From 00bdc011135bacfd12e054c7cc6bd6b05027e046 Mon Sep 17 00:00:00 2001 From: RVC-Boss <129054828+RVC-Boss@users.noreply.github.com> Date: Thu, 27 Feb 2025 16:08:26 +0800 Subject: [PATCH] =?UTF-8?q?=E6=94=AF=E6=8C=8124k=E9=9F=B3=E9=A2=91?= =?UTF-8?q?=E8=B6=85=E5=88=8648k=E9=87=87=E6=A0=B7=E7=8E=87?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 支持24k音频超分48k采样率 --- GPT_SoVITS/inference_webui.py | 95 +++++++++++++++++++++-------------- 1 file changed, 57 insertions(+), 38 deletions(-) diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 74e0cab..1f160ed 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -238,7 +238,7 @@ def change_sovits_weights(sovits_path,prompt_language=None,text_language=None): else: visible_sample_steps=False visible_inp_refs=True - yield {'__type__':'update', 'choices':list(dict_language.keys())}, {'__type__':'update', 'choices':list(dict_language.keys())}, prompt_text_update, prompt_language_update, text_update, text_language_update,{"__type__": "update", "visible": visible_sample_steps},{"__type__": "update", "visible": visible_inp_refs},{"__type__": "update", "value": False,"interactive":True if model_version!="v3"else False} + yield {'__type__':'update', 'choices':list(dict_language.keys())}, {'__type__':'update', 'choices':list(dict_language.keys())}, prompt_text_update, prompt_language_update, text_update, text_language_update,{"__type__": "update", "visible": visible_sample_steps},{"__type__": "update", "visible": visible_inp_refs},{"__type__": "update", "value": False,"interactive":True if model_version!="v3"else False},{"__type__": "update", "visible":True if model_version=="v3"else False} dict_s2 = load_sovits_new(sovits_path) hps = dict_s2["config"] @@ -289,7 +289,7 @@ def change_sovits_weights(sovits_path,prompt_language=None,text_language=None): ) vq_model.cfm = get_peft_model(vq_model.cfm, lora_config) print("loading sovits_v3_lora%s"%(lora_rank)) - vq_model.load_state_dict(dict_s2["weight"], strict=False)## + vq_model.load_state_dict(dict_s2["weight"], strict=False) vq_model.cfm = vq_model.cfm.merge_and_unload() # torch.save(vq_model.state_dict(),"merge_win.pth") vq_model.eval() @@ -332,18 +332,18 @@ now_dir = os.getcwd() import soundfile def init_bigvgan(): - global model + global bigvgan_model from BigVGAN import bigvgan - model = bigvgan.BigVGAN.from_pretrained("%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,), use_cuda_kernel=False) # if True, RuntimeError: Ninja is required to load C++ extensions + bigvgan_model = bigvgan.BigVGAN.from_pretrained("%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,), use_cuda_kernel=False) # if True, RuntimeError: Ninja is required to load C++ extensions # remove weight norm in the model and set to eval mode - model.remove_weight_norm() - model = model.eval() + bigvgan_model.remove_weight_norm() + bigvgan_model = bigvgan_model.eval() if is_half == True: - model = model.half().to(device) + bigvgan_model = bigvgan_model.half().to(device) else: - model = model.to(device) + bigvgan_model = bigvgan_model.to(device) -if model_version!="v3":model=None +if model_version!="v3":bigvgan_model=None else:init_bigvgan() @@ -460,7 +460,13 @@ def get_phones_and_bert(text,language,version,final=False): return phones,bert.to(dtype),norm_text from module.mel_processing import spectrogram_torch,mel_spectrogram_torch -mel_fn_args = { +spec_min = -12 +spec_max = 2 +def norm_spec(x): + return (x - spec_min) / (spec_max - spec_min) * 2 - 1 +def denorm_spec(x): + return (x + 1) / 2 * (spec_max - spec_min) + spec_min +mel_fn=lambda x: mel_spectrogram_torch(x, **{ "n_fft": 1024, "win_size": 1024, "hop_size": 256, @@ -469,16 +475,7 @@ mel_fn_args = { "fmin": 0, "fmax": None, "center": False -} - -spec_min = -12 -spec_max = 2 -def norm_spec(x): - return (x - spec_min) / (spec_max - spec_min) * 2 - 1 -def denorm_spec(x): - return (x + 1) / 2 * (spec_max - spec_min) + spec_min -mel_fn=lambda x: mel_spectrogram_torch(x, **mel_fn_args) - +}) def merge_short_text_in_array(texts, threshold): if (len(texts)) < 2: @@ -497,10 +494,23 @@ def merge_short_text_in_array(texts, threshold): result[len(result) - 1] += text return result +sr_model=None +def audio_sr(audio,sr): + global sr_model + if sr_model==None: + from tools.audio_sr import AP_BWE + try: + sr_model=AP_BWE(device) + except FileNotFoundError: + gr.Warning(i18n("你没有下载超分模型的参数，因此不进行超分。如想超分请先参照教程把文件下载好")) + return audio,sr + return sr_model(audio,sr) + + ##ref_wav_path+prompt_text+prompt_language+text(单个)+text_language+top_k+top_p+temperature # cache_tokens={}#暂未实现清理机制 cache= {} -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=None,sample_steps=8): +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=None,sample_steps=8,if_sr=False,pause_second=0.3): global cache if ref_wav_path:pass else:gr.Warning(i18n('请上传参考音频')) @@ -524,9 +534,14 @@ 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:"), text) zero_wav = np.zeros( - int(hps.data.sampling_rate * 0.3), + int(hps.data.sampling_rate * pause_second), dtype=np.float16 if is_half == True else np.float32, ) + zero_wav_torch = torch.from_numpy(zero_wav) + if is_half == True: + zero_wav_torch = zero_wav_torch.half().to(device) + else: + zero_wav_torch = zero_wav_torch.to(device) if not ref_free: with torch.no_grad(): wav16k, sr = librosa.load(ref_wav_path, sr=16000) @@ -534,13 +549,10 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, gr.Warning(i18n("参考音频在3~10秒范围外，请更换！")) raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) wav16k = torch.from_numpy(wav16k) - zero_wav_torch = torch.from_numpy(zero_wav) if is_half == True: wav16k = wav16k.half().to(device) - zero_wav_torch = zero_wav_torch.half().to(device) else: 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))[ "last_hidden_state" @@ -671,22 +683,26 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, cfm_resss.append(cfm_res) cmf_res = torch.cat(cfm_resss, 2) cmf_res = denorm_spec(cmf_res) - if model==None:init_bigvgan() + if bigvgan_model==None:init_bigvgan() with torch.inference_mode(): - wav_gen = model(cmf_res) - audio=wav_gen[0][0].cpu().detach().numpy() - max_audio=np.abs(audio).max()#简单防止16bit爆音 + wav_gen = bigvgan_model(cmf_res) + audio=wav_gen[0][0]#.cpu().detach().numpy() + max_audio=torch.abs(audio).max()#简单防止16bit爆音#np.abs(audio).max() if max_audio>1:audio/=max_audio audio_opt.append(audio) - audio_opt.append(zero_wav) + audio_opt.append(zero_wav_torch)#zero_wav t4 = ttime() t.extend([t2 - t1,t3 - t2, t4 - t3]) t1 = ttime() - print("%.3f\t%.3f\t%.3f\t%.3f" % - (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3])) - ) + print("%.3f\t%.3f\t%.3f\t%.3f" % (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3]))) + audio_opt=torch.cat(audio_opt, 0)#np.concatenate sr=hps.data.sampling_rate if model_version!="v3"else 24000 - yield sr, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16) + if if_sr==True and sr==24000: + print(i18n("音频超分中")) + audio_opt,sr=audio_sr(audio_opt.unsqueeze(0),sr) + else: + audio_opt=audio_opt.cpu().detach().numpy() + yield sr, (audio_opt * 32767).astype(np.int16) def split(todo_text): @@ -864,7 +880,8 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: label=i18n("参考音频的语种"), choices=list(dict_language.keys()), value=i18n("中文"), ) 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="multiple")if model_version!="v3"else 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="multiple",visible=False) - sample_steps = gr.Radio(label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),value=32,choices=[4,8,16,32],visible=True)if model_version=="v3"else gr.Radio(label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),value=8,choices=[4,8,16,32],visible=False) + sample_steps = gr.Radio(label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),value=32,choices=[4,8,16,32],visible=True)if model_version=="v3"else gr.Radio(label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),choices=[4,8,16,32],visible=False) + if_sr_Checkbox=gr.Checkbox(label=i18n("v3输出如果觉得闷可以试试开超分"), value=False, interactive=True, show_label=True,visible=False if model_version!="v3"else True) gr.Markdown(html_center(i18n("*请填写需要合成的目标文本和语种模式"),'h3')) with gr.Row(): with gr.Column(scale=13): @@ -881,7 +898,9 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: ) gr.Markdown(value=html_center(i18n("语速调整，高为更快"))) 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) - speed = gr.Slider(minimum=0.6,maximum=1.65,step=0.05,label=i18n("语速"),value=1,interactive=True, scale=1) + with gr.Row(): + speed = gr.Slider(minimum=0.6,maximum=1.65,step=0.05,label=i18n("语速"),value=1,interactive=True, scale=1) + pause_second_slider = gr.Slider(minimum=0.1,maximum=0.5,step=0.01,label=i18n("句间停顿秒数"),value=0.3,interactive=True, scale=1) 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)："))) top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=15,interactive=True, scale=1) top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True, scale=1) @@ -896,10 +915,10 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: inference_button.click( get_tts_wav, - [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,sample_steps], + [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,sample_steps,if_sr_Checkbox,pause_second_slider], [output], ) - SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown,prompt_language,text_language], [prompt_language,text_language,prompt_text,prompt_language,text,text_language,sample_steps,inp_refs,ref_text_free]) + SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown,prompt_language,text_language], [prompt_language,text_language,prompt_text,prompt_language,text,text_language,sample_steps,inp_refs,ref_text_free,if_sr_Checkbox]) GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], []) # gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))