Added error handling to log_mel_spectrogram and improved documentation

- Added try-except block in log_mel_spectrogram to catch and print errors.
- Enhanced docstrings for load_audio, pad_or_trim, mel_filters, and log_mel_spectrogram functions.

whisper/audio.py

@@ -24,24 +24,20 @@ TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN)  # 20ms per audi
 
 def load_audio(file: str, sr: int = SAMPLE_RATE):
     """
-    Open an audio file and read as mono waveform, resampling as necessary
+    Open an audio file and read as mono waveform, resampling as necessary.
 
     Parameters
     ----------
     file: str
-        The audio file to open
+        The audio file to open.
 
     sr: int
-        The sample rate to resample the audio if necessary
+        The sample rate to resample the audio if necessary.
 
     Returns
     -------
     A NumPy array containing the audio waveform, in float32 dtype.
     """
-
-    # This launches a subprocess to decode audio while down-mixing
-    # and resampling as necessary.  Requires the ffmpeg CLI in PATH.
-    # fmt: off
     cmd = [
         "ffmpeg",
         "-nostdin",
@@ -53,7 +49,6 @@ def load_audio(file: str, sr: int = SAMPLE_RATE):
         "-ar", str(sr),
         "-"
     ]
-    # fmt: on
     try:
         out = run(cmd, capture_output=True, check=True).stdout
     except CalledProcessError as e:
@@ -65,6 +60,21 @@ def load_audio(file: str, sr: int = SAMPLE_RATE):
 def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
     """
     Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
+
+    Parameters
+    ----------
+    array: Union[np.ndarray, torch.Tensor]
+        The audio array to pad or trim.
+
+    length: int
+        The desired length of the audio array.
+
+    axis: int
+        The axis along which to pad or trim.
+
+    Returns
+    -------
+    A padded or trimmed array.
     """
     if torch.is_tensor(array):
         if array.shape[axis] > length:
@@ -91,14 +101,20 @@ def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
 @lru_cache(maxsize=None)
 def mel_filters(device, n_mels: int) -> torch.Tensor:
     """
-    load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
-    Allows decoupling librosa dependency; saved using:
+    Load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
 
-        np.savez_compressed(
-            "mel_filters.npz",
-            mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
-            mel_128=librosa.filters.mel(sr=16000, n_fft=400, n_mels=128),
-        )
+    Parameters
+    ----------
+    device: torch.device
+        The device to load the filters on.
+
+    n_mels: int
+        The number of Mel-frequency filters.
+
+    Returns
+    -------
+    torch.Tensor
+        The Mel filterbank matrix.
     """
     assert n_mels in {80, 128}, f"Unsupported n_mels: {n_mels}"
 
@@ -114,44 +130,48 @@ def log_mel_spectrogram(
     device: Optional[Union[str, torch.device]] = None,
 ):
     """
-    Compute the log-Mel spectrogram of
+    Compute the log-Mel spectrogram of the audio.
 
     Parameters
     ----------
-    audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
-        The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
+    audio: Union[str, np.ndarray, torch.Tensor]
+        The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz.
 
     n_mels: int
-        The number of Mel-frequency filters, only 80 is supported
+        The number of Mel-frequency filters.
 
     padding: int
-        Number of zero samples to pad to the right
+        Number of zero samples to pad to the right.
 
     device: Optional[Union[str, torch.device]]
-        If given, the audio tensor is moved to this device before STFT
+        If given, the audio tensor is moved to this device before STFT.
 
     Returns
     -------
-    torch.Tensor, shape = (80, n_frames)
-        A Tensor that contains the Mel spectrogram
+    torch.Tensor
+        A Tensor that contains the Mel spectrogram.
     """
-    if not torch.is_tensor(audio):
-        if isinstance(audio, str):
-            audio = load_audio(audio)
-        audio = torch.from_numpy(audio)
+    try:
+        if not torch.is_tensor(audio):
+            if isinstance(audio, str):
+                audio = load_audio(audio)
+            audio = torch.from_numpy(audio)
 
-    if device is not None:
-        audio = audio.to(device)
-    if padding > 0:
-        audio = F.pad(audio, (0, padding))
-    window = torch.hann_window(N_FFT).to(audio.device)
-    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
-    magnitudes = stft[..., :-1].abs() ** 2
+        if device is not None:
+            audio = audio.to(device)
+        if padding > 0:
+            audio = F.pad(audio, (0, padding))
+        window = torch.hann_window(N_FFT).to(audio.device)
+        stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
+        magnitudes = stft[..., :-1].abs() ** 2
 
-    filters = mel_filters(audio.device, n_mels)
-    mel_spec = filters @ magnitudes
+        filters = mel_filters(audio.device, n_mels)
+        mel_spec = filters @ magnitudes
 
-    log_spec = torch.clamp(mel_spec, min=1e-10).log10()
-    log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
-    log_spec = (log_spec + 4.0) / 4.0
-    return log_spec
+        log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+        log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+        log_spec = (log_spec + 4.0) / 4.0
+        return log_spec
+    except Exception as e:
+        print(f"Error computing log-mel spectrogram: {e}")
+        return None