File size: 2,185 Bytes
157a4c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | import math
import torch
import torch.nn.functional as F
import torchaudio
def time_stretch_preserve_pitch(waveform: torch.Tensor, sample_rate: int, target_samples: int) -> torch.Tensor:
"""
Pitch-preserving time stretch using STFT phase vocoder.
waveform: [C, L] float tensor (CPU or GPU)
returns: [C, target_samples] float tensor
"""
if waveform.dim() == 1:
waveform = waveform.unsqueeze(0)
waveform = waveform.to(torch.float32)
src_len = waveform.shape[-1]
if src_len == 0 or target_samples <= 0:
return waveform[..., :0]
if src_len == target_samples:
return waveform
# rate > 1.0 speeds up (shorter), rate < 1.0 slows down (longer)
rate = float(src_len) / float(target_samples)
# Use sample_rate to pick STFT params
win_seconds = 0.046
hop_seconds = 0.0115
n_fft_target = int(sample_rate * win_seconds)
n_fft = 1 << max(8, int(math.floor(math.log2(max(256, n_fft_target))))) # >=256, pow2
win_length = n_fft
hop_length = max(64, int(sample_rate * hop_seconds))
hop_length = min(hop_length, win_length // 2)
window = torch.hann_window(win_length, device=waveform.device, dtype=waveform.dtype)
stft = torch.stft(
waveform,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=window,
center=True,
return_complex=True,
) # [C, F, T] complex
# n_freq must match STFT's frequency bins (n_fft//2 + 1)
stretcher = torchaudio.transforms.TimeStretch(
n_freq=stft.shape[-2],
hop_length=hop_length,
fixed_rate=rate,
).to(waveform.device)
stft_stretched = stretcher(stft) # [C, F, T']
stretched = torch.istft(
stft_stretched,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=window,
center=True,
length=target_samples,
)
if stretched.shape[-1] > target_samples:
stretched = stretched[..., :target_samples]
elif stretched.shape[-1] < target_samples:
stretched = F.pad(stretched, (0, target_samples - stretched.shape[-1]))
return stretched
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