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inference_main.py
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inference_main.py
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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')
# 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_2 = 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_2)+lg2*lg_2
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()