[skip ci] feat(audio recording): adding new asyn start_recording, stop_recording and read functions to avoid for loop delays

src/lerobot/datasets/lerobot_dataset.py

@@ -80,6 +80,7 @@ from lerobot.datasets.video_utils import (
     get_video_info,
 )
 from lerobot.microphones import Microphone
+from lerobot.microphones.utils import async_microphones_start_recording
 from lerobot.utils.constants import HF_LEROBOT_HOME
 
 CODEBASE_VERSION = "v3.0"
@@ -1312,7 +1313,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         self.episode_buffer["size"] += 1
 
-    def add_microphone_recording(self, microphone: Microphone, microphone_key: str) -> None:
+    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.
         """
@@ -1320,6 +1321,19 @@ class LeRobotDataset(torch.utils.data.Dataset):
         audio_file = self._get_raw_audio_file_path(self.num_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.num_episodes, "observation.audio." + microphone_key)
+            )
+
+        async_microphones_start_recording(microphones, output_files)
+
     def save_episode(
         self,
         episode_data: dict | None = None,

src/lerobot/microphones/portaudio/microphone_portaudio.py

@@ -185,9 +185,10 @@ class PortAudioMicrophone(Microphone):
             logging.warning(status)
         # Slicing makes copy unnecessary
         # Two separate queues are necessary because .get() also pops the data from the queue
+        # Remark: this also ensures that file-recorded data and chunk-audio data are the same.
         if self.is_writing:
-            self.record_queue.put(indata[:, self.channels_index])
-        self.read_queue.put(indata[:, self.channels_index])
+            self.record_queue.put_nowait(indata[:, self.channels_index])
+        self.read_queue.put_nowait(indata[:, self.channels_index])
 
     @staticmethod
     def _record_loop(queue, event: Event, sample_rate: int, channels: list[int], output_file: Path) -> None:
@@ -206,8 +207,8 @@ class PortAudioMicrophone(Microphone):
             while not event.is_set():
                 try:
                     file.write(
-                        queue.get(timeout=0.02)
-                    )  # Timeout set as twice the usual sounddevice buffer size
+                        queue.get(timeout=0.01)
+                    )  # 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
@@ -257,6 +258,7 @@ class PortAudioMicrophone(Microphone):
     ) -> None:
         """
         Starts the recording of the microphone. If output_file is provided, the audio will be written to this file.
+        Remark: multiprocessing is implemented, but does not work well with sounddevice (launching delays, tricky memory sharing, sounddevice streams are not picklable (even with dill #pathos), etc.).
         """
         if not self.is_connected:
             raise DeviceNotConnectedError(f"Microphone {self.microphone_index} is not connected.")

src/lerobot/microphones/utils.py

@@ -12,6 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from queue import Queue
+from threading import Thread
+
 from .configs import MicrophoneConfig
 from .microphone import Microphone
 
@@ -28,3 +31,70 @@ def make_microphones_from_configs(microphone_configs: dict[str, MicrophoneConfig
             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,
+    multiprocessing: bool = False,
+    overwrite: bool = True,
+):
+    """
+    Starts recording on multiple microphones asynchronously to avoid delays
+    """
+
+    start_recording_threads = []
+    if output_files is None:
+        output_files = [None] * 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))
+        )
+
+    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]):
+    """
+    Stops recording on multiple microphones asynchronously to avoid delays
+    """
+
+    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()
+
+
+def async_microphones_read(microphones: dict[str, Microphone]):
+    """
+    Reads from multiple microphones asynchronously to avoid delays
+    """
+
+    read_threads = []
+    read_queue = Queue()
+
+    for microphone_key, microphone in microphones.items():
+        read_threads.append(
+            Thread(
+                target=lambda microphone, output, microphone_key: output.put_nowait(
+                    {microphone_key: microphone.read()}
+                ),
+                args=(microphone, read_queue, microphone_key),
+            )
+        )
+
+    for thread in read_threads:
+        thread.start()
+    for thread in read_threads:
+        thread.join()
+
+    return dict(kv for d in read_queue.queue for kv in d.items())