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RVC-Boss 2024-02-07 00:24:37 +08:00 committed by GitHub
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commit c2b5b284ec
1 changed files with 8 additions and 6 deletions
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GPT_SoVITS/inference_webui.py
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@ -86,6 +86,7 @@ else:
def get_bert_feature(text, word2ph): def get_bert_feature(text, word2ph):
print(23333334444444,text,word2ph)
with torch.no_grad(): with torch.no_grad():
inputs = tokenizer(text, return_tensors="pt") inputs = tokenizer(text, return_tensors="pt")
for i in inputs: for i in inputs:
@ -332,11 +333,11 @@ def get_cleaned_text_final(text,language):
return phones, word2ph, norm_text return phones, word2ph, norm_text
def get_bert_final(phones, word2ph, text,language,device): def get_bert_final(phones, word2ph, text,language,device):
if text_language == "en": if language == "en":
bert = get_bert_inf(phones, word2ph, text, language) bert = get_bert_inf(phones, word2ph, text, language)
elif text_language in {"zh", "ja","auto"}: elif language in {"zh", "ja","auto"}:
bert = nonen_get_bert_inf(text, language) bert = nonen_get_bert_inf(text, language)
elif text_language == "all_zh": elif language == "all_zh":
bert = get_bert_feature(text, word2ph).to(device) bert = get_bert_feature(text, word2ph).to(device)
else: else:
bert = torch.zeros((1024, len(phones))).to(device) bert = torch.zeros((1024, len(phones))).to(device)
@ -376,9 +377,10 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
) # .float() ) # .float()
codes = vq_model.extract_latent(ssl_content) codes = vq_model.extract_latent(ssl_content)
prompt_semantic = codes[0, 0] prompt_semantic = codes[0, 0]
t1 = ttime() t1 = ttime()
phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language) phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
if (how_to_cut == i18n("凑四句一切")): if (how_to_cut == i18n("凑四句一切")):
text = cut1(text) text = cut1(text)
elif (how_to_cut == i18n("凑50字一切")): elif (how_to_cut == i18n("凑50字一切")):
@ -403,7 +405,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
print(i18n("实际输入的目标文本(每句):"), text) print(i18n("实际输入的目标文本(每句):"), text)
phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language) phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype) bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
print(bert1,bert2)
bert = torch.cat([bert1, bert2], 1) bert = torch.cat([bert1, bert2], 1)
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0) all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
@ -582,7 +584,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath") inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="") prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
prompt_language = gr.Dropdown( prompt_language = gr.Dropdown(
label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文")], value=i18n("中文") label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
) )
gr.Markdown(value=i18n("*请填写需要合成的目标文本。中英混合选中文，日英混合选日文，中日混合暂不支持，非目标语言文本自动遗弃。")) gr.Markdown(value=i18n("*请填写需要合成的目标文本。中英混合选中文，日英混合选日文，中日混合暂不支持，非目标语言文本自动遗弃。"))
with gr.Row(): with gr.Row():