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fix lint

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lyuxiang.lx
2025-12-10 02:17:00 +00:00
parent a051a09ba4
commit 927addadd8
14 changed files with 75 additions and 59 deletions
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15
cosyvoice/hifigan/generator.py
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@@ -342,11 +342,9 @@ class SourceModuleHnNSF(torch.nn.Module):
# to produce sine waveforms
if sinegen_type == '1':
self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
sine_amp, add_noise_std, voiced_threshod)
self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod)
else:
self.l_sin_gen = SineGen2(sampling_rate, upsample_scale, harmonic_num,
sine_amp, add_noise_std, voiced_threshod, causal=causal)
self.l_sin_gen = SineGen2(sampling_rate, upsample_scale, harmonic_num, sine_amp, add_noise_std, voiced_threshod, causal=causal)
# to merge source harmonics into a single excitation
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
@@ -675,7 +673,8 @@ class CausalHiFTGenerator(HiFTGenerator):
x = self.conv_pre(x)
else:
x = self.conv_pre(x[:, :, :-self.conv_pre_look_right], x[:, :, -self.conv_pre_look_right:])
s_stft_real, s_stft_imag = s_stft_real[:, :, :-int(np.prod(self.upsample_rates) * self.conv_pre_look_right)], s_stft_imag[:, :, :-int(np.prod(self.upsample_rates) * self.conv_pre_look_right)]
s_stft_real = s_stft_real[:, :, :-int(np.prod(self.upsample_rates) * self.conv_pre_look_right)]
s_stft_imag = s_stft_imag[:, :, :-int(np.prod(self.upsample_rates) * self.conv_pre_look_right)]
s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1)
for i in range(self.num_upsamples):
@@ -711,7 +710,7 @@ class CausalHiFTGenerator(HiFTGenerator):
@torch.inference_mode()
def inference(self, speech_feat: torch.Tensor, finalize: bool = True) -> torch.Tensor:
# mel->f0
# mel->f0 NOTE f0_predictor precision is crucial for causal inference, move self.f0_predictor to cpu if necessary
self.f0_predictor.to('cpu')
f0 = self.f0_predictor(speech_feat.cpu(), finalize=finalize).to(speech_feat)
# f0->source
@@ -729,7 +728,7 @@ if __name__ == '__main__':
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
from hyperpyyaml import load_hyperpyyaml
with open('./pretrained_models/CosyVoice3-0.5B/cosyvoice3.yaml', 'r') as f:
with open('./pretrained_models/Fun-CosyVoice3-0.5B/cosyvoice3.yaml', 'r') as f:
configs = load_hyperpyyaml(f, overrides={'llm': None, 'flow': None})
model = configs['hift']
device = 'cuda' if torch.cuda.is_available() else 'cpu'
@@ -742,4 +741,4 @@ if __name__ == '__main__':
finalize = True if i + chunk_size + context_size >= max_len else False
pred_chunk, _ = model.inference(mel[:, :, : i + chunk_size + context_size], finalize=finalize)
pred_chunk = pred_chunk[:, i * 480:]
print((pred_gt[:, i * 480:i * 480 + pred_chunk.shape[1]] - pred_chunk).abs().max().item())
print((pred_gt[:, i * 480:i * 480 + pred_chunk.shape[1]] - pred_chunk).abs().max().item())