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| 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/48k/G_1M111000_sing.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 = [] | |
| # 例如：clean_names = ["千千阙歌_1", "千千阙歌_2", "千千阙歌_3", "千千阙歌_4", "千千阙歌_5", "千千阙歌_6", "千千阙歌_7", "千千阙歌_8"] | |
| # 中文字符过多会报编码错误，分两批就好 | |
| trans = [0] # 音高调整，支持正负（半音） | |
| spk_list = ['suiji'] # 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 = 'wav' # 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) | |