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添加数据预处理环节的音频降噪功能,提高模型性能
添加数据预处理环节的音频降噪功能,提高模型性能
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6e224464c8
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17
webui.py
17
webui.py
@ -5,6 +5,8 @@ import json,yaml,warnings,torch
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import platform
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import psutil
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import signal
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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warnings.filterwarnings("ignore")
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torch.manual_seed(233333)
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@ -210,6 +212,17 @@ def close_asr():
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p_asr=None
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return "已终止ASR进程",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
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# 音频降噪
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def reset_tts_wav(audio):
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ans = pipeline(
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Tasks.acoustic_noise_suppression,
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model='damo/speech_frcrn_ans_cirm_16k')
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ans(audio,output_path='./output_ins.wav')
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return "./output_ins.wav"
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p_train_SoVITS=None
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def open1Ba(batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D):
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global p_train_SoVITS
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@ -634,7 +647,8 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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gr.Markdown(value=i18n("0b-语音切分工具"))
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with gr.Row():
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with gr.Row():
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slice_inp_path=gr.Textbox(label=i18n("音频自动切分输入路径,可文件可文件夹"),value="")
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#slice_inp_path=gr.Textbox(label=i18n("音频自动切分输入路径,可文件可文件夹"),value="")
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slice_inp_path = gr.Audio(label=i18n("请上传克隆对象音频"), type="filepath")
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slice_opt_root=gr.Textbox(label=i18n("切分后的子音频的输出根目录"),value="output/slicer_opt")
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threshold=gr.Textbox(label=i18n("threshold:音量小于这个值视作静音的备选切割点"),value="-34")
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min_length=gr.Textbox(label=i18n("min_length:每段最小多长,如果第一段太短一直和后面段连起来直到超过这个值"),value="4000")
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@ -648,6 +662,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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alpha=gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("alpha_mix:混多少比例归一化后音频进来"),value=0.25,interactive=True)
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n_process=gr.Slider(minimum=1,maximum=n_cpu,step=1,label=i18n("切割使用的进程数"),value=4,interactive=True)
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slicer_info = gr.Textbox(label=i18n("语音切割进程输出信息"))
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reset_inp_button.click(reset_tts_wav,[slice_inp_path],[slice_inp_path])
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gr.Markdown(value=i18n("0c-中文批量离线ASR工具"))
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with gr.Row():
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open_asr_button = gr.Button(i18n("开启离线批量ASR"), variant="primary",visible=True)
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