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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 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()