謬紗特
GitHub
commit
8bff4720bd
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@ -190,38 +190,59 @@ class Svc(object):
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# 清理显存
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torch.cuda.empty_cache()
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def slice_inference(self,raw_audio_path, spk, tran, slice_db,cluster_infer_ratio, auto_predict_f0,noice_scale, pad_seconds=0.5):
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def slice_inference(self,raw_audio_path, spk, tran, slice_db,cluster_infer_ratio, auto_predict_f0,noice_scale, pad_seconds=0.5, clip_seconds=0,lg_num=0,lgr_num =0.75):
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wav_path = raw_audio_path
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chunks = slicer.cut(wav_path, db_thresh=slice_db)
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audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
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per_size = int(clip_seconds*audio_sr)
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lg_size = int(lg_num*audio_sr)
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lg_size_r = int(lg_size*lgr_num)
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lg_size_c_l = (lg_size-lg_size_r)//2
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lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
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lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
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audio = []
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for (slice_tag, data) in audio_data:
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print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
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# padd
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pad_len = int(audio_sr * pad_seconds)
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data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])])
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length = int(np.ceil(len(data) / audio_sr * self.target_sample))
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raw_path = io.BytesIO()
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soundfile.write(raw_path, data, audio_sr, format="wav")
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raw_path.seek(0)
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if slice_tag:
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print('jump empty segment')
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_audio = np.zeros(length)
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audio.extend(list(pad_array(_audio, length)))
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continue
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if per_size != 0:
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datas = split_list_by_n(data, per_size,lg_size)
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else:
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datas = [data]
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for k,dat in enumerate(datas):
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per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length
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if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
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# padd
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pad_len = int(audio_sr * pad_seconds)
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dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
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raw_path = io.BytesIO()
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soundfile.write(raw_path, dat, audio_sr, format="wav")
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raw_path.seek(0)
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out_audio, out_sr = self.infer(spk, tran, raw_path,
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cluster_infer_ratio=cluster_infer_ratio,
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auto_predict_f0=auto_predict_f0,
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noice_scale=noice_scale
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)
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_audio = out_audio.cpu().numpy()
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pad_len = int(self.target_sample * pad_seconds)
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_audio = _audio[pad_len:-pad_len]
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_audio = pad_array(_audio, per_length)
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if lg_size!=0 and k!=0:
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lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr_num != 1 else audio[-lg_size:]
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lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r] if lgr_num != 1 else _audio[0:lg_size]
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lg_pre = lg1*(1-lg)+lg2*lg
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audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr_num != 1 else audio[0:-lg_size]
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audio.extend(lg_pre)
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_audio = _audio[lg_size_c_l+lg_size_r:] if lgr_num != 1 else _audio[lg_size:]
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audio.extend(list(_audio))
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return np.array(audio)
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class RealTimeVC:
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def __init__(self):
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self.last_chunk = None
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@ -0,0 +1,94 @@
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import io
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import os
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# os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
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import gradio as gr
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import librosa
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import numpy as np
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import soundfile
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from inference.infer_tool import Svc
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import logging
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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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logging.getLogger('urllib3').setLevel(logging.WARNING)
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logging.getLogger('matplotlib').setLevel(logging.WARNING)
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logging.getLogger('multipart').setLevel(logging.WARNING)
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model = None
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spk = None
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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):
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global model
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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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# print(audio.shape,sampling_rate)
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audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
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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, model.target_sample, 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)
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os.remove(temp_path)
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return "Success", (model.target_sample, _audio)
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except Exception as e:
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return "异常信息:"+str(e)+"\n请排障后重试",None
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app = gr.Blocks()
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with app:
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with gr.Tabs():
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with gr.TabItem("Sovits4.0"):
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gr.Markdown(value="""
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Sovits4.0 WebUI
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""")
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gr.Markdown(value="""
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<font size=3>下面是模型文件选择：</font>
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""")
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model_path = gr.File(label="模型文件")
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gr.Markdown(value="""
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<font size=3>下面是配置文件选择：</font>
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""")
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config_path = gr.File(label="配置文件")
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gr.Markdown(value="""
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<font size=3>下面是聚类模型文件选择，没有可以不填：</font>
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""")
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cluster_model_path = gr.File(label="聚类模型文件")
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device = gr.Dropdown(label="推理设备，留白则为自动选择cpu和gpu",choices=[None,"gpu","cpu"],value=None)
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gr.Markdown(value="""
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<font size=3>全部上传完毕后(全部文件模块显示download),点击模型解析进行解析：</font>
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""")
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model_analysis_button = gr.Button(value="模型解析")
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sid = gr.Dropdown(label="音色（说话人）")
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sid_output = gr.Textbox(label="Output Message")
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vc_input3 = gr.Audio(label="上传音频")
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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，默认为0不启用聚类，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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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，歌声不要勾选此项会究极跑调）", value=False)
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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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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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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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lg_num = gr.Number(label="两端音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，注意，该设置会影响推理速度，单位为秒/s", value=0)
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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,interactive=True)
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vc_submit = gr.Button("转换", 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 modelAnalysis(model_path,config_path,cluster_model_path,device):
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try:
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global model
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model = Svc(model_path.name, config_path.name,device=device if device!="" else None,cluster_model_path= cluster_model_path.name if cluster_model_path!=None else "")
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spks = list(model.spk2id.keys())
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return sid.update(choices = spks,value=spks[0]),"ok"
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except Exception as e:
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return "","异常信息:"+str(e)+"\n请排障后重试"
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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], [vc_output1, vc_output2])
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model_analysis_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device],[sid,sid_output])
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app.launch()
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