import io import logging import time from pathlib import Path import librosa import numpy as np import soundfile from inference import infer_tool from inference import slicer from inference.infer_tool import Svc logging.getLogger('numba').setLevel(logging.WARNING) chunks_dict = infer_tool.read_temp("inference/chunks_temp.json") model_path = "logs/32k/G_174000-Copy1.pth" config_path = "configs/config.json" svc_model = Svc(model_path, config_path) infer_tool.mkdir(["raw", "results"]) # 支持多个wav文件,放在raw文件夹下 clean_names = ["君の知らない物語-src"] trans = [-5] # 音高调整,支持正负(半音) spk_list = ['yunhao'] # 每次同时合成多语者音色 slice_db = -40 # 默认-40,嘈杂的音频可以-30,干声保留呼吸可以-50 wav_format = 'flac' # 音频输出格式 infer_tool.fill_a_to_b(trans, clean_names) for clean_name, tran in zip(clean_names, trans): raw_audio_path = f"raw/{clean_name}" if "." not in raw_audio_path: raw_audio_path += ".wav" infer_tool.format_wav(raw_audio_path) wav_path = Path(raw_audio_path).with_suffix('.wav') chunks = slicer.cut(wav_path, db_thresh=slice_db) audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks) for spk in spk_list: audio = [] for (slice_tag, data) in audio_data: print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======') length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample)) raw_path = io.BytesIO() soundfile.write(raw_path, data, audio_sr, format="wav") raw_path.seek(0) if slice_tag: print('jump empty segment') _audio = np.zeros(length) else: out_audio, out_sr = svc_model.infer(spk, tran, raw_path) _audio = out_audio.cpu().numpy() audio.extend(list(_audio)) res_path = f'./results/{clean_name}_{tran}key_{spk}.{wav_format}' soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format)