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https://github.com/RVC-Boss/GPT-SoVITS.git
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support speed adjust支持合成语速调整
support speed adjust支持合成语速调整
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RVC-Boss
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commit
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@ -312,7 +312,11 @@ def merge_short_text_in_array(texts, threshold):
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result[len(result) - 1] += text
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return result
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False,speed=1):
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##ref_wav_path+prompt_text+prompt_language+text(单个)+text_language+top_k+top_p+temperature
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# cache_tokens={}#暂未实现清理机制
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cache=None
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False,speed=1,if_freeze=False):
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global cache
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if prompt_text is None or len(prompt_text) == 0:
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ref_free = True
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t0 = ttime()
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@ -395,38 +399,30 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
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t2 = ttime()
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with torch.no_grad():
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# pred_semantic = t2s_model.model.infer(
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pred_semantic, idx = t2s_model.model.infer_panel(
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all_phoneme_ids,
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all_phoneme_len,
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None if ref_free else prompt,
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bert,
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# prompt_phone_len=ph_offset,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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early_stop_num=hz * max_sec,
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)
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# cache_key="%s-%s-%s-%s-%s-%s-%s-%s"%(ref_wav_path,prompt_text,prompt_language,text,text_language,top_k,top_p,temperature)
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if(type(cache)!=type(None)and if_freeze==True):pred_semantic=cache
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else:
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with torch.no_grad():
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pred_semantic, idx = t2s_model.model.infer_panel(
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all_phoneme_ids,
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all_phoneme_len,
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None if ref_free else prompt,
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bert,
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# prompt_phone_len=ph_offset,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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early_stop_num=hz * max_sec,
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)
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pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
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cache=pred_semantic
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t3 = ttime()
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# print(pred_semantic.shape,idx)
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pred_semantic = pred_semantic[:, -idx:].unsqueeze(
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0
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) # .unsqueeze(0)#mq要多unsqueeze一次
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refer = get_spepc(hps, ref_wav_path) # .to(device)
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if is_half == True:
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refer = refer.half().to(device)
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else:
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refer = refer.to(device)
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# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
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audio = (
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vq_model.decode(
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pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer,speed=speed
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)
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.detach()
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.cpu()
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.numpy()[0, 0]
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) ###试试重建不带上prompt部分
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audio = (vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer,speed=speed).detach().cpu().numpy()[0, 0])
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max_audio=np.abs(audio).max()#简单防止16bit爆音
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if max_audio>1:audio/=max_audio
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audio_opt.append(audio)
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@ -611,29 +607,36 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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)
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gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
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with gr.Row():
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text = gr.Textbox(label=i18n("需要合成的文本"), value="")
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text_language = gr.Dropdown(
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label=i18n("需要合成的语种"), 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")
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)
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how_to_cut = gr.Radio(
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label=i18n("怎么切"),
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choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
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value=i18n("凑四句一切"),
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interactive=True,
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)
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with gr.Row():
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gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
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with gr.Column():
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text = gr.Textbox(label=i18n("需要合成的文本"), value="")
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text_language = gr.Dropdown(
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label=i18n("需要合成的语种"), 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")
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)
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how_to_cut = gr.Radio(
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label=i18n("怎么切"),
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choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
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value=i18n("凑四句一切"),
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interactive=True,
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)
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with gr.Column():
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gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低。不懂就用默认)："))
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top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=10,interactive=True)
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top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
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temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
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with gr.Row():
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speed = gr.Slider(minimum=0.5,maximum=2,step=0.05,label=i18n("speed"),value=1,interactive=True)
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with gr.Column():
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gr.Markdown(value=i18n("语速调整，高为更快"))
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if_freeze=gr.Checkbox(label=i18n("是否直接对上次合成结果调整语速。防止随机性。"), value=False, interactive=True, show_label=True)
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speed = gr.Slider(minimum=0.6,maximum=1.65,step=0.05,label=i18n("语速"),value=1,interactive=True)
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# with gr.Column():
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# gr.Markdown(value=i18n("手工调整音素。当音素框不为空时使用手工音素输入推理，无视目标文本框。"))
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# phoneme=gr.Textbox(label=i18n("音素框"), value="")
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# get_phoneme_button = gr.Button(i18n("目标文本转音素"), variant="primary")
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inference_button = gr.Button(i18n("合成语音"), variant="primary")
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output = gr.Audio(label=i18n("输出的语音"))
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inference_button.click(
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get_tts_wav,
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[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free,speed],
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[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free,speed,if_freeze],
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[output],
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)
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