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fix(audio): normalize negative axis in pad_or_trim #2833

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fix(audio): normalize negative axis in pad_or_trim #2833

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18 changes: 10 additions & 8 deletions whisper/audio.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,22 +67,24 @@ 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.
"""
if torch.is_tensor(array):
if array.shape[axis] > length:
dim = axis if axis >= 0 else array.ndim + axis
if array.shape[dim] > length:
array = array.index_select(
dim=axis, index=torch.arange(length, device=array.device)
dim=dim, index=torch.arange(length, device=array.device)
)

if array.shape[axis] < length:
if array.shape[dim] < length:
pad_widths = [(0, 0)] * array.ndim
pad_widths[axis] = (0, length - array.shape[axis])
pad_widths[dim] = (0, length - array.shape[dim])
array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
else:
if array.shape[axis] > length:
array = array.take(indices=range(length), axis=axis)
dim = axis if axis >= 0 else array.ndim + axis
if array.shape[dim] > length:
array = array.take(indices=range(length), axis=dim)

if array.shape[axis] < length:
if array.shape[dim] < length:
pad_widths = [(0, 0)] * array.ndim
pad_widths[axis] = (0, length - array.shape[axis])
pad_widths[dim] = (0, length - array.shape[dim])
array = np.pad(array, pad_widths)

return array
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