revert back to using PyAV instead of torch audio

faster_whisper/audio.py

@@ -1,7 +1,20 @@
+"""We use the PyAV library to decode the audio: https://github.com/PyAV-Org/PyAV
+
+The advantage of PyAV is that it bundles the FFmpeg libraries so there is no additional
+system dependencies. FFmpeg does not need to be installed on the system.
+
+However, the API is quite low-level so we need to manipulate audio frames directly.
+"""
+
+import gc
+import io
+import itertools
+
 from typing import BinaryIO, Union
 
+import av
+import numpy as np
 import torch
-import torchaudio
 
 
 def decode_audio(
@@ -17,22 +30,83 @@ def decode_audio(
       split_stereo: Return separate left and right channels.
 
     Returns:
-      A float32 Torch Tensor.
+      A float32 Numpy array.
 
       If `split_stereo` is enabled, the function returns a 2-tuple with the
       separated left and right channels.
     """
+    resampler = av.audio.resampler.AudioResampler(
+        format="s16",
+        layout="mono" if not split_stereo else "stereo",
+        rate=sampling_rate,
+    )
 
-    waveform, audio_sf = torchaudio.load(input_file)  # waveform: channels X T
+    raw_buffer = io.BytesIO()
+    dtype = None
+
+    with av.open(input_file, mode="r", metadata_errors="ignore") as container:
+        frames = container.decode(audio=0)
+        frames = _ignore_invalid_frames(frames)
+        frames = _group_frames(frames, 500000)
+        frames = _resample_frames(frames, resampler)
+
+        for frame in frames:
+            array = frame.to_ndarray()
+            dtype = array.dtype
+            raw_buffer.write(array)
+
+    # It appears that some objects related to the resampler are not freed
+    # unless the garbage collector is manually run.
+    # https://github.com/SYSTRAN/faster-whisper/issues/390
+    # note that this slows down loading the audio a little bit
+    # if that is a concern, please use ffmpeg directly as in here:
+    # https://github.com/openai/whisper/blob/25639fc/whisper/audio.py#L25-L62
+    del resampler
+    gc.collect()
+
+    audio = np.frombuffer(raw_buffer.getbuffer(), dtype=dtype)
+
+    # Convert s16 back to f32.
+    audio = audio.astype(np.float32) / 32768.0
 
-    if audio_sf != sampling_rate:
-        waveform = torchaudio.functional.resample(
-            waveform, orig_freq=audio_sf, new_freq=sampling_rate
-        )
     if split_stereo:
-        return waveform[0], waveform[1]
+        left_channel = audio[0::2]
+        right_channel = audio[1::2]
+        return torch.from_numpy(left_channel), torch.from_numpy(right_channel)
+
+    return torch.from_numpy(audio)
+
+
+def _ignore_invalid_frames(frames):
+    iterator = iter(frames)
+
+    while True:
+        try:
+            yield next(iterator)
+        except StopIteration:
+            break
+        except av.error.InvalidDataError:
+            continue
+
+
+def _group_frames(frames, num_samples=None):
+    fifo = av.audio.fifo.AudioFifo()
+
+    for frame in frames:
+        frame.pts = None  # Ignore timestamp check.
+        fifo.write(frame)
+
+        if num_samples is not None and fifo.samples >= num_samples:
+            yield fifo.read()
+
+    if fifo.samples > 0:
+        yield fifo.read()
+
 
-    return waveform.mean(0)
+def _resample_frames(frames, resampler):
+    # Add None to flush the resampler.
+    for frame in itertools.chain(frames, [None]):
+        yield from resampler.resample(frame)
 
 
 def pad_or_trim(array, length: int, *, axis: int = -1):

requirements.txt

@@ -4,5 +4,5 @@ tokenizers>=0.13,<1
 onnxruntime>=1.14,<2 
 pyannote-audio>=3.1.1
 torch>=2.1.1 
-torchaudio>=2.1.2
+av>=11
 tqdm