lerobot/benchmarks/audio/run_microphone_benchmark.py

#!/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)