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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import json | |
| import os | |
| import tempfile | |
| import shutil | |
| import requests | |
| from pathlib import Path | |
| ################################################### | |
| from utils.hparams import hparams | |
| from preprocessing.data_gen_utils import get_pitch_parselmouth,get_pitch_crepe | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import IPython.display as ipd | |
| import utils | |
| import librosa | |
| import torchcrepe | |
| from infer import * | |
| import logging | |
| from infer_tools.infer_tool import * | |
| import io | |
| spk_dict = { | |
| "东海帝皇": {"model_name": './models/uma/teio.ckpt', "config_name": './models/uma/config.yaml'} | |
| } | |
| print(spk_dict) | |
| project_name = "teio" | |
| model_path = spk_dict['东海帝皇']['model_name'] | |
| config_path= spk_dict['东海帝皇']['config_name'] | |
| hubert_gpu = False | |
| svc_model = Svc(project_name, config_path, hubert_gpu, model_path) | |
| print(svc_model) | |
| def vc_fn(sid, audio_record, audio_upload, tran, pndm_speedup=20): | |
| print(sid) | |
| if audio_upload is not None: | |
| audio_path = audio_upload | |
| elif audio_record is not None: | |
| audio_path = audio_record | |
| else: | |
| return "你需要上传wav文件或使用网页内置的录音！", None | |
| tran = int(tran) | |
| pndm_speedup = int(pndm_speedup) | |
| print('model loaded') | |
| # demoaudio, sr = librosa.load(audio_path) | |
| key = tran # 音高调整，支持正负（半音） | |
| # 加速倍数 | |
| wav_gen='./output.wav' | |
| # Show the spinner and run the run_clip function inside the 'with' block | |
| f0_tst, f0_pred, audio = run_clip(svc_model, file_path=audio_path, key=key, acc=pndm_speedup, use_crepe=True, use_pe=True, thre=0.1, | |
| use_gt_mel=False, add_noise_step=500, project_name=project_name, out_path=wav_gen) | |
| audio, sr = librosa.load(wav_gen) | |
| f0_gen,_=get_pitch_parselmouth(*svc_model.vocoder.wav2spec(wav_gen),hparams) | |
| f0_tst[f0_tst==0]=np.nan#ground truth f0 | |
| f0_pred[f0_pred==0]=np.nan#f0 pe predicted | |
| f0_gen[f0_gen==0]=np.nan#f0 generated | |
| fig=plt.figure(figsize=[15,5]) | |
| plt.plot(np.arange(0,len(f0_tst)),f0_tst,color='black',label="f0_tst") | |
| plt.plot(np.arange(0,len(f0_pred)),f0_pred,color='orange',label="f0_pred") | |
| plt.plot(np.arange(0,len(f0_gen)),f0_gen,color='red',label="f0_gen") | |
| plt.axhline(librosa.note_to_hz('C4'),ls=":",c="blue",label="C4") | |
| plt.axhline(librosa.note_to_hz('G4'),ls=":",c="green",label="G4") | |
| plt.axhline(librosa.note_to_hz('C5'),ls=":",c="orange",label="C5") | |
| plt.axhline(librosa.note_to_hz('F#5'),ls=":",c="red",label="F#5") | |
| #plt.axhline(librosa.note_to_hz('A#5'),ls=":",c="black",label="分割线") | |
| plt.legend() | |
| plt.savefig('./temp.png') | |
| return "Success", (sr, audio), gr.Image.update("temp.png") # hparams['audio_sample_rate'] | |
| app = gr.Blocks() | |
| with app: | |
| with gr.Tabs(): | |
| with gr.TabItem("Basic"): | |
| gr.Markdown(value=""" | |
| 本模型基于diffsvc训练，改自ulysses115/diffsvc_test | |
| 感谢uly大佬 | |
| 请尽量使用比较短的音频 | |
| """) | |
| speaker_id = gr.Dropdown(label="音色", choices=['东海帝皇'], value="东海帝皇") | |
| record_input = gr.Audio(source="microphone", label="录制你的声音", type="filepath", elem_id="audio_inputs") | |
| upload_input = gr.Audio(source="upload", label="上传音频（长度小于60秒）", type="filepath", | |
| elem_id="audio_inputs") | |
| 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) | |
| vc_speedup = gr.Number(label="加速倍数", value=20) | |
| vc_submit = gr.Button("转换", variant="primary") | |
| out_audio = gr.Audio(label="Output Audio") | |
| gr.Markdown(value=""" | |
| 无用信息 | |
| """) | |
| out_message = gr.Textbox(label="Output") | |
| gr.Markdown(value="""f0曲线可以直观的显示跑调情况： | |
| """) | |
| f0_image = gr.Image(label="f0曲线") | |
| vc_submit.click(vc_fn, [speaker_id, record_input, upload_input, vc_transform, vc_speedup], | |
| [out_message, out_audio, f0_image]) | |
| app.launch() | |