Update webUI.py
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umoubuton
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webUI.py
124
webUI.py
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@ -22,6 +22,7 @@ import time
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import traceback
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from itertools import chain
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from utils import mix_model
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from compress_model import removeOptimizer
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logging.getLogger('numba').setLevel(logging.WARNING)
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logging.getLogger('markdown_it').setLevel(logging.WARNING)
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@ -74,18 +75,38 @@ def updata_mix_info(files):
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if debug: traceback.print_exc()
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raise gr.Error(e)
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def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance):
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def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix):
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global model
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try:
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device = cuda[device] if "CUDA" in device else device
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model = Svc(model_path.name, config_path.name, device=device if device!="Auto" else None, cluster_model_path = cluster_model_path.name if cluster_model_path != None else "",nsf_hifigan_enhance=enhance)
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cluster_filepath = os.path.split(cluster_model_path.name) if cluster_model_path is not None else "no_cluster"
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fr = ".pkl" in cluster_filepath[1]
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#model = Svc(model_path.name, config_path.name, device=device if device!="Auto" else None, cluster_model_path = cluster_model_path.name if cluster_model_path != None else "",nsf_hifigan_enhance=enhance)
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model = Svc(model_path.name,
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config_path.name,
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device=device if device != "Auto" else None,
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cluster_model_path = cluster_model_path.name if cluster_model_path is not None else "",
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nsf_hifigan_enhance=enhance,
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diffusion_model_path = diff_model_path.name if diff_model_path is not None else "",
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diffusion_config_path = diff_config_path.name if diff_config_path is not None else "",
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shallow_diffusion = True if diff_model_path is not None else False,
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only_diffusion = only_diffusion,
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spk_mix_enable = use_spk_mix,
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feature_retrieval = fr
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)
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spks = list(model.spk2id.keys())
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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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msg = f"成功加载模型到设备{device_name}上\n"
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if cluster_model_path is None:
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msg += "未加载聚类模型\n"
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msg += "未加载聚类模型或特征检索模型\n"
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elif fr:
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msg += f"特征检索模型{cluster_filepath[1]}加载成功\n"
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else:
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msg += f"聚类模型{cluster_model_path.name}加载成功\n"
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msg += f"聚类模型{cluster_filepath[1]}加载成功\n"
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if diff_model_path is None:
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msg += "未加载扩散模型\n"
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else:
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msg += f"扩散模型{diff_model_path.name}加载成功\n"
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msg += "当前模型的可用音色：\n"
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for i in spks:
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msg += i + " "
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@ -105,39 +126,55 @@ def modelUnload():
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torch.cuda.empty_cache()
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return sid.update(choices = [],value=""),"模型卸载完毕!"
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def vc_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):
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def vc_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):
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global model
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try:
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if input_audio is None:
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raise gr.Error("你需要上传音频")
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return "You need to upload an audio", None
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if model is None:
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raise gr.Error("你需要指定模型")
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return "You need to upload an model", None
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print(input_audio)
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sampling_rate, audio = input_audio
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# print(audio.shape,sampling_rate)
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print(audio.shape,sampling_rate)
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audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
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print(audio.dtype)
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if len(audio.shape) > 1:
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audio = librosa.to_mono(audio.transpose(1, 0))
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temp_path = "temp.wav"
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soundfile.write(temp_path, audio, sampling_rate, format="wav")
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_audio = model.slice_inference(temp_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold)
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_audio = model.slice_inference(
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temp_path,
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sid,
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vc_transform,
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slice_db,
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cluster_ratio,
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auto_f0,
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noise_scale,
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pad_seconds,
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cl_num,
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lg_num,
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lgr_num,
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f0_predictor,
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enhancer_adaptive_key,
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cr_threshold,
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k_step,
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use_spk_mix,
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second_encoding,
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loudness_envelope_adjustment,
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)
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model.clear_empty()
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os.remove(temp_path)
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#构建保存文件的路径，并保存到results文件夹内
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try:
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timestamp = str(int(time.time()))
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filename = sid + "_" + timestamp + ".wav"
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output_file = os.path.join("./results", filename)
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soundfile.write(output_file, _audio, model.target_sample, format="wav")
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return f"推理成功，音频文件保存为results/{filename}", (model.target_sample, _audio)
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except Exception as e:
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if debug: traceback.print_exc()
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return f"文件保存失败，请手动保存", (model.target_sample, _audio)
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timestamp = str(int(time.time()))
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if not os.path.exists("results"):
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os.makedirs("results")
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output_file = os.path.join("results", sid + "_" + timestamp + ".wav")
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soundfile.write(output_file, _audio, model.target_sample, format="wav")
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return "Success", output_file
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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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def tts_func(_text,_rate,_voice):
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#使用edge-tts把文字转成音频
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# voice = "zh-CN-XiaoyiNeural"#女性，较高音
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@ -189,6 +226,17 @@ def vc_fn2(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, nois
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os.remove(save_path2)
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return a,b
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def model_compression(_model):
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if _model == "":
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return "请先选择要压缩的模型"
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else:
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model_path = os.path.split(_model.name)
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filename, extension = os.path.splitext(model_path[1])
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output_model_name = f"{filename}_compressed{extension}"
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output_path = os.path.join(os.getcwd(), output_model_name)
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removeOptimizer(_model.name, output_path)
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return f"模型已成功被保存在了{output_path}"
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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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@ -210,11 +258,16 @@ with gr.Blocks(
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gr.Markdown(value="""
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<font size=2> 模型设置</font>
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""")
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model_path = gr.File(label="选择模型文件")
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config_path = gr.File(label="选择配置文件")
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cluster_model_path = gr.File(label="选择聚类模型文件（没有可以不选）")
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device = gr.Dropdown(label="推理设备，默认为自动选择CPU和GPU", choices=["Auto",*cuda.keys(),"CPU"], value="Auto")
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with gr.Row():
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model_path = gr.File(label="选择模型文件")
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config_path = gr.File(label="选择配置文件")
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with gr.Row():
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diff_model_path = gr.File(label="选择扩散模型文件")
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diff_config_path = gr.File(label="选择扩散模型配置文件")
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cluster_model_path = gr.File(label="选择聚类模型或特征检索文件（没有可以不选）")
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device = gr.Dropdown(label="推理设备，默认为自动选择CPU和GPU", choices=["Auto",*cuda.keys(),"cpu"], value="Auto")
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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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with gr.Column():
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gr.Markdown(value="""
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<font size=3>左侧文件全部选择完毕后(全部文件模块显示download)，点击“加载模型”进行解析：</font>
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@ -233,9 +286,10 @@ with gr.Blocks(
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auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声勾选此项会究极跑调）", value=False)
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f0_predictor = gr.Dropdown(label="选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意：crepe为原F0使用均值滤波器)", choices=["pm","dio","harvest","crepe"], value="pm")
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vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
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cluster_ratio = gr.Number(label="聚类模型混合比例，0-1之间，0即不启用聚类。使用聚类模型能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0)
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cluster_ratio = gr.Number(label="聚类模型/特征检索混合比例，0-1之间，0即不启用聚类/特征检索。使用聚类/特征检索能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0)
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slice_db = gr.Number(label="切片阈值", value=-40)
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noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
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k_step = gr.Slider(label="浅扩散步数，只有使用了扩散模型才有效，步数越大越接近扩散模型的结果", value=100, minimum = 1, maximum = 1000)
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with gr.Column():
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pad_seconds = gr.Number(label="推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现", value=0.5)
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cl_num = gr.Number(label="音频自动切片，0为不切片，单位为秒(s)", value=0)
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@ -243,6 +297,9 @@ with gr.Blocks(
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lgr_num = gr.Number(label="自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭", value=0.75)
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enhancer_adaptive_key = gr.Number(label="使增强器适应更高的音域(单位为半音数)|默认为0", value=0)
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cr_threshold = gr.Number(label="F0过滤阈值，只有启动crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音", value=0.05)
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loudness_envelope_adjustment = gr.Number(label="输入源响度包络替换输出响度包络融合比例，越靠近1越使用输出响度包络", value = 0)
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second_encoding = gr.Checkbox(label = "二次编码，浅扩散前会对原始音频进行二次编码，玄学选项，效果时好时差，默认关闭", value=False)
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use_spk_mix = gr.Checkbox(label = "动态声线融合", value = False, interactive = False)
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with gr.Tabs():
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with gr.TabItem("音频转音频"):
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vc_input3 = gr.Audio(label="选择音频")
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@ -278,7 +335,7 @@ with gr.Blocks(
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</font>
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""")
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mix_model_path = gr.Files(label="选择需要混合模型文件")
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mix_model_upload_button = gr.UploadButton("选择/追加需要混合模型文件", file_count="multiple", variant="primary")
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mix_model_upload_button = gr.UploadButton("选择/追加需要混合模型文件", file_count="multiple")
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mix_model_output1 = gr.Textbox(
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label="混合比例调整，单位/%",
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interactive = True
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@ -291,6 +348,17 @@ with gr.Blocks(
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mix_model_path.change(updata_mix_info,[mix_model_path],[mix_model_output1])
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mix_model_upload_button.upload(upload_mix_append_file, [mix_model_upload_button,mix_model_path], [mix_model_path,mix_model_output1])
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mix_submit.click(mix_submit_click, [mix_model_output1,mix_mode], [mix_model_output2])
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with gr.TabItem("模型压缩工具"):
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gr.Markdown(value="""
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该工具可以实现对模型的体积压缩，在**不影响模型推理功能**的情况下，将原本约600M的So-VITS模型压缩至约200M, 大大减少了硬盘的压力。
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**注意：压缩后的模型将无法继续训练，请在确认封炉后再压缩。**
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""")
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model_to_compress = gr.File(label="模型上传")
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compress_model_btn = gr.Button("压缩模型", variant="primary")
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compress_model_output = gr.Textbox(label="输出信息", value="")
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compress_model_btn.click(model_compression, [model_to_compress], [compress_model_output])
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with gr.Tabs():
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@ -300,10 +368,10 @@ with gr.Blocks(
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<font size=2> WebUI设置</font>
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""")
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debug_button = gr.Checkbox(label="Debug模式，如果向社区反馈BUG需要打开，打开后控制台可以显示具体错误提示", value=debug)
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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], [vc_output1, vc_output2])
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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])
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vc_submit2.click(vc_fn2, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,tts_voice,f0_predictor,enhancer_adaptive_key,cr_threshold], [vc_output1, vc_output2])
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debug_button.change(debug_change,[],[])
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model_load_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device,enhance],[sid,sid_output])
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model_load_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix],[sid,sid_output])
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model_unload_button.click(modelUnload,[],[sid,sid_output])
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app.launch()
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