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support for large-v3

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This commit is contained in:
MahmoudAshraf97
2023-11-25 12:09:00 +00:00
parent d97cdb7bcf
commit 71a5281bde
3 changed files with 11 additions and 5 deletions
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7
whisperx/audio.py
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@ -12,7 +12,6 @@ from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000 samples in a 30-second chunk
@ -93,7 +92,7 @@ def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
@lru_cache(maxsize=None)
def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
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:
@ -103,7 +102,7 @@ def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
)
"""
assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
assert n_mels in [80, 128], f"Unsupported n_mels: {n_mels}"
with np.load(
os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")
) as f:
@ -112,7 +111,7 @@ def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
def log_mel_spectrogram(
audio: Union[str, np.ndarray, torch.Tensor],
n_mels: int = N_MELS,
n_mels: int,
padding: int = 0,
device: Optional[Union[str, torch.device]] = None,
):