blackteay/GPT-SoVITS
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RVC-Boss 2024-01-21 22:47:32 +08:00 committed by GitHub
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1 changed files with 9 additions and 6 deletions
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GPT_SoVITS/inference_webui.py
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@ -115,7 +115,6 @@ vq_model.eval()
print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
hz = 50 hz = 50
max_sec = config["data"]["max_sec"] max_sec = config["data"]["max_sec"]
# t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo
t2s_model = Text2SemanticLightningModule(config, "ojbk", is_train=False) t2s_model = Text2SemanticLightningModule(config, "ojbk", is_train=False)
t2s_model.load_state_dict(dict_s1["weight"]) t2s_model.load_state_dict(dict_s1["weight"])
if is_half == True: if is_half == True:
@ -149,13 +148,21 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
t0 = ttime() t0 = ttime()
prompt_text = prompt_text.strip("\n") prompt_text = prompt_text.strip("\n")
prompt_language, text = prompt_language, text.strip("\n") prompt_language, text = prompt_language, text.strip("\n")
zero_wav = np.zeros(
int(hps.data.sampling_rate * 0.3),
dtype=np.float16 if is_half == True else np.float32,
)
with torch.no_grad(): with torch.no_grad():
wav16k, sr = librosa.load(ref_wav_path, sr=16000) # 派蒙 wav16k, sr = librosa.load(ref_wav_path, sr=16000)
wav16k = torch.from_numpy(wav16k) wav16k = torch.from_numpy(wav16k)
zero_wav_torch = torch.from_numpy(zero_wav)
if is_half == True: if is_half == True:
wav16k = wav16k.half().to(device) wav16k = wav16k.half().to(device)
zero_wav_torch = zero_wav_torch.half().to(device)
else: else:
wav16k = wav16k.to(device) wav16k = wav16k.to(device)
zero_wav_torch = zero_wav_torch.to(device)
wav16k=torch.cat([wav16k,zero_wav_torch])
ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
"last_hidden_state" "last_hidden_state"
].transpose( ].transpose(
@ -170,10 +177,6 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
phones1 = cleaned_text_to_sequence(phones1) phones1 = cleaned_text_to_sequence(phones1)
texts = text.split("\n") texts = text.split("\n")
audio_opt = [] audio_opt = []
zero_wav = np.zeros(
int(hps.data.sampling_rate * 0.3),
dtype=np.float16 if is_half == True else np.float32,
)
for text in texts: for text in texts:
# 解决输入目标文本的空行导致报错的问题 # 解决输入目标文本的空行导致报错的问题
if (len(text.strip()) == 0): if (len(text.strip()) == 0):