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Fix device mismatch error in Whisper model during feature extraction #35866

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Merged
merged 6 commits into from
Feb 4, 2025
Merged

Fix device mismatch error in Whisper model during feature extraction #35866

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1c1f4ac
Fix device mismatch error in whisper feature extraction
thedebugger Jan 24, 2025
2f2a914
Set default device
thedebugger Jan 27, 2025
d133b3f
Address code review feedback
thedebugger Jan 28, 2025
802adb5
Merge branch 'main' into svij-whisper-device-mismatch
eustlb Jan 28, 2025
9e4d757
Merge branch 'main' into svij-whisper-device-mismatch
eustlb Jan 28, 2025
d5d40bb
Merge branch 'main' into svij-whisper-device-mismatch
eustlb Feb 4, 2025
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11 changes: 3 additions & 8 deletions src/transformers/models/whisper/feature_extraction_whisper.py
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Expand Up @@ -129,18 +129,13 @@ def _torch_extract_fbank_features(self, waveform: np.array, device: str = "cpu")
Compute the log-mel spectrogram of the audio using PyTorch's GPU-accelerated STFT implementation with batching,
yielding results similar to cpu computing with 1e-5 tolerance.
"""
waveform = torch.from_numpy(waveform).type(torch.float32)
waveform = torch.from_numpy(waveform).to(device, torch.float32)
window = torch.hann_window(self.n_fft, device=device)

window = torch.hann_window(self.n_fft)
if device != "cpu":
waveform = waveform.to(device)
window = window.to(device)
stft = torch.stft(waveform, self.n_fft, self.hop_length, window=window, return_complex=True)
magnitudes = stft[..., :-1].abs() ** 2

mel_filters = torch.from_numpy(self.mel_filters).type(torch.float32)
if device != "cpu":
mel_filters = mel_filters.to(device)
mel_filters = torch.from_numpy(self.mel_filters).to(device, torch.float32)
mel_spec = mel_filters.T @ magnitudes

log_spec = torch.clamp(mel_spec, min=1e-10).log10()
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7 changes: 4 additions & 3 deletions tests/models/whisper/test_feature_extraction_whisper.py
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Expand Up @@ -298,8 +298,9 @@ def test_torch_integration_batch(self):
)
# fmt: on

input_speech = self._load_datasamples(3)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
with torch.device("cuda"):
input_speech = self._load_datasamples(3)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
self.assertEqual(input_features.shape, (3, 80, 3000))
torch.testing.assert_close(input_features[:, 0, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)