio files."""
+        return self.info["audio_path"]
+
     @property
     def robot_type(self) -> str | None:
         """Robot type used in recording this dataset."""
@@ -309,6 +342,11 @@ class LeRobotDatasetMetadata:
         """Keys to access visual modalities (regardless of their storage method)."""
         return [key for key, ft in self.features.items() if ft["dtype"] in ["video", "image"]]
 
+    @property
+    def audio_keys(self) -> list[str]:
+        """Keys to access audio modalities."""
+        return [key for key, ft in self.features.items() if ft["dtype"] == "audio"]
+
     @property
     def names(self) -> dict[str, list | dict]:
         """Names of the various dimensions of vector modalities."""
@@ -349,6 +387,11 @@ class LeRobotDatasetMetadata:
         """Max size of video file in mega bytes."""
         return self.info["video_files_size_in_mb"]
 
+    @property
+    def audio_files_size_in_mb(self) -> int:
+        """Max size of audio file in mega bytes."""
+        return self.info["audio_files_size_in_mb"]
+
     def get_task_index(self, task: str) -> int | None:
         """
         Given a task in natural language, returns its task_index if the task already exists in the dataset,
@@ -515,11 +558,27 @@ class LeRobotDatasetMetadata:
                 video_path = self.root / self.video_path.format(video_key=key, chunk_index=0, file_index=0)
                 self.info["features"][key]["info"] = get_video_info(video_path)
 
+    def update_audio_info(self, audio_key: str | None = None) -> None:
+        """
+        Warning: this function writes info from first episode audio, implicitly assuming that all audio have
+        been encoded the same way. Also, this means it assumes the first episode exists.
+        """
+        if audio_key is not None and audio_key not in self.audio_keys:
+            raise ValueError(f"Audio key {audio_key} not found in dataset")
+
+        audio_keys = [audio_key] if audio_key is not None else self.audio_keys
+        for key in audio_keys:
+            if not self.features[key].get("info", None):
+                audio_path = self.root / self.audio_path.format(audio_key=key, chunk_index=0, file_index=0)
+                self.info["features"][key]["info"] = get_audio_info(audio_path)
+                self.info["features"][key]["info"]["start_time_s"] = DEFAULT_INITIAL_AUDIO_BUFFER_DURATION
+
     def update_chunk_settings(
         self,
         chunks_size: int | None = None,
         data_files_size_in_mb: int | None = None,
         video_files_size_in_mb: int | None = None,
+        audio_files_size_in_mb: int | None = None,
     ) -> None:
         """Update chunk and file size settings after dataset creation.
 
@@ -531,6 +590,7 @@ class LeRobotDatasetMetadata:
             chunks_size: Maximum number of files per chunk directory. If None, keeps current value.
             data_files_size_in_mb: Maximum size for data parquet files in MB. If None, keeps current value.
             video_files_size_in_mb: Maximum size for video files in MB. If None, keeps current value.
+            audio_files_size_in_mb: Maximum size for audio files in MB. If None, keeps current value.
         """
         if chunks_size is not None:
             if chunks_size <= 0:
@@ -547,6 +607,11 @@ class LeRobotDatasetMetadata:
                 raise ValueError(f"video_files_size_in_mb must be positive, got {video_files_size_in_mb}")
             self.info["video_files_size_in_mb"] = video_files_size_in_mb
 
+        if audio_files_size_in_mb is not None:
+            if audio_files_size_in_mb <= 0:
+                raise ValueError(f"audio_files_size_in_mb must be positive, got {audio_files_size_in_mb}")
+            self.info["audio_files_size_in_mb"] = audio_files_size_in_mb
+
         # Update the info file on disk
         write_info(self.info, self.root)
 
@@ -554,12 +619,13 @@ class LeRobotDatasetMetadata:
         """Get current chunk and file size settings.
 
         Returns:
-            Dict containing chunks_size, data_files_size_in_mb, and video_files_size_in_mb.
+            Dict containing chunks_size, data_files_size_in_mb, video_files_size_in_mb, and audio_files_size_in_mb.
         """
         return {
             "chunks_size": self.chunks_size,
             "data_files_size_in_mb": self.data_files_size_in_mb,
             "video_files_size_in_mb": self.video_files_size_in_mb,
+            "audio_files_size_in_mb": self.audio_files_size_in_mb,
         }
 
     def __repr__(self):
@@ -586,6 +652,7 @@ class LeRobotDatasetMetadata:
         chunks_size: int | None = None,
         data_files_size_in_mb: int | None = None,
         video_files_size_in_mb: int | None = None,
+        audio_files_size_in_mb: int | None = None,
     ) -> "LeRobotDatasetMetadata":
         """Create metadata for a new LeRobot dataset from scratch.
 
@@ -636,6 +703,7 @@ class LeRobotDatasetMetadata:
             chunks_size,
             data_files_size_in_mb,
             video_files_size_in_mb,
+            audio_files_size_in_mb,
         )
         if len(obj.video_keys) > 0 and not use_videos:
             raise ValueError(

src/lerobot/datasets/dataset_reader.py

@@ -21,12 +21,14 @@ from pathlib import Path
 import datasets
 import torch
 
+from lerobot.datasets.audio_utils import decode_audio
 from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
 from lerobot.datasets.feature_utils import (
     check_delta_timestamps,
     get_delta_indices,
     get_hf_features_from_features,
 )
+from lerobot.datasets.utils import DEFAULT_AUDIO_CHUNK_DURATION
 from lerobot.datasets.io_utils import (
     hf_transform_to_torch,
     load_nested_dataset,
@@ -130,7 +132,7 @@ class DatasetReader:
         return hf_dataset
 
     def _check_cached_episodes_sufficient(self) -> bool:
-        """Check if the cached dataset contains all requested episodes and their video files."""
+        """Check if the cached dataset contains all requested episodes and their video and audio files."""
         if self.hf_dataset is None or len(self.hf_dataset) == 0:
             return False
 
@@ -154,6 +156,13 @@ class DatasetReader:
                     if not video_path.exists():
                         return False
 
+        if len(self._meta.audio_keys) > 0:
+            for ep_idx in requested_episodes:
+                for audio_key in self._meta.audio_keys:
+                    audio_path = self.root / self._meta.get_compressed_audio_file_path(ep_idx, audio_key)
+                    if not audio_path.exists():
+                        return False
+
         return True
 
     def get_episodes_file_paths(self) -> list[Path]:
@@ -170,6 +179,15 @@ class DatasetReader:
                 for ep_idx in episodes
             ]
             fpaths += video_files
+
+        if len(self._meta.audio_keys) > 0:
+            audio_files = [
+                str(self._meta.get_compressed_audio_file_path(ep_idx, audio_key))
+                for audio_key in self._meta.audio_keys
+                for ep_idx in episodes
+            ]
+            fpaths += audio_files
+
         # episodes are stored in the same files, so we return unique paths only
         fpaths = list(set(fpaths))
         return fpaths
@@ -199,7 +217,7 @@ class DatasetReader:
         query_indices: dict[str, list[int]] | None = None,
     ) -> dict[str, list[float]]:
         query_timestamps = {}
-        for key in self._meta.video_keys:
+        for key in self._meta.video_keys + self._meta.audio_keys:
             if query_indices is not None and key in query_indices:
                 if self._absolute_to_relative_idx is not None:
                     relative_indices = [self._absolute_to_relative_idx[idx] for idx in query_indices[key]]
@@ -213,10 +231,10 @@ class DatasetReader:
         return query_timestamps
 
     def _query_hf_dataset(self, query_indices: dict[str, list[int]]) -> dict:
-        """Query dataset for indices across keys, skipping video keys."""
+        """Query dataset for indices across keys, skipping video and audio keys."""
         result: dict = {}
         for key, q_idx in query_indices.items():
-            if key in self._meta.video_keys:
+            if key in self._meta.video_keys or key in self._meta.audio_keys:
                 continue
             relative_indices = (
                 q_idx
@@ -246,6 +264,28 @@ class DatasetReader:
 
         return item
 
+    # TODO(CarolinePascal): add variable query durations
+    def _query_audio(
+        self, query_timestamps: dict[str, list[float]], query_duration: float, ep_idx: int
+    ) -> dict[str, torch.Tensor]:
+        ep = self.meta.episodes[ep_idx]
+        item = {}
+        for audio_key, query_ts in query_timestamps.items():
+            # Episodes are stored sequentially on a single mp4 to reduce the number of files.
+            # Thus we load the start timestamp of the episode on this mp4 and,
+            # shift the query timestamp accordingly.
+            from_timestamp = ep[f"audio/{audio_key}/from_timestamp"]
+            shifted_query_ts = [from_timestamp + ts for ts in query_ts]
+
+            audio_path = self.root / self.meta.get_audio_file_path(ep_idx, audio_key)
+            start_time_s = self.meta.features[audio_key]["info"].get("start_time_s", 0.0)
+            audio_chunk = decode_audio(
+                audio_path, shifted_query_ts, query_duration, start_time_s, self.audio_backend
+            )
+            item[audio_key] = audio_chunk.squeeze(0)
+
+        return item
+
     def get_item(self, idx) -> dict:
         """Core __getitem__ logic. Assumes hf_dataset is loaded.
 
@@ -265,11 +305,12 @@ class DatasetReader:
             for key, val in query_result.items():
                 item[key] = val
 
-        if len(self._meta.video_keys) > 0:
+        if len(self._meta.video_keys) > 0 or len(self._meta.audio_keys) > 0:
             current_ts = item["timestamp"].item()
             query_timestamps = self._get_query_timestamps(current_ts, query_indices)
             video_frames = self._query_videos(query_timestamps, ep_idx)
-            item = {**video_frames, **item}
+            audio_chunks = self._query_audio(query_timestamps, DEFAULT_AUDIO_CHUNK_DURATION, ep_idx)
+            item = {**video_frames, **audio_chunks, **item}
 
         if self._image_transforms is not None:
             image_keys = self._meta.camera_keys

src/lerobot/datasets/dataset_writer.py

@@ -31,6 +31,7 @@ import PIL.Image
 import pyarrow.parquet as pq
 import torch
 
+from lerobot.datasets.audio_utils import encode_audio
 from lerobot.datasets.compute_stats import compute_episode_stats
 from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
 from lerobot.datasets.feature_utils import (
@@ -48,14 +49,17 @@ from lerobot.datasets.io_utils import (
 from lerobot.datasets.utils import (
     DEFAULT_EPISODES_PATH,
     DEFAULT_IMAGE_PATH,
+    DEFAULT_RAW_AUDIO_PATH,
     update_chunk_file_indices,
 )
 from lerobot.datasets.video_utils import (
     StreamingVideoEncoder,
-    concatenate_video_files,
+    concatenate_media_files,
     encode_video_frames,
-    get_video_duration_in_s,
+    get_media_duration_in_s,
 )
+from lerobot.microphones.microphone import Microphone
+from lerobot.microphones.utils import async_microphones_start_recording
 
 logger = logging.getLogger(__name__)
 
@@ -144,6 +148,10 @@ class DatasetWriter:
     def _get_image_file_dir(self, episode_index: int, image_key: str) -> Path:
         return self._get_image_file_path(episode_index, image_key, frame_index=0).parent
 
+    def _get_raw_audio_file_path(self, episode_index: int, audio_key: str) -> Path:
+        fpath = DEFAULT_RAW_AUDIO_PATH.format(audio_key=audio_key, episode_index=episode_index)
+        return self._root / fpath
+
     def _save_image(
         self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1
     ) -> None:
@@ -208,11 +216,43 @@ class DatasetWriter:
                 compress_level = 1 if self._meta.features[key]["dtype"] == "video" else 6
                 self._save_image(frame[key], img_path, compress_level)
                 self.episode_buffer[key].append(str(img_path))
+            elif self._meta.features[key]["dtype"] == "audio":
+                if (
+                    self._meta.robot_type == "lekiwi"
+                ):  # Raw data storage should only be triggered for LeKiwi robot, for which audio is stored chunk by chunk in a visual frame-like manner
+                    self.episode_buffer[key].append(frame[key])
+                else:  # Otherwise, only the audio file path is stored in the episode buffer
+                    if frame_index == 0:
+                        audio_path = self._get_raw_audio_file_path(
+                            episode_index=self.episode_buffer["episode_index"], audio_key=key
+                        )
+                        self.episode_buffer[key].append(str(audio_path))
             else:
                 self.episode_buffer[key].append(frame[key])
 
         self.episode_buffer["size"] += 1
 
+    def add_microphone_recording(self, microphone_key: str, microphone: Microphone) -> None:
+        """
+        Starts recording audio data provided by the microphone and directly writes it in a .wav file.
+        """
+
+        audio_file = self._get_raw_audio_file_path(self._meta.total_episodes, "observation.audio." + microphone_key)
+        microphone.start_recording(output_file=audio_file)
+
+    def add_microphones_recordings(self, microphones: dict[str, Microphone]) -> None:
+        """
+        Starts recording audio data provided by multiple microphones and directly writes it in appropriate .wav files.
+        """
+
+        output_files = []
+        for microphone_key in microphones:
+            output_files.append(
+                self._get_raw_audio_file_path(self._meta.total_episodes, "observation.audio." + microphone_key)
+            )
+
+        async_microphones_start_recording(microphones, output_files)
+
     def save_episode(
         self,
         episode_data: dict | None = None,
@@ -241,12 +281,19 @@ class DatasetWriter:
         for key, ft in self._meta.features.items():
             if key in ["index", "episode_index", "task_index"] or ft["dtype"] in ["image", "video"]:
                 continue
+            elif ft["dtype"] == "audio":
+                if (
+                    self._meta.robot_type == "lekiwi"
+                ):  # Raw data storage should only be triggered for LeKiwi robot, for which audio is stored chunk by chunk in a visual frame-like manner
+                    episode_buffer[key] = np.concatenate(episode_buffer[key], axis=0)
+                continue
             episode_buffer[key] = np.stack(episode_buffer[key])
 
         # Wait for image writer to end, so that episode stats over images can be computed
         self._wait_image_writer()
 
         has_video_keys = len(self._meta.video_keys) > 0
+        has_audio_keys = len(self._meta.audio_keys) > 0
         use_streaming = self._streaming_encoder is not None and has_video_keys
         use_batched_encoding = self._batch_encoding_size > 1
 
@@ -273,7 +320,7 @@ class DatasetWriter:
                         for k, v in video_stats.items()
                     }
                 ep_metadata.update(self._save_episode_video(video_key, episode_index, temp_path=temp_path))
-        elif has_video_keys and not use_batched_encoding:
+        elif (has_video_keys or has_audio_keys) and not use_batched_encoding:
             num_cameras = len(self._meta.video_keys)
             if parallel_encoding and num_cameras > 1:
                 with concurrent.futures.ProcessPoolExecutor(max_workers=num_cameras) as executor:
@@ -309,19 +356,28 @@ class DatasetWriter:
                 for video_key in self._meta.video_keys:
                     ep_metadata.update(self._save_episode_video(video_key, episode_index))
 
+            # TODO(Caroline): add parallel encoding for audio as well
+            for audio_key in self._meta.audio_keys:
+                ep_metadata.update(self._save_episode_audio(audio_key, episode_index))
+
         # `meta.save_episode` need to be executed after encoding the videos
         self._meta.save_episode(episode_index, episode_length, episode_tasks, ep_stats, ep_metadata)
 
-        if has_video_keys and use_batched_encoding:
+        if (has_video_keys or has_audio_keys) and use_batched_encoding:
             self._episodes_since_last_encoding += 1
             if self._episodes_since_last_encoding == self._batch_encoding_size:
                 start_ep = self._meta.total_episodes - self._batch_encoding_size
                 end_ep = self._meta.total_episodes
-                self._batch_save_episode_video(start_ep, end_ep)
+                if has_video_keys:
+                    self._batch_save_episode_video(start_ep, end_ep)
+                if has_audio_keys:
+                    self._batch_save_episode_audio(start_ep, end_ep)
                 self._episodes_since_last_encoding = 0
 
         if episode_data is None:
-            self.clear_episode_buffer(delete_images=len(self._meta.image_keys) > 0)
+            self.clear_episode_buffer(
+                delete_images=len(self._meta.image_keys) > 0, delete_audio=len(self._meta.audio_keys) > 0
+            )
 
     def _batch_save_episode_video(self, start_episode: int, end_episode: int | None = None) -> None:
         """Batch save videos for multiple episodes."""
@@ -368,6 +424,59 @@ class DatasetWriter:
             episode_df.to_parquet(episode_df_path)
             self._meta.episodes = load_episodes(self._root)
 
+    def _batch_save_episode_audio(self, start_episode: int, end_episode: int | None = None) -> None:
+        """
+        Batch save audio for multiple episodes.
+
+        Args:
+            start_episode: Starting episode index (inclusive)
+            end_episode: Ending episode index (exclusive). If None, encodes all episodes from start_episode to the current episode.
+        """
+        if end_episode is None:
+            end_episode = self._meta.total_episodes
+
+        logging.info(
+            f"Batch encoding {self.batch_encoding_size} audio for episodes {start_episode} to {end_episode - 1}"
+        )
+
+        chunk_idx = self._meta.episodes[start_episode]["data/chunk_index"]
+        file_idx = self._meta.episodes[start_episode]["data/file_index"]
+        episode_df_path = self._root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
+        episode_df = pd.read_parquet(episode_df_path)
+
+        for ep_idx in range(start_episode, end_episode):
+            logging.info(f"Encoding audio for episode {ep_idx}")
+
+            if (
+                self._meta.episodes[ep_idx]["data/chunk_index"] != chunk_idx
+                or self._meta.episodes[ep_idx]["data/file_index"] != file_idx
+            ):
+                # The current episode is in a new chunk or file.
+                # Save previous episode dataframe and update the Hugging Face dataset by reloading it.
+                episode_df.to_parquet(episode_df_path)
+                self._meta.episodes = load_episodes(self._root)
+
+                # Load new episode dataframe
+                chunk_idx = self._meta.episodes[ep_idx]["data/chunk_index"]
+                file_idx = self._meta.episodes[ep_idx]["data/file_index"]
+                episode_df_path = self._root / DEFAULT_EPISODES_PATH.format(
+                    chunk_index=chunk_idx, file_index=file_idx
+                )
+                episode_df = pd.read_parquet(episode_df_path)
+
+            # Save the current episode's video metadata to the dataframe
+            audio_ep_metadata = {}
+            for audio_key in self._meta.audio_keys:
+                audio_ep_metadata.update(self._save_episode_audio(audio_key, ep_idx))
+            audio_ep_metadata.pop("episode_index")
+            audio_ep_df = pd.DataFrame(audio_ep_metadata, index=[ep_idx]).convert_dtypes(
+                dtype_backend="pyarrow"
+            )  # allows NaN values along with integers
+
+            episode_df = episode_df.combine_first(audio_ep_df)
+            episode_df.to_parquet(episode_df_path)
+            self._meta.episodes = load_episodes(self._root)
+
     def _save_episode_data(self, episode_buffer: dict) -> dict:
         """Save episode data to a parquet file."""
         # Use metadata features as the authoritative schema
@@ -445,7 +554,7 @@ class DatasetWriter:
             ep_path = temp_path
 
         ep_size_in_mb = get_file_size_in_mb(ep_path)
-        ep_duration_in_s = get_video_duration_in_s(ep_path)
+        ep_duration_in_s = get_media_duration_in_s(ep_path, media_type="video")
 
         if (
             episode_index == 0
@@ -485,7 +594,7 @@ class DatasetWriter:
                 shutil.move(str(ep_path), str(new_path))
                 latest_duration_in_s = 0.0
             else:
-                concatenate_video_files(
+                concatenate_media_files(
                     [latest_path, ep_path],
                     latest_path,
                 )
@@ -507,7 +616,91 @@ class DatasetWriter:
         }
         return metadata
 
-    def clear_episode_buffer(self, delete_images: bool = True) -> None:
+    def _encode_temporary_episode_audio(self, audio_key: str, episode_index: int) -> Path:
+        """
+        Use ffmpeg to convert raw audio files into m4a audio files.
+        Note: `encode_episode_audio` is a blocking call. Making it asynchronous shouldn't speedup encoding,
+        since audio encoding with ffmpeg is already using multithreading.
+        """
+        temp_path = Path(tempfile.mkdtemp(dir=self._root)) / f"{audio_key}_{episode_index:03d}.m4a"
+        raw_audio_file = self._get_raw_audio_file_path(episode_index, audio_key)
+        encode_audio(raw_audio_file, temp_path, overwrite=True)
+        raw_audio_file.unlink()
+        return temp_path
+
+    def _save_episode_audio(self, audio_key: str, episode_index: int) -> dict:
+        # Encode episode audio into a temporary audio file
+        ep_path = self._encode_temporary_episode_audio(audio_key, episode_index)
+        ep_size_in_mb = get_file_size_in_mb(ep_path)
+        ep_duration_in_s = get_media_duration_in_s(ep_path, media_type="audio")
+
+        if (
+            episode_index == 0
+            or self._meta.latest_episode is None
+            or f"audio/{audio_key}/chunk_index" not in self._meta.latest_episode
+        ):
+            # Initialize indices for a new dataset made of the first episode data
+            chunk_idx, file_idx = 0, 0
+            if self._meta.episodes is not None and len(self._meta.episodes) > 0:
+                # It means we are resuming recording, so we need to load the latest episode
+                # Update the indices to avoid overwriting the latest episode
+                old_chunk_idx = self._meta.episodes[-1][f"audio/{audio_key}/chunk_index"]
+                old_file_idx = self._meta.episodes[-1][f"audio/{audio_key}/file_index"]
+                chunk_idx, file_idx = update_chunk_file_indices(
+                    old_chunk_idx, old_file_idx, self._meta.chunks_size
+                )
+            latest_duration_in_s = 0.0
+            new_path = self._root / self._meta.audio_path.format(
+                audio_key=audio_key, chunk_index=chunk_idx, file_index=file_idx
+            )
+            new_path.parent.mkdir(parents=True, exist_ok=True)
+            shutil.move(str(ep_path), str(new_path))
+        else:
+            # Retrieve information from the latest updated audio file using latest_episode
+            latest_ep = self._meta.latest_episode
+            chunk_idx = latest_ep[f"audio/{audio_key}/chunk_index"][0]
+            file_idx = latest_ep[f"audio/{audio_key}/file_index"][0]
+
+            latest_path = self._root / self._meta.audio_path.format(
+                audio_key=audio_key, chunk_index=chunk_idx, file_index=file_idx
+            )
+            latest_size_in_mb = get_file_size_in_mb(latest_path)
+            latest_duration_in_s = latest_ep[f"audio/{audio_key}/to_timestamp"][0]
+
+            if latest_size_in_mb + ep_size_in_mb >= self._meta.audio_files_size_in_mb:
+                # Move temporary episode audio to a new audio file in the dataset
+                chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self._meta.chunks_size)
+                new_path = self._root / self._meta.audio_path.format(
+                    audio_key=audio_key, chunk_index=chunk_idx, file_index=file_idx
+                )
+                new_path.parent.mkdir(parents=True, exist_ok=True)
+                shutil.move(str(ep_path), str(new_path))
+                latest_duration_in_s = 0.0
+            else:
+                # Update latest audio file
+                concatenate_media_files(
+                    [latest_path, ep_path],
+                    latest_path,
+                )
+
+        # Remove temporary directory
+        shutil.rmtree(str(ep_path.parent))
+
+        # Update audio info (only needed when first episode is encoded since it reads from episode 0)
+        if episode_index == 0:
+            self._meta.update_audio_info(audio_key)
+            write_info(self._meta.info, self._meta.root)  # ensure audio info always written properly
+
+        metadata = {
+            "episode_index": episode_index,
+            f"audio/{audio_key}/chunk_index": chunk_idx,
+            f"audio/{audio_key}/file_index": file_idx,
+            f"audio/{audio_key}/from_timestamp": latest_duration_in_s,
+            f"audio/{audio_key}/to_timestamp": latest_duration_in_s + ep_duration_in_s,
+        }
+        return metadata
+
+    def clear_episode_buffer(self, delete_images: bool = True, delete_audio: bool = True) -> None:
         """Discard the current episode buffer and optionally delete temp images.
 
         Args:
@@ -531,6 +724,15 @@ class DatasetWriter:
                 if img_dir.is_dir():
                     shutil.rmtree(img_dir)
 
+        if delete_audio:
+            episode_index = self.episode_buffer["episode_index"]
+            if isinstance(episode_index, np.ndarray):
+                episode_index = episode_index.item() if episode_index.size == 1 else episode_index[0]
+            for audio_key in self._meta.audio_keys:
+                audio_file = self._get_raw_audio_file_path(episode_index, audio_key)
+                if audio_file.is_file():
+                    audio_file.unlink()
+
         self.episode_buffer = self._create_episode_buffer()
 
     def start_image_writer(self, num_processes: int = 0, num_threads: int = 4) -> None:
@@ -596,7 +798,7 @@ class DatasetWriter:
             self._streaming_encoder.cancel_episode()
 
     def cleanup_interrupted_episode(self, episode_index: int) -> None:
-        """Remove temporary image directories for an interrupted episode."""
+        """Remove temporary image and audio directories for an interrupted episode."""
         for key in self._meta.video_keys:
             img_dir = self._get_image_file_path(
                 episode_index=episode_index, image_key=key, frame_index=0
@@ -607,6 +809,14 @@ class DatasetWriter:
                 )
                 shutil.rmtree(img_dir)
 
+        for key in self._meta.audio_keys:
+            audio_file = self._get_raw_audio_file_path(episode_index=episode_index, audio_key=key)
+            if audio_file.exists():
+                logger.debug(
+                    f"Cleaning up interrupted episode audio for episode {episode_index}, microphone {key}"
+                )
+                audio_file.unlink()
+
     def finalize(self) -> None:
         """Flush all pending work and release all resources.
 

src/lerobot/datasets/feature_utils.py

@@ -22,6 +22,8 @@ from PIL import Image as PILImage
 
 from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.datasets.utils import (
+    DEFAULT_AUDIO_FILE_SIZE_IN_MB,
+    DEFAULT_AUDIO_PATH,
     DEFAULT_CHUNK_SIZE,
     DEFAULT_DATA_FILE_SIZE_IN_MB,
     DEFAULT_DATA_PATH,
@@ -47,7 +49,7 @@ def get_hf_features_from_features(features: dict) -> datasets.Features:
     """
     hf_features = {}
     for key, ft in features.items():
-        if ft["dtype"] == "video":
+        if ft["dtype"] == "video" or ft["dtype"] == "audio":
             continue
         elif ft["dtype"] == "image":
             hf_features[key] = datasets.Image()
@@ -110,7 +112,12 @@ def hw_to_dataset_features(
         for key, ftype in hw_features.items()
         if ftype is float or (isinstance(ftype, PolicyFeature) and ftype.type != FeatureType.VISUAL)
     }
-    cam_fts = {key: shape for key, shape in hw_features.items() if isinstance(shape, tuple)}
+    cam_fts = {
+        key: shape for key, shape in hw_features.items() if isinstance(shape, tuple) and len(shape) == 3
+    }
+    mic_fts = {
+        key: shape for key, shape in hw_features.items() if isinstance(shape, tuple) and len(shape) == 2
+    }
 
     if joint_fts and prefix == ACTION:
         features[prefix] = {
@@ -133,6 +140,14 @@ def hw_to_dataset_features(
             "names": ["height", "width", "channels"],
         }
 
+    for key, parameters in mic_fts.items():
+        features[f"{prefix}.audio.{key}"] = {
+            "dtype": "audio",
+            "shape": (len(parameters[1]),),
+            "names": ["channels"],
+            "info": {"sample_rate": parameters[0]},
+        }
+
     _validate_feature_names(features)
     return features
 
@@ -162,6 +177,8 @@ def build_dataset_frame(
             frame[key] = np.array([values[name] for name in ft["names"]], dtype=np.float32)
         elif ft["dtype"] in ["image", "video"]:
             frame[key] = values[key.removeprefix(f"{prefix}.images.")]
+        elif ft["dtype"] == "audio":
+            frame[key] = values[key.removeprefix(f"{prefix}.audio.")]
 
     return frame
 
@@ -195,6 +212,10 @@ def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFea
             # Backward compatibility for "channel" which is an error introduced in LeRobotDataset v2.0 for ported datasets.
             if names[2] in ["channel", "channels"]:  # (h, w, c) -> (c, h, w)
                 shape = (shape[2], shape[0], shape[1])
+        elif ft["dtype"] == "audio":
+            type = FeatureType.AUDIO
+            if len(shape) != 2:
+                raise ValueError(f"Number of dimensions of {key} != 2 (shape={shape})")
         elif key == OBS_ENV_STATE:
             type = FeatureType.ENV
         elif key.startswith(OBS_STR):
@@ -273,6 +294,7 @@ def create_empty_dataset_info(
     chunks_size: int | None = None,
     data_files_size_in_mb: int | None = None,
     video_files_size_in_mb: int | None = None,
+    audio_files_size_in_mb: int | None = None,
 ) -> dict:
     """Create a template dictionary for a new dataset's `info.json`.
 
@@ -282,7 +304,10 @@ def create_empty_dataset_info(
         features (dict): The LeRobot features dictionary for the dataset.
         use_videos (bool): Whether the dataset will store videos.
         robot_type (str | None): The type of robot used, if any.
-
+        chunks_size (int | None): The number of files per chunk.
+        data_files_size_in_mb (int | None): The maximum size per data file in MB.
+        video_files_size_in_mb (int | None): The maximum size per video file in MB.
+        audio_files_size_in_mb (int | None): The maximum size per audio file in MB.
     Returns:
         dict: A dictionary with the initial dataset metadata.
     """
@@ -295,10 +320,12 @@ def create_empty_dataset_info(
         "chunks_size": chunks_size or DEFAULT_CHUNK_SIZE,
         "data_files_size_in_mb": data_files_size_in_mb or DEFAULT_DATA_FILE_SIZE_IN_MB,
         "video_files_size_in_mb": video_files_size_in_mb or DEFAULT_VIDEO_FILE_SIZE_IN_MB,
+        "audio_files_size_in_mb": audio_files_size_in_mb or DEFAULT_AUDIO_FILE_SIZE_IN_MB,
         "fps": fps,
         "splits": {},
         "data_path": DEFAULT_DATA_PATH,
         "video_path": DEFAULT_VIDEO_PATH if use_videos else None,
+        "audio_path": DEFAULT_AUDIO_PATH,
         "features": features,
     }
 
@@ -435,6 +462,8 @@ def validate_feature_dtype_and_shape(
         return validate_feature_numpy_array(name, expected_dtype, expected_shape, value)
     elif expected_dtype in ["image", "video"]:
         return validate_feature_image_or_video(name, expected_shape, value)
+    elif expected_dtype == "audio":
+        return validate_feature_audio(name, expected_shape, value)
     elif expected_dtype == "string":
         return validate_feature_string(name, value)
     else:
@@ -501,6 +530,33 @@ def validate_feature_image_or_video(
     return error_message
 
 
+def validate_feature_audio(name: str, expected_shape: list[str], value: np.ndarray):
+    """Validate a feature that is expected to be an audio frame.
+
+    Args:
+        name (str): The name of the feature.
+        expected_shape (list[str]): The expected shape (C,).
+        value: The audio data to validate.
+
+    Returns:
+        str: An error message if validation fails, otherwise an empty string.
+    """
+    error_message = ""
+    if isinstance(value, np.ndarray):
+        actual_shape = value.shape
+        c = expected_shape
+        if (len(actual_shape) != 2 and len(actual_shape) != 1) or actual_shape[-1] != c[
+            -1
+        ]:  # The number of frames might be different
+            error_message += (
+                f"The feature '{name}' of shape '{actual_shape}' does not have the expected shape '{c}'.\n"
+            )
+    else:
+        error_message += f"The feature '{name}' is expected to be of type 'np.ndarray', but type '{type(value)}' provided instead.\n"
+
+    return error_message
+
+
 def validate_feature_string(name: str, value: str) -> str:
     """Validate a feature that is expected to be a string.
 

src/lerobot/datasets/io_utils.py

@@ -23,6 +23,7 @@ import pandas
 import pandas as pd
 import pyarrow.dataset as pa_ds
 import pyarrow.parquet as pq
+import soundfile as sf
 import torch
 from datasets import Dataset
 from datasets.table import embed_table_storage
@@ -280,6 +281,24 @@ def load_image_as_numpy(
     return img_array
 
 
+def load_audio_from_path(fpath: str | Path) -> np.ndarray:
+    """Load an audio file from a path into a numpy array.
+
+    Args:
+        fpath (str | Path): Path to the audio file.
+
+    Returns:
+        np.ndarray: The audio as a numpy array.
+    """
+    audio_data, _ = sf.read(fpath, dtype="float32")
+
+    # Fill missing channel dimension when loading mono audio data
+    if audio_data.ndim == 1:
+        audio_data = np.expand_dims(audio_data, axis=1)
+
+    return audio_data
+
+
 def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[torch.Tensor | str]]:
     """Convert a batch from a Hugging Face dataset to torch tensors.
 

src/lerobot/datasets/lerobot_dataset.py

@@ -54,7 +54,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
         revision: str | None = None,
         force_cache_sync: bool = False,
         download_videos: bool = True,
+        download_audio: bool = True,
         video_backend: str | None = None,
+        audio_backend: str | None = None,
         batch_encoding_size: int = 1,
         vcodec: str = "libsvtav1",
         streaming_encoding: bool = False,
@@ -91,6 +93,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
                   task-conditioned training.
             - data (backed by datasets.Dataset), which reads values from parquet files.
             - videos (optional) from which frames are loaded to be synchronous with data from parquet files.
+            - audio (optional) from which audio is loaded to be synchronous with data from parquet files.
 
         A typical LeRobotDataset looks like this from its root path:
         .
@@ -116,19 +119,37 @@ class LeRobotDataset(torch.utils.data.Dataset):
         │   ├── info.json
         │   ├── stats.json
         │   └── tasks.parquet
-        └── videos
-            ├── observation.images.laptop
+        ├── videos
+        │   ├── observation.images.laptop
+        │   │   ├── chunk-000
+        │   │   │   ├── file-000.mp4
+        │   │   │   ├── file-001.mp4
+        │   │   │   └── ...
+        │   │   ├── chunk-001
+        │   │   │   └── ...
+        │   │   └── ...
+        │   ├── observation.images.phone
+        │   │   ├── chunk-000
+        │   │   │   ├── file-000.mp4
+        │   │   │   ├── file-001.mp4
+        │   │   │   └── ...
+        │   │   ├── chunk-001
+        │   │   │   └── ...
+        │   │   └── ...
+        │   └── ...
+        └── audio
+            ├── observation.audio.laptop
             │   ├── chunk-000
-            │   │   ├── file-000.mp4
-            │   │   ├── file-001.mp4
+            │   │   ├── file-000.m4a
+            │   │   ├── file-001.m4a
             │   │   └── ...
             │   ├── chunk-001
             │   │   └── ...
             │   └── ...
-            ├── observation.images.phone
+            ├── observation.audio.phone
             │   ├── chunk-000
-            │   │   ├── file-000.mp4
-            │   │   ├── file-001.mp4
+            │   │   ├── file-000.m4a
+            │   │   ├── file-001.m4a
             │   │   └── ...
             │   ├── chunk-001
             │   │   └── ...
@@ -169,8 +190,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
             download_videos (bool, optional): Flag to download the videos. Note that when set to True but the
                 video files are already present on local disk, they won't be downloaded again. Defaults to
                 True.
+            download_audio (bool, optional): Flag to download the audio. Defaults to True.
             video_backend (str | None, optional): Video backend to use for decoding videos. Defaults to torchcodec when available int the platform; otherwise, defaults to 'pyav'.
                 You can also use the 'pyav' decoder used by Torchvision, which used to be the default option, or 'video_reader' which is another decoder of Torchvision.
+            audio_backend (str | None, optional): Audio backend to use for decoding audio. Defaults to 'torchcodec'.
             batch_encoding_size (int, optional): Number of episodes to accumulate before batch encoding videos.
                 Set to 1 for immediate encoding (default), or higher for batched encoding. Defaults to 1.
             vcodec (str, optional): Video codec for encoding videos during recording. Options: 'h264', 'hevc',
@@ -198,6 +221,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.tolerance_s = tolerance_s
         self.revision = revision if revision else CODEBASE_VERSION
         self._video_backend = video_backend if video_backend else get_safe_default_codec()
+        self._audio_backend = audio_backend if audio_backend else "torchcodec"
         self._batch_encoding_size = batch_encoding_size
         self._vcodec = resolve_vcodec(vcodec)
         self._encoder_threads = encoder_threads
@@ -219,6 +243,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             episodes=episodes,
             tolerance_s=tolerance_s,
             video_backend=self._video_backend,
+            audio_backend=self._audio_backend,
             delta_timestamps=delta_timestamps,
             image_transforms=image_transforms,
         )
@@ -227,7 +252,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         if force_cache_sync or not self.reader.try_load():
             if is_valid_version(self.revision):
                 self.revision = get_safe_version(self.repo_id, self.revision)
-            self._download(download_videos)
+            self._download(download_videos, download_audio)
             self.reader.load_and_activate()
 
         # Detect write-mode params for backward compatibility
@@ -281,6 +306,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 episodes=self.episodes,
                 tolerance_s=self.tolerance_s,
                 video_backend=self._video_backend,
+                audio_backend=self._audio_backend,
                 delta_timestamps=self.delta_timestamps,
                 image_transforms=self.image_transforms,
             )
@@ -360,6 +386,14 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self._require_writer("add_frame")
         self.writer.add_frame(frame)
 
+    def add_microphones_recordings(self, microphones: dict) -> None:
+        """Add microphone recordings to the current episode buffer.
+
+        Delegates to :meth:`DatasetWriter.add_microphones_recordings`.
+        """
+        self._require_writer("add_microphones_recordings")
+        self.writer.add_microphones_recordings(microphones)
+
     def save_episode(self, episode_data: dict | None = None, parallel_encoding: bool = True) -> None:
         """Save the current episode buffer to disk.
 
@@ -484,6 +518,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         license: str | None = "apache-2.0",
         tag_version: bool = True,
         push_videos: bool = True,
+        push_audio: bool = True,
         private: bool = False,
         allow_patterns: list[str] | str | None = None,
         upload_large_folder: bool = False,
@@ -513,6 +548,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         ignore_patterns = ["images/"]
         if not push_videos:
             ignore_patterns.append("videos/")
+        if not push_audio:
+            ignore_patterns.append("audio/")
 
         hub_api = HfApi()
         hub_api.create_repo(
@@ -553,10 +590,15 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 hub_api.delete_tag(self.repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
             hub_api.create_tag(self.repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset")
 
-    def _download(self, download_videos: bool = True) -> None:
+    def _download(self, download_videos: bool = True, download_audio: bool = True) -> None:
         """Downloads the dataset from the given 'repo_id' at the provided version."""
         ignore_patterns = None if download_videos else "videos/"
         files = None
+        ignore_patterns = []
+        if not download_videos:
+            ignore_patterns.append("videos/")
+        if not download_audio:
+            ignore_patterns.append("audio/")
         if self.episodes is not None:
             # Reader is guaranteed to exist here (created in __init__ before _download)
             files = self.reader.get_episodes_file_paths()
@@ -603,6 +645,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         image_writer_processes: int = 0,
         image_writer_threads: int = 0,
         video_backend: str | None = None,
+        audio_backend: str | None = None,
         batch_encoding_size: int = 1,
         vcodec: str = "libsvtav1",
         metadata_buffer_size: int = 10,

src/lerobot/datasets/utils.py

@@ -73,6 +73,7 @@ class ForwardCompatibilityError(CompatibilityError):
 DEFAULT_CHUNK_SIZE = 1000  # Max number of files per chunk
 DEFAULT_DATA_FILE_SIZE_IN_MB = 100  # Max size per file
 DEFAULT_VIDEO_FILE_SIZE_IN_MB = 200  # Max size per file
+DEFAULT_AUDIO_FILE_SIZE_IN_MB = 100  # Max size per file
 
 INFO_PATH = "meta/info.json"
 STATS_PATH = "meta/stats.json"
@@ -80,6 +81,7 @@ STATS_PATH = "meta/stats.json"
 EPISODES_DIR = "meta/episodes"
 DATA_DIR = "data"
 VIDEO_DIR = "videos"
+AUDIO_DIR = "audio"
 
 CHUNK_FILE_PATTERN = "chunk-{chunk_index:03d}/file-{file_index:03d}"
 DEFAULT_TASKS_PATH = "meta/tasks.parquet"
@@ -87,7 +89,12 @@ DEFAULT_SUBTASKS_PATH = "meta/subtasks.parquet"
 DEFAULT_EPISODES_PATH = EPISODES_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_DATA_PATH = DATA_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_VIDEO_PATH = VIDEO_DIR + "/{video_key}/" + CHUNK_FILE_PATTERN + ".mp4"
+DEFAULT_AUDIO_PATH = AUDIO_DIR + "/{audio_key}/" + CHUNK_FILE_PATTERN + ".m4a"
 DEFAULT_IMAGE_PATH = "images/{image_key}/episode-{episode_index:06d}/frame-{frame_index:06d}.png"
+DEFAULT_RAW_AUDIO_PATH = "raw_audio/{audio_key}/episode_{episode_index:06d}.wav"
+
+DEFAULT_AUDIO_CHUNK_DURATION = 0.5  # seconds
+DEFAULT_INITIAL_AUDIO_BUFFER_DURATION = 1.0  # seconds
 
 LEGACY_EPISODES_PATH = "meta/episodes.jsonl"
 LEGACY_EPISODES_STATS_PATH = "meta/episodes_stats.jsonl"

src/lerobot/datasets/video_utils.py

@@ -486,42 +486,42 @@ def encode_video_frames(
         raise OSError(f"Video encoding did not work. File not found: {video_path}.")
 
 
-def concatenate_video_files(
-    input_video_paths: list[Path | str], output_video_path: Path, overwrite: bool = True
+def concatenate_media_files(
+    input_media_paths: list[Path | str], output_media_path: Path, overwrite: bool = True
 ):
     """
-    Concatenate multiple video files into a single video file using pyav.
+    Concatenate multiple media files (video & audio) into a single media file using pyav.
 
-    This function takes a list of video input file paths and concatenates them into a single
-    output video file. It uses ffmpeg's concat demuxer with stream copy mode for fast
+    This function takes a list of input media file paths and concatenates them into a single
+    output media file. It uses ffmpeg's concat demuxer with stream copy mode for fast
     concatenation without re-encoding.
 
     Args:
-        input_video_paths: Ordered list of input video file paths to concatenate.
-        output_video_path: Path to the output video file.
-        overwrite: Whether to overwrite the output video file if it already exists. Default is True.
+        input_media_paths: Ordered list of input media file paths to concatenate.
+        output_media_path: Path to the output media file.
+        overwrite: Whether to overwrite the output media file if it already exists. Default is True.
 
     Note:
-        - Creates a temporary directory for intermediate files that is cleaned up after use.
-        - Uses ffmpeg's concat demuxer which requires all input videos to have the same
+        - Creates a temporary .ffconcat file and container audio/video file that are cleaned up after use.
+        - Uses ffmpeg's concat demuxer which requires all input media files to have the same
           codec, resolution, and frame rate for proper concatenation.
     """
 
-    output_video_path = Path(output_video_path)
+    output_media_path = Path(output_media_path)
 
-    if output_video_path.exists() and not overwrite:
-        logger.warning(f"Video file already exists: {output_video_path}. Skipping concatenation.")
+    if output_media_path.exists() and not overwrite:
+        logging.warning(f"Media file already exists: {output_media_path}. Skipping concatenation.")
         return
 
-    output_video_path.parent.mkdir(parents=True, exist_ok=True)
+    output_media_path.parent.mkdir(parents=True, exist_ok=True)
 
-    if len(input_video_paths) == 0:
-        raise FileNotFoundError("No input video paths provided.")
+    if len(input_media_paths) == 0:
+        raise FileNotFoundError("No input media paths provided.")
 
-    # Create a temporary .ffconcat file to list the input video paths
+    # Create a temporary .ffconcat file to list the input media paths
     with tempfile.NamedTemporaryFile(mode="w", suffix=".ffconcat", delete=False) as tmp_concatenate_file:
         tmp_concatenate_file.write("ffconcat version 1.0\n")
-        for input_path in input_video_paths:
+        for input_path in input_media_paths:
             tmp_concatenate_file.write(f"file '{str(input_path.resolve())}'\n")
         tmp_concatenate_file.flush()
         tmp_concatenate_path = tmp_concatenate_file.name
@@ -531,11 +531,12 @@ def concatenate_video_files(
         tmp_concatenate_path, mode="r", format="concat", options={"safe": "0"}
     )  # safe = 0 allows absolute paths as well as relative paths
 
-    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_named_file:
-        tmp_output_video_path = tmp_named_file.name
+    # Using an intermediate container to store the concatenated media file is necessary to avoid inplace concatenation read-write race conditions.
+    with tempfile.NamedTemporaryFile(suffix=output_media_path.suffix, delete=False) as tmp_named_file:
+        tmp_output_media_path = tmp_named_file.name
 
     output_container = av.open(
-        tmp_output_video_path, mode="w", options={"movflags": "faststart"}
+        tmp_output_media_path, mode="w", options={"movflags": "faststart"}
     )  # faststart is to move the metadata to the beginning of the file to speed up loading
 
     # Replicate input streams in output container
@@ -550,6 +551,7 @@ def concatenate_video_files(
             stream_map[input_stream.index].time_base = input_stream.time_base
 
     # Demux + remux packets (no re-encode)
+    last_dts = None
     for packet in input_container.demux():
         # Skip packets from un-mapped streams
         if packet.stream.index not in stream_map:
@@ -558,6 +560,16 @@ def concatenate_video_files(
         # Skip demux flushing packets
         if packet.dts is None:
             continue
+        else:
+            # Enforce strictly increasing decoding timestamps (DTS)
+            if last_dts is not None and packet.dts <= last_dts:
+                shift = last_dts - packet.dts + 1
+                packet.dts += shift
+                packet.pts += shift  # Presenting timestamps (PTS) are the same as DTS here
+                logging.warning(
+                    f"Non-monotonic DTS; previous: {last_dts}, current: {packet.dts - shift}; changing to {packet.dts}. This may result in incorrect timestamps in the output file."
+                )
+            last_dts = packet.dts
 
         output_stream = stream_map[packet.stream.index]
         packet.stream = output_stream
@@ -565,7 +577,7 @@ def concatenate_video_files(
 
     input_container.close()
     output_container.close()
-    shutil.move(tmp_output_video_path, output_video_path)
+    shutil.move(tmp_output_media_path, output_media_path)
     Path(tmp_concatenate_path).unlink()
 
 
@@ -947,38 +959,6 @@ with warnings.catch_warnings():
     register_feature(VideoFrame, "VideoFrame")
 
 
-def get_audio_info(video_path: Path | str) -> dict:
-    # Set logging level
-    logging.getLogger("libav").setLevel(av.logging.WARNING)
-
-    # Getting audio stream information
-    audio_info = {}
-    with av.open(str(video_path), "r") as audio_file:
-        try:
-            audio_stream = audio_file.streams.audio[0]
-        except IndexError:
-            # Reset logging level
-            av.logging.restore_default_callback()
-            return {"has_audio": False}
-
-        audio_info["audio.channels"] = audio_stream.channels
-        audio_info["audio.codec"] = audio_stream.codec.canonical_name
-        # In an ideal loseless case : bit depth x sample rate x channels = bit rate.
-        # In an actual compressed case, the bit rate is set according to the compression level : the lower the bit rate, the more compression is applied.
-        audio_info["audio.bit_rate"] = audio_stream.bit_rate
-        audio_info["audio.sample_rate"] = audio_stream.sample_rate  # Number of samples per second
-        # In an ideal loseless case : fixed number of bits per sample.
-        # In an actual compressed case : variable number of bits per sample (often reduced to match a given depth rate).
-        audio_info["audio.bit_depth"] = audio_stream.format.bits
-        audio_info["audio.channel_layout"] = audio_stream.layout.name
-        audio_info["has_audio"] = True
-
-    # Reset logging level
-    av.logging.restore_default_callback()
-
-    return audio_info
-
-
 def get_video_info(video_path: Path | str) -> dict:
     # Set logging level
     logging.getLogger("libav").setLevel(av.logging.WARNING)
@@ -1008,9 +988,6 @@ def get_video_info(video_path: Path | str) -> dict:
     # Reset logging level
     av.logging.restore_default_callback()
 
-    # Adding audio stream information
-    video_info.update(**get_audio_info(video_path))
-
     return video_info
 
 
@@ -1025,22 +1002,22 @@ def get_video_pixel_channels(pix_fmt: str) -> int:
         raise ValueError("Unknown format")
 
 
-def get_video_duration_in_s(video_path: Path | str) -> float:
+def get_media_duration_in_s(media_path: Path | str, media_type: str = "video") -> float:
     """
-    Get the duration of a video file in seconds using PyAV.
+    Get the duration of a media file (video & audio) in seconds using PyAV.
 
     Args:
-        video_path: Path to the video file.
+        media_path: Path to the media file.
 
     Returns:
-        Duration of the video in seconds.
+        Duration of the media file in seconds.
     """
-    with av.open(str(video_path)) as container:
-        # Get the first video stream
-        video_stream = container.streams.video[0]
+    with av.open(str(media_path)) as container:
+        # Get the first stream
+        stream = container.streams.video[0] if media_type == "video" else container.streams.audio[0]
         # Calculate duration: stream.duration * stream.time_base gives duration in seconds
-        if video_stream.duration is not None:
-            duration = float(video_stream.duration * video_stream.time_base)
+        if stream.duration is not None:
+            duration = float(stream.duration * stream.time_base)
         else:
             # Fallback to container duration if stream duration is not available
             duration = float(container.duration / av.time_base)
@@ -1049,12 +1026,12 @@ def get_video_duration_in_s(video_path: Path | str) -> float:
 
 class VideoEncodingManager:
     """
-    Context manager that ensures proper video encoding and data cleanup even if exceptions occur.
+    Context manager that ensures proper video and audio encoding and data cleanup even if exceptions occur.
 
     This manager handles:
     - Batch encoding for any remaining episodes when recording interrupted
-    - Cleaning up temporary image files from interrupted episodes
-    - Removing empty image directories
+    - Cleaning up temporary image and audio files from interrupted episodes
+    - Removing empty image and audio directories
 
     Args:
         dataset: The LeRobotDataset instance
@@ -1091,4 +1068,16 @@ class VideoEncodingManager:
             else:
                 logger.debug(f"Images directory is not empty, containing {len(png_files)} PNG files")
 
+        # Clean up any remaining audio directory if it's empty
+        audio_dir = self.dataset.root / "raw_audio"
+        # Check for any remaining WAV files
+        wav_files = list(audio_dir.rglob("*.wav"))
+        if len(wav_files) == 0:
+            # Only remove the raw_audio directory if no WAV files remain
+            if audio_dir.exists():
+                shutil.rmtree(audio_dir)
+                logging.debug("Cleaned up empty audio directory")
+        else:
+            logging.debug(f"Audio directory is not empty, containing {len(wav_files)} WAV files")
+
         return False  # Don't suppress the original exception

src/lerobot/microphones/__init__.py

@@ -0,0 +1,17 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .configs import MicrophoneConfig
+from .microphone import Microphone
+from .utils import make_microphones_from_configs

src/lerobot/microphones/configs.py

@@ -0,0 +1,28 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import abc
+from dataclasses import dataclass
+
+import draccus
+
+
+@dataclass(kw_only=True)
+class MicrophoneConfig(draccus.ChoiceRegistry, abc.ABC):
+    sample_rate: int | None = None
+    channels: list[int] | None = None
+
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)

src/lerobot/microphones/microphone.py

@@ -0,0 +1,140 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import abc
+from pathlib import Path
+from threading import Barrier
+from typing import Any
+
+import numpy as np
+
+from .configs import MicrophoneConfig
+
+
+class Microphone(abc.ABC):
+    """Base class for microphone implementations.
+
+    Defines a standard interface for microphone operations across different backends.
+    Subclasses must implement all abstract methods.
+
+    Manages basic microphone properties (sample rate, channels) and core operations:
+    - Connection/disconnection
+    - Start/stop recording
+    - Audio chunk reading
+
+    Attributes:
+        sample_rate (int | None): Configured sample rate in Hz
+        channels (list[int] | None): List of channel numbers to record
+
+    Example:
+        class MyMicrophone(Microphone):
+            def __init__(self, config): ...
+            @property
+            def is_connected(self) -> bool: ...
+            def connect(self): ...
+            # Plus other required methods
+    """
+
+    def __init__(self, config: MicrophoneConfig):
+        """Initialize the microphone with the given configuration.
+
+        Args:
+            config: Microphone configuration containing sample rate and channels.
+        """
+        self.sample_rate: int | None = config.sample_rate
+        self.channels: list[int] | None = config.channels
+
+    @property
+    @abc.abstractmethod
+    def is_connected(self) -> bool:
+        """Check if the microphone is currently connected.
+
+        Returns:
+            bool: True if the microphone is connected and ready to start recording,
+                  False otherwise.
+        """
+        pass
+
+    @property
+    @abc.abstractmethod
+    def is_recording(self) -> bool:
+        """Check if the microphone is currently recording.
+
+        Returns:
+            bool: True if the microphone is recording, False otherwise.
+        """
+        pass
+
+    @property
+    @abc.abstractmethod
+    def is_writing(self) -> bool:
+        """Check if the microphone is currently writing to a file.
+
+        Returns:
+            bool: True if the microphone is writing to a file, False otherwise.
+        """
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def find_microphones() -> list[dict[str, Any]]:
+        """Detects available microphones connected to the system.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries,
+            where each dictionary contains information about a detected microphone.
+        """
+        pass
+
+    @abc.abstractmethod
+    def connect(self) -> None:
+        """Establish connection to the microphone."""
+        pass
+
+    @abc.abstractmethod
+    def start_recording(
+        self,
+        output_file: str | Path | None = None,
+        multiprocessing: bool | None = False,
+        overwrite: bool | None = True,
+        barrier: Barrier | None = None,
+    ) -> None:
+        """Start recording audio from the microphone.
+
+        Args:
+            output_file: Optional path to save the recorded audio.
+            multiprocessing: If True, enables multiprocessing for recording. Defaults to multithreading otherwise.
+            overwrite: If True, overwrites existing files at output_file path.
+            barrier: If not None, ensures that multiple microphones start recording at the same time.
+        """
+        pass
+
+    @abc.abstractmethod
+    def read(self) -> np.ndarray:
+        """Capture and return a single audio chunk from the microphone.
+
+        Returns:
+            np.ndarray: Captured audio chunk as a numpy array.
+        """
+        pass
+
+    @abc.abstractmethod
+    def stop_recording(self) -> None:
+        """Stop recording audio from the microphone."""
+        pass
+
+    @abc.abstractmethod
+    def disconnect(self) -> None:
+        """Disconnect the microphone and release any resources."""
+        pass

src/lerobot/microphones/portaudio/__init__.py

@@ -0,0 +1,16 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .configuration_portaudio import PortAudioMicrophoneConfig
+from .microphone_portaudio import PortAudioMicrophone

src/lerobot/microphones/portaudio/configuration_portaudio.py

@@ -0,0 +1,41 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+
+from ..configs import MicrophoneConfig
+
+
+@MicrophoneConfig.register_subclass("portaudio")
+@dataclass
+class PortAudioMicrophoneConfig(MicrophoneConfig):
+    """Configuration class for PortAudio-based microphone devices.
+
+    This class provides configuration options for microphones accessed through PortAudio with the sounddevice Python package.
+    including device index, sample rate and channels.
+
+    Example configurations:
+    ```python
+    # Basic configurations
+    PortAudioMicrophoneConfig(0, 16000, [1])  # Device index 0, 16000Hz, mono
+    PortAudioMicrophoneConfig(1, 44100, [1, 2])  # Device index 1, 44100Hz, stereo
+    ```
+
+    Attributes:
+        microphone_index: Device index for the microphone.
+        sample_rate: Sample rate in Hz for the microphone.
+        channels: List of channel numbers to use for the microphone.
+    """
+
+    microphone_index: int

src/lerobot/microphones/portaudio/interface_sounddevice_sdk.py

@@ -0,0 +1,394 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import abc
+import time
+from collections.abc import Callable
+from threading import Event, Thread
+from typing import Any
+
+import numpy as np
+from sounddevice import PortAudioError
+
+from lerobot.utils.robot_utils import precise_sleep
+
+
+# --- Interface definitions for InputStream ---
+class IInputStream(abc.ABC):
+    @abc.abstractmethod
+    def __init__(
+        self,
+        samplerate: float | None = None,
+        blocksize: int | None = None,
+        device: int | str | None = None,
+        channels: int | None = None,
+        dtype: str | np.dtype | None = None,
+        latency: float | str | None = None,
+        callback: Callable[[Any, int, Any, Any], None] | None = None,
+    ):
+        pass
+
+    @abc.abstractmethod
+    def start(self) -> None:
+        pass
+
+    @abc.abstractmethod
+    def stop(self) -> None:
+        pass
+
+    @abc.abstractmethod
+    def close(self) -> None:
+        pass
+
+
+class ISounddeviceSDK(abc.ABC):
+    """Interface defining the contract for the Sounddevice SDK."""
+
+    InputStream: type[IInputStream]
+
+    @abc.abstractmethod
+    def query_devices(self, device: int | str | None = None, kind: str | None = None) -> list[dict[str, Any]]:
+        pass
+
+
+# --- Real SDK Adapter ---
+
+
+class SounddeviceSDKAdapter(ISounddeviceSDK):
+    """Adapts the real sounddevice library to the ISounddeviceSDK interface."""
+
+    _sounddevice = None
+
+    def __init__(self):
+        try:
+            import sounddevice
+
+            SounddeviceSDKAdapter._sounddevice = sounddevice
+        except ImportError as e:
+            raise ImportError("sounddevice library not found") from e
+
+    # --- Inner Class Implementation ---
+    class RealInputStream(IInputStream):
+        def __init__(
+            self,
+            samplerate: int | None = None,
+            blocksize: int | None = None,
+            device: int | None = None,
+            channels: int | None = None,
+            dtype: str | np.dtype | None = None,
+            latency: float | str | None = None,
+            callback: Callable[[Any, int, Any, Any], None] | None = None,
+        ):
+            import sounddevice
+
+            self._input_stream = sounddevice.InputStream(
+                samplerate=samplerate,
+                blocksize=blocksize,
+                device=device,
+                channels=channels,
+                dtype=dtype,
+                latency=latency,
+                callback=callback,
+            )
+
+        def start(self) -> None:
+            self._input_stream.start()
+
+        def stop(self) -> None:
+            self._input_stream.stop()
+
+        def close(self) -> None:
+            self._input_stream.close()
+
+        def __del__(self):
+            self._input_stream.stop()
+            self._input_stream.close()
+
+        @property
+        def active(self) -> bool:
+            return self._input_stream.active
+
+        @property
+        def stopped(self) -> bool:
+            return self._input_stream.stopped
+
+        @property
+        def closed(self) -> bool:
+            return self._input_stream.closed
+
+    InputStream = RealInputStream
+
+    def query_devices(self, device: int | str | None = None, kind: str | None = None) -> list[dict[str, Any]]:
+        return SounddeviceSDKAdapter._sounddevice.query_devices(device, kind)
+
+
+# Emulates a 48kHz stereo microphone
+VALID_DTYPE = {
+    "float32",
+    "int32",
+    "int16",
+    "int8",
+    "uint8",
+    np.float32,
+    np.int32,
+    np.int16,
+    np.int8,
+    np.uint8,
+}
+VALID_LATENCY = {"low", "high"}
+
+VALID_DEVICES = [
+    {
+        "index": 0,
+        "name": "Built-in Microphone",
+        "hostapi": 0,
+        "max_input_channels": 2,
+        "max_output_channels": 0,
+        "default_low_input_latency": 0.01,
+        "default_low_output_latency": 0.001,
+        "default_high_input_latency": 0.1,
+        "default_high_output_latency": 0.01,
+        "default_samplerate": 48000.0,
+    },
+    {
+        "index": 1,
+        "name": "Built-in Output",
+        "hostapi": 0,
+        "max_input_channels": 0,
+        "max_output_channels": 2,
+        "default_low_input_latency": 0.04,
+        "default_low_output_latency": 0.04,
+        "default_high_input_latency": 0.12,
+        "default_high_output_latency": 0.12,
+        "default_samplerate": 48000.0,
+    },
+    {
+        "index": 2,
+        "name": "USB Audio Device",
+        "hostapi": 0,
+        "max_input_channels": 1,
+        "max_output_channels": 0,
+        "default_low_input_latency": 0.03,
+        "default_low_output_latency": 0.01,
+        "default_high_input_latency": 0.04,
+        "default_high_output_latency": 0.03,
+        "default_samplerate": 16000.0,
+    },
+]
+
+# -- Fake SDK Adapter ---
+
+
+class FakeSounddeviceSDKAdapter(ISounddeviceSDK):
+    """Implements the ISounddeviceSDK interface with fake behaviour for testing."""
+
+    # --- Inner Class Implementation ---
+    class FakeInputStream(IInputStream):
+        def __init__(
+            self,
+            samplerate: float | None = None,
+            blocksize: int | None = None,
+            device: int | str | None = None,
+            channels: int | None = None,
+            dtype: str | None = None,
+            latency: str | None = None,
+            callback: Callable[[Any, int, Any, Any], None] | None = None,
+        ):
+            self.samplerate = samplerate
+            self.blocksize = blocksize
+            self.device = device
+            self.channels = channels
+            self.dtype = dtype
+            self.latency = latency
+            self.callback = callback
+
+            self._validate_settings()
+
+            self._active = False
+            self._closed = False
+
+            if self.callback is not None:
+                self._streaming_thread = Thread(target=self._streaming_loop, daemon=True)
+                self._streaming_thread_stop_event = Event()
+
+        @property
+        def active(self) -> bool:
+            """True when the stream is active, False otherwise."""
+            return self._active
+
+        @property
+        def stopped(self) -> bool:
+            """True when the stream is stopped, False otherwise."""
+            return not self._active
+
+        @property
+        def closed(self) -> bool:
+            """True after a call to close(), False otherwise."""
+            return self._closed
+
+        def _get_device_info(self):
+            """Returns the device info for the device."""
+            for device in VALID_DEVICES:
+                if (isinstance(self.device, int) and device["index"] == self.device) or (
+                    isinstance(self.device, str) and device["name"] == self.device
+                ):
+                    return device
+            raise PortAudioError(f"No input device matching {self.device}")
+
+        def _validate_device(self):
+            """Validates the device against the valid devices."""
+            valid_device_indices = [device["index"] for device in VALID_DEVICES]
+            valid_device_names = [device["name"] for device in VALID_DEVICES]
+
+            if self.device is not None:
+                if isinstance(self.device, (int, str)):
+                    # Check if device index is valid
+                    if isinstance(self.device, int) and self.device not in valid_device_indices:
+                        raise PortAudioError(f"Error querying device {self.device}")
+
+                    # Check if device name is valid
+                    if isinstance(self.device, str) and self.device not in valid_device_names:
+                        raise PortAudioError(f"No input device matching {self.device}")
+                else:
+                    raise PortAudioError(f"Device must be int or str, got {type(self.device)}")
+            else:
+                # Default to first input device
+                input_devices = [d for d in VALID_DEVICES if d["max_input_channels"] > 0]
+                if input_devices:
+                    self.device = input_devices[0]["index"]
+
+        def _validate_samplerate(self):
+            """Validates the samplerate against the device's maximum samplerate."""
+            device_info = self._get_device_info()
+            if self.samplerate is None:
+                self.samplerate = device_info["default_samplerate"]
+            elif self.samplerate > device_info["default_samplerate"] or self.samplerate < 1000:
+                raise PortAudioError("Error opening InputStream: Invalid sample rate")
+
+        def _validate_channels(self):
+            """Validates the channels against the device's maximum channels."""
+            device_info = self._get_device_info()
+            if self.channels is None:
+                self.channels = device_info["max_input_channels"]
+            elif self.channels > device_info["max_input_channels"] or self.channels < 1:
+                raise PortAudioError("Error opening InputStream: Invalid number of channels")
+
+        def _validate_dtype(self):
+            """Validates the dtype against the valid dtypes."""
+            if self.dtype is not None:
+                if self.dtype not in VALID_DTYPE:
+                    raise PortAudioError("Invalid input sample format")
+            else:
+                self.dtype = "float32"  # Default dtype
+
+        def _validate_latency(self):
+            """Validates the latency against the valid latencies."""
+            if self.latency is not None:
+                if self.latency not in VALID_LATENCY:
+                    raise PortAudioError("Invalid latency")
+            else:
+                self.latency = "low"  # Default latency
+
+            if isinstance(self.latency, str):
+                device_info = self._get_device_info()
+                if self.latency == "low":
+                    self.latency = device_info["default_low_input_latency"]
+                elif self.latency == "high":
+                    self.latency = device_info["default_high_input_latency"]
+
+        def _validate_settings(self):
+            """Validates the input parameters against available devices and valid options."""
+            self._validate_device()
+            self._validate_samplerate()
+            self._validate_channels()
+            self._validate_dtype()
+            self._validate_latency()
+
+        def _simulated_audio_data(self) -> np.ndarray:
+            """Generates a simulated audio signal for testing purposes with proper value ranges."""
+            duration_samples = int(self.samplerate * self.latency)
+
+            # Generate output according to dtype
+            if self.dtype in {"float32", np.float32}:
+                # Generate values between -1 and 1 for float32
+                data = np.random.uniform(-1.0, 1.0, (duration_samples, self.channels)).astype(self.dtype)
+            else:
+                # Use np.iinfo to get proper range for integer types
+                info = np.iinfo(self.dtype)
+                data = np.random.randint(
+                    info.min, info.max + 1, (duration_samples, self.channels), dtype=self.dtype
+                )
+
+            return data
+
+        def _streaming_loop(self):
+            if self.callback is not None:
+                while not self._streaming_thread_stop_event.is_set():
+                    precise_sleep(self.latency)
+                    tmp_data = self._simulated_audio_data()
+                    self.callback(
+                        tmp_data,
+                        len(tmp_data),
+                        time.perf_counter(),
+                        None,
+                    )
+
+        def start(self) -> None:
+            """Start the fake input stream."""
+            if not self.active and self.callback is not None:
+                self._streaming_thread.start()
+            self._active = True
+
+        def stop(self) -> None:
+            """Stop the fake input stream."""
+            if self.callback is not None:
+                self._streaming_thread_stop_event.set()
+                self._streaming_thread.join()
+            self._active = False
+
+        def close(self) -> None:
+            """Close the fake input stream."""
+            if self.active and self.callback is not None:
+                self.stop()
+            self._active = False
+            self._closed = True
+
+        def __del__(self):
+            self.close()
+
+    InputStream = FakeInputStream
+
+    def query_devices(self, device: int | str | None = None, kind: str | None = None) -> list[dict[str, Any]]:
+        """Returns a realistic list of audio devices including speakers and microphones."""
+        if device is not None:
+            # Return specific device
+            for valid_device in VALID_DEVICES:
+                if (isinstance(device, int) and valid_device["index"] == device) or (
+                    isinstance(device, str) and valid_device["name"] == device
+                ):
+                    return valid_device
+            raise PortAudioError(f"Error querying device {device}")
+
+        elif kind is not None:
+            for valid_device in VALID_DEVICES:
+                if (
+                    valid_device["max_input_channels"] > 0
+                    and kind == "input"
+                    or valid_device["max_output_channels"] > 0
+                    and kind == "output"
+                ):
+                    return valid_device
+            raise PortAudioError(f"No {kind} device found")
+
+        return VALID_DEVICES

src/lerobot/microphones/portaudio/microphone_portaudio.py

@@ -0,0 +1,566 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Provides the PortAudioMicrophone class for capturing audio from microphones using the PortAudio library through the sounddevice Python package.
+"""
+
+import logging
+import time
+from multiprocessing import (
+    Event as process_Event,
+    JoinableQueue as process_Queue,
+    Process,
+)
+from pathlib import Path
+from queue import Empty
+from threading import Barrier, Event, Event as thread_Event, Thread
+from typing import Any
+
+import numpy as np
+from soundfile import SoundFile
+
+from lerobot.microphones.portaudio.interface_sounddevice_sdk import ISounddeviceSDK, SounddeviceSDKAdapter
+from lerobot.utils.errors import (
+    DeviceAlreadyConnectedError,
+    DeviceAlreadyRecordingError,
+    DeviceNotConnectedError,
+    DeviceNotRecordingError,
+)
+from lerobot.utils.shared_array import SharedArray
+
+from ..microphone import Microphone
+from .configuration_portaudio import PortAudioMicrophoneConfig
+
+logger = logging.getLogger(__name__)
+
+
+class PortAudioMicrophone(Microphone):
+    """
+    The PortAudioMicrophone class handles all microphones compatible with sounddevice (and the underlying PortAudio library). Most microphones and sound cards are compatible, across all OS (Linux, Mac, Windows).
+
+    A PortAudioMicrophone instance requires the sounddevice index of the microphone, which may be obtained using `python -m sounddevice`. It also requires the recording sample rate as well as the list of recorded channels.
+
+    Example of usage:
+    ```python
+    from lerobot.common.robot_devices.microphones.configs import PortAudioMicrophoneConfig
+
+    config = PortAudioMicrophoneConfig(microphone_index=0, sample_rate=16000, channels=[1])
+    microphone = PortAudioMicrophone(config)
+
+    microphone.connect()
+    microphone.start_recording("some/output/file.wav")
+    ...
+    audio_readings = microphone.read()  # Gets all recorded audio data since the last read or since the beginning of the recording. The longer the period the longer the reading time !
+    ...
+    microphone.stop_recording()
+    microphone.disconnect()
+    ```
+    """
+
+    def __init__(self, config: PortAudioMicrophoneConfig, sounddevice_sdk: ISounddeviceSDK = None):
+        """
+        Initializes the PortAudioMicrophone instance.
+
+        Args:
+            config: The configuration settings for the microphone.
+        """
+        super().__init__(config)
+
+        if sounddevice_sdk is None:
+            self.sounddevice_sdk = SounddeviceSDKAdapter()
+        else:
+            self.sounddevice_sdk = sounddevice_sdk
+
+        # Microphone index
+        self.microphone_index = config.microphone_index
+
+        # Input audio recording process and events
+        self.record_process = None
+        self.record_stop_event = process_Event()
+        self.record_start_event = process_Event()
+        self.record_close_event = process_Event()
+        self.record_is_started_event = process_Event()
+        self.audio_callback_start_event = process_Event()
+
+        # Process-safe concurrent queue to send audio from the recording process to the writing process/thread
+        self.write_queue = process_Queue()
+
+        # SharedArray to store audio from the recording process.
+        self.read_shared_array = None
+        self.local_read_shared_array = None
+        # Thread/Process to handle data writing in a separate thread/process (safely)
+        self.write_thread = None
+        self.write_stop_event = None
+        self.write_is_started_event = None
+
+        self.logs = {}
+
+    def __str__(self) -> str:
+        return f"{self.__class__.__name__}({self.microphone_index})"
+
+    @property
+    def is_connected(self) -> bool:
+        return self.record_process is not None and self.record_process.is_alive()
+
+    @property
+    def is_recording(self) -> bool:
+        return self.record_is_started_event.is_set()
+
+    @property
+    def is_writing(self) -> bool:
+        return self.write_thread is not None and self.write_is_started_event.is_set()
+
+    @staticmethod
+    def find_microphones(
+        device: int | str | None = None, sounddevice_sdk: ISounddeviceSDK = None
+    ) -> list[dict[str, Any]] | dict[str, Any]:
+        """
+        Detects available microphones connected to the system.
+
+        Args:
+            device: The device to find microphones for. If None, all microphones are found.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries,
+            where each dictionary contains information about a detected microphone : index, name, sample rate, channels.
+        """
+
+        if sounddevice_sdk is None:
+            sounddevice_sdk = SounddeviceSDKAdapter()
+
+        found_microphones_info = []
+
+        devices = sounddevice_sdk.query_devices()
+        for d in devices:
+            if d["max_input_channels"] > 0:
+                microphone_info = {
+                    "index": d["index"],
+                    "name": d["name"],
+                    "sample_rate": int(d["default_samplerate"]),
+                    "channels": np.arange(1, d["max_input_channels"] + 1),
+                }
+
+                if device is None or (
+                    (isinstance(device, int) and d["index"] == device)
+                    or (isinstance(device, str) and d["name"] == device)
+                ):
+                    found_microphones_info.append(microphone_info)
+
+        if device is not None:
+            if len(found_microphones_info) == 0:
+                raise RuntimeError(f"No microphone found for device {device}")
+            else:
+                return found_microphones_info[0]
+
+        if len(found_microphones_info) == 0:
+            logger.warning("No microphone found !")
+
+        return found_microphones_info
+
+    def _configure_capture_settings(self) -> None:
+        """
+        Validates the microphone index, sample rate and channels settings specified in the constructor's config to the un-connected microphone.
+
+        This method actually checks the specified settings and fills the sample rate and channels settings if not specified before attempting to start a PortAudio stream.
+
+        Raises:
+            RuntimeError: If one of the specified settings is not compatible with the microphone.
+            DeviceAlreadyConnectedError: If the microphone is connected when attempting to configure settings.
+        """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(
+                f"Cannot configure settings for {self} as it is already connected."
+            )
+
+        self._validate_microphone_index()
+        self._validate_sample_rate()
+        self._validate_channels()
+
+    def _validate_microphone_index(self) -> None:
+        """ "Validates the microphone index against available devices by checking if it has at least one input channel."""
+
+        try:
+            PortAudioMicrophone.find_microphones(self.microphone_index, self.sounddevice_sdk)
+        except RuntimeError as e:
+            raise RuntimeError(
+                f"{e}. Available microphones: {PortAudioMicrophone.find_microphones(sounddevice_sdk=self.sounddevice_sdk)}"
+            ) from e
+
+    def _validate_sample_rate(self) -> None:
+        """Validates the sample rate against the actual microphone's default sample rate."""
+
+        actual_sample_rate = PortAudioMicrophone.find_microphones(
+            self.microphone_index, self.sounddevice_sdk
+        )["sample_rate"]
+
+        if self.sample_rate is not None:
+            try:
+                self.sample_rate = int(self.sample_rate)
+            except ValueError as e:
+                raise RuntimeError(
+                    f"Cannot convert the provided sample rate ({self.sample_rate} Hz) to an integer."
+                ) from e
+
+            if self.sample_rate > actual_sample_rate or self.sample_rate < 1000:
+                raise RuntimeError(
+                    f"Provided sample rate {self.sample_rate} is either too low or too high compared to the sample rate of the microphone {actual_sample_rate}."
+                )
+            else:
+                if self.sample_rate < actual_sample_rate:
+                    logger.warning(
+                        "Provided sample rate is lower than the sample rate of the microphone. Performance may be impacted."
+                    )
+        else:
+            self.sample_rate = actual_sample_rate
+
+    def _validate_channels(self) -> None:
+        """Validates the channels against the actual microphone's maximum input channels."""
+
+        actual_channels = PortAudioMicrophone.find_microphones(self.microphone_index, self.sounddevice_sdk)[
+            "channels"
+        ]
+
+        if self.channels is not None and len(self.channels) > 0:
+            if not all(channel in actual_channels for channel in self.channels):
+                raise RuntimeError(
+                    f"Some of the provided channels {self.channels} are outside the possible channel range of the microphone {actual_channels}."
+                )
+        else:
+            self.channels = actual_channels
+
+        # Get channels index instead of number for slicing
+        self.channels_index = np.array(self.channels) - 1
+
+    def connect(self) -> None:
+        """
+        Connects the microphone and checks if the requested acquisition parameters are compatible with the microphone.
+        """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"Microphone {self.microphone_index} is already connected.")
+
+        self._configure_capture_settings()
+
+        # Create or reset queue and shared array
+        self.read_shared_array = SharedArray(
+            shape=(self.sample_rate * 10, len(self.channels)),
+            dtype=np.dtype("float32"),
+        )
+        self.local_read_shared_array = self.read_shared_array.get_local_array()
+        self.write_queue = process_Queue()
+
+        # Reset events
+        self.record_start_event.clear()
+        self.record_stop_event.clear()
+        self.record_close_event.clear()
+        self.record_is_started_event.clear()
+        self.audio_callback_start_event.clear()
+
+        # Create and start an audio input stream with a recording callback
+        # Remark: this is done in a separate process so that audio recording is not impacted by the main thread CPU usage, especially the precise_sleep function.
+        process_init_event = process_Event()
+        self.record_process = Process(
+            target=self._record_process,
+            args=(
+                self.microphone_index,
+                self.sample_rate,
+                self.channels,
+                process_init_event,
+                self.record_start_event,
+                self.record_stop_event,
+                self.record_close_event,
+                self.record_is_started_event,
+                self.audio_callback_start_event,
+                self.write_queue,
+                self.read_shared_array,
+                self.sounddevice_sdk,
+            ),
+        )
+        self.record_process.daemon = True
+        self.record_process.start()
+
+        is_init = process_init_event.wait(
+            timeout=5.0
+        )  # Wait for the recording process to be started, and to potentially raise an error on failure.
+        if not self.is_connected or not is_init:
+            raise RuntimeError(f"Error connecting microphone {self.microphone_index}.")
+
+        logger.info(f"{self} connected.")
+
+    def disconnect(self) -> None:
+        """
+        Disconnects the microphone and stops the recording.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Microphone {self.microphone_index} is not connected.")
+
+        if self.is_recording:
+            self.stop_recording()
+
+        self.record_close_event.set()
+        self.read_shared_array.delete()
+        self.write_queue.close()
+        self.record_process.join()
+
+        if self.is_connected:
+            raise RuntimeError(f"Error disconnecting microphone {self.microphone_index}.")
+
+        logger.info(f"{self} disconnected.")
+
+    def _read(self) -> np.ndarray:
+        """
+        Thread/Process-safe callback to read available audio data
+        """
+        return self.read_shared_array.read(self.local_read_shared_array, flush=True)
+
+    def read(self) -> np.ndarray:
+        """
+        Reads the last audio chunk recorded by the microphone, e.g. all samples recorded since the last read or since the beginning of the recording.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Microphone {self.microphone_index} is not connected.")
+        if not self.is_recording:
+            raise RuntimeError(f"Microphone {self.microphone_index} is not recording.")
+
+        start_time = time.perf_counter()
+
+        audio_readings = self._read()
+
+        # log the number of seconds it took to read the audio chunk
+        self.logs["delta_timestamp_s"] = time.perf_counter() - start_time
+
+        # log the utc time at which the audio chunk was received
+        self.logs["timestamp_utc"] = time.perf_counter()
+
+        return audio_readings
+
+    @staticmethod
+    def _record_process(
+        microphone_index,
+        sample_rate,
+        channels,
+        process_init_event,
+        record_start_event,
+        record_stop_event,
+        record_close_event,
+        record_is_started_event,
+        audio_callback_start_event,
+        write_queue,
+        read_shared_array,
+        sounddevice_sdk,
+    ) -> None:
+        """
+        Process callback used to create an unpickable sounddevice audio input stream with a recording callback and start, stop and close it based on multiprocessing events.
+        """
+
+        channels_index = np.array(channels) - 1
+        local_read_shared_array = read_shared_array.get_local_array()
+
+        def audio_callback(indata, frames, timestamp, status) -> None:
+            """
+            Low-level sounddevice callback.
+            """
+            if status:
+                logger.warning(status)
+            if audio_callback_start_event.is_set():
+                write_queue.put_nowait(indata[:, channels_index])
+                read_shared_array.write(local_read_shared_array, indata[:, channels_index])
+
+        # Create the audio stream
+        # InputStream must be instantiated in the process as it is not pickable.
+        stream = sounddevice_sdk.InputStream(
+            device=microphone_index,
+            samplerate=sample_rate,
+            channels=max(channels),
+            dtype="float32",
+            blocksize=0,  # Varying input buffer length, but no additional latency
+            latency="low",  # Low latency mode (not enabled by default !)
+            # never_drop_input=True, # Disabled as it generates an error for some devices
+            callback=audio_callback,
+        )
+        process_init_event.set()
+
+        while True:
+            start_flag = record_start_event.wait(timeout=0.1)
+            if record_close_event.is_set():
+                break
+            elif not start_flag:
+                continue
+            stream.start()
+            record_is_started_event.set()
+            record_stop_event.wait()
+            stream.stop()  # stream.stop() waits for all buffers to be processed, stream.abort() flushes the buffers !
+            record_is_started_event.clear()
+        stream.close()
+
+    def start_recording(
+        self,
+        output_file: str | None = None,
+        multiprocessing: bool | None = False,
+        overwrite: bool | None = True,
+        barrier: Barrier | None = None,
+    ) -> None:
+        """
+        Starts the recording of the microphone. If output_file is provided, the audio will be written to this file.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Microphone {self.microphone_index} is not connected.")
+        if self.is_recording:
+            raise DeviceAlreadyRecordingError(f"Microphone {self.microphone_index} is already recording.")
+
+        # Reset queue and shared memory
+        self.read_shared_array.reset()
+        self._clear_queue(self.write_queue)
+
+        # Reset stop event
+        self.record_stop_event.clear()
+
+        # Write recordings into a file if output_file is provided
+        if output_file is not None:
+            output_file = Path(output_file)
+            output_file.parent.mkdir(parents=True, exist_ok=True)
+
+            if output_file.exists():
+                if overwrite:
+                    output_file.unlink()
+                else:
+                    raise FileExistsError(
+                        f"Output file {output_file} already exists. Set overwrite to True to overwrite it."
+                    )
+
+            if multiprocessing:
+                self.write_stop_event = process_Event()
+                self.write_is_started_event = process_Event()
+                self.write_thread = Process(
+                    target=PortAudioMicrophone._write_loop,
+                    args=(
+                        self.write_queue,
+                        self.write_stop_event,
+                        self.write_is_started_event,
+                        self.sample_rate,
+                        self.channels,
+                        output_file,
+                    ),
+                )
+            else:
+                self.write_stop_event = thread_Event()
+                self.write_is_started_event = thread_Event()
+                self.write_thread = Thread(
+                    target=PortAudioMicrophone._write_loop,
+                    args=(
+                        self.write_queue,
+                        self.write_stop_event,
+                        self.write_is_started_event,
+                        self.sample_rate,
+                        self.channels,
+                        output_file,
+                    ),
+                )
+            self.write_thread.daemon = True
+            self.write_thread.start()
+            self.write_is_started_event.wait()  # Wait for the writing thread/process to be started.
+
+        self.record_start_event.set()  # Start the input audio stream process
+        self.record_is_started_event.wait()  # Wait for the input audio stream process to be actually started
+
+        if barrier is not None:
+            barrier.wait()  # Wait for multiple input audio streams to be started at the same time
+
+        self.audio_callback_start_event.set()
+
+        if not self.is_recording:
+            raise RuntimeError(f"Error starting recording for microphone {self.microphone_index}.")
+        if output_file is not None and not self.is_writing:
+            raise RuntimeError(f"Error starting writing for microphone {self.microphone_index}.")
+
+    def stop_recording(self) -> None:
+        """
+        Stops the recording of the microphones.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Microphone {self.microphone_index} is not connected.")
+        if not self.is_recording:
+            raise DeviceNotRecordingError(f"Microphone {self.microphone_index} is not recording.")
+
+        self.audio_callback_start_event.clear()
+        self.record_start_event.clear()  # Ensures the audio stream is not started again !
+        self.record_stop_event.set()
+
+        # Wait for the stream to be stopped (might lead to race condition if the stream is not properly stopped on array reset and queue clearing)
+        timeout = 1.0
+        while self.is_recording and timeout > 0:
+            time.sleep(0.01)
+            timeout -= 0.01
+
+        self.read_shared_array.reset()
+        self._clear_queue(self.write_queue, join_queue=True)
+
+        if self.is_writing:
+            self.write_stop_event.set()
+            self.write_thread.join()
+
+        if self.is_recording:
+            raise RuntimeError(f"Error stopping recording for microphone {self.microphone_index}.")
+        if self.is_writing:
+            raise RuntimeError(f"Error stopping writing for microphone {self.microphone_index}.")
+
+    @staticmethod
+    def _write_loop(
+        queue,
+        write_stop_event: Event,
+        write_is_started_event: Event,
+        sample_rate: int,
+        channels: list[int],
+        output_file: Path,
+    ) -> None:
+        """
+        Thread/Process-safe loop to write audio data into a file.
+        """
+        # Can only be run on a single process/thread for file writing safety
+        with SoundFile(
+            output_file,
+            mode="w",
+            samplerate=sample_rate,
+            channels=len(channels),
+            format="WAV",
+            subtype="FLOAT",  # By default, a much lower quality WAV file is created !
+        ) as file:
+            write_is_started_event.set()
+            while not write_stop_event.is_set():
+                try:
+                    file.write(
+                        queue.get(timeout=0.005)
+                    )  # Timeout set as the usual sounddevice buffer size. get_nowait is not possible here as it saturates the thread.
+                    queue.task_done()
+                except Empty:
+                    continue
+        write_is_started_event.clear()
+
+    def __del__(self) -> None:
+        if self.is_connected:
+            self.disconnect()
+
+    @staticmethod
+    def _clear_queue(queue, join_queue: bool = False):
+        """
+        Clears the queue by getting all items until it is empty. The longer the queue, the longer it takes to clear it.
+        """
+        try:
+            while True:
+                queue.get_nowait()
+                queue.task_done()
+        except Empty:
+            if join_queue:
+                queue.join()
+            return

src/lerobot/microphones/touchlab/__init__.py

@@ -0,0 +1,16 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .configuration_touchlab import TouchLabSensorConfig
+from .sensor_touchlab import TouchLabSensor

src/lerobot/microphones/touchlab/configuration_touchlab.py

@@ -0,0 +1,42 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+
+from ..configs import MicrophoneConfig
+
+
+@MicrophoneConfig.register_subclass("touchlab")
+@dataclass
+class TouchLabSensorConfig(MicrophoneConfig):
+    """Configuration class for TouchLab tactile sensors (technically not a microphone, but behaves like one acquisition-wise).
+
+    This class provides configuration options for TouchLab tactile sensors, including serial port, sample rate and channels.
+
+    Example configurations:
+    ```python
+    # Basic configurations
+    TouchLabSensorConfig("/dev/ttyACM0", 16000)  # Serial port /dev/ttyACM0, 16000Hz
+    TouchLabSensorConfig("/dev/ttyACM1", 44100)  # Serial port /dev/ttyACM1, 44100Hz
+    ```
+
+    Attributes:
+        sensor_port: Serial port of the tactile sensor.
+        baud_rate: Baud rate of the tactile sensor.
+        sample_rate: Sample rate in Hz for the tactile sensor.
+        channels: List of channel numbers to use for the tactile sensor.
+    """
+
+    sensor_port: str
+    baud_rate: int = 115_200

src/lerobot/microphones/touchlab/sensor_touchlab.py

@@ -0,0 +1,469 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Provides the TouchLabSensor class for capturing tactile data from TouchLab tactile sensors.
+"""
+
+import logging
+import time
+from multiprocessing import (
+    Event as process_Event,
+    JoinableQueue as process_Queue,
+    Process,
+)
+from pathlib import Path
+from queue import Empty
+from threading import Barrier, Event, Event as thread_Event, Thread
+from typing import Any
+
+import numpy as np
+from serial import Serial
+from soundfile import SoundFile
+
+from lerobot.utils.errors import (
+    DeviceAlreadyConnectedError,
+    DeviceAlreadyRecordingError,
+    DeviceNotConnectedError,
+    DeviceNotRecordingError,
+)
+from lerobot.utils.shared_array import SharedArray
+
+from ..microphone import Microphone
+from .configuration_touchlab import TouchLabSensorConfig
+
+logger = logging.getLogger(__name__)
+
+MAX_SERIAL_READ_SIZE = 512
+
+
+class TouchLabSensor(Microphone):
+    """
+    The TouchLabSensor class handles all TouchLab tactile sensors.
+
+    A TouchLabSensor instance requires the serial port of the tactile sensor, which may be obtained using `python -m lerobot.find_port`. It also requires the recording sample rate as well as the list of recorded channels.
+
+    Example of usage:
+    ```python
+    from lerobot.common.robot_devices.microphones.configs import TouchLabSensorConfig
+
+    config = TouchLabSensorConfig(sensor_port="/dev/ttyACM0", baud_rate=115200, sample_rate=115, channels=[1])
+    microphone = TouchLabSensor(config)
+
+    microphone.connect()
+    microphone.start_recording("some/output/file.wav")
+    ...
+    audio_readings = microphone.read()  # Gets all recorded audio data since the last read or since the beginning of the recording. The longer the period the longer the reading time !
+    ...
+    microphone.stop_recording()
+    microphone.disconnect()
+    ```
+    """
+
+    def __init__(self, config: TouchLabSensorConfig):
+        """ "
+        Initializes the TouchLabSensor instance.
+
+        Args:
+            config: The configuration settings for the sensor.
+        """
+        super().__init__(config)
+
+        # Sensor port
+        self.sensor_port = config.sensor_port
+
+        # Baud rate
+        self.baud_rate = config.baud_rate
+
+        # Input audio recording process and events
+        self.record_process = None
+        self.record_stop_event = process_Event()
+        self.record_start_event = process_Event()
+        self.record_close_event = process_Event()
+        self.record_is_started_event = process_Event()
+        self.audio_callback_start_event = process_Event()
+
+        # Process-safe concurrent queue to send audio from the recording process to the writing process/thread
+        self.write_queue = process_Queue()
+
+        # SharedArray to store audio from the recording process.
+        self.read_shared_array = None
+        self.local_read_shared_array = None
+        # Thread/Process to handle data writing in a separate thread/process (safely)
+        self.write_thread = None
+        self.write_stop_event = None
+        self.write_is_started_event = None
+
+        self.logs = {}
+
+    def __str__(self) -> str:
+        return f"{self.__class__.__name__}({self.sensor_port})"
+
+    @property
+    def is_connected(self) -> bool:
+        """Check if the sensor is currently connected.
+
+        Returns:
+            bool: True if the sensor is connected and ready to start recording,
+                  False otherwise.
+        """
+        return self.record_process is not None and self.record_process.is_alive()
+
+    @property
+    def is_recording(self) -> bool:
+        """Check if the sensor is currently recording.
+
+        Returns:
+            bool: True if the sensor is recording, False otherwise.
+        """
+        return self.record_is_started_event.is_set()
+
+    @property
+    def is_writing(self) -> bool:
+        """Check if the sensor is currently writing to a file.
+
+        Returns:
+            bool: True if the sensor is writing to a file, False otherwise.
+        """
+        return self.write_thread is not None and self.write_is_started_event.is_set()
+
+    @staticmethod
+    def find_microphones() -> list[dict[str, Any]]:
+        """Detects available sensors connected to the system.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries,
+            where each dictionary contains information about a detected sensor.
+        """
+        pass
+
+    def connect(self) -> None:
+        """
+        Establish connection to the sensor.
+        """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"Sensor connected to {self.sensor_port} is already connected.")
+
+        # Create or reset queue and shared array
+        self.read_shared_array = SharedArray(
+            shape=(self.sample_rate * 10, len(self.channels)),
+            dtype=np.dtype("int16"),
+        )
+        self.local_read_shared_array = self.read_shared_array.get_local_array()
+        self.write_queue = process_Queue()
+
+        # Reset events
+        self.record_start_event.clear()
+        self.record_stop_event.clear()
+        self.record_close_event.clear()
+        self.record_is_started_event.clear()
+        self.audio_callback_start_event.clear()
+
+        # Create and start an audio input stream with a recording callback
+        # Remark: this is done in a separate process so that audio recording is not impacted by the main thread CPU usage, especially the precise_sleep function.
+        process_init_event = process_Event()
+        self.record_process = Process(
+            target=self._record_process,
+            args=(
+                self.sensor_port,
+                self.baud_rate,
+                self.channels,
+                process_init_event,
+                self.record_start_event,
+                self.record_stop_event,
+                self.record_close_event,
+                self.record_is_started_event,
+                self.audio_callback_start_event,
+                self.write_queue,
+                self.read_shared_array,
+            ),
+        )
+        self.record_process.daemon = True
+        self.record_process.start()
+
+        is_init = process_init_event.wait(
+            timeout=5.0
+        )  # Wait for the recording process to be started, and to potentially raise an error on failure.
+        if not self.is_connected or not is_init:
+            raise RuntimeError(f"Error connecting sensor connected to {self.sensor_port}.")
+
+        logger.info(f"{self} connected.")
+
+    @staticmethod
+    def _record_process(
+        sensor_port,
+        baud_rate,
+        channels,
+        process_init_event,
+        record_start_event,
+        record_stop_event,
+        record_close_event,
+        record_is_started_event,
+        audio_callback_start_event,
+        write_queue,
+        read_shared_array,
+    ) -> None:
+        channels_index = np.array(channels) - 1
+        local_read_shared_array = read_shared_array.get_local_array()
+
+        def tactile_callback(serial_connection):
+            """
+            Parse the tactile data from the raw input data.
+            """
+            buffer = serial_connection.readline()
+
+            if audio_callback_start_event.is_set():
+                strings = buffer.decode("utf8").split(",")
+                num_taxels = len(strings)
+
+                if num_taxels > 0 and num_taxels < MAX_SERIAL_READ_SIZE:  # Make sure we didn't read rubbish
+                    indata = np.empty((1, num_taxels))
+                    for i in range(num_taxels):
+                        indata[0, i] = int(strings[i])
+
+                    write_queue.put_nowait(indata[:, channels_index])
+                    read_shared_array.write(local_read_shared_array, indata[:, channels_index])
+
+        process_init_event.set()
+
+        while True:
+            start_flag = record_start_event.wait(timeout=0.1)
+            if record_close_event.is_set():
+                break
+            elif not start_flag:
+                continue
+
+            with Serial(sensor_port, baud_rate, timeout=0.5) as serial_connection:
+                serial_connection.flush()
+                record_is_started_event.set()
+                while not record_stop_event.is_set():
+                    tactile_callback(serial_connection)
+                record_is_started_event.clear()
+        serial_connection.close()
+
+    def disconnect(self) -> None:
+        """
+        Disconnect the sensor and release any resources.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Sensor connected to {self.sensor_port} is not connected.")
+
+        if self.is_recording:
+            self.stop_recording()
+
+        self.record_close_event.set()
+        self.read_shared_array.delete()
+        self.write_queue.close()
+        self.record_process.join()
+
+        if self.is_connected:
+            raise RuntimeError(f"Error disconnecting sensor connected to {self.sensor_port}.")
+
+        logger.info(f"{self} disconnected.")
+
+    def start_recording(
+        self,
+        output_file: str | Path | None = None,
+        multiprocessing: bool | None = False,
+        overwrite: bool | None = True,
+        barrier: Barrier | None = None,
+    ) -> None:
+        """
+        Start recording tactile data from the sensor.
+
+        Args:
+            output_file: Optional path to save the recorded tactile data.
+            multiprocessing: If True, enables multiprocessing for recording. Defaults to multithreading otherwise.
+            overwrite: If True, overwrites existing files at output_file path.
+            barrier: If not None, ensures that multiple sensors start recording at the same time.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Sensor connected to {self.sensor_port} is not connected.")
+        if self.is_recording:
+            raise DeviceAlreadyRecordingError(f"Sensor connected to {self.sensor_port} is already recording.")
+
+        # Reset queue and shared memory
+        self.read_shared_array.reset()
+        self._clear_queue(self.write_queue)
+
+        # Reset stop event
+        self.record_stop_event.clear()
+
+        # Write recordings into a file if output_file is provided
+        if output_file is not None:
+            output_file = Path(output_file)
+            output_file.parent.mkdir(parents=True, exist_ok=True)
+
+            if output_file.exists():
+                if overwrite:
+                    output_file.unlink()
+                else:
+                    raise FileExistsError(
+                        f"Output file {output_file} already exists. Set overwrite to True to overwrite it."
+                    )
+
+            if multiprocessing:
+                self.write_stop_event = process_Event()
+                self.write_is_started_event = process_Event()
+                self.write_thread = Process(
+                    target=TouchLabSensor._write_loop,
+                    args=(
+                        self.write_queue,
+                        self.write_stop_event,
+                        self.write_is_started_event,
+                        self.sample_rate,
+                        self.channels,
+                        output_file,
+                    ),
+                )
+            else:
+                self.write_stop_event = thread_Event()
+                self.write_is_started_event = thread_Event()
+                self.write_thread = Thread(
+                    target=TouchLabSensor._write_loop,
+                    args=(
+                        self.write_queue,
+                        self.write_stop_event,
+                        self.write_is_started_event,
+                        self.sample_rate,
+                        self.channels,
+                        output_file,
+                    ),
+                )
+            self.write_thread.daemon = True
+            self.write_thread.start()
+            self.write_is_started_event.wait()  # Wait for the writing thread/process to be started.
+
+        self.record_start_event.set()  # Start the input audio stream process
+        self.record_is_started_event.wait()  # Wait for the input audio stream process to be actually started
+
+        if barrier is not None:
+            barrier.wait()  # Wait for multiple input audio streams to be started at the same time
+
+        self.audio_callback_start_event.set()
+
+        if not self.is_recording:
+            raise RuntimeError(f"Error starting recording for sensor connected to {self.sensor_port}.")
+        if output_file is not None and not self.is_writing:
+            raise RuntimeError(f"Error starting writing for sensor connected to {self.sensor_port}.")
+
+    def _read(self) -> np.ndarray:
+        """
+        Thread/Process-safe callback to read available audio data
+        """
+        return self.read_shared_array.read(self.local_read_shared_array, flush=True)
+
+    def read(self) -> np.ndarray:
+        """Capture and return a single audio chunk from the sensor.
+
+        Returns:
+            np.ndarray: Captured audio chunk as a numpy array.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Sensor connected to {self.sensor_port} is not connected.")
+        if not self.is_recording:
+            raise RuntimeError(f"Sensor connected to {self.sensor_port} is not recording.")
+
+        start_time = time.perf_counter()
+
+        tactile_readings = self._read()
+
+        # log the number of seconds it took to read the audio chunk
+        self.logs["delta_timestamp_s"] = time.perf_counter() - start_time
+
+        # log the utc time at which the audio chunk was received
+        self.logs["timestamp_utc"] = time.perf_counter()
+
+        return tactile_readings
+
+    def _read_loop(self) -> None:
+        """Internal loop run by the background thread for asynchronous reading."""
+
+    def stop_recording(self) -> None:
+        """Stop recording audio from the sensor."""
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Sensor connected to {self.sensor_port} is not connected.")
+        if not self.is_recording:
+            raise DeviceNotRecordingError(f"Sensor connected to {self.sensor_port} is not recording.")
+
+        self.audio_callback_start_event.clear()
+        self.record_start_event.clear()  # Ensures the audio stream is not started again !
+        self.record_stop_event.set()
+
+        self.read_shared_array.reset()
+        self._clear_queue(self.write_queue, join_queue=True)
+
+        if self.is_writing:
+            self.write_stop_event.set()
+            self.write_thread.join()
+
+        timeout = 1.0
+        while self.is_recording and timeout > 0:
+            time.sleep(0.01)
+            timeout -= 0.01
+
+        if self.is_recording:
+            raise RuntimeError(f"Error stopping recording for sensor connected to {self.sensor_port}.")
+        if self.is_writing:
+            raise RuntimeError(f"Error stopping writing for sensor connected to {self.sensor_port}.")
+
+    def __del__(self) -> None:
+        if self.is_connected:
+            self.disconnect()
+
+    @staticmethod
+    def _clear_queue(queue, join_queue: bool = False):
+        """
+        Clears the queue by getting all items until it is empty. The longer the queue, the longer it takes to clear it.
+        """
+        try:
+            while True:
+                queue.get_nowait()
+                queue.task_done()
+        except Empty:
+            if join_queue:
+                queue.join()
+            return
+
+    @staticmethod
+    def _write_loop(
+        queue,
+        write_stop_event: Event,
+        write_is_started_event: Event,
+        sample_rate: int,
+        channels: list[int],
+        output_file: Path,
+    ) -> None:
+        """
+        Thread/Process-safe loop to write audio data into a file.
+        """
+        # Can only be run on a single process/thread for file writing safety
+        with SoundFile(
+            output_file,
+            mode="w",
+            samplerate=sample_rate,
+            channels=len(channels),
+            format="WAV",
+            subtype="PCM_16",  # Subtype for int16 values
+        ) as file:
+            write_is_started_event.set()
+            while not write_stop_event.is_set():
+                try:
+                    file.write(
+                        queue.get(timeout=0.005)
+                    )  # Timeout set as the usual sounddevice buffer size. get_nowait is not possible here as it saturates the thread.
+                    queue.task_done()
+                except Empty:
+                    continue
+        write_is_started_event.clear()

src/lerobot/microphones/utils.py

@@ -0,0 +1,89 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from multiprocessing import Barrier
+from threading import Thread
+
+from .configs import MicrophoneConfig
+from .microphone import Microphone
+
+
+def make_microphones_from_configs(microphone_configs: dict[str, MicrophoneConfig]) -> dict[str, Microphone]:
+    microphones = {}
+
+    for key, cfg in microphone_configs.items():
+        if cfg.type == "portaudio":
+            from .portaudio import PortAudioMicrophone
+
+            microphones[key] = PortAudioMicrophone(cfg)
+        elif cfg.type == "touchlab":
+            from .touchlab import TouchLabSensor
+
+            microphones[key] = TouchLabSensor(cfg)
+        else:
+            raise ValueError(f"The microphone type '{cfg.type}' is not valid.")
+
+    return microphones
+
+
+def async_microphones_start_recording(
+    microphones: dict[str, Microphone],
+    output_files: list[str | None] | None = None,
+    multiprocessing: bool = False,
+    overwrite: bool = True,
+) -> None:
+    """
+    Starts recording on multiple microphones asynchronously to avoid delays.
+
+    Args:
+        microphones: A dictionary of microphones.
+        output_files: A list of output files.
+        multiprocessing: If True, enables multiprocessing for recording.
+        overwrite: If True, overwrites existing files at output_file path.
+    """
+
+    start_recording_threads = []
+    if output_files is None:
+        output_files = [None] * len(microphones)
+
+    barrier = Barrier(len(microphones))
+
+    for microphone, output_file in zip(microphones.values(), output_files, strict=False):
+        start_recording_threads.append(
+            Thread(target=microphone.start_recording, args=(output_file, multiprocessing, overwrite, barrier))
+        )
+
+    for thread in start_recording_threads:
+        thread.start()
+    for thread in start_recording_threads:
+        thread.join()
+
+
+def async_microphones_stop_recording(microphones: dict[str, Microphone]) -> None:
+    """
+    Stops recording on multiple microphones asynchronously to avoid delays.
+
+    Args:
+        microphones: A dictionary of microphones.
+    """
+
+    stop_recording_threads = []
+
+    for microphone in microphones.values():
+        stop_recording_threads.append(Thread(target=microphone.stop_recording))
+
+    for thread in stop_recording_threads:
+        thread.start()
+    for thread in stop_recording_threads:
+        thread.join()

src/lerobot/policies/act/configuration_act.py

@@ -89,6 +89,7 @@ class ACTConfig(PreTrainedConfig):
     normalization_mapping: dict[str, NormalizationMode] = field(
         default_factory=lambda: {
             "VISUAL": NormalizationMode.MEAN_STD,
+            "AUDIO": NormalizationMode.IDENTITY,
             "STATE": NormalizationMode.MEAN_STD,
             "ACTION": NormalizationMode.MEAN_STD,
         }
@@ -99,6 +100,10 @@ class ACTConfig(PreTrainedConfig):
     vision_backbone: str = "resnet18"
     pretrained_backbone_weights: str | None = "ResNet18_Weights.IMAGENET1K_V1"
     replace_final_stride_with_dilation: int = False
+    # Audio backbone.
+    audio_backbone: str = vision_backbone
+    pretrained_backbone_weights_audio: str | None = None
+    replace_final_stride_with_dilation_audio: int = False
     # Transformer layers.
     pre_norm: bool = False
     dim_model: int = 512
@@ -161,8 +166,10 @@ class ACTConfig(PreTrainedConfig):
         return None
 
     def validate_features(self) -> None:
-        if not self.image_features and not self.env_state_feature:
-            raise ValueError("You must provide at least one image or the environment state among the inputs.")
+        if not (self.image_features or self.audio_features) and not self.env_state_feature:
+            raise ValueError(
+                "You must provide at least one image/audio or the environment state among the inputs."
+            )
 
     @property
     def observation_delta_indices(self) -> None:

src/lerobot/policies/act/modeling_act.py

@@ -35,7 +35,7 @@ from torchvision.ops.misc import FrozenBatchNorm2d
 
 from lerobot.policies.act.configuration_act import ACTConfig
 from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_IMAGES, OBS_STATE
+from lerobot.utils.constants import ACTION, OBS_AUDIO, OBS_ENV_STATE, OBS_IMAGES, OBS_STATE
 
 
 class ACTPolicy(PreTrainedPolicy):
@@ -106,6 +106,8 @@ class ACTPolicy(PreTrainedPolicy):
         """
         self.eval()  # keeping the policy in eval mode as it could be set to train mode while queue is consumed
 
+        # If we are doing temporal ensembling, do online updates where we keep track of the number of actions
+        # we are ensembling over.
         if self.config.temporal_ensemble_coeff is not None:
             actions = self.predict_action_chunk(batch)
             action = self.temporal_ensembler.update(actions)
@@ -331,12 +333,26 @@ class ACT(nn.Module):
             # Note: The forward method of this returns a dict: {"feature_map": output}.
             self.backbone = IntermediateLayerGetter(backbone_model, return_layers={"layer4": "feature_map"})
 
+        # Backbone for audio feature extraction.
+        if self.config.audio_features:
+            audio_backbone_model = getattr(torchvision.models, config.audio_backbone)(
+                replace_stride_with_dilation=[False, False, config.replace_final_stride_with_dilation_audio],
+                weights=config.pretrained_backbone_weights_audio,
+                norm_layer=FrozenBatchNorm2d,
+            )
+            # Note: The assumption here is that we are using a ResNet model (and hence layer4 is the final
+            # feature map).
+            # Note: The forward method of this returns a dict: {"feature_map": output}.
+            self.audio_backbone = IntermediateLayerGetter(
+                audio_backbone_model, return_layers={"layer4": "feature_map"}
+            )
+
         # Transformer (acts as VAE decoder when training with the variational objective).
         self.encoder = ACTEncoder(config)
         self.decoder = ACTDecoder(config)
 
         # Transformer encoder input projections. The tokens will be structured like
-        # [latent, (robot_state), (env_state), (image_feature_map_pixels)].
+        # [latent, (robot_state), (env_state), (image_feature_map_pixels), (audio_feature)].
         if self.config.robot_state_feature:
             self.encoder_robot_state_input_proj = nn.Linear(
                 self.config.robot_state_feature.shape[0], config.dim_model
@@ -350,6 +366,10 @@ class ACT(nn.Module):
             self.encoder_img_feat_input_proj = nn.Conv2d(
                 backbone_model.fc.in_features, config.dim_model, kernel_size=1
             )
+        if self.config.audio_features:
+            self.encoder_audio_feat_input_proj = nn.Conv2d(
+                audio_backbone_model.fc.in_features, config.dim_model, kernel_size=1
+            )
         # Transformer encoder positional embeddings.
         n_1d_tokens = 1  # for the latent
         if self.config.robot_state_feature:
@@ -359,6 +379,8 @@ class ACT(nn.Module):
         self.encoder_1d_feature_pos_embed = nn.Embedding(n_1d_tokens, config.dim_model)
         if self.config.image_features:
             self.encoder_cam_feat_pos_embed = ACTSinusoidalPositionEmbedding2d(config.dim_model // 2)
+        if self.config.audio_features:
+            self.encoder_audio_feat_pos_embed = ACTSinusoidalPositionEmbedding2d(config.dim_model // 2)
 
         # Transformer decoder.
         # Learnable positional embedding for the transformer's decoder (in the style of DETR object queries).
@@ -483,6 +505,21 @@ class ACT(nn.Module):
                 encoder_in_tokens.extend(list(cam_features))
                 encoder_in_pos_embed.extend(list(cam_pos_embed))
 
+        if self.config.audio_features:
+            for audio in batch[OBS_AUDIO]:
+                audio_features = self.audio_backbone(audio)["feature_map"]
+                audio_pos_embed = self.encoder_audio_feat_pos_embed(audio_features).to(
+                    dtype=audio_features.dtype
+                )
+                audio_features = self.encoder_audio_feat_input_proj(audio_features)
+
+                # Rearrange features to (sequence, batch, dim).
+                audio_features = einops.rearrange(audio_features, "b c h w -> (h w) b c")
+                audio_pos_embed = einops.rearrange(audio_pos_embed, "b c h w -> (h w) b c")
+
+                encoder_in_tokens.extend(list(audio_features))
+                encoder_in_pos_embed.extend(list(audio_pos_embed))
+
         # Stack all tokens along the sequence dimension.
         encoder_in_tokens = torch.stack(encoder_in_tokens, axis=0)
         encoder_in_pos_embed = torch.stack(encoder_in_pos_embed, axis=0)

src/lerobot/policies/act/processor_act.py

@@ -17,9 +17,11 @@ from typing import Any
 
 import torch
 
+from lerobot.datasets.utils import DEFAULT_AUDIO_CHUNK_DURATION
 from lerobot.policies.act.configuration_act import ACTConfig
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
+    AudioProcessorStep,
     DeviceProcessorStep,
     NormalizerProcessorStep,
     PolicyAction,
@@ -63,6 +65,15 @@ def make_act_pre_post_processors(
             stats=dataset_stats,
             device=config.device,
         ),
+        AudioProcessorStep(
+            output_height=224,
+            output_width=224,
+            output_channels=3,
+            input_audio_chunk_duration=DEFAULT_AUDIO_CHUNK_DURATION,
+            input_sample_rate=48000,
+            intermediate_sample_rate=16000,
+            n_fft=1024,
+        ),
     ]
     output_steps = [
         UnnormalizerProcessorStep(

src/lerobot/policies/utils.py

@@ -106,7 +106,7 @@ def prepare_observation_for_inference(
     This function takes a dictionary of NumPy arrays, performs necessary
     preprocessing, and prepares it for model inference. The steps include:
     1. Converting NumPy arrays to PyTorch tensors.
-    2. Normalizing and permuting image data (if any).
+    2. Normalizing and permuting image data and audio data (if any).
     3. Adding a batch dimension to each tensor.
     4. Moving all tensors to the specified compute device.
     5. Adding task and robot type information to the dictionary.
@@ -129,6 +129,9 @@ def prepare_observation_for_inference(
         if "image" in name:
             observation[name] = observation[name].type(torch.float32) / 255
             observation[name] = observation[name].permute(2, 0, 1).contiguous()
+        elif "audio" in name:
+            observation[name] = observation[name].type(torch.float32)
+            observation[name] = observation[name].permute(1, 0).contiguous()
         observation[name] = observation[name].unsqueeze(0)
         observation[name] = observation[name].to(device)
 

src/lerobot/processor/__init__.py

@@ -23,6 +23,7 @@ from lerobot.types import (
     TransitionKey,
 )
 
+from .audio_processor import AudioProcessorStep
 from .batch_processor import AddBatchDimensionProcessorStep
 from .converters import (
     batch_to_transition,
@@ -88,6 +89,7 @@ __all__ = [
     "ActionProcessorStep",
     "AddTeleopActionAsComplimentaryDataStep",
     "AddTeleopEventsAsInfoStep",
+    "AudioProcessorStep",
     "ComplementaryDataProcessorStep",
     "batch_to_transition",
     "create_transition",

src/lerobot/processor/audio_processor.py

@@ -0,0 +1,130 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass, field
+
+from torch import Tensor
+from torchaudio.functional import amplitude_to_DB
+from torchaudio.transforms import MelSpectrogram, Resample
+from torchvision.transforms import Compose, Lambda, Resize
+
+from lerobot.datasets.utils import DEFAULT_AUDIO_CHUNK_DURATION
+from lerobot.utils.constants import OBS_AUDIO
+
+from .pipeline import ObservationProcessorStep, ProcessorStepRegistry
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="audio_processor")
+class AudioProcessorStep(ObservationProcessorStep):
+    """
+    Processes audio waveform data into a mel-spectrogram image representation.
+
+    **Audio Processing:**
+    -   Averages waveform data over all channels.
+    -   Resamples the waveform to 16kHz.
+    -   Converts the waveform to a mel-spectrogram.
+    -   Converts the mel-spectrogram to decibels.
+    -   Resizes the mel-spectrogram to 224×224.
+    -   Converts the mel-spectrogram to a channel-first, normalized tensor.
+
+    Attributes:
+        output_height: Height of the output mel-spectrogram image in pixels.
+        output_width: Width of the output mel-spectrogram image in pixels.
+        output_channels: Number of channels in the output image (3 for RGB-like format).
+        input_audio_chunk_duration: Duration of the input audio chunk in seconds.
+        input_sample_rate: Original sample rate of the input audio in Hz.
+
+        intermediate_sample_rate: Reduced intermediate sample rate in Hz.
+                                  Downsampling improves the temporal resolution but reduces the frequency range.
+        n_fft: Size of the FFT window for spectrogram computation.
+               Increasing the window size increases the frequency resolution but decreases the temporal resolution.
+
+        hop_length: Number of samples between successive frames, computed automatically to match the output_width.
+                    Decreasing the hop length increases the temporal resolution but decreases the frequency resolution.
+        n_mels: Number of mel filter banks, computed automatically to match the output_height.
+                Increasing the number of banks increases the number of rows in the spectrogram and the frequency resolution.
+        mel_spectrogram_transform: The complete audio processing pipeline.
+    """
+
+    output_height: int = 224
+    output_width: int = 224
+    output_channels: int = 3
+    input_audio_chunk_duration: float = DEFAULT_AUDIO_CHUNK_DURATION
+
+    input_sample_rate: int = 48000
+    intermediate_sample_rate: int = 16000
+
+    n_fft: int = 1024
+
+    # Parameters computed from other parameters at initialization
+    hop_length: int = field(init=False)
+    n_mels: int = field(init=False)
+    mel_spectrogram_transform: Compose = field(init=False, repr=False)
+
+    def __post_init__(self):
+        self.hop_length = int(
+            self.intermediate_sample_rate * self.input_audio_chunk_duration
+            - self.n_fft // self.output_width
+            - 1
+        )
+        self.n_mels = self.output_height
+
+        self.mel_spectrogram_transform = Compose(
+            [
+                Lambda(lambda x: x.mean(dim=1)),  # Average over all channels (second dimension after batch)
+                Resample(orig_freq=self.input_sample_rate, new_freq=self.intermediate_sample_rate),
+                MelSpectrogram(
+                    sample_rate=self.intermediate_sample_rate,
+                    n_fft=self.n_fft,
+                    hop_length=self.hop_length,
+                    n_mels=self.n_mels,
+                    power=2,  # Power spectrum
+                ),
+                Lambda(
+                    lambda x: amplitude_to_DB(x, multiplier=10, amin=1e-10, db_multiplier=0)
+                ),  # Convert to decibels
+                Resize(
+                    (self.output_height, self.output_width)
+                ),  # Resize spectrogram to output_height×output_width
+                Lambda(
+                    lambda x: x.unsqueeze(1).expand(-1, self.output_channels, -1, -1)
+                ),  # Duplicate across 3 channels to mimic RGB images. Dimensions are [batch, rgb, height, width].
+            ]
+        )
+
+    def _process_observation(self, observation: dict[str, Tensor]) -> dict[str, Tensor]:
+        """
+        Processes audio data contained in the provided observation.
+        """
+        processed_obs = observation.copy()
+
+        # Process single audio observation
+        if OBS_AUDIO in processed_obs:
+            audio_data = processed_obs[OBS_AUDIO]
+            if isinstance(audio_data, Tensor) and audio_data.dim() == 3:  # Batch, Channels, Samples
+                processed_obs[OBS_AUDIO] = self.mel_spectrogram_transform(audio_data)
+
+        # Process multiple audio observations
+        for key, value in processed_obs.items():
+            if (
+                key.startswith(f"{OBS_AUDIO}.") and isinstance(value, Tensor) and value.dim() == 3
+            ):  # Batch, Channels, Samples
+                processed_obs[key] = self.mel_spectrogram_transform(value)
+
+        return processed_obs
+
+    def observation(self, observation: dict[str, Tensor]) -> dict[str, Tensor]:
+        return self._process_observation(observation)

src/lerobot/processor/batch_processor.py

@@ -25,8 +25,7 @@ from dataclasses import dataclass, field
 from torch import Tensor
 
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
-from lerobot.types import EnvTransition, PolicyAction
-from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
+from lerobot.utils.constants import OBS_AUDIO, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
 
 from .pipeline import (
     ComplementaryDataProcessorStep,
@@ -36,6 +35,7 @@ from .pipeline import (
     ProcessorStepRegistry,
     TransitionKey,
 )
+from lerobot.types import PolicyAction, EnvTransition
 
 
 @dataclass
@@ -88,6 +88,8 @@ class AddBatchDimensionObservationStep(ObservationProcessorStep):
     - State vectors (1D tensors).
     - Single images (3D tensors).
     - Dictionaries of multiple images (3D tensors).
+    - Single audio waveforms (2D tensors).
+    - Dictionaries of multiple audio waveforms (2D tensors).
     """
 
     def observation(self, observation: dict[str, Tensor]) -> dict[str, Tensor]:
@@ -117,6 +119,18 @@ class AddBatchDimensionObservationStep(ObservationProcessorStep):
         for key, value in observation.items():
             if key.startswith(f"{OBS_IMAGES}.") and isinstance(value, Tensor) and value.dim() == 3:
                 observation[key] = value.unsqueeze(0)
+
+        # Process single audio observation - add batch dim if 2D
+        if OBS_AUDIO in observation:
+            audio_value = observation[OBS_AUDIO]
+            if isinstance(audio_value, Tensor) and audio_value.dim() == 2:
+                observation[OBS_AUDIO] = audio_value.unsqueeze(0)
+
+        # Process multiple audio observations - add batch dim if 2D
+        for key, value in observation.items():
+            if key.startswith(f"{OBS_AUDIO}.") and isinstance(value, Tensor) and value.dim() == 2:
+                observation[key] = value.unsqueeze(0)
+
         return observation
 
     def transform_features(

src/lerobot/robots/config.py

@@ -34,6 +34,13 @@ class RobotConfig(draccus.ChoiceRegistry, abc.ABC):
                         raise ValueError(
                             f"Specifying '{attr}' is required for the camera to be used in a robot"
                         )
+        if hasattr(self, "microphones") and self.microphones:
+            for _, config in self.microphones.items():
+                for attr in ["sample_rate", "channels"]:
+                    if getattr(config, attr) is None:
+                        raise ValueError(
+                            f"Specifying '{attr}' is required for the microphone to be used in a robot"
+                        )
 
     @property
     def type(self) -> str:

src/lerobot/robots/koch_follower/config_koch_follower.py

@@ -15,6 +15,7 @@
 from dataclasses import dataclass, field
 
 from lerobot.cameras import CameraConfig
+from lerobot.microphones import MicrophoneConfig
 
 from ..config import RobotConfig
 
@@ -35,5 +36,8 @@ class KochFollowerConfig(RobotConfig):
     # cameras
     cameras: dict[str, CameraConfig] = field(default_factory=dict)
 
+    # microphones
+    microphones: dict[str, MicrophoneConfig] = field(default_factory=dict)
+
     # Set to `True` for backward compatibility with previous policies/dataset
     use_degrees: bool = False

src/lerobot/robots/koch_follower/koch_follower.py

@@ -19,6 +19,7 @@ import time
 from functools import cached_property
 
 from lerobot.cameras.utils import make_cameras_from_configs
+from lerobot.microphones.utils import make_microphones_from_configs
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
 from lerobot.motors.dynamixel import (
     DynamixelMotorsBus,
@@ -61,6 +62,7 @@ class KochFollower(Robot):
             calibration=self.calibration,
         )
         self.cameras = make_cameras_from_configs(config.cameras)
+        self.microphones = make_microphones_from_configs(config.microphones)
 
     @property
     def _motors_ft(self) -> dict[str, type]:
@@ -72,9 +74,16 @@ class KochFollower(Robot):
             cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
         }
 
+    @property
+    def _microphones_ft(self) -> dict[str, tuple]:
+        return {
+            mic: (self.config.microphones[mic].sample_rate, self.config.microphones[mic].channels)
+            for mic in self.microphones
+        }
+
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
-        return {**self._motors_ft, **self._cameras_ft}
+        return {**self._motors_ft, **self._cameras_ft, **self._microphones_ft}
 
     @cached_property
     def action_features(self) -> dict[str, type]:
@@ -82,7 +91,11 @@ class KochFollower(Robot):
 
     @property
     def is_connected(self) -> bool:
-        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+        return (
+            self.bus.is_connected
+            and all(cam.is_connected for cam in self.cameras.values())
+            and all(mic.is_connected for mic in self.microphones.values())
+        )
 
     @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
@@ -101,6 +114,9 @@ class KochFollower(Robot):
         for cam in self.cameras.values():
             cam.connect()
 
+        for mic in self.microphones.values():
+            mic.connect()
+
         self.configure()
         logger.info(f"{self} connected.")
 
@@ -197,6 +213,13 @@ class KochFollower(Robot):
             dt_ms = (time.perf_counter() - start) * 1e3
             logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
 
+        # Read audio frames from microphones
+        for mic_key, mic in self.microphones.items():
+            start = time.perf_counter()
+            obs_dict[mic_key] = mic.read()
+            dt_ms = (time.perf_counter() - start) * 1e3
+            logger.debug(f"{self} read {mic_key}: {dt_ms:.1f}ms")
+
         return obs_dict
 
     @check_if_not_connected
@@ -232,5 +255,7 @@ class KochFollower(Robot):
         self.bus.disconnect(self.config.disable_torque_on_disconnect)
         for cam in self.cameras.values():
             cam.disconnect()
+        for mic in self.microphones.values():
+            mic.disconnect()
 
         logger.info(f"{self} disconnected.")

src/lerobot/robots/lekiwi/config_lekiwi.py

@@ -16,6 +16,7 @@ from dataclasses import dataclass, field
 
 from lerobot.cameras.configs import CameraConfig, Cv2Rotation
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.microphones import MicrophoneConfig
 
 from ..config import RobotConfig
 
@@ -45,6 +46,8 @@ class LeKiwiConfig(RobotConfig):
 
     cameras: dict[str, CameraConfig] = field(default_factory=lekiwi_cameras_config)
 
+    microphones: dict[str, MicrophoneConfig] = field(default_factory=dict)
+
     # Set to `True` for backward compatibility with previous policies/dataset
     use_degrees: bool = False
 
@@ -92,5 +95,7 @@ class LeKiwiClientConfig(RobotConfig):
 
     cameras: dict[str, CameraConfig] = field(default_factory=lekiwi_cameras_config)
 
+    microphones: dict[str, MicrophoneConfig] = field(default_factory=dict)
+
     polling_timeout_ms: int = 15
     connect_timeout_s: int = 5

src/lerobot/robots/lekiwi/lekiwi.py

@@ -23,6 +23,7 @@ from typing import Any
 import numpy as np
 
 from lerobot.cameras.utils import make_cameras_from_configs
+from lerobot.microphones.utils import make_microphones_from_configs
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
 from lerobot.motors.feetech import (
     FeetechMotorsBus,
@@ -73,6 +74,7 @@ class LeKiwi(Robot):
         self.arm_motors = [motor for motor in self.bus.motors if motor.startswith("arm")]
         self.base_motors = [motor for motor in self.bus.motors if motor.startswith("base")]
         self.cameras = make_cameras_from_configs(config.cameras)
+        self.microphones = make_microphones_from_configs(config.microphones)
 
     @property
     def _state_ft(self) -> dict[str, type]:
@@ -97,9 +99,16 @@ class LeKiwi(Robot):
             cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
         }
 
+    @property
+    def _microphones_ft(self) -> dict[str, tuple]:
+        return {
+            mic: (self.config.microphones[mic].sample_rate, self.config.microphones[mic].channels)
+            for mic in self.microphones
+        }
+
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
-        return {**self._state_ft, **self._cameras_ft}
+        return {**self._state_ft, **self._cameras_ft, **self._microphones_ft}
 
     @cached_property
     def action_features(self) -> dict[str, type]:
@@ -107,7 +116,11 @@ class LeKiwi(Robot):
 
     @property
     def is_connected(self) -> bool:
-        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+        return (
+            self.bus.is_connected
+            and all(cam.is_connected for cam in self.cameras.values())
+            and all(mic.is_connected for mic in self.microphones.values())
+        )
 
     @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
@@ -121,6 +134,9 @@ class LeKiwi(Robot):
         for cam in self.cameras.values():
             cam.connect()
 
+        for mic in self.microphones.values():
+            mic.connect()
+
         self.configure()
         logger.info(f"{self} connected.")
 
@@ -364,6 +380,13 @@ class LeKiwi(Robot):
             dt_ms = (time.perf_counter() - start) * 1e3
             logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
 
+        # Read audio frames from microphones
+        for mic_key, mic in self.microphones.items():
+            start = time.perf_counter()
+            obs_dict[mic_key] = mic.read()
+            dt_ms = (time.perf_counter() - start) * 1e3
+            logger.debug(f"{self} read {mic_key}: {dt_ms:.1f}ms")
+
         return obs_dict
 
     @check_if_not_connected
@@ -413,5 +436,7 @@ class LeKiwi(Robot):
         self.bus.disconnect(self.config.disable_torque_on_disconnect)
         for cam in self.cameras.values():
             cam.disconnect()
+        for mic in self.microphones.values():
+            mic.disconnect()
 
         logger.info(f"{self} disconnected.")

src/lerobot/robots/lekiwi/lekiwi_client.py

@@ -18,6 +18,7 @@ import base64
 import json
 import logging
 from functools import cached_property
+from time import perf_counter
 
 import cv2
 import numpy as np
@@ -58,8 +59,9 @@ class LeKiwiClient(Robot):
         self.zmq_observation_socket = None
 
         self.last_frames = {}
-
         self.last_remote_state = {}
+        self.last_frame_timestamp = None
+        self.last_frame_delay = 0.0
 
         # Define three speed levels and a current index
         self.speed_levels = [
@@ -97,9 +99,13 @@ class LeKiwiClient(Robot):
     def _cameras_ft(self) -> dict[str, tuple[int, int, int]]:
         return {name: (cfg.height, cfg.width, 3) for name, cfg in self.config.cameras.items()}
 
+    @cached_property
+    def _microphones_ft(self) -> dict[str, tuple]:
+        return {name: (cfg.sample_rate, cfg.channels) for name, cfg in self.config.microphones.items()}
+
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
-        return {**self._state_ft, **self._cameras_ft}
+        return {**self._state_ft, **self._cameras_ft, **self._microphones_ft}
 
     @cached_property
     def action_features(self) -> dict[str, type]:
@@ -135,6 +141,7 @@ class LeKiwiClient(Robot):
         if self.zmq_observation_socket not in socks or socks[self.zmq_observation_socket] != zmq.POLLIN:
             raise DeviceNotConnectedError("Timeout waiting for LeKiwi Host to connect expired.")
 
+        self.last_frame_timestamp = perf_counter()
         self._is_connected = True
 
     def calibrate(self) -> None:
@@ -167,6 +174,8 @@ class LeKiwiClient(Robot):
         if last_msg is None:
             logging.warning("Poller indicated data, but failed to retrieve message.")
 
+        self.last_frame_delay = perf_counter() - self.last_frame_timestamp
+        self.last_frame_timestamp = perf_counter()
         return last_msg
 
     def _parse_observation_json(self, obs_string: str) -> RobotObservation | None:
@@ -203,14 +212,16 @@ class LeKiwiClient(Robot):
 
         obs_dict: RobotObservation = {**flat_state, OBS_STATE: state_vec}
 
-        # Decode images
+        # Decode images and audio data
         current_frames: dict[str, np.ndarray] = {}
-        for cam_name, image_b64 in observation.items():
-            if cam_name not in self._cameras_ft:
-                continue
-            frame = self._decode_image_from_b64(image_b64)
-            if frame is not None:
-                current_frames[cam_name] = frame
+        for frame_name, frame_data in observation.items():
+            if frame_name in self._cameras_ft:
+                image = self._decode_image_from_b64(frame_data)
+                if image is not None:
+                    current_frames[frame_name] = image
+            elif frame_name in self._microphones_ft:
+                if frame_data is not None:
+                    current_frames[frame_name] = frame_data
 
         return current_frames, obs_dict
 
@@ -254,17 +265,27 @@ class LeKiwiClient(Robot):
         """
         Capture observations from the remote robot: current follower arm positions,
         present wheel speeds (converted to body-frame velocities: x, y, theta),
-        and a camera frame. Receives over ZMQ, translate to body-frame vel
+        and cameras and microphones data. Receives over ZMQ, translate to body-frame vel
         """
 
         frames, obs_dict = self._get_data()
 
-        # Loop over each configured camera
-        for cam_name, frame in frames.items():
-            if frame is None:
-                logging.warning("Frame is None")
-                frame = np.zeros((640, 480, 3), dtype=np.uint8)
-            obs_dict[cam_name] = frame
+        # Loop over each configured camera and microphone
+        for frame_name, frame_data in frames.items():
+            if frame_data is None:
+                if frame_name in self._cameras_ft:
+                    logging.warning("Image frame is None")
+                    image = np.zeros((640, 480, 3), dtype=np.uint8)
+                    obs_dict[frame_name] = image
+                elif frame_name in self._microphones_ft:
+                    logging.warning("Audio frame is None")
+                    obs_dict[frame_name] = np.zeros(
+                        (
+                            int(self._microphones_ft[frame_name][0] * self.last_frame_delay),
+                            self._microphones_ft[frame_name][1],
+                        ),
+                        dtype=np.float32,
+                    )
 
         return obs_dict
 

src/lerobot/robots/so_follower/config_so_follower.py

@@ -17,6 +17,7 @@
 from dataclasses import dataclass, field
 
 from lerobot.cameras import CameraConfig
+from lerobot.microphones import MicrophoneConfig
 
 from ..config import RobotConfig
 
@@ -38,6 +39,9 @@ class SOFollowerConfig:
     # cameras
     cameras: dict[str, CameraConfig] = field(default_factory=dict)
 
+    # microphones
+    microphones: dict[str, MicrophoneConfig] = field(default_factory=dict)
+
     # Set to `True` for backward compatibility with previous policies/dataset
     use_degrees: bool = True
 

src/lerobot/robots/so_follower/so_follower.py

@@ -19,6 +19,7 @@ import time
 from functools import cached_property
 
 from lerobot.cameras.utils import make_cameras_from_configs
+from lerobot.microphones.utils import make_microphones_from_configs
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
 from lerobot.motors.feetech import (
     FeetechMotorsBus,
@@ -61,6 +62,7 @@ class SOFollower(Robot):
             calibration=self.calibration,
         )
         self.cameras = make_cameras_from_configs(config.cameras)
+        self.microphones = make_microphones_from_configs(config.microphones)
 
     @property
     def _motors_ft(self) -> dict[str, type]:
@@ -72,9 +74,16 @@ class SOFollower(Robot):
             cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
         }
 
+    @property
+    def _microphones_ft(self) -> dict[str, tuple]:
+        return {
+            mic: (self.config.microphones[mic].sample_rate, self.config.microphones[mic].channels)
+            for mic in self.microphones
+        }
+
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
-        return {**self._motors_ft, **self._cameras_ft}
+        return {**self._motors_ft, **self._cameras_ft, **self._microphones_ft}
 
     @cached_property
     def action_features(self) -> dict[str, type]:
@@ -82,7 +91,11 @@ class SOFollower(Robot):
 
     @property
     def is_connected(self) -> bool:
-        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+        return (
+            self.bus.is_connected
+            and all(cam.is_connected for cam in self.cameras.values())
+            and all(mic.is_connected for mic in self.microphones.values())
+        )
 
     @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
@@ -101,6 +114,9 @@ class SOFollower(Robot):
         for cam in self.cameras.values():
             cam.connect()
 
+        for mic in self.microphones.values():
+            mic.connect()
+
         self.configure()
         logger.info(f"{self} connected.")
 
@@ -190,6 +206,13 @@ class SOFollower(Robot):
             dt_ms = (time.perf_counter() - start) * 1e3
             logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
 
+        # Read audio frames from microphones
+        for mic_key, mic in self.microphones.items():
+            start = time.perf_counter()
+            obs_dict[mic_key] = mic.read()
+            dt_ms = (time.perf_counter() - start) * 1e3
+            logger.debug(f"{self} read {mic_key}: {dt_ms:.1f}ms")
+
         return obs_dict
 
     @check_if_not_connected
@@ -225,6 +248,8 @@ class SOFollower(Robot):
         self.bus.disconnect(self.config.disable_torque_on_disconnect)
         for cam in self.cameras.values():
             cam.disconnect()
+        for mic in self.microphones.values():
+            mic.disconnect()
 
         logger.info(f"{self} disconnected.")
 

src/lerobot/scripts/convert_dataset_v21_to_v30.py

@@ -85,7 +85,7 @@ from lerobot.datasets.utils import (
     flatten_dict,
     update_chunk_file_indices,
 )
-from lerobot.datasets.video_utils import concatenate_video_files, get_video_duration_in_s
+from lerobot.datasets.video_utils import concatenate_media_files, get_media_duration_in_s
 from lerobot.utils.constants import HF_LEROBOT_HOME
 from lerobot.utils.utils import init_logging
 
@@ -318,12 +318,12 @@ def convert_videos_of_camera(root: Path, new_root: Path, video_key: str, video_f
 
     for ep_path in tqdm.tqdm(ep_paths, desc=f"convert videos of {video_key}"):
         ep_size_in_mb = get_file_size_in_mb(ep_path)
-        ep_duration_in_s = get_video_duration_in_s(ep_path)
+        ep_duration_in_s = get_media_duration_in_s(ep_path, media_type="video")
 
         # Check if adding this episode would exceed the limit
         if size_in_mb + ep_size_in_mb >= video_file_size_in_mb and len(paths_to_cat) > 0:
             # Size limit would be exceeded, save current accumulation WITHOUT this episode
-            concatenate_video_files(
+            concatenate_media_files(
                 paths_to_cat,
                 new_root
                 / DEFAULT_VIDEO_PATH.format(video_key=video_key, chunk_index=chunk_idx, file_index=file_idx),
@@ -359,7 +359,7 @@ def convert_videos_of_camera(root: Path, new_root: Path, video_key: str, video_f
 
     # Write remaining videos if any
     if paths_to_cat:
-        concatenate_video_files(
+        concatenate_media_files(
             paths_to_cat,
             new_root
             / DEFAULT_VIDEO_PATH.format(video_key=video_key, chunk_index=chunk_idx, file_index=file_idx),
@@ -402,7 +402,12 @@ def generate_episode_metadata_dict(
         if len(ep_ids_set) != 1:
             raise ValueError(f"Number of episodes is not the same ({ep_ids_set}).")
 
-        ep_dict = {**ep_metadata, **ep_video, **ep_legacy_metadata, **flatten_dict({"stats": ep_stats})}
+        ep_dict = {
+            **ep_metadata,
+            **ep_video,
+            **ep_legacy_metadata,
+            **flatten_dict({"stats": ep_stats}),
+        }
         ep_dict["meta/episodes/chunk_index"] = 0
         ep_dict["meta/episodes/file_index"] = 0
         yield ep_dict
@@ -423,7 +428,10 @@ def convert_episodes_metadata(root, new_root, episodes_metadata, episodes_video_
 
     ds_episodes = Dataset.from_generator(
         lambda: generate_episode_metadata_dict(
-            episodes_legacy_metadata, episodes_metadata, episodes_stats, episodes_video_metadata
+            episodes_legacy_metadata,
+            episodes_metadata,
+            episodes_stats,
+            episodes_video_metadata,
         )
     )
     write_episodes(ds_episodes, new_root)

src/lerobot/scripts/lerobot_calibrate.py

@@ -33,6 +33,8 @@ import draccus
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.microphones.portaudio.configuration_portaudio import PortAudioMicrophoneConfig  # noqa: F401
+from lerobot.microphones.touchlab.configuration_touchlab import TouchLabSensorConfig  # noqa: F401
 from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,

src/lerobot/scripts/lerobot_record.py

@@ -69,11 +69,14 @@ lerobot-record \
 
 import logging
 import time
+from copy import copy
 from dataclasses import asdict, dataclass, field
 from pathlib import Path
 from pprint import pformat
 from typing import Any
 
+import numpy as np
+
 from lerobot.cameras import (  # noqa: F401
     CameraConfig,  # noqa: F401
 )
@@ -87,7 +90,20 @@ from lerobot.datasets.feature_utils import build_dataset_frame, combine_feature_
 from lerobot.datasets.image_writer import safe_stop_image_writer
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import (
+    DEFAULT_AUDIO_CHUNK_DURATION,
+    DEFAULT_INITIAL_AUDIO_BUFFER_DURATION,
+)
 from lerobot.datasets.video_utils import VideoEncodingManager
+from lerobot.microphones import (
+    MicrophoneConfig,  # noqa: F401
+)
+from lerobot.microphones.portaudio.configuration_portaudio import PortAudioMicrophoneConfig  # noqa: F401
+from lerobot.microphones.touchlab.configuration_touchlab import TouchLabSensorConfig  # noqa: F401
+from lerobot.microphones.utils import (
+    async_microphones_start_recording,
+    async_microphones_stop_recording,
+)
 from lerobot.policies.factory import make_policy, make_pre_post_processors
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.utils import make_robot_action
@@ -131,6 +147,7 @@ from lerobot.teleoperators import (  # noqa: F401
     unitree_g1,
 )
 from lerobot.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop
+from lerobot.utils.audio_utils import rolling_vstack
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.control_utils import (
     init_keyboard_listener,
@@ -300,6 +317,13 @@ def record_loop(
     display_data: bool = False,
     display_compressed_images: bool = False,
 ):
+    if display_data:
+        init_rerun(
+            session_name="recording",
+            robot=robot,
+            reset_time=True,
+        )
+
     if dataset is not None and dataset.fps != fps:
         raise ValueError(f"The dataset fps should be equal to requested fps ({dataset.fps} != {fps}).")
 
@@ -334,6 +358,36 @@ def record_loop(
         preprocessor.reset()
         postprocessor.reset()
 
+    # Create a buffer for audio observations (shifting window of fixed size over audio samples)
+    if robot.microphones and (policy is not None or dataset is not None):
+        audio_buffer = {
+            microphone_name: np.zeros(
+                (int(microphone.sample_rate * DEFAULT_AUDIO_CHUNK_DURATION), len(microphone.channels))
+            )
+            for microphone_name, microphone in robot.microphones.items()
+        }
+
+    if (
+        dataset is not None and robot.name != "lekiwi"
+    ):  # For now, LeKiwi only supports frame audio recording (which may lead to audio chunks loss, extended post-processing, increased memory usage)
+        dataset.add_microphones_recordings(robot.microphones)
+    else:
+        async_microphones_start_recording(robot.microphones)
+
+    # Fill audio buffers if needed
+    if (
+        robot.microphones
+        and (policy is not None or dataset is not None)
+        and DEFAULT_INITIAL_AUDIO_BUFFER_DURATION > 0.0
+    ):
+        # This initial wait might be longer than the audio chunk duration to
+        # (1) ensure that the audio buffers are filled with enough data
+        # (2) add additional initial samples to the dataset in case of variable audio chunk duration during training
+        precise_sleep(DEFAULT_INITIAL_AUDIO_BUFFER_DURATION)
+        for microphone_name, microphone in robot.microphones.items():
+            audio_chunk = microphone.read()
+            audio_buffer[microphone_name] = rolling_vstack(audio_buffer[microphone_name], audio_chunk)
+
     no_action_count = 0
     timestamp = 0
     start_episode_t = time.perf_counter()
@@ -355,8 +409,14 @@ def record_loop(
 
         # Get action from either policy or teleop
         if policy is not None and preprocessor is not None and postprocessor is not None:
+            # Transform instantaneous audio samples into a buffer of fixed size
+            buffered_observation_frame = copy(observation_frame)
+            for name in audio_buffer:
+                # Add the audio buffer to the observation
+                buffered_observation_frame[name] = rolling_vstack(audio_buffer[name], observation_frame[name])
+
             action_values = predict_action(
-                observation=observation_frame,
+                observation=buffered_observation_frame,
                 policy=policy,
                 device=get_safe_torch_device(policy.config.device),
                 preprocessor=preprocessor,
@@ -415,7 +475,10 @@ def record_loop(
 
         if display_data:
             log_rerun_data(
-                observation=obs_processed, action=action_values, compress_images=display_compressed_images
+                observation=obs_processed,
+                action=action_values,
+                compress_images=display_compressed_images,
+                log_time=time.perf_counter() - start_episode_t,
             )
 
         dt_s = time.perf_counter() - start_loop_t
@@ -430,6 +493,8 @@ def record_loop(
 
         timestamp = time.perf_counter() - start_episode_t
 
+    async_microphones_stop_recording(robot.microphones)
+
 
 @parser.wrap()
 def record(cfg: RecordConfig) -> LeRobotDataset:

src/lerobot/scripts/lerobot_teleoperate.py

@@ -62,6 +62,8 @@ from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # no
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
 from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig  # noqa: F401
 from lerobot.configs import parser
+from lerobot.microphones.portaudio.configuration_portaudio import PortAudioMicrophoneConfig  # noqa: F401
+from lerobot.microphones.touchlab.configuration_touchlab import TouchLabSensorConfig  # noqa: F401
 from lerobot.processor import (
     RobotAction,
     RobotObservation,
@@ -151,8 +153,18 @@ def teleop_loop(
         robot_action_processor: An optional pipeline to process actions before they are sent to the robot.
         robot_observation_processor: An optional pipeline to process raw observations from the robot.
     """
+    if display_data:
+        init_rerun(
+            session_name="teleoperation",
+            robot=robot,
+            reset_time=True,
+        )
 
     display_len = max(len(key) for key in robot.action_features)
+
+    for _, microphone in robot.microphones.items():
+        microphone.start_recording()
+
     start = time.perf_counter()
     while True:
         loop_start = time.perf_counter()
@@ -186,6 +198,7 @@ def teleop_loop(
                 observation=obs_transition,
                 action=teleop_action,
                 compress_images=display_compressed_images,
+                log_time=time.perf_counter() - start,
             )
 
             print("\n" + "-" * (display_len + 10))
@@ -202,7 +215,10 @@ def teleop_loop(
         move_cursor_up(1)
 
         if duration is not None and time.perf_counter() - start >= duration:
-            return
+            break
+
+    for _, microphone in robot.microphones.items():
+        microphone.stop_recording()
 
 
 @parser.wrap()

src/lerobot/utils/audio_utils.py

@@ -0,0 +1,37 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+
+
+def rolling_vstack(buffer: np.ndarray, new_data: np.ndarray) -> np.ndarray:
+    """
+    Rolling implementation of numpy.vstack to add new data in at the end of a fixed shape buffer in a rolling fashion.
+
+    Args:
+        buffer: The *fixed* shape buffer to update.
+        new_data: The new data to add to the buffer.
+
+    Returns:
+        The updated buffer.
+    """
+
+    buffer_size = buffer.shape[0]
+    # Remove as many old audio samples as needed
+    buffer[: -len(new_data)] = buffer[len(new_data) :]
+    # Add new audio samples, only the newest if the buffer is already full
+    buffer[-len(new_data) :] = new_data[-buffer_size:]
+    return buffer

src/lerobot/utils/constants.py

@@ -23,6 +23,7 @@ OBS_ENV_STATE = OBS_STR + ".environment_state"
 OBS_STATE = OBS_STR + ".state"
 OBS_IMAGE = OBS_STR + ".image"
 OBS_IMAGES = OBS_IMAGE + "s"
+OBS_AUDIO = OBS_STR + ".audio"
 OBS_LANGUAGE = OBS_STR + ".language"
 OBS_LANGUAGE_TOKENS = OBS_LANGUAGE + ".tokens"
 OBS_LANGUAGE_ATTENTION_MASK = OBS_LANGUAGE + ".attention_mask"

src/lerobot/utils/control_utils.py

@@ -103,7 +103,7 @@ def predict_action(
         torch.inference_mode(),
         torch.autocast(device_type=device.type) if device.type == "cuda" and use_amp else nullcontext(),
     ):
-        # Convert to pytorch format: channel first and float32 in [0,1] with batch dimension
+        # Convert to pytorch format: normalizing and permuting (channel first)
         observation = prepare_observation_for_inference(observation, device, task, robot_type)
         observation = preprocessor(observation)
 

src/lerobot/utils/errors.py

@@ -30,3 +30,22 @@ class DeviceAlreadyConnectedError(ConnectionError):
     ):
         self.message = message
         super().__init__(self.message)
+
+
+class DeviceNotRecordingError(Exception):
+    """Exception raised when the robot device is not recording."""
+
+    def __init__(self, message="This robot device is not recording. Try calling `start_recording()` first."):
+        self.message = message
+        super().__init__(self.message)
+
+
+class DeviceAlreadyRecordingError(Exception):
+    """Exception raised when the robot device is already recording."""
+
+    def __init__(
+        self,
+        message="This robot device is already recording. Try not calling `start_recording()` twice.",
+    ):
+        self.message = message
+        super().__init__(self.message)

src/lerobot/utils/shared_array.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from multiprocessing import Lock, Value, shared_memory
+
+import numpy as np
+
+
+class SharedArray:
+    """
+    A SharedArray is a numpy array shared between multiple processes in a shared_memory object.
+    - Data is written to the array using the `write` method, which appends data to the array.
+    - Data is read from the array (and eventually flushed) using the `read` method, which copies the _whole_ array.
+
+    SharedArray offers quasi-instantaneous array-wide read and flush capabilities in comparison to Queues, but has a limited size defined at initialization.
+
+    Example:
+    _Main_process_
+    shared_array = SharedArray(shape=(10, 10), dtype=np.dtype("float32"))
+    local_array = shared_array.get_local_array()
+    shared_array.write(local_array, np.array([[1, 2, 3], [4, 5, 6]]))
+
+    _Child_process_
+    local_array = shared_array.get_local_array()
+    data = shared_array.read(local_array, flush=True)
+    """
+
+    def __init__(self, shape: tuple[int], dtype: np.dtype | str):
+        """
+        Initialize a SharedArray.
+
+        Args:
+            shape: The shape of the shared array.
+            dtype: The dtype of the shared array.
+        """
+        self.shape = shape
+        self.dtype = dtype
+
+        self.shared_memory = shared_memory.SharedMemory(
+            create=True, size=np.prod(shape) * np.dtype(dtype).itemsize
+        )
+        self.read_index = Value("i", 0)
+        self.lock = Lock()
+
+    def get_local_array(self) -> np.ndarray:
+        """
+        Get a process-local instance of the shared array.
+
+        Returns:
+            A process-local instance of the shared array.
+        """
+        return np.ndarray(self.shape, dtype=np.dtype(self.dtype), buffer=self.shared_memory.buf)
+
+    def delete(self):
+        """
+        Delete the shared array.
+        """
+        self.shared_memory.close()
+        self.shared_memory.unlink()
+
+    def write(self, local_array: np.ndarray, data: np.ndarray):
+        """
+        Write data to the shared array.
+
+        Args:
+            local_array: The process-local instance of the shared array to write to.
+            data: The data to write to the shared array.
+        """
+        with self.lock:
+            local_array[self.read_index.value : self.read_index.value + len(data)] = data
+            self.read_index.value += len(data)
+
+    def read(self, local_array: np.ndarray, flush: bool = True) -> np.ndarray:
+        """
+        Read data from the shared array.
+
+        Args:
+            local_array: The process-local instance of the shared array to read from.
+            flush: Whether to flush the shared array after reading.
+        """
+        with self.lock:
+            data = np.copy(local_array[: self.read_index.value])
+            if flush:
+                self.read_index.value = 0
+            return data
+
+    def reset(self):
+        """
+        Reset the read index to 0.
+        """
+        with self.lock:
+            self.read_index.value = 0

src/lerobot/utils/visualization_utils.py

@@ -14,17 +14,25 @@
 
 import numbers
 import os
+import time
+from uuid import uuid4
 
 import numpy as np
 import rerun as rr
 
-from lerobot.types import RobotAction, RobotObservation
+from lerobot.datasets.utils import DEFAULT_AUDIO_CHUNK_DURATION
+from lerobot.processor import RobotAction, RobotObservation
+from lerobot.robots import Robot
 
 from .constants import ACTION, ACTION_PREFIX, OBS_PREFIX, OBS_STR
 
 
 def init_rerun(
-    session_name: str = "lerobot_control_loop", ip: str | None = None, port: int | None = None
+    session_name: str = "lerobot_control_loop",
+    ip: str | None = None,
+    port: int | None = None,
+    robot: Robot | None = None,
+    reset_time: bool = False,
 ) -> None:
     """
     Initializes the Rerun SDK for visualizing the control loop.
@@ -33,16 +41,26 @@ def init_rerun(
         session_name: Name of the Rerun session.
         ip: Optional IP for connecting to a Rerun server.
         port: Optional port for connecting to a Rerun server.
+        robot: A Robot object. If provided, Rerun will be initialized with a blueprint that includes the object's cameras and microphones.
+        reset_time: Whether to reset the timer "episode_time" to 0.
     """
     batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000")
     os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size
-    rr.init(session_name)
+    rr.init(
+        application_id=session_name,
+        recording_id=uuid4(),
+    )
+    if robot is not None:
+        rr.send_blueprint(build_rerun_blueprint(robot))
     memory_limit = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "10%")
     if ip and port:
         rr.connect_grpc(url=f"rerun+http://{ip}:{port}/proxy")
     else:
         rr.spawn(memory_limit=memory_limit)
 
+    if reset_time:
+        rr.set_time("episode_time", timestamp=0.0)
+
 
 def _is_scalar(x):
     return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
@@ -50,10 +68,47 @@ def _is_scalar(x):
     )
 
 
+def build_rerun_blueprint(robot: Robot) -> rr.blueprint.Grid:
+    """ "
+    Builds a Rerun blueprint for optimized visualization of the robot's observations and actions :
+    -   Time series views for all scalar observations and actions (e.g. position, velocity, torque, etc.).
+    -   Spatial 2D views for all camera observations.
+    -   Time series views for all microphone observations.
+
+    Args:
+        robot: A Robot object.
+    Returns:
+        A Rerun blueprint.
+    """
+    contents = [
+        rr.blueprint.TimeSeriesView(
+            origin="data",
+            plot_legend=rr.blueprint.PlotLegend(visible=True),
+        )
+    ]
+    if robot.microphones:
+        contents += [
+            rr.blueprint.TimeSeriesView(
+                origin="audio",
+                plot_legend=rr.blueprint.PlotLegend(visible=True),
+            )
+        ]
+    if robot.cameras:
+        contents += [
+            rr.blueprint.Spatial2DView(
+                origin=OBS_PREFIX + camera_name,
+            )
+            for camera_name in robot.cameras
+        ]
+
+    return rr.blueprint.Grid(*contents)
+
+
 def log_rerun_data(
     observation: RobotObservation | None = None,
     action: RobotAction | None = None,
     compress_images: bool = False,
+    log_time: float | None = None,
 ) -> None:
     """
     Logs observation and action data to Rerun for real-time visualization.
@@ -72,7 +127,13 @@ def log_rerun_data(
         observation: An optional dictionary containing observation data to log.
         action: An optional dictionary containing action data to log.
         compress_images: Whether to compress images before logging to save bandwidth & memory in exchange for cpu and quality.
+        log_time: The time to log the data in the "episode_time" timeline.
+                  If None, the current time is used in Rerun's default timeline.
     """
+    if log_time is None:
+        log_time = time.perf_counter()
+    rr.set_time("episode_time", timestamp=log_time)
+
     if observation:
         for k, v in observation.items():
             if v is None:
@@ -80,15 +141,41 @@ def log_rerun_data(
             key = k if str(k).startswith(OBS_PREFIX) else f"{OBS_STR}.{k}"
 
             if _is_scalar(v):
-                rr.log(key, rr.Scalars(float(v)))
+                rr.log("data/" + key, rr.Scalars(float(v)))
             elif isinstance(v, np.ndarray):
                 arr = v
                 # Convert CHW -> HWC when needed
                 if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
                     arr = np.transpose(arr, (1, 2, 0))
+                # Convert samples x channels -> channels x samples when needed
+                elif arr.ndim == 2 and arr.shape[1] < arr.shape[0]:
+                    arr = np.transpose(arr, (1, 0))
+
                 if arr.ndim == 1:
                     for i, vi in enumerate(arr):
-                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
+                        rr.log("data/" + f"{key}_{i}", rr.Scalars(float(vi)))
+                elif arr.ndim == 2:
+                    for i, channel_arr in enumerate(arr):
+                        rr.send_columns(
+                            "audio/"
+                            + key
+                            + f"_channel_{i}",  # TODO(CarolinePascal): Get actual channel number/name
+                            indexes=[
+                                rr.TimeColumn(
+                                    "episode_time",
+                                    timestamp=log_time
+                                    + np.linspace(
+                                        -DEFAULT_AUDIO_CHUNK_DURATION,
+                                        0,
+                                        len(channel_arr),
+                                        endpoint=False,
+                                    ),
+                                )
+                            ],
+                            columns=rr.Scalars.columns(scalars=channel_arr),
+                        )
+                elif arr.ndim == 3:
+                    rr.log(key, rr.Image(arr), static=True)
                 else:
                     img_entity = rr.Image(arr).compress() if compress_images else rr.Image(arr)
                     rr.log(key, entity=img_entity, static=True)
@@ -100,13 +187,13 @@ def log_rerun_data(
             key = k if str(k).startswith(ACTION_PREFIX) else f"{ACTION}.{k}"
 
             if _is_scalar(v):
-                rr.log(key, rr.Scalars(float(v)))
+                rr.log("data/" + key, rr.Scalars(float(v)))
             elif isinstance(v, np.ndarray):
                 if v.ndim == 1:
                     for i, vi in enumerate(v):
-                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
+                        rr.log("data/" + f"{key}_{i}", rr.Scalars(float(vi)))
                 else:
                     # Fall back to flattening higher-dimensional arrays
                     flat = v.flatten()
                     for i, vi in enumerate(flat):
-                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
+                        rr.log("data/" + f"{key}_{i}", rr.Scalars(float(vi)))

tests/conftest.py

@@ -57,6 +57,8 @@ def _check_component_availability(component_type, available_components, make_com
             print("\nNo physical device detected.")
         elif isinstance(e, ValueError) and "camera_index" in str(e):
             print("\nNo physical camera detected.")
+        elif isinstance(e, ValueError) and "microphone_index" in str(e):
+            print("\nNo physical microphone detected.")
         else:
             traceback.print_exc()
 

tests/datasets/test_compute_stats.py

@@ -26,16 +26,22 @@ from lerobot.datasets.compute_stats import (
     compute_episode_stats,
     estimate_num_samples,
     get_feature_stats,
+    sample_audio_from_data,
+    sample_audio_from_path,
     sample_images,
     sample_indices,
 )
-from lerobot.utils.constants import OBS_IMAGE, OBS_STATE
+from lerobot.utils.constants import OBS_AUDIO, OBS_IMAGE, OBS_STATE
 
 
 def mock_load_image_as_numpy(path, dtype, channel_first):
     return np.ones((3, 32, 32), dtype=dtype) if channel_first else np.ones((32, 32, 3), dtype=dtype)
 
 
+def mock_load_audio(path):
+    return np.ones((16000, 2), dtype=np.float32)
+
+
 @pytest.fixture
 def sample_array():
     return np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
@@ -73,6 +79,25 @@ def test_sample_images(mock_load):
     assert len(images) == estimate_num_samples(100)
 
 
+@patch("lerobot.datasets.compute_stats.load_audio_from_path", side_effect=mock_load_audio)
+def test_sample_audio_from_path(mock_load):
+    audio_path = "audio.wav"
+    audio_samples = sample_audio_from_path(audio_path)
+    assert isinstance(audio_samples, np.ndarray)
+    assert audio_samples.shape[1] == 2
+    assert audio_samples.dtype == np.float32
+    assert len(audio_samples) == estimate_num_samples(16000)
+
+
+def test_sample_audio_from_data():
+    audio_data = np.ones((16000, 2), dtype=np.float32)
+    audio_samples = sample_audio_from_data(audio_data)
+    assert isinstance(audio_samples, np.ndarray)
+    assert audio_samples.shape[1] == 2
+    assert audio_samples.dtype == np.float32
+    assert len(audio_samples) == estimate_num_samples(16000)
+
+
 def test_get_feature_stats_images():
     data = np.random.rand(100, 3, 32, 32)
     stats = get_feature_stats(data, axis=(0, 2, 3), keepdims=True)
@@ -81,6 +106,14 @@ def test_get_feature_stats_images():
     assert stats["min"].shape == stats["max"].shape == stats["mean"].shape == stats["std"].shape
 
 
+def test_get_feature_stats_audio():
+    data = np.random.uniform(-1, 1, (16000, 2))
+    stats = get_feature_stats(data, axis=0, keepdims=True)
+    assert "min" in stats and "max" in stats and "mean" in stats and "std" in stats and "count" in stats
+    np.testing.assert_equal(stats["count"], np.array([16000]))
+    assert stats["min"].shape == stats["max"].shape == stats["mean"].shape == stats["std"].shape
+
+
 def test_get_feature_stats_axis_0_keepdims(sample_array):
     expected = {
         "min": np.array([[1, 2, 3]]),
@@ -145,20 +178,27 @@ def test_get_feature_stats_single_value():
 def test_compute_episode_stats():
     episode_data = {
         OBS_IMAGE: [f"image_{i}.jpg" for i in range(100)],
+        OBS_AUDIO: "audio.wav",
         OBS_STATE: np.random.rand(100, 10),
     }
     features = {
         OBS_IMAGE: {"dtype": "image"},
+        OBS_AUDIO: {"dtype": "audio"},
         OBS_STATE: {"dtype": "numeric"},
     }
 
-    with patch("lerobot.datasets.compute_stats.load_image_as_numpy", side_effect=mock_load_image_as_numpy):
+    with (
+        patch("lerobot.datasets.compute_stats.load_image_as_numpy", side_effect=mock_load_image_as_numpy),
+        patch("lerobot.datasets.compute_stats.load_audio_from_path", side_effect=mock_load_audio),
+    ):
         stats = compute_episode_stats(episode_data, features)
 
-    assert OBS_IMAGE in stats and OBS_STATE in stats
-    assert stats[OBS_IMAGE]["count"].item() == 100
-    assert stats[OBS_STATE]["count"].item() == 100
+    assert OBS_IMAGE in stats and OBS_AUDIO in stats and OBS_STATE in stats
+    assert stats[OBS_IMAGE]["count"].item() == estimate_num_samples(100)
+    assert stats[OBS_AUDIO]["count"].item() == estimate_num_samples(16000)
+    assert stats[OBS_STATE]["count"].item() == estimate_num_samples(100)
     assert stats[OBS_IMAGE]["mean"].shape == (3, 1, 1)
+    assert stats[OBS_AUDIO]["mean"].shape == (1, 2)
 
 
 def test_assert_type_and_shape_valid():

tests/datasets/test_datasets.py

@@ -24,6 +24,7 @@ import torch
 from huggingface_hub import HfApi
 from PIL import Image
 from safetensors.torch import load_file
+from soundfile import write
 
 import lerobot
 from lerobot.configs.default import DatasetConfig
@@ -35,6 +36,7 @@ from lerobot.datasets.io_utils import hf_transform_to_torch
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.multi_dataset import MultiLeRobotDataset
 from lerobot.datasets.utils import (
+    DEFAULT_AUDIO_CHUNK_DURATION,
     DEFAULT_CHUNK_SIZE,
     DEFAULT_DATA_FILE_SIZE_IN_MB,
     DEFAULT_VIDEO_FILE_SIZE_IN_MB,
@@ -45,7 +47,13 @@ from lerobot.envs.factory import make_env_config
 from lerobot.policies.factory import make_policy_config
 from lerobot.robots import make_robot_from_config
 from lerobot.utils.constants import ACTION, DONE, OBS_IMAGES, OBS_STATE, OBS_STR, REWARD
-from tests.fixtures.constants import DUMMY_CHW, DUMMY_HWC, DUMMY_REPO_ID
+from tests.fixtures.constants import (
+    DEFAULT_SAMPLE_RATE,
+    DUMMY_AUDIO_CHANNELS,
+    DUMMY_CHW,
+    DUMMY_HWC,
+    DUMMY_REPO_ID,
+)
 from tests.mocks.mock_robot import MockRobotConfig
 from tests.utils import require_x86_64_kernel
 
@@ -66,6 +74,36 @@ def image_dataset(tmp_path, empty_lerobot_dataset_factory):
     return empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
 
 
+@pytest.fixture
+def audio_dataset_le_kiwi(tmp_path, empty_lerobot_dataset_factory):
+    features = {
+        "audio": {
+            "dtype": "audio",
+            "shape": (1, DUMMY_AUDIO_CHANNELS),
+            "names": [
+                "channels",
+            ],
+            "info": {"sample_rate": DEFAULT_SAMPLE_RATE},
+        }
+    }
+    return empty_lerobot_dataset_factory(root=tmp_path / "test", features=features, robot_type="lekiwi")
+
+
+@pytest.fixture
+def audio_dataset(tmp_path, empty_lerobot_dataset_factory):
+    features = {
+        "audio": {
+            "dtype": "audio",
+            "shape": (1, DUMMY_AUDIO_CHANNELS),
+            "names": [
+                "channels",
+            ],
+            "info": {"sample_rate": DEFAULT_SAMPLE_RATE},
+        }
+    }
+    return empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
+
+
 def test_same_attributes_defined(tmp_path, lerobot_dataset_factory):
     """
     Instantiate a LeRobotDataset both ways with '__init__()' and 'create()' and verify that instantiated
@@ -420,6 +458,78 @@ def test_tmp_mixed_deletion(tmp_path, empty_lerobot_dataset_factory):
     )
 
 
+def test_add_frame_audio_array(audio_dataset_le_kiwi):
+    dataset = audio_dataset_le_kiwi
+    dataset.add_frame(
+        {
+            "audio": np.random.rand(
+                int(DEFAULT_AUDIO_CHUNK_DURATION * DEFAULT_SAMPLE_RATE), DUMMY_AUDIO_CHANNELS
+            )
+        },
+        task="Dummy task",
+    )
+    dataset.save_episode()
+
+    assert dataset[0]["audio"].shape == torch.Size(
+        (
+            DUMMY_AUDIO_CHANNELS,
+            int(DEFAULT_AUDIO_CHUNK_DURATION * DEFAULT_SAMPLE_RATE),
+        )
+    )
+
+
+def test_add_frame_audio_array_wrong_shape(audio_dataset_le_kiwi):
+    dataset = audio_dataset_le_kiwi
+    with pytest.raises(ValueError):
+        dataset.add_frame(
+            {
+                "audio": np.random.rand(
+                    int(DEFAULT_AUDIO_CHUNK_DURATION * DEFAULT_SAMPLE_RATE), DUMMY_AUDIO_CHANNELS, 99
+                )
+            },
+            task="Dummy task",
+        )
+
+
+def test_add_frame_audio_array_wrong_channels_number(audio_dataset_le_kiwi):
+    dataset = audio_dataset_le_kiwi
+    with pytest.raises(ValueError):
+        dataset.add_frame(
+            {"audio": np.random.rand(int(DEFAULT_AUDIO_CHUNK_DURATION * DEFAULT_SAMPLE_RATE), 99)},
+            task="Dummy task",
+        )
+
+
+def test_add_frame_audio_file(audio_dataset):
+    dataset = audio_dataset
+    dataset.add_frame(
+        {
+            "audio": np.random.rand(
+                int(DEFAULT_AUDIO_CHUNK_DURATION * DEFAULT_SAMPLE_RATE), DUMMY_AUDIO_CHANNELS
+            )
+        },
+        task="Dummy task",
+    )
+    # Create the audio file that should be created in the background by the Microphone class
+    for audio_key in dataset.meta.audio_keys:
+        fpath = dataset.writer._get_raw_audio_file_path(0, audio_key)
+        fpath.parent.mkdir(parents=True, exist_ok=True)
+        write(
+            fpath,
+            np.random.rand(int(DEFAULT_AUDIO_CHUNK_DURATION * DEFAULT_SAMPLE_RATE), DUMMY_AUDIO_CHANNELS),
+            DEFAULT_SAMPLE_RATE,
+        )
+
+    dataset.save_episode()
+
+    assert dataset[0]["audio"].shape == torch.Size(
+        (
+            DUMMY_AUDIO_CHANNELS,
+            int(DEFAULT_AUDIO_CHUNK_DURATION * DEFAULT_SAMPLE_RATE),
+        )
+    )
+
+
 # TODO(aliberts):
 # - [ ] test various attributes & state from init and create
 # - [ ] test init with episodes and check num_frames
@@ -459,6 +569,7 @@ def test_factory(env_name, repo_id, policy_name):
     dataset = make_dataset(cfg)
     delta_timestamps = dataset.delta_timestamps
     camera_keys = dataset.meta.camera_keys
+    audio_keys = dataset.meta.audio_keys
 
     item = dataset[0]
 
@@ -501,6 +612,11 @@ def test_factory(env_name, repo_id, policy_name):
                 # test c,h,w
                 assert item[key].shape[0] == 3, f"{key}"
 
+        for key in audio_keys:
+            assert item[key].dtype == torch.float32, f"{key}"
+            assert item[key].max() <= 1.0, f"{key}"
+            assert item[key].min() >= -1.0, f"{key}"
+
     if delta_timestamps is not None:
         # test missing keys in delta_timestamps
         for key in delta_timestamps:

tests/fixtures/constants.py

@@ -40,5 +40,18 @@ DUMMY_VIDEO_INFO = {
     "video.is_depth_map": False,
     "has_audio": False,
 }
+DUMMY_MICROPHONE_FEATURES = {
+    "laptop": {"dtype": "audio", "shape": (1, 2), "names": ["channels"], "info": None},
+    "phone": {"dtype": "audio", "shape": (1, 2), "names": ["channels"], "info": None},
+}
+DEFAULT_SAMPLE_RATE = 48000
+DUMMY_AUDIO_CHANNELS = 2
+DUMMY_AUDIO_INFO = {
+    "has_audio": True,
+    "audio.sample_rate": DEFAULT_SAMPLE_RATE,
+    "audio.codec": "aac",
+    "audio.channels": DUMMY_AUDIO_CHANNELS,
+    "audio.channel_layout": "stereo",
+}
 DUMMY_CHW = (3, 96, 128)
 DUMMY_HWC = (96, 128, 3)

tests/fixtures/dataset_factories.py

@@ -31,6 +31,7 @@ from lerobot.datasets.feature_utils import get_hf_features_from_features
 from lerobot.datasets.io_utils import hf_transform_to_torch
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.utils import (
+    DEFAULT_AUDIO_PATH,
     DEFAULT_CHUNK_SIZE,
     DEFAULT_DATA_FILE_SIZE_IN_MB,
     DEFAULT_DATA_PATH,
@@ -43,6 +44,7 @@ from lerobot.datasets.video_utils import encode_video_frames
 from tests.fixtures.constants import (
     DEFAULT_FPS,
     DUMMY_CAMERA_FEATURES,
+    DUMMY_MICROPHONE_FEATURES,
     DUMMY_MOTOR_FEATURES,
     DUMMY_REPO_ID,
     DUMMY_ROBOT_TYPE,
@@ -131,6 +133,7 @@ def features_factory():
     def _create_features(
         motor_features: dict = DUMMY_MOTOR_FEATURES,
         camera_features: dict = DUMMY_CAMERA_FEATURES,
+        audio_features: dict = DUMMY_MICROPHONE_FEATURES,
         use_videos: bool = True,
     ) -> dict:
         if use_videos:
@@ -142,6 +145,7 @@ def features_factory():
         return {
             **motor_features,
             **camera_ft,
+            **audio_features,
             **DEFAULT_FEATURES,
         }
 
@@ -158,16 +162,19 @@ def info_factory(features_factory):
         total_frames: int = 0,
         total_tasks: int = 0,
         total_videos: int = 0,
+        total_audio: int = 0,
         chunks_size: int = DEFAULT_CHUNK_SIZE,
         data_files_size_in_mb: float = DEFAULT_DATA_FILE_SIZE_IN_MB,
         video_files_size_in_mb: float = DEFAULT_VIDEO_FILE_SIZE_IN_MB,
         data_path: str = DEFAULT_DATA_PATH,
         video_path: str = DEFAULT_VIDEO_PATH,
+        audio_path: str = DEFAULT_AUDIO_PATH,
         motor_features: dict = DUMMY_MOTOR_FEATURES,
         camera_features: dict = DUMMY_CAMERA_FEATURES,
+        audio_features: dict = DUMMY_MICROPHONE_FEATURES,
         use_videos: bool = True,
     ) -> dict:
-        features = features_factory(motor_features, camera_features, use_videos)
+        features = features_factory(motor_features, camera_features, audio_features, use_videos)
         return {
             "codebase_version": codebase_version,
             "robot_type": robot_type,
@@ -175,6 +182,7 @@ def info_factory(features_factory):
             "total_frames": total_frames,
             "total_tasks": total_tasks,
             "total_videos": total_videos,
+            "total_audio": total_audio,
             "chunks_size": chunks_size,
             "data_files_size_in_mb": data_files_size_in_mb,
             "video_files_size_in_mb": video_files_size_in_mb,
@@ -182,6 +190,7 @@ def info_factory(features_factory):
             "splits": {},
             "data_path": data_path,
             "video_path": video_path if use_videos else None,
+            "audio_path": audio_path,
             "features": features,
         }
 
@@ -205,6 +214,14 @@ def stats_factory():
                     "std": np.full((3, 1, 1), 0.25, dtype=np.float32).tolist(),
                     "count": [10],
                 }
+            elif dtype == "audio":
+                stats[key] = {
+                    "mean": np.full((shape[0],), 0.0, dtype=np.float32).tolist(),
+                    "max": np.full((shape[0],), 1, dtype=np.float32).tolist(),
+                    "min": np.full((shape[0],), -1, dtype=np.float32).tolist(),
+                    "std": np.full((shape[0],), 0.5, dtype=np.float32).tolist(),
+                    "count": [10],
+                }
             else:
                 stats[key] = {
                     "max": np.full(shape, 1, dtype=dtype).tolist(),

tests/microphones/test_portaudio.py

@@ -0,0 +1,532 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from copy import deepcopy
+from pathlib import Path
+
+import numpy as np
+import pytest
+from soundfile import read
+
+from lerobot.microphones.portaudio.configuration_portaudio import PortAudioMicrophoneConfig
+from lerobot.microphones.portaudio.interface_sounddevice_sdk import (
+    FakeSounddeviceSDKAdapter,
+    SounddeviceSDKAdapter,
+)
+from lerobot.microphones.portaudio.microphone_portaudio import PortAudioMicrophone
+from lerobot.microphones.utils import async_microphones_start_recording, async_microphones_stop_recording
+from lerobot.utils.errors import (
+    DeviceAlreadyConnectedError,
+    DeviceAlreadyRecordingError,
+    DeviceNotConnectedError,
+    DeviceNotRecordingError,
+)
+from lerobot.utils.robot_utils import precise_sleep
+
+MODULE_PATH = "lerobot.microphones.portaudio.microphone_portaudio"
+RECORDING_DURATION = 1.0
+
+LEROBOT_USE_REAL_PORTAUDIO_MICROPHONE_TESTS = (
+    os.getenv("LEROBOT_USE_REAL_PORTAUDIO_MICROPHONE_TESTS", "False").lower() == "true"
+)
+
+
+@pytest.fixture
+def test_sdk():
+    """Fixture to provide either real or fake SDK based on environment variable."""
+    if LEROBOT_USE_REAL_PORTAUDIO_MICROPHONE_TESTS:
+        return SounddeviceSDKAdapter()
+    else:
+        return FakeSounddeviceSDKAdapter()
+
+
+# Configuration Tests
+
+
+def test_config_creation():
+    """Test creating a valid configuration."""
+    config = PortAudioMicrophoneConfig(microphone_index=0, sample_rate=48000, channels=[1, 2])
+    assert config.microphone_index == 0
+    assert config.sample_rate == 48000
+    assert config.channels == [1, 2]
+
+
+def test_config_creation_missing_microphone_index():
+    """Test creating a configuration with missing microphone index."""
+    with pytest.raises(TypeError):
+        PortAudioMicrophoneConfig(sample_rate=48000, channels=[1, 2])
+
+
+def test_config_creation_missing_sample_rate():
+    """Test creating a configuration with missing sample rate."""
+    config = PortAudioMicrophoneConfig(microphone_index=0, channels=[1, 2])
+    assert config.sample_rate is None
+
+
+def test_config_creation_missing_channels():
+    """Test creating a configuration with missing channels."""
+    config = PortAudioMicrophoneConfig(microphone_index=0, sample_rate=48000)
+    assert config.channels is None
+
+
+@pytest.fixture
+def default_config(test_sdk):
+    """Fixture to provide a default configuration for input devices."""
+    device_info = test_sdk.query_devices(kind="input")
+    return PortAudioMicrophoneConfig(
+        microphone_index=device_info["index"],
+        sample_rate=device_info["default_samplerate"],
+        channels=np.arange(device_info["max_input_channels"]) + 1,
+    )
+
+
+# Microphone Tests
+
+
+def test_find_microphones(test_sdk):
+    """Test finding microphones."""
+    microphones = PortAudioMicrophone.find_microphones(sounddevice_sdk=test_sdk)
+
+    for microphone in microphones:
+        assert isinstance(microphone["index"], int)
+        assert isinstance(microphone["name"], str)
+        assert isinstance(microphone["sample_rate"], int)
+        assert isinstance(microphone["channels"], np.ndarray)
+        assert len(microphone["channels"]) > 0
+
+
+def test_init_defaults(default_config, test_sdk):
+    """Test microphone initialization with defaults."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+
+    device_info = test_sdk.query_devices(kind="input")
+    assert microphone is not None
+    assert microphone.microphone_index == device_info["index"]
+    assert microphone.sample_rate == device_info["default_samplerate"]
+    np.testing.assert_array_equal(microphone.channels, np.arange(device_info["max_input_channels"]) + 1)
+    assert not microphone.is_connected
+    assert not microphone.is_recording
+
+
+def test_connect_success(default_config, test_sdk):
+    """Test successful connection."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+
+    assert microphone.is_connected
+    assert not microphone.is_recording
+    assert not microphone.is_writing
+
+
+def test_connect_empty_config(default_config, test_sdk):
+    """Test connection with empty config values."""
+    config = deepcopy(default_config)
+    config.sample_rate = None
+    config.channels = None
+    microphone = PortAudioMicrophone(config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+
+    device_info = test_sdk.query_devices(kind="input")
+    assert microphone.sample_rate == device_info["default_samplerate"]
+    np.testing.assert_array_equal(microphone.channels, np.arange(device_info["max_input_channels"]) + 1)
+
+
+def test_connect_already_connected(default_config, test_sdk):
+    """Test connecting when already connected."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+
+    with pytest.raises(DeviceAlreadyConnectedError):
+        microphone.connect()
+
+
+def test_connect_invalid_device(test_sdk):
+    """Test connecting with invalid device (output device)."""
+    device_info = test_sdk.query_devices(kind="output")
+    config = PortAudioMicrophoneConfig(
+        microphone_index=device_info["index"],
+        sample_rate=device_info["default_samplerate"],
+        channels=np.arange(device_info["max_input_channels"]) + 1,
+    )
+    microphone = PortAudioMicrophone(config, sounddevice_sdk=test_sdk)
+
+    with pytest.raises(RuntimeError):
+        microphone.connect()
+
+
+def test_connect_invalid_index(default_config, test_sdk):
+    """Test connecting with invalid device index."""
+    config = deepcopy(default_config)
+    config.microphone_index = -1
+    microphone = PortAudioMicrophone(config, sounddevice_sdk=test_sdk)
+
+    with pytest.raises(RuntimeError):
+        microphone.connect()
+
+
+def test_connect_invalid_sample_rate(default_config, test_sdk):
+    """Test connecting with invalid sample rate."""
+    config = deepcopy(default_config)
+    config.sample_rate = -1
+    microphone = PortAudioMicrophone(config, sounddevice_sdk=test_sdk)
+
+    with pytest.raises(RuntimeError):
+        microphone.connect()
+
+
+def test_connect_float_sample_rate(default_config, test_sdk):
+    """Test connecting with float sample rate."""
+    config = deepcopy(default_config)
+    config.sample_rate = int(config.sample_rate) - 0.5
+    microphone = PortAudioMicrophone(config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+
+    assert isinstance(microphone.sample_rate, int)
+    assert microphone.sample_rate == int(config.sample_rate)
+
+
+def test_connect_lower_sample_rate(default_config, test_sdk):
+    """Test connecting with lower sample rate."""
+    config = deepcopy(default_config)
+    config.sample_rate = 1000  # Lowest possible sample rate
+    microphone = PortAudioMicrophone(config, sounddevice_sdk=test_sdk)
+
+    microphone.connect()
+    assert microphone.sample_rate == 1000
+
+
+def test_connect_invalid_channels(default_config, test_sdk):
+    """Test connecting with invalid channels."""
+    config = deepcopy(default_config)
+    config.channels = np.append(default_config.channels, -1)
+    microphone = PortAudioMicrophone(config, sounddevice_sdk=test_sdk)
+
+    with pytest.raises(RuntimeError):
+        microphone.connect()
+
+
+def test_disconnect_success(default_config, test_sdk):
+    """Test successful disconnection."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+    microphone.disconnect()
+
+    assert not microphone.is_connected
+    assert not microphone.is_recording
+    assert not microphone.is_writing
+
+
+def test_disconnect_not_connected(default_config, test_sdk):
+    """Test disconnecting when not connected."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+
+    with pytest.raises(DeviceNotConnectedError):
+        microphone.disconnect()
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_start_recording_success(default_config, test_sdk, multiprocessing):
+    """Test successful recording start."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+    microphone.start_recording(multiprocessing=multiprocessing)
+
+    assert microphone.is_recording
+    assert microphone.is_connected
+    assert not microphone.is_writing
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_recording_not_connected(default_config, test_sdk, multiprocessing):
+    """Test starting recording when not connected."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+
+    with pytest.raises(DeviceNotConnectedError):
+        microphone.start_recording(multiprocessing=multiprocessing)
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_start_recording_already_recording(default_config, test_sdk, multiprocessing):
+    """Test starting recording when already recording."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+    microphone.start_recording(multiprocessing=multiprocessing)
+
+    with pytest.raises(DeviceAlreadyRecordingError):
+        microphone.start_recording(multiprocessing=multiprocessing)
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_start_writing_success(tmp_path, default_config, test_sdk, multiprocessing):
+    """Test successful writing start."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+    microphone.start_recording(output_file=tmp_path / "test.wav", multiprocessing=multiprocessing)
+
+    assert microphone.is_recording
+    assert microphone.is_connected
+    assert microphone.is_writing
+    assert (tmp_path / "test.wav").exists()
+
+    (tmp_path / "test.wav").unlink()
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_start_writing_file_already_exists_no_overwrite(tmp_path, default_config, test_sdk, multiprocessing):
+    """Test writing with file that already exists."""
+    (tmp_path / "test.wav").touch()
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+
+    with pytest.raises(FileExistsError):
+        microphone.start_recording(
+            output_file=tmp_path / "test.wav", multiprocessing=multiprocessing, overwrite=False
+        )
+
+    (tmp_path / "test.wav").unlink()
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_stop_recording_success(default_config, test_sdk, multiprocessing):
+    """Test successful recording stop."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+    microphone.start_recording(multiprocessing=multiprocessing)
+    precise_sleep(RECORDING_DURATION)
+    microphone.stop_recording()
+
+    assert not microphone.is_recording
+    assert microphone.is_connected
+    assert not microphone.is_writing
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_stop_writing_success(tmp_path, default_config, test_sdk, multiprocessing):
+    """Test successful writing stop."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+    microphone.start_recording(output_file=tmp_path / "test.wav", multiprocessing=multiprocessing)
+    precise_sleep(RECORDING_DURATION)
+    microphone.stop_recording()
+
+    assert not microphone.is_recording
+    assert microphone.is_connected
+    assert not microphone.is_writing
+    assert (tmp_path / "test.wav").exists()
+
+    (tmp_path / "test.wav").unlink()
+
+
+def test_stop_recording_not_connected(default_config, test_sdk):
+    """Test stopping recording when not connected."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+
+    with pytest.raises(DeviceNotConnectedError):
+        microphone.stop_recording()
+
+
+def test_stop_recording_not_recording(default_config, test_sdk):
+    """Test stopping recording when not recording."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+
+    with pytest.raises(DeviceNotRecordingError):
+        microphone.stop_recording()
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_disconnect_while_recording(default_config, test_sdk, multiprocessing):
+    """Test disconnecting while recording."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+    microphone.start_recording(multiprocessing=multiprocessing)
+    precise_sleep(RECORDING_DURATION)
+    microphone.disconnect()
+
+    assert not microphone.is_connected
+    assert not microphone.is_recording
+    assert not microphone.is_writing
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_disconnect_while_writing(tmp_path, default_config, test_sdk, multiprocessing):
+    """Test disconnecting while writing."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+    microphone.start_recording(output_file=tmp_path / "test.wav", multiprocessing=multiprocessing)
+    precise_sleep(RECORDING_DURATION)
+    microphone.disconnect()
+
+    assert not microphone.is_connected
+    assert not microphone.is_recording
+    assert not microphone.is_writing
+    assert Path(tmp_path / "test.wav").exists()
+
+    (tmp_path / "test.wav").unlink()
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_read_success(default_config, test_sdk, multiprocessing):
+    """Test successful reading of audio data."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+    microphone.start_recording(multiprocessing=multiprocessing)
+
+    precise_sleep(RECORDING_DURATION)
+
+    data = microphone.read()
+
+    device_info = test_sdk.query_devices(kind="input")
+    assert data is not None
+    assert data.shape[1] == len(default_config.channels)
+    assert (
+        abs(data.shape[0] - RECORDING_DURATION * default_config.sample_rate)
+        <= 2 * default_config.sample_rate * device_info["default_low_input_latency"]
+    )
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_writing_success(tmp_path, default_config, test_sdk, multiprocessing):
+    """Test successful writing to file."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+    microphone.start_recording(output_file=tmp_path / "test.wav", multiprocessing=multiprocessing)
+
+    precise_sleep(RECORDING_DURATION)
+
+    microphone.stop_recording()
+
+    data, samplerate = read(tmp_path / "test.wav")
+
+    device_info = test_sdk.query_devices(kind="input")
+    assert samplerate == default_config.sample_rate
+    assert data.shape[1] == len(default_config.channels)
+    assert (
+        abs(data.shape[0] - RECORDING_DURATION * default_config.sample_rate)
+        <= 2 * default_config.sample_rate * device_info["default_low_input_latency"]
+    )
+
+    (tmp_path / "test.wav").unlink()
+
+
+@pytest.mark.parametrize("multiprocessing", [True, False])
+def test_read_while_writing(tmp_path, default_config, test_sdk, multiprocessing):
+    """Test reading while writing."""
+    microphone = PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk)
+    microphone.connect()
+    microphone.start_recording(output_file=tmp_path / "test.wav", multiprocessing=multiprocessing)
+
+    precise_sleep(RECORDING_DURATION)
+
+    read_data = microphone.read()
+    microphone.stop_recording()
+
+    writing_data, _ = read(tmp_path / "test.wav")
+
+    device_info = test_sdk.query_devices(kind="input")
+    assert (
+        abs(writing_data.shape[0] - RECORDING_DURATION * default_config.sample_rate)
+        <= 2 * default_config.sample_rate * device_info["default_low_input_latency"]
+    )
+    assert (
+        abs(read_data.shape[0] - RECORDING_DURATION * default_config.sample_rate)
+        <= 2 * default_config.sample_rate * device_info["default_low_input_latency"]
+    )
+
+    (tmp_path / "test.wav").unlink()
+
+
+def test_async_start_recording(default_config, test_sdk):
+    """Test async recording start."""
+    microphones = {
+        "microphone_1": PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk),
+        "microphone_2": PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk),
+    }
+    for microphone in microphones.values():
+        microphone.connect()
+
+    async_microphones_start_recording(microphones)
+
+    for microphone in microphones.values():
+        assert microphone.is_recording
+        assert microphone.is_connected
+        assert not microphone.is_writing
+
+
+def test_async_start_writing(tmp_path, default_config, test_sdk):
+    """Test async writing start."""
+    microphones = {
+        "microphone_1": PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk),
+        "microphone_2": PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk),
+    }
+    for microphone in microphones.values():
+        microphone.connect()
+
+    async_microphones_start_recording(
+        microphones, output_files=[tmp_path / "test_1.wav", tmp_path / "test_2.wav"]
+    )
+
+    for microphone in microphones.values():
+        assert microphone.is_recording
+        assert microphone.is_connected
+        assert microphone.is_writing
+    assert Path(tmp_path / "test_1.wav").exists()
+    assert Path(tmp_path / "test_2.wav").exists()
+
+    (tmp_path / "test_1.wav").unlink()
+    (tmp_path / "test_2.wav").unlink()
+
+
+def test_async_stop_recording(default_config, test_sdk):
+    """Test async recording stop."""
+    microphones = {
+        "microphone_1": PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk),
+        "microphone_2": PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk),
+    }
+    for microphone in microphones.values():
+        microphone.connect()
+
+    async_microphones_start_recording(microphones)
+    async_microphones_stop_recording(microphones)
+
+    for microphone in microphones.values():
+        assert not microphone.is_recording
+        assert microphone.is_connected
+        assert not microphone.is_writing
+
+
+def test_async_stop_writing(tmp_path, default_config, test_sdk):
+    """Test async writing stop."""
+    microphones = {
+        "microphone_1": PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk),
+        "microphone_2": PortAudioMicrophone(default_config, sounddevice_sdk=test_sdk),
+    }
+    for microphone in microphones.values():
+        microphone.connect()
+
+    async_microphones_start_recording(
+        microphones, output_files=[tmp_path / "test_1.wav", tmp_path / "test_2.wav"]
+    )
+    async_microphones_stop_recording(microphones)
+
+    for microphone in microphones.values():
+        assert not microphone.is_recording
+        assert microphone.is_connected
+        assert not microphone.is_writing
+    assert Path(tmp_path / "test_1.wav").exists()
+    assert Path(tmp_path / "test_2.wav").exists()
+
+    (tmp_path / "test_1.wav").unlink()
+    (tmp_path / "test_2.wav").unlink()

tests/microphones/test_shared_array.py

@@ -0,0 +1,508 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import multiprocessing
+import time
+from multiprocessing import Event, Process, Queue
+
+import numpy as np
+import pytest
+
+from lerobot.utils.shared_array import SharedArray
+
+
+def writer_process(shared_array, data_queue, stop_event, barrier, process_id):
+    """Writer process that continuously writes data to shared array."""
+    local_array = shared_array.get_local_array()
+
+    # Wait for all processes to be ready
+    barrier.wait()
+
+    write_count = 0
+    while not stop_event.is_set() and write_count < 10:
+        # Generate unique data for this process and write iteration
+        data = np.full((5, 2), process_id * 100 + write_count, dtype=np.float32)
+
+        try:
+            shared_array.write(local_array, data)
+            data_queue.put(f"writer_{process_id}_wrote_{write_count}")
+            write_count += 1
+            time.sleep(0.01)  # Small delay to allow race conditions
+        except IndexError:
+            # Array is full, stop writing
+            break
+
+
+def reader_process(shared_array, data_queue, stop_event, barrier, process_id):
+    """Reader process that continuously reads data from shared array."""
+    local_array = shared_array.get_local_array()
+
+    # Wait for all processes to be ready
+    barrier.wait()
+
+    read_count = 0
+    while not stop_event.is_set() and read_count < 5:
+        time.sleep(0.02)  # Allow some writes to accumulate
+
+        data = shared_array.read(local_array, flush=True)
+        data_queue.put(f"reader_{process_id}_read_{len(data)}_items")
+        read_count += 1
+
+
+def stress_writer_process(shared_array, data_queue, stop_event, barrier, process_id):
+    """High-frequency writer process for stress testing."""
+    local_array = shared_array.get_local_array()
+
+    barrier.wait()
+
+    write_count = 0
+    while not stop_event.is_set() and write_count < 50:
+        # Write single row at a time for more frequent operations
+        data = np.array([[process_id, write_count]], dtype=np.float32)
+
+        try:
+            shared_array.write(local_array, data)
+            write_count += 1
+            # No sleep - stress test
+        except IndexError:
+            break
+
+    data_queue.put(f"stress_writer_{process_id}_completed_{write_count}")
+
+
+# Basic functionality tests
+
+
+def test_shared_array_creation():
+    """Test basic SharedArray creation and properties."""
+    shape = (100, 4)
+    dtype = np.float32
+
+    shared_array = SharedArray(shape=shape, dtype=dtype)
+
+    assert shared_array.shape == shape
+    assert shared_array.dtype == dtype
+    assert shared_array.read_index.value == 0
+
+    # Clean up
+    shared_array.delete()
+
+
+def test_local_array_access():
+    """Test getting local array instances."""
+    shape = (50, 2)
+    shared_array = SharedArray(shape=shape, dtype=np.float32)
+
+    local_array = shared_array.get_local_array()
+
+    assert local_array.shape == shape
+    assert local_array.dtype == np.float32
+    assert isinstance(local_array, np.ndarray)
+
+    # Test that we can get multiple local array instances
+    local_array2 = shared_array.get_local_array()
+    assert local_array2.shape == shape
+
+    shared_array.delete()
+
+
+def test_write_and_read_single_process():
+    """Test basic write and read operations in single process."""
+    shape = (20, 3)
+    shared_array = SharedArray(shape=shape, dtype=np.float32)
+    local_array = shared_array.get_local_array()
+
+    # Write some data
+    data1 = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
+    shared_array.write(local_array, data1)
+
+    assert shared_array.read_index.value == 2
+
+    # Write more data
+    data2 = np.array([[7, 8, 9]], dtype=np.float32)
+    shared_array.write(local_array, data2)
+
+    assert shared_array.read_index.value == 3
+
+    # Read all data
+    read_data = shared_array.read(local_array, flush=False)
+    expected = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
+    np.testing.assert_array_equal(read_data, expected)
+
+    # Read with flush
+    read_data_flush = shared_array.read(local_array, flush=True)
+    np.testing.assert_array_equal(read_data_flush, expected)
+    assert shared_array.read_index.value == 0
+
+    shared_array.delete()
+
+
+def test_array_overflow():
+    """Test behavior when writing more data than array capacity."""
+    shape = (5, 2)  # Small array
+    shared_array = SharedArray(shape=shape, dtype=np.float32)
+    local_array = shared_array.get_local_array()
+
+    # Fill the array
+    data = np.ones((5, 2), dtype=np.float32)
+    shared_array.write(local_array, data)
+
+    # Try to write more data - should raise IndexError
+    with pytest.raises(ValueError):
+        extra_data = np.ones((2, 2), dtype=np.float32)
+        shared_array.write(local_array, extra_data)
+
+    shared_array.delete()
+
+
+def test_reset_functionality():
+    """Test the reset method."""
+    shape = (10, 2)
+    shared_array = SharedArray(shape=shape, dtype=np.float32)
+    local_array = shared_array.get_local_array()
+
+    # Write some data
+    data = np.ones((3, 2), dtype=np.float32)
+    shared_array.write(local_array, data)
+    assert shared_array.read_index.value == 3
+
+    # Reset
+    shared_array.reset()
+    assert shared_array.read_index.value == 0
+
+    # Read should return empty array
+    read_data = shared_array.read(local_array, flush=False)
+    assert len(read_data) == 0
+
+    shared_array.delete()
+
+
+# Multi-process tests
+
+
+def test_single_writer_single_reader():
+    """Test basic writer-reader scenario with one process each."""
+    shape = (100, 2)
+    shared_array = SharedArray(shape=shape, dtype=np.float32)
+
+    data_queue = Queue()
+    stop_event = Event()
+    barrier = multiprocessing.Barrier(2)  # Writer + reader
+
+    # Start writer process
+    writer = Process(target=writer_process, args=(shared_array, data_queue, stop_event, barrier, 1))
+
+    # Start reader process
+    reader = Process(target=reader_process, args=(shared_array, data_queue, stop_event, barrier, 1))
+
+    writer.start()
+    reader.start()
+
+    # Let them run for a bit
+    time.sleep(0.5)
+    stop_event.set()
+
+    # Wait for completion
+    writer.join(timeout=2.0)
+    reader.join(timeout=2.0)
+
+    # Verify both processes completed
+    assert not writer.is_alive()
+    assert not reader.is_alive()
+
+    # Check that we got messages from both processes
+    messages = []
+    while not data_queue.empty():
+        messages.append(data_queue.get())
+
+    writer_messages = [msg for msg in messages if msg.startswith("writer_")]
+    reader_messages = [msg for msg in messages if msg.startswith("reader_")]
+
+    assert len(writer_messages) > 0
+    assert len(reader_messages) > 0
+
+    shared_array.delete()
+
+
+def test_multiple_writers_single_reader():
+    """Test multiple writers with single reader - check for race conditions."""
+    shape = (200, 2)
+    shared_array = SharedArray(shape=shape, dtype=np.float32)
+
+    data_queue = Queue()
+    stop_event = Event()
+    num_writers = 3
+    barrier = multiprocessing.Barrier(num_writers + 1)  # Writers + reader
+
+    processes = []
+
+    # Start multiple writer processes
+    for i in range(num_writers):
+        writer = Process(target=writer_process, args=(shared_array, data_queue, stop_event, barrier, i + 1))
+        processes.append(writer)
+        writer.start()
+
+    # Start reader process
+    reader = Process(target=reader_process, args=(shared_array, data_queue, stop_event, barrier, 1))
+    processes.append(reader)
+    reader.start()
+
+    # Let them run
+    time.sleep(1.0)
+    stop_event.set()
+
+    # Wait for all processes
+    for process in processes:
+        process.join(timeout=3.0)
+        assert not process.is_alive()
+
+    # Verify we got messages from all processes
+    messages = []
+    while not data_queue.empty():
+        messages.append(data_queue.get())
+
+    writer_messages = [msg for msg in messages if msg.startswith("writer_")]
+    reader_messages = [msg for msg in messages if msg.startswith("reader_")]
+
+    # Should have messages from all writers
+    assert len(writer_messages) >= num_writers
+    assert len(reader_messages) > 0
+
+    shared_array.delete()
+
+
+def test_data_integrity_with_concurrent_access():
+    """Test that data integrity is maintained under concurrent access using standard reader/writer processes."""
+    shape = (500, 2)  # Use standard 2-column format
+    shared_array = SharedArray(shape=shape, dtype=np.float32)
+
+    data_queue = Queue()
+    stop_event = Event()
+    barrier = multiprocessing.Barrier(3)  # 2 writers + 1 reader
+
+    # Start two writer processes
+    writer1 = Process(target=writer_process, args=(shared_array, data_queue, stop_event, barrier, 1))
+    writer2 = Process(target=writer_process, args=(shared_array, data_queue, stop_event, barrier, 2))
+
+    # Start one reader process
+    reader = Process(target=reader_process, args=(shared_array, data_queue, stop_event, barrier, 1))
+
+    writer1.start()
+    writer2.start()
+    reader.start()
+
+    # Let them run for integrity test duration
+    time.sleep(1.0)
+    stop_event.set()
+
+    # Wait for completion
+    writer1.join(timeout=3.0)
+    writer2.join(timeout=3.0)
+    reader.join(timeout=3.0)
+
+    # Verify all processes completed successfully
+    assert not writer1.is_alive()
+    assert not writer2.is_alive()
+    assert not reader.is_alive()
+
+    # Verify data integrity by checking messages
+    messages = []
+    while not data_queue.empty():
+        messages.append(data_queue.get())
+
+    writer1_messages = [msg for msg in messages if "writer_1_wrote" in msg]
+    writer2_messages = [msg for msg in messages if "writer_2_wrote" in msg]
+    reader_messages = [msg for msg in messages if "reader_1_read" in msg]
+
+    # Verify both writers wrote data
+    assert len(writer1_messages) > 0
+    assert len(writer2_messages) > 0
+    # Verify reader read data
+    assert len(reader_messages) > 0
+
+    # Verify the shared array is in a consistent state
+    local_array = shared_array.get_local_array()
+    final_data = shared_array.read(local_array, flush=False)
+
+    # Should have some data written by the writers
+    assert len(final_data) >= 0  # Could be empty if reader flushed everything
+    # Should not exceed array capacity
+    assert len(final_data) <= shape[0]
+
+    # If there's data, verify it contains the expected writer signatures
+    if len(final_data) > 0:
+        # Data should contain values like 100, 101, 102... (writer 1) or 200, 201, 202... (writer 2)
+        unique_values = np.unique(final_data.flatten())
+        writer1_values = unique_values[(unique_values >= 100) & (unique_values < 200)]
+        writer2_values = unique_values[(unique_values >= 200) & (unique_values < 300)]
+
+        # Should have data from at least one writer
+        assert len(writer1_values) > 0 or len(writer2_values) > 0
+
+    shared_array.delete()
+
+
+def test_stress_test_high_frequency_operations():
+    """Stress test with high frequency read/write operations."""
+    shape = (1000, 2)
+    shared_array = SharedArray(shape=shape, dtype=np.float32)
+
+    data_queue = Queue()
+    stop_event = Event()
+    num_writers = 4
+    barrier = multiprocessing.Barrier(num_writers)
+
+    processes = []
+
+    # Start multiple high-frequency writers
+    for i in range(num_writers):
+        writer = Process(
+            target=stress_writer_process, args=(shared_array, data_queue, stop_event, barrier, i + 1)
+        )
+        processes.append(writer)
+        writer.start()
+
+    # Let them run for stress test duration
+    time.sleep(0.5)
+    stop_event.set()
+
+    # Wait for completion
+    for process in processes:
+        process.join(timeout=3.0)
+        assert not process.is_alive()
+
+    # Verify all writers completed successfully
+    messages = []
+    while not data_queue.empty():
+        messages.append(data_queue.get())
+
+    completed_messages = [msg for msg in messages if "completed" in msg]
+    assert len(completed_messages) == num_writers
+
+    # Verify the shared array is in a consistent state
+    local_array = shared_array.get_local_array()
+    final_data = shared_array.read(local_array, flush=False)
+
+    # Should have some data written
+    assert len(final_data) > 0
+    # Should not exceed array capacity
+    assert len(final_data) <= shape[0]
+
+    shared_array.delete()
+
+
+def test_concurrent_readers():
+    """Test multiple concurrent readers with writers to ensure thread safety."""
+    shape = (200, 2)
+    shared_array = SharedArray(shape=shape, dtype=np.float32)
+
+    data_queue = Queue()
+    stop_event = Event()
+    num_readers = 3
+    num_writers = 2
+    barrier = multiprocessing.Barrier(num_readers + num_writers)
+
+    processes = []
+
+    # Start multiple writer processes to generate data
+    for i in range(num_writers):
+        writer = Process(target=writer_process, args=(shared_array, data_queue, stop_event, barrier, i + 1))
+        processes.append(writer)
+        writer.start()
+
+    # Start multiple reader processes
+    for i in range(num_readers):
+        reader = Process(target=reader_process, args=(shared_array, data_queue, stop_event, barrier, i + 1))
+        processes.append(reader)
+        reader.start()
+
+    # Let them run to test concurrent access
+    time.sleep(1.0)
+    stop_event.set()
+
+    # Wait for all processes to complete
+    for process in processes:
+        process.join(timeout=3.0)
+        assert not process.is_alive()
+
+    # Verify all readers and writers completed
+    messages = []
+    while not data_queue.empty():
+        messages.append(data_queue.get())
+
+    reader_messages = [msg for msg in messages if msg.startswith("reader_")]
+    writer_messages = [msg for msg in messages if msg.startswith("writer_")]
+
+    # Should have messages from all readers and writers
+    assert len(reader_messages) >= num_readers
+    assert len(writer_messages) >= num_writers
+
+    # Verify different readers generated different messages (proving they ran concurrently)
+    reader_ids = set()
+    for msg in reader_messages:
+        # Extract reader ID from message like "reader_1_read_5_items"
+        parts = msg.split("_")
+        if len(parts) >= 2:
+            reader_ids.add(parts[1])
+
+    assert len(reader_ids) == num_readers  # All readers should have participated
+
+    shared_array.delete()
+
+
+def test_edge_case_empty_reads():
+    """Test reading from empty array and after flushes."""
+    shape = (10, 2)
+    shared_array = SharedArray(shape=shape, dtype=np.float32)
+    local_array = shared_array.get_local_array()
+
+    # Read from empty array
+    empty_data = shared_array.read(local_array, flush=False)
+    assert len(empty_data) == 0
+
+    # Write some data
+    data = np.ones((3, 2), dtype=np.float32)
+    shared_array.write(local_array, data)
+
+    # Read with flush
+    read_data = shared_array.read(local_array, flush=True)
+    assert len(read_data) == 3
+
+    # Read again after flush - should be empty
+    empty_again = shared_array.read(local_array, flush=False)
+    assert len(empty_again) == 0
+
+    shared_array.delete()
+
+
+def test_different_dtypes():
+    """Test SharedArray with different numpy dtypes."""
+    dtypes_to_test = [np.float32, np.float64, np.int32, np.int16]
+
+    for dtype in dtypes_to_test:
+        shape = (20, 2)
+        shared_array = SharedArray(shape=shape, dtype=dtype)
+        local_array = shared_array.get_local_array()
+
+        assert local_array.dtype == dtype
+
+        # Write and read data of this dtype
+        data = np.ones((5, 2), dtype=dtype)
+        shared_array.write(local_array, data)
+
+        read_data = shared_array.read(local_array, flush=True)
+        assert read_data.dtype == dtype
+        assert len(read_data) == 5
+
+        shared_array.delete()

tests/utils.py

@@ -20,7 +20,7 @@ from functools import wraps
 import pytest
 import torch
 
-from lerobot import available_cameras, available_motors, available_robots
+from lerobot import available_cameras, available_microphones, available_motors, available_robots
 from lerobot.utils.device_utils import auto_select_torch_device
 from lerobot.utils.import_utils import is_package_available
 
@@ -34,6 +34,10 @@ TEST_CAMERA_TYPES = []
 for camera_type in available_cameras:
     TEST_CAMERA_TYPES += [(camera_type, True), (camera_type, False)]
 
+TEST_MICROPHONE_TYPES = []
+for microphone_type in available_microphones:
+    TEST_MICROPHONE_TYPES += [(microphone_type, True), (microphone_type, False)]
+
 TEST_MOTOR_TYPES = []
 for motor_type in available_motors:
     TEST_MOTOR_TYPES += [(motor_type, True), (motor_type, False)]
@@ -42,6 +46,9 @@ for motor_type in available_motors:
 OPENCV_CAMERA_INDEX = int(os.environ.get("LEROBOT_TEST_OPENCV_CAMERA_INDEX", 0))
 INTELREALSENSE_SERIAL_NUMBER = int(os.environ.get("LEROBOT_TEST_INTELREALSENSE_SERIAL_NUMBER", 128422271614))
 
+# Microphone indices used for connecting physical microphones
+MICROPHONE_INDEX = int(os.environ.get("LEROBOT_TEST_MICROPHONE_INDEX", 0))
+
 DYNAMIXEL_PORT = os.environ.get("LEROBOT_TEST_DYNAMIXEL_PORT", "/dev/tty.usbmodem575E0032081")
 DYNAMIXEL_MOTORS = {
     "shoulder_pan": [1, "xl430-w250"],

tests/utils/test_visualization_utils.py

@@ -37,6 +37,14 @@ def mock_rerun(monkeypatch):
         def __init__(self, value):
             self.value = float(value)
 
+        @staticmethod
+        def columns(scalars):
+            return DummyScalarsColumn(scalars)
+
+    class DummyScalarsColumn:
+        def __init__(self, values):
+            self.values = values
+
     class DummyImage:
         def __init__(self, arr):
             self.arr = arr
@@ -47,12 +55,46 @@ def mock_rerun(monkeypatch):
             obj = kwargs.pop("entity")
         calls.append((key, obj, kwargs))
 
+    def dummy_send_columns(key, indexes, columns, **kwargs):
+        calls.append((key, columns, kwargs))
+
+    def dummy_time_column(timeline, timestamp):
+        return timestamp
+
+    def dummy_set_time(timeline, timestamp):
+        return None
+
+    class DummyTimeSeriesView:
+        def __call__(self, origin, plot_legend=None):
+            return None
+
+    class DummySpatial2DView:
+        def __call__(self, origin):
+            return None
+
+    class DummyGrid:
+        def __call__(self, *args):
+            return None
+
+    class DummyPlotLegend:
+        def __call__(self, visible=True):
+            return None
+
     dummy_rr = SimpleNamespace(
         Scalars=DummyScalar,
         Image=DummyImage,
         log=dummy_log,
+        TimeColumn=dummy_time_column,
+        send_columns=dummy_send_columns,
+        set_time=dummy_set_time,
         init=lambda *a, **k: None,
         spawn=lambda *a, **k: None,
+        blueprint=SimpleNamespace(
+            TimeSeriesView=DummyTimeSeriesView,
+            Spatial2DView=DummySpatial2DView,
+            Grid=DummyGrid,
+            PlotLegend=DummyPlotLegend,
+        ),
     )
 
     # Inject fake module into sys.modules
@@ -87,7 +129,7 @@ def _kwargs_for(calls, key):
     raise KeyError(f"Key {key} not found in calls: {calls}")
 
 
-def test_log_rerun_data_envtransition_scalars_and_image(mock_rerun):
+def test_log_rerun_data_envtransition_scalars_image_audio(mock_rerun):
     vu, calls = mock_rerun
 
     # Build EnvTransition dict
@@ -95,6 +137,8 @@ def test_log_rerun_data_envtransition_scalars_and_image(mock_rerun):
         f"{OBS_STATE}.temperature": np.float32(25.0),
         # CHW image should be converted to HWC for rr.Image
         "observation.camera": np.zeros((3, 10, 20), dtype=np.uint8),
+        # Multiple channels audio data should be split into separate channels and logged as rr.Scalars.columns
+        "observation.audio": np.zeros((100, 2), dtype=np.float32),
     }
     act = {
         "action.throttle": 0.7,
@@ -117,25 +161,27 @@ def test_log_rerun_data_envtransition_scalars_and_image(mock_rerun):
     # - action.throttle -> Scalars
     # - action.vector_0, action.vector_1 -> Scalars
     expected_keys = {
-        f"{OBS_STATE}.temperature",
+        "data/" + f"{OBS_STATE}.temperature",
         "observation.camera",
-        "action.throttle",
-        "action.vector_0",
-        "action.vector_1",
+        "data/action.throttle",
+        "data/action.vector_0",
+        "data/action.vector_1",
+        "audio/observation.audio_channel_0",
+        "audio/observation.audio_channel_1",
     }
     assert set(_keys(calls)) == expected_keys
 
     # Check scalar types and values
-    temp_obj = _obj_for(calls, f"{OBS_STATE}.temperature")
+    temp_obj = _obj_for(calls, f"data/{OBS_STATE}.temperature")
     assert type(temp_obj).__name__ == "DummyScalar"
     assert temp_obj.value == pytest.approx(25.0)
 
-    throttle_obj = _obj_for(calls, "action.throttle")
+    throttle_obj = _obj_for(calls, "data/action.throttle")
     assert type(throttle_obj).__name__ == "DummyScalar"
     assert throttle_obj.value == pytest.approx(0.7)
 
-    v0 = _obj_for(calls, "action.vector_0")
-    v1 = _obj_for(calls, "action.vector_1")
+    v0 = _obj_for(calls, "data/action.vector_0")
+    v1 = _obj_for(calls, "data/action.vector_1")
     assert type(v0).__name__ == "DummyScalar"
     assert type(v1).__name__ == "DummyScalar"
     assert v0.value == pytest.approx(1.0)
@@ -147,6 +193,14 @@ def test_log_rerun_data_envtransition_scalars_and_image(mock_rerun):
     assert img_obj.arr.shape == (10, 20, 3)  # transposed
     assert _kwargs_for(calls, "observation.camera").get("static", False) is True  # static=True for images
 
+    # Check audio handling: split channels + rr.Scalars.columns
+    audio_obj_0 = _obj_for(calls, "audio/observation.audio_channel_0")
+    audio_obj_1 = _obj_for(calls, "audio/observation.audio_channel_1")
+    assert type(audio_obj_0).__name__ == "DummyScalarsColumn"
+    assert type(audio_obj_1).__name__ == "DummyScalarsColumn"
+    assert audio_obj_0.values.shape == (100,)
+    assert audio_obj_1.values.shape == (100,)
+
 
 def test_log_rerun_data_plain_list_ordering_and_prefixes(mock_rerun):
     vu, calls = mock_rerun
@@ -157,6 +211,8 @@ def test_log_rerun_data_plain_list_ordering_and_prefixes(mock_rerun):
         "temp": 1.5,
         # Already HWC image => should stay as-is
         "img": np.zeros((5, 6, 3), dtype=np.uint8),
+        # Multiple channels audio data should be split into separate channels
+        "audio": np.zeros((100, 2), dtype=np.float32),
         "none": None,  # should be skipped
     }
     act_plain = {
@@ -170,22 +226,24 @@ def test_log_rerun_data_plain_list_ordering_and_prefixes(mock_rerun):
 
     # Expected keys with auto-prefixes
     expected = {
-        "observation.temp",
+        "data/observation.temp",
         "observation.img",
-        "action.throttle",
-        "action.vec_0",
-        "action.vec_1",
-        "action.vec_2",
+        "data/action.throttle",
+        "data/action.vec_0",
+        "data/action.vec_1",
+        "data/action.vec_2",
+        "audio/observation.audio_channel_0",
+        "audio/observation.audio_channel_1",
     }
     logged = set(_keys(calls))
     assert logged == expected
 
     # Scalars
-    t = _obj_for(calls, "observation.temp")
+    t = _obj_for(calls, "data/observation.temp")
     assert type(t).__name__ == "DummyScalar"
     assert t.value == pytest.approx(1.5)
 
-    throttle = _obj_for(calls, "action.throttle")
+    throttle = _obj_for(calls, "data/action.throttle")
     assert type(throttle).__name__ == "DummyScalar"
     assert throttle.value == pytest.approx(0.3)
 
@@ -197,25 +255,39 @@ def test_log_rerun_data_plain_list_ordering_and_prefixes(mock_rerun):
 
     # Vectors
     for i, val in enumerate([9, 8, 7]):
-        o = _obj_for(calls, f"action.vec_{i}")
+        o = _obj_for(calls, f"data/action.vec_{i}")
         assert type(o).__name__ == "DummyScalar"
         assert o.value == pytest.approx(val)
 
+    # Audio
+    audio_obj_0 = _obj_for(calls, "audio/observation.audio_channel_0")
+    audio_obj_1 = _obj_for(calls, "audio/observation.audio_channel_1")
+    assert type(audio_obj_0).__name__ == "DummyScalarsColumn"
+    assert type(audio_obj_1).__name__ == "DummyScalarsColumn"
+    assert audio_obj_0.values.shape == (100,)
+    assert audio_obj_1.values.shape == (100,)
+
 
 def test_log_rerun_data_kwargs_only(mock_rerun):
     vu, calls = mock_rerun
 
     vu.log_rerun_data(
-        observation={"observation.temp": 10.0, "observation.gray": np.zeros((8, 8, 1), dtype=np.uint8)},
+        observation={
+            "observation.temp": 10.0,
+            "observation.gray": np.zeros((8, 8, 1), dtype=np.uint8),
+            "observation.audio": np.zeros((100, 2), dtype=np.float32),
+        },
         action={"action.a": 1.0},
     )
 
     keys = set(_keys(calls))
-    assert "observation.temp" in keys
+    assert "data/observation.temp" in keys
     assert "observation.gray" in keys
-    assert "action.a" in keys
+    assert "data/action.a" in keys
+    assert "audio/observation.audio_channel_0" in keys
+    assert "audio/observation.audio_channel_1" in keys
 
-    temp = _obj_for(calls, "observation.temp")
+    temp = _obj_for(calls, "data/observation.temp")
     assert type(temp).__name__ == "DummyScalar"
     assert temp.value == pytest.approx(10.0)
 
@@ -224,6 +296,13 @@ def test_log_rerun_data_kwargs_only(mock_rerun):
     assert img.arr.shape == (8, 8, 1)  # remains HWC
     assert _kwargs_for(calls, "observation.gray").get("static", False) is True
 
-    a = _obj_for(calls, "action.a")
+    a = _obj_for(calls, "data/action.a")
     assert type(a).__name__ == "DummyScalar"
     assert a.value == pytest.approx(1.0)
+
+    audio_obj_0 = _obj_for(calls, "audio/observation.audio_channel_0")
+    audio_obj_1 = _obj_for(calls, "audio/observation.audio_channel_1")
+    assert type(audio_obj_0).__name__ == "DummyScalarsColumn"
+    assert type(audio_obj_1).__name__ == "DummyScalarsColumn"
+    assert audio_obj_0.values.shape == (100,)
+    assert audio_obj_1.values.shape == (100,)