129 lines
7.0 KiB
Python
129 lines
7.0 KiB
Python
import io
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import logging
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import time
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from pathlib import Path
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import librosa
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import matplotlib.pyplot as plt
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import numpy as np
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import soundfile
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from inference import infer_tool
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from inference import slicer
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from inference.infer_tool import Svc
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logging.getLogger('numba').setLevel(logging.WARNING)
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chunks_dict = infer_tool.read_temp("inference/chunks_temp.json")
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def main():
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import argparse
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parser = argparse.ArgumentParser(description='sovits4 inference')
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# 一定要设置的部分
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parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='模型路径')
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parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='配置文件路径')
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parser.add_argument('-cl', '--clip', type=float, default=0, help='音频自动切片，0为不切片，单位为秒/s')
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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')
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parser.add_argument('-t', '--trans', type=int, nargs='+', default=[0], help='音高调整，支持正负（半音）')
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parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['nen'], help='合成目标说话人名称')
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# 可选项部分
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parser.add_argument('-a', '--auto_predict_f0', action='store_true', default=False,
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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')
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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')
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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')
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parser.add_argument('-lg', '--linear_gradient', type=float, default=0, help='两段音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，单位为秒/s')
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# 不用动的部分
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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')
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parser.add_argument('-d', '--device', type=str, default=None, help='推理设备，None则为自动选择cpu和gpu')
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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')
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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')
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parser.add_argument('-wf', '--wav_format', type=str, default='flac', help='音频输出格式')
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parser.add_argument('-lgr', '--linear_gradient_retain', type=float, default=0.75, help='自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭')
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args = parser.parse_args()
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svc_model = Svc(args.model_path, args.config_path, args.device, args.cluster_model_path)
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infer_tool.mkdir(["raw", "results"])
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clean_names = args.clean_names
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trans = args.trans
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spk_list = args.spk_list
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slice_db = args.slice_db
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wav_format = args.wav_format
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auto_predict_f0 = args.auto_predict_f0
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cluster_infer_ratio = args.cluster_infer_ratio
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noice_scale = args.noice_scale
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pad_seconds = args.pad_seconds
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clip = args.clip
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lg = args.linear_gradient
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lgr = args.linear_gradient_retain
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infer_tool.fill_a_to_b(trans, clean_names)
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for clean_name, tran in zip(clean_names, trans):
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raw_audio_path = f"raw/{clean_name}"
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if "." not in raw_audio_path:
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raw_audio_path += ".wav"
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infer_tool.format_wav(raw_audio_path)
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wav_path = Path(raw_audio_path).with_suffix('.wav')
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chunks = slicer.cut(wav_path, db_thresh=slice_db)
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audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
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per_size = int(clip*audio_sr)
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lg_size = int(lg*audio_sr)
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lg_size_r = int(lg_size*lgr)
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lg_size_c_l = (lg_size-lg_size_r)//2
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lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
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lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
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for spk in spk_list:
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audio = []
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for (slice_tag, data) in audio_data:
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print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
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length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample))
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if slice_tag:
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print('jump empty segment')
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_audio = np.zeros(length)
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audio.extend(list(infer_tool.pad_array(_audio, length)))
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continue
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if per_size != 0:
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datas = infer_tool.split_list_by_n(data, per_size,lg_size)
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else:
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datas = [data]
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for k,dat in enumerate(datas):
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per_length = int(np.ceil(len(dat) / audio_sr * svc_model.target_sample)) if clip!=0 else length
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if clip!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
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# padd
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pad_len = int(audio_sr * pad_seconds)
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dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
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raw_path = io.BytesIO()
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soundfile.write(raw_path, dat, audio_sr, format="wav")
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raw_path.seek(0)
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out_audio, out_sr = svc_model.infer(spk, tran, raw_path,
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cluster_infer_ratio=cluster_infer_ratio,
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auto_predict_f0=auto_predict_f0,
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noice_scale=noice_scale
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)
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_audio = out_audio.cpu().numpy()
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pad_len = int(svc_model.target_sample * pad_seconds)
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_audio = _audio[pad_len:-pad_len]
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_audio = infer_tool.pad_array(_audio, per_length)
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if lg_size!=0 and k!=0:
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lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr != 1 else audio[-lg_size:]
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lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r] if lgr != 1 else _audio[0:lg_size]
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lg_pre = lg1*(1-lg)+lg2*lg
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audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr != 1 else audio[0:-lg_size]
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audio.extend(lg_pre)
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_audio = _audio[lg_size_c_l+lg_size_r:] if lgr != 1 else _audio[lg_size:]
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audio.extend(list(_audio))
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key = "auto" if auto_predict_f0 else f"{tran}key"
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cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}"
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res_path = f'./results/{clean_name}_{key}_{spk}{cluster_name}.{wav_format}'
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soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format)
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if __name__ == '__main__':
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main()
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