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') # Required parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='Path to the model.') parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='Path to the configuration file.') parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['nen'], help='Target speaker name for conversion.') parser.add_argument('-n', '--clean_names', type=str, nargs='+', default=["君の知らない物語-src.wav"], help='A list of wav file names located in the raw folder.') parser.add_argument('-t', '--trans', type=int, nargs='+', default=[0], help='Pitch adjustment, supports positive and negative (semitone) values.') # Optional parser.add_argument('-a', '--auto_predict_f0', action='store_true', default=False, help='Automatic pitch prediction for voice conversion. Do not enable this when converting songs as it can cause serious pitch issues.') parser.add_argument('-cl', '--clip', type=float, default=0, help='Voice forced slicing. Set to 0 to turn off(default), duration in seconds.') parser.add_argument('-lg', '--linear_gradient', type=float, default=0, help='The cross fade length of two audio slices in seconds. If there is a discontinuous voice after forced slicing, you can adjust this value. Otherwise, it is recommended to use. Default 0.') parser.add_argument('-cm', '--cluster_model_path', type=str, default="logs/44k/kmeans_10000.pt", help='Path to the clustering model. Fill in any value if clustering is not trained.') parser.add_argument('-cr', '--cluster_infer_ratio', type=float, default=0, help='Proportion of the clustering solution, range 0-1. Fill in 0 if the clustering model is not trained.') parser.add_argument('-fmp', '--f0_mean_pooling', action='store_true', default=False, help='Apply mean filter (pooling) to f0, which may improve some hoarse sounds. Enabling this option will reduce inference speed.') parser.add_argument('-eh', '--enhance', action='store_true', default=False, help='Whether to use NSF_HIFIGAN enhancer. This option has certain effect on sound quality enhancement for some models with few training sets, but has negative effect on well-trained models, so it is turned off by default.') # generally keep default parser.add_argument('-sd', '--slice_db', type=int, default=-40, help='Loudness for automatic slicing. For noisy audio it can be set to -30') parser.add_argument('-d', '--device', type=str, default=None, help='Device used for inference. None means auto selecting.') parser.add_argument('-ns', '--noice_scale', type=float, default=0.4, help='Affect pronunciation and sound quality.') parser.add_argument('-p', '--pad_seconds', type=float, default=0.5, help='Due to unknown reasons, there may be abnormal noise at the beginning and end. It will disappear after padding a short silent segment.') parser.add_argument('-wf', '--wav_format', type=str, default='flac', help='output format') parser.add_argument('-lgr', '--linear_gradient_retain', type=float, default=0.75, help='Proportion of cross length retention, range (0-1]. After forced slicing, the beginning and end of each segment need to be discarded.') parser.add_argument('-eak', '--enhancer_adaptive_key', type=int, default=0, help='Adapt the enhancer to a higher range of sound. The unit is the semitones, default 0.') parser.add_argument('-ft', '--f0_filter_threshold', type=float, default=0.05, help='F0 Filtering threshold: This parameter is valid only when f0_mean_pooling is enabled. Values range from 0 to 1. Reducing this value reduces the probability of being out of tune, but increases matte.') args = parser.parse_args() 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 clip = args.clip lg = args.linear_gradient lgr = args.linear_gradient_retain F0_mean_pooling = args.f0_mean_pooling enhance = args.enhance enhancer_adaptive_key = args.enhancer_adaptive_key cr_threshold = args.f0_filter_threshold svc_model = Svc(args.model_path, args.config_path, args.device, args.cluster_model_path,enhance) infer_tool.mkdir(["raw", "results"]) 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) per_size = int(clip*audio_sr) lg_size = int(lg*audio_sr) lg_size_r = int(lg_size*lgr) lg_size_c_l = (lg_size-lg_size_r)//2 lg_size_c_r = lg_size-lg_size_r-lg_size_c_l lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0 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) audio.extend(list(infer_tool.pad_array(_audio, length))) continue if per_size != 0: datas = infer_tool.split_list_by_n(data, per_size,lg_size) else: datas = [data] for k,dat in enumerate(datas): per_length = int(np.ceil(len(dat) / audio_sr * svc_model.target_sample)) if clip!=0 else length if clip!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======') # padd pad_len = int(audio_sr * pad_seconds) dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])]) raw_path = io.BytesIO() soundfile.write(raw_path, dat, 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, F0_mean_pooling = F0_mean_pooling, enhancer_adaptive_key = enhancer_adaptive_key, cr_threshold = cr_threshold ) _audio = out_audio.cpu().numpy() pad_len = int(svc_model.target_sample * pad_seconds) _audio = _audio[pad_len:-pad_len] _audio = infer_tool.pad_array(_audio, per_length) if lg_size!=0 and k!=0: lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr != 1 else audio[-lg_size:] lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r] if lgr != 1 else _audio[0:lg_size] lg_pre = lg1*(1-lg)+lg2*lg audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr != 1 else audio[0:-lg_size] audio.extend(lg_pre) _audio = _audio[lg_size_c_l+lg_size_r:] if lgr != 1 else _audio[lg_size:] audio.extend(list(_audio)) 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) svc_model.clear_empty() if __name__ == '__main__': main()