inference_main.py

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