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support gpt-sovits v4

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support gpt-sovits v4
This commit is contained in:
RVC-Boss
2025-04-20 14:53:42 +08:00
committed by GitHub
parent c6cb6b45f3
commit 50e9ba0218
3 changed files with 255 additions and 62 deletions
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95
GPT_SoVITS/module/mel_processing.py
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@@ -38,89 +38,72 @@ hann_window = {}
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("max value is ", torch.max(y))
if torch.min(y) < -1.2:
print('min value is ', torch.min(y))
if torch.max(y) > 1.2:
print('max value is ', torch.max(y))
global hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
wnsize_dtype_device = str(win_size) + "_" + dtype_device
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
dtype_device = str(y.dtype) + '_' + str(y.device)
# wnsize_dtype_device = str(win_size) + '_' + dtype_device
key = "%s-%s-%s-%s-%s" %(dtype_device,n_fft, sampling_rate, hop_size, win_size)
# if wnsize_dtype_device not in hann_window:
if key not in hann_window:
# hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
hann_window[key] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
y = y.squeeze(1)
spec = torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=False,
)
# spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[key],
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-8)
return spec
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
global mel_basis
dtype_device = str(spec.dtype) + "_" + str(spec.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
dtype_device = str(spec.dtype) + '_' + str(spec.device)
# fmax_dtype_device = str(fmax) + '_' + dtype_device
key = "%s-%s-%s-%s-%s-%s"%(dtype_device,n_fft, num_mels, sampling_rate, fmin, fmax)
# if fmax_dtype_device not in mel_basis:
if key not in mel_basis:
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
# mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
mel_basis[key] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
# spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
spec = torch.matmul(mel_basis[key], spec)
spec = spectral_normalize_torch(spec)
return spec
def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("max value is ", torch.max(y))
if torch.min(y) < -1.2:
print('min value is ', torch.min(y))
if torch.max(y) > 1.2:
print('max value is ', torch.max(y))
global mel_basis, hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
wnsize_dtype_device = str(win_size) + "_" + dtype_device
dtype_device = str(y.dtype) + '_' + str(y.device)
# fmax_dtype_device = str(fmax) + '_' + dtype_device
fmax_dtype_device = "%s-%s-%s-%s-%s-%s-%s-%s"%(dtype_device,n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax)
# wnsize_dtype_device = str(win_size) + '_' + dtype_device
wnsize_dtype_device = fmax_dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
y = y.squeeze(1)
spec = torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=False,
)
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-9)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-8)
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
spec = spectral_normalize_torch(spec)