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| import io | |
| import logging | |
| import time | |
| from pathlib import Path | |
| import librosa | |
| import matplotlib.pyplot as plt | |
| 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") | |
| def infer(file_path, spk_list=['tokaiteio'], trans=[0], config_path="configs/config.json", device="cpu", cluster_model_path="logs/44k/kmeans_10000.pt", slice_db=-40, wav_format='flac', auto_predict_f0=False, cluster_infer_ratio=0, noice_scale=0.4, pad_seconds=0.5, model_path="logs/44k/G_318400.pth"): | |
| # import argparse | |
| # parser = argparse.ArgumentParser(description='sovits4 inference') | |
| # # 一定要设置的部分 | |
| # parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='模型路径') | |
| # parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='配置文件路径') | |
| # parser.add_argument('-n', '--clean_names', type=str, nargs='+', default=["君の知らない物語-src.wav"], help='wav文件名列表，放在raw文件夹下') | |
| # parser.add_argument('-t', '--trans', type=int, nargs='+', default=[0], help='音高调整，支持正负（半音）') | |
| # parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['nen'], help='合成目标说话人名称') | |
| # # 可选项部分 | |
| # parser.add_argument('-a', '--auto_predict_f0', action='store_true', default=False, | |
| # help='语音转换自动预测音高，转换歌声时不要打开这个会严重跑调') | |
| # parser.add_argument('-cm', '--cluster_model_path', type=str, default="logs/44k/kmeans_10000.pt", help='聚类模型路径，如果没有训练聚类则随便填') | |
| # parser.add_argument('-cr', '--cluster_infer_ratio', type=float, default=0, help='聚类方案占比，范围0-1，若没有训练聚类模型则填0即可') | |
| # # 不用动的部分 | |
| # parser.add_argument('-sd', '--slice_db', type=int, default=-40, help='默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50') | |
| # parser.add_argument('-d', '--device', type=str, default=None, help='推理设备，None则为自动选择cpu和gpu') | |
| # parser.add_argument('-ns', '--noice_scale', type=float, default=0.4, help='噪音级别，会影响咬字和音质，较为玄学') | |
| # parser.add_argument('-p', '--pad_seconds', type=float, default=0.5, help='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') | |
| # parser.add_argument('-wf', '--wav_format', type=str, default='flac', help='音频输出格式') | |
| # args = parser.parse_args() | |
| svc_model = Svc(model_path, config_path, device, cluster_model_path) | |
| # infer_tool.mkdir(["raw", "results"]) | |
| # clean_names = args.clean_names | |
| # trans = trans | |
| # spk_list = args.spk_list | |
| # slice_db = args.slice_db | |
| # wav_format = args.wav_format | |
| # auto_predict_f0 = args.auto_predict_f0 | |
| # cluster_infer_ratio = args.cluster_infer_ratio | |
| # noice_scale = args.noice_scale | |
| # pad_seconds = args.pad_seconds | |
| # if there is a lot of file, let the trans be the same length as the file | |
| # infer_tool.fill_a_to_b(trans, clean_names) | |
| if "." not in file_path: | |
| file_path += ".wav" | |
| infer_tool.format_wav(file_path) | |
| wav_path = Path(file_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)) | |
| if slice_tag: | |
| print('jump empty segment') | |
| _audio = np.zeros(length) | |
| else: | |
| # padd | |
| pad_len = int(audio_sr * pad_seconds) | |
| data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])]) | |
| raw_path = io.BytesIO() | |
| soundfile.write(raw_path, data, audio_sr, format="wav") | |
| raw_path.seek(0) | |
| out_audio, out_sr = svc_model.infer(spk, trans[0], raw_path, | |
| cluster_infer_ratio=cluster_infer_ratio, | |
| auto_predict_f0=auto_predict_f0, | |
| noice_scale=noice_scale | |
| ) | |
| _audio = out_audio.cpu().numpy() | |
| pad_len = int(svc_model.target_sample * pad_seconds) | |
| _audio = _audio[pad_len:-pad_len] | |
| audio.extend(list(infer_tool.pad_array(_audio, length))) | |
| key = "auto" if auto_predict_f0 else f"{trans[0]}key" | |
| cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}" | |
| res_path = f"results/{wav_path.stem}_{spk}_{key}{cluster_name}.{wav_format}" | |
| soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format) | |
| return res_path |