blackteay/GPT-SoVITS
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RVC-Boss 2024-07-08 20:52:39 +08:00 committed by GitHub
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1 changed files with 24 additions and 22 deletions
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GPT_SoVITS/inference_webui.py
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@ -331,27 +331,29 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
int(hps.data.sampling_rate * 0.3), int(hps.data.sampling_rate * 0.3),
dtype=np.float16 if is_half == True else np.float32, dtype=np.float16 if is_half == True else np.float32,
) )
with torch.no_grad(): if not ref_free:
wav16k, sr = librosa.load(ref_wav_path, sr=16000) with torch.no_grad():
if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): wav16k, sr = librosa.load(ref_wav_path, sr=16000)
raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
wav16k = torch.from_numpy(wav16k) raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
zero_wav_torch = torch.from_numpy(zero_wav) wav16k = torch.from_numpy(wav16k)
if is_half == True: zero_wav_torch = torch.from_numpy(zero_wav)
wav16k = wav16k.half().to(device) if is_half == True:
zero_wav_torch = zero_wav_torch.half().to(device) wav16k = wav16k.half().to(device)
else: zero_wav_torch = zero_wav_torch.half().to(device)
wav16k = wav16k.to(device) else:
zero_wav_torch = zero_wav_torch.to(device) wav16k = wav16k.to(device)
wav16k = torch.cat([wav16k, zero_wav_torch]) zero_wav_torch = zero_wav_torch.to(device)
ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ wav16k = torch.cat([wav16k, zero_wav_torch])
"last_hidden_state" ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
].transpose( "last_hidden_state"
1, 2 ].transpose(
) # .float() 1, 2
codes = vq_model.extract_latent(ssl_content) ) # .float()
codes = vq_model.extract_latent(ssl_content)
prompt_semantic = codes[0, 0] prompt_semantic = codes[0, 0]
prompt = prompt_semantic.unsqueeze(0).to(device)
t1 = ttime() t1 = ttime()
if (how_to_cut == i18n("凑四句一切")): if (how_to_cut == i18n("凑四句一切")):
@ -391,7 +393,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
bert = bert.to(device).unsqueeze(0) bert = bert.to(device).unsqueeze(0)
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device) all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
prompt = prompt_semantic.unsqueeze(0).to(device)
t2 = ttime() t2 = ttime()
with torch.no_grad(): with torch.no_grad():
# pred_semantic = t2s_model.model.infer( # pred_semantic = t2s_model.model.infer(