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 main(): 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(args.model_path, args.config_path, args.device, args.cluster_model_path) infer_tool.mkdir(["raw", "results"]) clean_names = args.clean_names trans = args.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 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)) 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, tran, 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"{tran}key" cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}" res_path = f'./results/{clean_name}_{key}_{spk}{cluster_name}.{wav_format}' soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format) if __name__ == '__main__': main()