[skip ci] feat(torchcodec): adding support for torchcodec audio decoding

src/lerobot/datasets/audio_utils.py

@@ -20,6 +20,7 @@ from pathlib import Path
 import av
 import torch
 import torchaudio
+import torchcodec
 from numpy import ceil
 
 CHANNELS_LAYOUTS_MAPPING = {
@@ -56,13 +57,42 @@ def decode_audio(
     Currently supports torchaudio.
     """
     if backend == "torchcodec":
-        raise NotImplementedError("torchcodec is not yet supported for audio decoding")
+        #   return decode_audio_torchcodec(audio_path, timestamps, duration)    #TODO(CarolinePascal): uncomment this line at next torchcodec release
+        raise ValueError("torchcodec backend is not available yet.")
     elif backend == "torchaudio":
         return decode_audio_torchaudio(audio_path, timestamps, duration)
     else:
         raise ValueError(f"Unsupported video backend: {backend}")
 
 
+def decode_audio_torchcodec(
+    audio_path: Path | str,
+    timestamps: list[float],
+    duration: float,
+    log_loaded_timestamps: bool = False,
+) -> torch.Tensor:
+    # TODO(CarolinePascal) : add channels selection
+    audio_decoder = torchcodec.decoders.AudioDecoder(audio_path)
+
+    audio_chunks = []
+    for ts in timestamps:
+        current_audio_chunk = audio_decoder.get_samples_played_in_range(
+            start_seconds=ts, stop_seconds=ts + duration
+        )
+
+        if log_loaded_timestamps:
+            logging.info(
+                f"audio chunk loaded at starting 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],

src/lerobot/datasets/lerobot_dataset.py

@@ -756,7 +756,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             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 'ffmpeg' decoder used by 'torchaudio'.
+            audio_backend (str | None, optional): Audio backend to use for decoding audio. Defaults to 'torchaudio'.
             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',
@@ -775,7 +775,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         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 "ffmpeg"
+            audio_backend if audio_backend else "trochaudio"
         )  # Waiting for torchcodec release #TODO(CarolinePascal)
         self.delta_indices = None
         self.batch_encoding_size = batch_encoding_size
@@ -1945,7 +1945,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj._recorded_frames = 0
         obj._writer_closed_for_reading = False
         obj.audio_backend = (
-            audio_backend if audio_backend is not None else "ffmpeg"
+            audio_backend if audio_backend is not None else "trochaudio"
         )  # Waiting for torchcodec release #TODO(CarolinePascal)
         return obj