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https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 19:41:56 +08:00
support timebre mixing
support timebre mixing
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RVC-Boss
GitHub
parent
2b142405b8
commit
21f05ee471
@ -7,6 +7,8 @@
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全部按日文识别
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'''
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import logging
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import traceback
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logging.getLogger("markdown_it").setLevel(logging.ERROR)
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logging.getLogger("urllib3").setLevel(logging.ERROR)
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logging.getLogger("httpcore").setLevel(logging.ERROR)
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@ -14,6 +16,7 @@ logging.getLogger("httpx").setLevel(logging.ERROR)
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logging.getLogger("asyncio").setLevel(logging.ERROR)
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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logging.getLogger("multipart.multipart").setLevel(logging.ERROR)
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import LangSegment, os, re, sys, json
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import pdb
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import torch
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@ -257,6 +260,8 @@ change_gpt_weights(gpt_path)
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def get_spepc(hps, filename):
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audio = load_audio(filename, int(hps.data.sampling_rate))
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audio = torch.FloatTensor(audio)
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maxx=audio.abs().max()
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if(maxx>1):audio/=min(2,maxx)
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audio_norm = audio
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audio_norm = audio_norm.unsqueeze(0)
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spec = spectrogram_torch(
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@ -387,7 +392,7 @@ def merge_short_text_in_array(texts, threshold):
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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= {}
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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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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,inp_refs=123):
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global cache
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if ref_wav_path:pass
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else:gr.Warning(i18n('请上传参考音频'))
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@ -498,12 +503,16 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
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cache[i_text]=pred_semantic
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t3 = ttime()
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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, torch.LongTensor(phones2).to(device).unsqueeze(0), refer,speed=speed).detach().cpu().numpy()[0, 0])
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refers=[]
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if(inp_refs):
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for path in inp_refs:
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try:
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refer = get_spepc(hps, path.name).to(dtype).to(device)
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refers.append(refer)
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except:
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traceback.print_exc()
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if(len(refers)==0):refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
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audio = (vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers,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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@ -692,13 +701,14 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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prompt_language = gr.Dropdown(
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label=i18n("参考音频的语种"), choices=list(dict_language.keys()), value=i18n("中文"), scale=14
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)
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inp_refs = gr.File(label=i18n("可选项：通过拖拽多个文件上传多个参考音频（建议同性），平均融合他们的音色。如不填写此项，音色由左侧单个参考音频控制。如是微调模型，建议参考音频全部在微调训练集音色内，底模不用管。"),file_count="file_count",scale=13)
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gr.Markdown(html_center(i18n("*请填写需要合成的目标文本和语种模式"),'h3'))
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with gr.Row():
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with gr.Column(scale=13):
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text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=26, max_lines=26)
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with gr.Column(scale=7):
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text_language = gr.Dropdown(
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label=i18n("需要合成的语种"), choices=list(dict_language.keys()), value=i18n("中文"), scale=1
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label=i18n("需要合成的语种")+i18n(".限制范围越小判别效果越好。"), choices=list(dict_language.keys()), value=i18n("中文"), scale=1
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)
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how_to_cut = gr.Dropdown(
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label=i18n("怎么切"),
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@ -707,10 +717,10 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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interactive=True, scale=1
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)
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gr.Markdown(value=html_center(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, scale=1)
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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和音色。防止随机性。"), value=False, interactive=True,show_label=True, scale=1)
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speed = gr.Slider(minimum=0.6,maximum=1.65,step=0.05,label=i18n("语速"),value=1,interactive=True, scale=1)
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gr.Markdown(html_center(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, scale=1)
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top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=15,interactive=True, scale=1)
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top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True, scale=1)
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temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True, scale=1)
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# with gr.Column():
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@ -723,7 +733,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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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,if_freeze],
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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,inp_refs],
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[output],
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)
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SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown,prompt_language,text_language], [prompt_language,text_language,prompt_text,prompt_language,text,text_language])
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