COM
Browse files
app.py
CHANGED
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@@ -39,8 +39,6 @@ debug = False
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sovits_params = {}
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diff_params = {}
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loaded = None
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-
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def debug_change():
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global debug
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debug = debug_button.value
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@@ -142,51 +140,6 @@ def load_model_func(ckpt_name,cluster_name,config_name,enhance,diff_model_name,d
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output_msg = f"模型被成功加载到了{device_name}上\n{index_or_kmeans}：{clu_load}\n扩散模型：{diff_load}"
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return output_msg, gr.Dropdown.update(choices=spk_list, value=spk_choice), clip
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def Newload_model_func(ckpt_name,cluster_name,config_name2,enhance2,diff_model_name2,diff_config_name2,only_diffusion2,encoder2,using_device2):
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global model, loaded
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config_name = config_name2.value
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enhance = enhance2.value
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diff_model_name = diff_model_name2.value
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diff_config_name = (diff_config_name2).value
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only_diffusion = (only_diffusion2).value
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encoder = (encoder2).value
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using_device = (using_device2).value
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config_path = os.path.join(config_dir, config_name)
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diff_config_path = os.path.join(config_dir, diff_config_name) if diff_config_name != "no_diff_config" else "configs/diffusion.yaml"
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with open(config_path, 'r') as f:
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config = json.load(f)
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spk_dict = config["spk"]
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spk_name = config.get('spk', None)
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spk_choice = next(iter(spk_name)) if spk_name else "未检测到音色"
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ckpt_path = os.path.join(workdir, ckpt_name)
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_, _suffix = os.path.splitext(cluster_name)
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fr = True if _suffix == ".pkl" else False #如果是pkl后缀就启用特征检索
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cluster_path = os.path.join(workdir, cluster_name)
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diff_model_path = os.path.join(diff_workdir, diff_model_name)
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shallow_diffusion = True if diff_model_name != "no_diff" else False
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use_spk_mix = False
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device = None if using_device == "Auto" else using_device
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model = Svc(ckpt_path,
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config_path,
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device,
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cluster_path,
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enhance,
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diff_model_path,
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diff_config_path,
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shallow_diffusion,
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only_diffusion,
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use_spk_mix,
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fr)
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spk_list = list(spk_dict.keys())
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clip = 25 if encoder == "Whisper-PPG" else 0 #Whisper必须强制切片25秒
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device_name = torch.cuda.get_device_properties(model.dev).name if "cuda" in str(model.dev) else str(model.dev)
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index_or_kmeans = "特征索引" if fr is True else "聚类模型"
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clu_load = "未加载" if cluster_name == "no_clu" else cluster_name
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diff_load = "未加载" if diff_model_name == "no_diff" else diff_model_name
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loaded = cluster_name
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#output_msg = f"模型被成功加载到了{device_name}上\n{index_or_kmeans}：{clu_load}\n扩散模型：{diff_load}"
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#return output_msg, gr.Dropdown.update(choices=spk_list, value=spk_choice), clip
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-
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def get_file_options(directory, extension):
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return [file for file in os.listdir(directory) if file.endswith(extension)]
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@@ -709,22 +662,6 @@ gpus="-".join([i[0]for i in gpu_infos])
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sovits_params, diff_params = get_default_settings()
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app = gr.Blocks()
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def Newget_model_info(choice_ckpt2):
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choice_ckpt = str(choice_ckpt2)
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pthfile = os.path.join(workdir, choice_ckpt)
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net = torch.load(pthfile, map_location=torch.device('cpu')) #cpu load
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spk_emb = net["model"].get("emb_g.weight")
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if spk_emb is None:
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return "所选模型缺少emb_g.weight，你可能选择了一个底模"
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_dim, _layer = spk_emb.size()
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model_type = {
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768: "Vec768-Layer12",
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256: "Vec256-Layer9 / HubertSoft",
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1024: "Whisper-PPG"
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}
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return gr.Textbox(visible=False, value=model_type.get(_layer, "不受支持的模型"))
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with app:
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gr.Markdown(value="""
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### So-VITS-SVC 4.1-Stable
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@@ -739,40 +676,31 @@ with app:
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""")
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with gr.Tabs():
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with gr.TabItem("
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#with gr.Row():
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# choice_ckpt = gr.Dropdown(label="模型选择", choices=ckpt_list, value="no_model")
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# model_branch = gr.Textbox(label="模型编码器", placeholder="请先选择模型", interactive=False)
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#choice_ckpt = gr.Dropdown(value="G_82400.pth", visible=False)
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#with gr.Row():
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# config_choice = gr.Dropdown(label="配置文件", choices=config_list, value="no_config")
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# config_info = gr.Textbox(label="配置文件编码器", placeholder="请选择配置文件")
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config_choice = gr.Dropdown(value="config.json", visible=False)
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#gr.Markdown(value="""**请检查模型和配置文件的编码器是否匹配**""")
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#with gr.Row():
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# diff_choice = gr.Dropdown(label="（可选）选择扩散模型", choices=diff_list, value="no_diff", interactive=True)
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# diff_config_choice = gr.Dropdown(label="扩散模型配置文件", choices=diff_config_list, value="no_diff_config", interactive=True)
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diff_choice = gr.Dropdown(value="no_diff", visible=False)
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diff_config_choice = gr.Dropdown(value="no_diff_config", visible=False)
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with gr.Row():
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with gr.Row():
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enhance = gr.Checkbox(label="是否使用NSF_HIFIGAN增强，该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭", value=False)
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# model_message = gr.Textbox(label="Output Message")
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# sid = gr.Dropdown(label="So-VITS说话人", value="speaker0")
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sid = gr.Dropdown(value="1001", visible=False)
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#refresh.click(refresh_options,[],[choice_ckpt,config_choice,cluster_choice,diff_choice,diff_config_choice])
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gr.Markdown(value="""
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请稍等片刻，模型加载大约需要10秒。后续操作不需要重新加载模型
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@@ -827,121 +755,101 @@ with app:
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vc_tts_submit = gr.Button("文本转语音", variant="primary")
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vc_output1 = gr.Textbox(label="Output Message")
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vc_output2 = gr.Audio(label="Output Audio")
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def Newvc_fn(sid, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment, clus2):
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global model, loaded
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if loaded != clus2:
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Newload_model_func("G_82400.pth",clus2,config_choice,enhance,diff_choice,diff_config_choice,only_diffusion,model_branch,using_device)
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loaded = clus2
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try:
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if input_audio is None:
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return "You need to upload an audio", None
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if model is None:
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return "You need to upload an model", None
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sampling_rate, audio = input_audio
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temp_path = "temp.wav"
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sf.write(temp_path, audio, sampling_rate, format="wav")
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output_file_path = vc_infer(sid, audio, temp_path, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment)
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os.remove(temp_path)
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return "Success", output_file_path
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except Exception as e:
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if debug: traceback.print_exc()
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raise gr.Error(e)
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vc_submit.click(
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vc_batch_submit.click(vc_batch_fn, [sid, vc_batch_files, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1])
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vc_tts_submit.click(tts_fn, [text_input, tts_spk, sid, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2])
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with gr.TabItem("训练"):
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with gr.TabItem("小工具/实验室特性"):
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gr.Markdown(value="""
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sovits_params = {}
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diff_params = {}
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def debug_change():
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global debug
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debug = debug_button.value
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output_msg = f"模型被成功加载到了{device_name}上\n{index_or_kmeans}：{clu_load}\n扩散模型：{diff_load}"
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return output_msg, gr.Dropdown.update(choices=spk_list, value=spk_choice), clip
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def get_file_options(directory, extension):
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return [file for file in os.listdir(directory) if file.endswith(extension)]
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sovits_params, diff_params = get_default_settings()
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app = gr.Blocks()
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with app:
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gr.Markdown(value="""
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### So-VITS-SVC 4.1-Stable
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""")
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with gr.Tabs():
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with gr.TabItem("特别周 (Special Week)"):
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with gr.Row():
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choice_ckpt = gr.Dropdown(label="模型选择", choices=ckpt_list, value="no_model")
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model_branch = gr.Textbox(label="模型编码器", placeholder="请先选择模型", interactive=False)
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with gr.Row():
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config_choice = gr.Dropdown(label="配置文件", choices=config_list, value="no_config")
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config_info = gr.Textbox(label="配置文件编码器", placeholder="请选择配置文件")
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gr.Markdown(value="""**请检查模型和配置文件的编码器是否匹配**""")
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with gr.Row():
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diff_choice = gr.Dropdown(label="（可选）选择扩散模型", choices=diff_list, value="no_diff", interactive=True)
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diff_config_choice = gr.Dropdown(label="扩散模型配置文件", choices=diff_config_list, value="no_diff_config", interactive=True)
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cluster_choice = gr.Dropdown(label="（可选）选择聚类模型/特征检索模型", choices=cluster_list, value="no_clu")
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with gr.Row():
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enhance = gr.Checkbox(label="是否使用NSF_HIFIGAN增强，该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭", value=False)
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only_diffusion = gr.Checkbox(label="是否使用全扩散推理，开启后将不使用So-VITS模型，仅使用扩散模型进行完整扩散推理，默认关闭", value=False)
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using_device = gr.Dropdown(label="推理设备，默认为自动选择", choices=["Auto","cuda","cpu"], value="Auto")
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refresh = gr.Button("刷新选项")
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loadckpt = gr.Button("加载模型", variant="primary")
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with gr.Row():
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model_message = gr.Textbox(label="Output Message")
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sid = gr.Dropdown(label="So-VITS说话人", value="speaker0")
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choice_ckpt.change(get_model_info, [choice_ckpt], [model_branch])
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config_choice.change(load_json_encoder, [config_choice], [config_info])
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refresh.click(refresh_options,[],[choice_ckpt,config_choice,cluster_choice,diff_choice,diff_config_choice])
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gr.Markdown(value="""
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请稍等片刻，模型加载大约需要10秒。后续操作不需要重新加载模型
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vc_tts_submit = gr.Button("文本转语音", variant="primary")
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vc_output1 = gr.Textbox(label="Output Message")
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vc_output2 = gr.Audio(label="Output Audio")
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| 758 |
|
| 759 |
+
loadckpt.click(load_model_func,[choice_ckpt,cluster_choice,config_choice,enhance,diff_choice,diff_config_choice,only_diffusion,model_branch,using_device],[model_message, sid, cl_num])
|
| 760 |
+
vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2])
|
| 761 |
vc_batch_submit.click(vc_batch_fn, [sid, vc_batch_files, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1])
|
| 762 |
vc_tts_submit.click(tts_fn, [text_input, tts_spk, sid, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2])
|
| 763 |
+
#with gr.TabItem("训练"):
|
| 764 |
+
# gr.Markdown(value="""请将数据集文件夹放置在dataset_raw文件夹下，确认放置正确后点击下方获取数据集名称""")
|
| 765 |
+
# raw_dirs_list=gr.Textbox(label="Raw dataset directory(s):")
|
| 766 |
+
# get_raw_dirs=gr.Button("识别数据集", variant="primary")
|
| 767 |
+
# gr.Markdown(value="""确认数据集正确识别后请选择训练使用的特征编码器和f0预测器，**如果要训练扩散模型，请选择Vec768l12或hubertsoft，并确保So-VITS和扩散模型使用同一个编码器**""")
|
| 768 |
+
# with gr.Row():
|
| 769 |
+
# gr.Markdown(value="""**vec256l9**: ContentVec(256Layer9)，旧版本叫v1，So-VITS-SVC 4.0的基础版本，**暂不支持扩散模型**
|
| 770 |
+
# **vec768l12**: 特征输入更换为ContentVec的第12层Transformer输出，模型理论上会更加还原训练集音色
|
| 771 |
+
# **hubertsoft**: So-VITS-SVC 3.0使用的编码器，咬字更为准确，但可能存在多说话人音色泄露问题
|
| 772 |
+
# **whisper-ppg**: 来自OpenAI，咬字最为准确，但和Hubertsoft一样存在多说话人音色泄露，且显存占用和训练时间有明显增加。**暂不支持扩散模型**
|
| 773 |
+
# """)
|
| 774 |
+
# gr.Markdown(value="""**crepe**: 抗噪能力最强，但预处理速度慢（不过如果你的显卡很强的话速度会很快）
|
| 775 |
+
# **pm**: 预处理速度快，但抗噪能力较弱
|
| 776 |
+
# **dio**: 先前版本预处理默认使用的f0预测器
|
| 777 |
+
# **harvest**: 有一定抗噪能力，预处理显存占用友好，速度比较慢
|
| 778 |
+
# """)
|
| 779 |
+
# with gr.Row():
|
| 780 |
+
# branch_selection = gr.Radio(label="选择训练使用的编码器", choices=["vec256l9","vec768l12","hubertsoft","whisper-ppg"], value="vec768l12", interactive=True)
|
| 781 |
+
# f0_predictor_selection = gr.Radio(label="选择训练使用的f0预测器", choices=["crepe","pm","dio","harvest"], value="crepe", interactive=True)
|
| 782 |
+
# use_diff = gr.Checkbox(label="是否使用浅扩散模型，如要训练浅扩散模型请勾选此项", value=True)
|
| 783 |
+
# vol_aug=gr.Checkbox(label="是否启用响度嵌入和音量增强，启用后可以根据输入源控制输出响度，但对数据集质量的要求更高。**仅支持vec768l12编码器**", value=False)
|
| 784 |
+
# with gr.Row():
|
| 785 |
+
# skip_loudnorm = gr.Checkbox(label="是否跳过响度匹配，如果你已经用音频处理软件做过响度匹配，请勾选此处")
|
| 786 |
+
# num_processes = gr.Slider(label="预处理使用的CPU线程数，可以大幅加快预处理速度，但线程数过大容易爆显存，建议12G显存设置为2", minimum=1, maximum=multiprocessing.cpu_count(), value=1, step=1)
|
| 787 |
+
# with gr.Row():
|
| 788 |
+
# raw_preprocess=gr.Button("数据预处理", variant="primary")
|
| 789 |
+
# regenerate_config_btn=gr.Button("重新生成配置文件", variant="primary")
|
| 790 |
+
# preprocess_output=gr.Textbox(label="预处理输出信息，完成后请检查一下是否有报错信息，如无则可以进行下一步", max_lines=999)
|
| 791 |
+
# clear_preprocess_output=gr.Button("清空输出信息")
|
| 792 |
+
# with gr.Group():
|
| 793 |
+
# gr.Markdown(value="""填写训练设置和超参数""")
|
| 794 |
+
# with gr.Row():
|
| 795 |
+
# gr.Textbox(label="当前使用显卡信息", value=gpu_info)
|
| 796 |
+
# gpu_selection=gr.Textbox(label="多卡用户请指定希望训练使用的显卡ID（0,1,2...）", value=gpus, interactive=True)
|
| 797 |
+
# with gr.Row():
|
| 798 |
+
# log_interval=gr.Textbox(label="每隔多少步(steps)生成一次评估日志", value=sovits_params['log_interval'])
|
| 799 |
+
# eval_interval=gr.Textbox(label="每隔多少步(steps)验证并保存一次模型", value=sovits_params['eval_interval'])
|
| 800 |
+
# keep_ckpts=gr.Textbox(label="仅保留最新的X个模型，超出该数字的旧模型会被删除。设置为0则永不删除", value=sovits_params['keep_ckpts'])
|
| 801 |
+
# with gr.Row():
|
| 802 |
+
# batch_size=gr.Textbox(label="批量大小，每步取多少条数据进行训练，大batch有助于训练但显著增加显存占用。6G显存建议设定为4", value=sovits_params['batch_size'])
|
| 803 |
+
# lr=gr.Textbox(label="学习率，一般不用动，批量大小较大时可以适当增大学习率，但强烈不建议超过0.0002，有炸炉风险", value=sovits_params['learning_rate'])
|
| 804 |
+
# fp16_run=gr.Checkbox(label="是否使用fp16混合精度训练，fp16训练可能降低显存占用和训练时间，但对模型质量的影响尚未查证", value=sovits_params['fp16_run'])
|
| 805 |
+
# all_in_mem=gr.Checkbox(label="是否加载所有数据集到内存中，硬盘IO过于低下、同时内存容量远大于数据集体积时可以启用，能显著加快训练速度", value=sovits_params['all_in_mem'])
|
| 806 |
+
# with gr.Row():
|
| 807 |
+
# gr.Markdown("请检查右侧的说话人列表是否和你要训练的目标说话人一致，确认无误后点击写入配置文件，然后就可以开始训练了")
|
| 808 |
+
# speakers=gr.Textbox(label="说话人列表")
|
| 809 |
+
# with gr.Accordion(label = "扩散模型配置（训练扩散模型需要写入此处）", open=True):
|
| 810 |
+
# with gr.Row():
|
| 811 |
+
# diff_num_workers = gr.Number(label="num_workers, 如果你的电脑配置较高，可以将这里设置为0加快训练速度", value=diff_params['num_workers'])
|
| 812 |
+
# diff_cache_all_data = gr.Checkbox(label="是否缓存数据，启用后可以加快训练速度，关闭后可以节省显存或内存，但会减慢训练速度", value=diff_params['cache_all_data'])
|
| 813 |
+
# diff_cache_device = gr.Radio(label="若启用缓存数据，使用显存(cuda)还是内存(cpu)缓存，如果显卡显存充足，选择cuda以加快训练速度", choices=["cuda","cpu"], value=diff_params['cache_device'])
|
| 814 |
+
# diff_amp_dtype = gr.Radio(label="训练数据类型，fp16可能会有更快的训练速度，前提是你的显卡支持", choices=["fp32","fp16"], value=diff_params['amp_dtype'])
|
| 815 |
+
# with gr.Row():
|
| 816 |
+
# diff_batch_size = gr.Number(label="批量大小(batch_size)，根据显卡显存设置，小显存适当降低该项，6G显存可以设定为48，但该数值不要超过数据集总数量的1/4", value=diff_params['diff_batch_size'])
|
| 817 |
+
# diff_lr = gr.Number(label="学习率（一般不需要动）", value=diff_params['diff_lr'])
|
| 818 |
+
# diff_interval_log = gr.Number(label="每隔多少步(steps)生成一次评估日志", value = diff_params['diff_interval_log'])
|
| 819 |
+
# diff_interval_val = gr.Number(label="每隔多少步(steps)验证并保存一次模型，如果你的批量大小较大，可以适当减少这里的数字，但不建议设置为1000以下", value=diff_params['diff_interval_val'])
|
| 820 |
+
# diff_force_save = gr.Number(label="每隔多少步强制保留模型，只有该步数的倍数保存的模型会被保留，其余会被删除。设置为与验证步数相同的值则每个模型都会被保留", value=diff_params['diff_force_save'])
|
| 821 |
+
# with gr.Row():
|
| 822 |
+
# save_params=gr.Button("将当前设置保存为默认设置", variant="primary")
|
| 823 |
+
# write_config=gr.Button("写入配置文件", variant="primary")
|
| 824 |
+
# write_config_output=gr.Textbox(label="输出信息")
|
| 825 |
+
|
| 826 |
+
# gr.Markdown(value="""**点击从头开始训练**将会自动将已有的训练进度保存到models_backup文件夹，并自动装载预训练模型。
|
| 827 |
+
# **继续上一次的训练进度**将从上一个保存模型的进度继续训练。继续训练进度无需重新预处理和写入配置文件。
|
| 828 |
+
# 关于扩散、聚类和特征检索的详细说明请看[此处](https://www.yuque.com/umoubuton/ueupp5/kmui02dszo5zrqkz)。
|
| 829 |
+
# """)
|
| 830 |
+
# with gr.Row():
|
| 831 |
+
# with gr.Column():
|
| 832 |
+
# start_training=gr.Button("从头开始训练", variant="primary")
|
| 833 |
+
# training_output=gr.Textbox(label="训练输出信息")
|
| 834 |
+
# with gr.Column():
|
| 835 |
+
# continue_training_btn=gr.Button("继续上一次的训练进度", variant="primary")
|
| 836 |
+
# continue_training_output=gr.Textbox(label="训练输出信息")
|
| 837 |
+
# with gr.Row():
|
| 838 |
+
# with gr.Column():
|
| 839 |
+
# diff_training_btn=gr.Button("从头训练扩散模型", variant="primary")
|
| 840 |
+
# diff_training_output=gr.Textbox(label="训练输出信息")
|
| 841 |
+
# with gr.Column():
|
| 842 |
+
# diff_continue_training_btn=gr.Button("继续训练扩散模型", variant="primary")
|
| 843 |
+
# diff_continue_training_output=gr.Textbox(label="训练输出信息")
|
| 844 |
+
# with gr.Accordion(label = "聚类、特征检索训练", open=False):
|
| 845 |
+
# with gr.Row():
|
| 846 |
+
# with gr.Column():
|
| 847 |
+
# kmeans_button=gr.Button("训练聚类模型", variant="primary")
|
| 848 |
+
# kmeans_gpu = gr.Checkbox(label="使用GPU训练", value=True)
|
| 849 |
+
# kmeans_output=gr.Textbox(label="训练输出信息")
|
| 850 |
+
# with gr.Column():
|
| 851 |
+
# index_button=gr.Button("训练特征检索模型", variant="primary")
|
| 852 |
+
# index_output=gr.Textbox(label="训练输出信息")
|
| 853 |
|
| 854 |
with gr.TabItem("小工具/实验室特性"):
|
| 855 |
gr.Markdown(value="""
|