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infer.py
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infer.py
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import io
import time
from pathlib import Path
import librosa
import numpy as np
import soundfile
from infer_tools import infer_tool
from infer_tools import slicer
from infer_tools.infer_tool import Svc
from utils.hparams import hparams
chunks_dict = infer_tool.read_temp("./infer_tools/new_chunks_temp.json")
def run_clip(svc_model, key, acc, use_pe, use_crepe, thre, use_gt_mel, add_noise_step, project_name='', f_name=None,
file_path=None, out_path=None, slice_db=-40,**kwargs):
print(f'code version:2022-12-04')
use_pe = use_pe if hparams['audio_sample_rate'] == 24000 else False
if file_path is None:
raw_audio_path = f"./raw/{f_name}"
clean_name = f_name[:-4]
else:
raw_audio_path = file_path
clean_name = str(Path(file_path).name)[:-4]
infer_tool.format_wav(raw_audio_path)
wav_path = Path(raw_audio_path).with_suffix('.wav')
global chunks_dict
audio, sr = librosa.load(wav_path, mono=True,sr=None)
wav_hash = infer_tool.get_md5(audio)
if wav_hash in chunks_dict.keys():
print("load chunks from temp")
chunks = chunks_dict[wav_hash]["chunks"]
else:
chunks = slicer.cut(wav_path, db_thresh=slice_db)
chunks_dict[wav_hash] = {"chunks": chunks, "time": int(time.time())}
infer_tool.write_temp("./infer_tools/new_chunks_temp.json", chunks_dict)
audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
count = 0
f0_tst = []
f0_pred = []
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 * hparams['audio_sample_rate']))
raw_path = io.BytesIO()
soundfile.write(raw_path, data, audio_sr, format="wav")
if hparams['debug']:
print(np.mean(data), np.var(data))
raw_path.seek(0)
if slice_tag:
print('jump empty segment')
_f0_tst, _f0_pred, _audio = (
np.zeros(int(np.ceil(length / hparams['hop_size']))), np.zeros(int(np.ceil(length / hparams['hop_size']))),
np.zeros(length))
else:
_f0_tst, _f0_pred, _audio = svc_model.infer(raw_path, key=key, acc=acc, use_pe=use_pe, use_crepe=use_crepe,
thre=thre, use_gt_mel=use_gt_mel, add_noise_step=add_noise_step)
fix_audio = np.zeros(length)
fix_audio[:] = np.mean(_audio)
fix_audio[:len(_audio)] = _audio[0 if len(_audio)<len(fix_audio) else len(_audio)-len(fix_audio):]
f0_tst.extend(_f0_tst)
f0_pred.extend(_f0_pred)
audio.extend(list(fix_audio))
count += 1
if out_path is None:
out_path = f'./results/{clean_name}_{key}key_{project_name}_{hparams["residual_channels"]}_{hparams["residual_layers"]}_{int(step / 1000)}k_{accelerate}x.{kwargs["format"]}'
soundfile.write(out_path, audio, hparams["audio_sample_rate"], 'PCM_16',format=out_path.split('.')[-1])
return np.array(f0_tst), np.array(f0_pred), audio
if __name__ == '__main__':
# Project folder name used for training
project_name = "test"
model_path = f'./checkpoints/{project_name}/model_ckpt_steps_246000.ckpt' # change ckpt file name to your best ckpt file name
config_path = f'./checkpoints/{project_name}/config.yaml'
# Support multiple wav/ogg files, put them in the raw folder, with extension
file_names = ["test.wav"]
trans = [0] # Pitch adjustment,
# support positive and negative (semitones),
# the number corresponds to the previous line,
# if it is insufficient,
# it will be filled automatically according to the first transpose parameter
# Acceleration factor
accelerate = 20
hubert_gpu = True
format='flac'
step = int(model_path.split("_")[-1].split(".")[0])
# don't move below
infer_tool.mkdir(["./raw", "./results"])
infer_tool.fill_a_to_b(trans, file_names)
model = Svc(project_name, config_path, hubert_gpu, model_path)
for f_name, tran in zip(file_names, trans):
if "." not in f_name:
f_name += ".wav"
run_clip(model, key=tran, acc=accelerate, use_crepe=True, thre=0.05, use_pe=True, use_gt_mel=False,
add_noise_step=500, f_name=f_name, project_name=project_name, format=format)