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import io
from pathlib import Path
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
def run_clip(raw_audio_path, svc_model, key, acc, use_crepe, spk_id=0, auto_key=False, out_path=None, slice_db=-40,
**kwargs):
print(f'code version:2023-01-22')
clean_name = Path(raw_audio_path).name.split(".")[0]
infer_tool.format_wav(raw_audio_path)
wav_path = Path(raw_audio_path).with_suffix('.wav')
key = svc_model.evaluate_key(wav_path, key, auto_key)
chunks = slicer.cut(wav_path, db_thresh=slice_db)
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")
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, spk_id=spk_id, key=key, acc=acc, use_crepe=use_crepe)
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__':
# 工程文件夹名，训练时用的那个
project_name = "xiaxiaobai"
model_path = f'./checkpoints/{project_name}/model_ckpt_steps_150000.ckpt'
config_path = f'./checkpoints/{project_name}/config.yaml'
# 支持多个wav/ogg文件，放在raw文件夹下，带扩展名
file_names = ["横竖撇点折-main-2key.wav"]
spk_id = "single"
# 自适应变调（仅支持单人模型）
auto_key = False
trans = [0] # 音高调整，支持正负（半音），数量与上一行对应，不足的自动按第一个移调参数补齐
# 加速倍数
accelerate = 1
hubert_gpu = True
wav_format = 'wav'
step = int(model_path.split("_")[-1].split(".")[0])
# 下面不动
infer_tool.mkdir(["./raw", "./results"])
infer_tool.fill_a_to_b(trans, file_names)
model = Svc(project_name, config_path, hubert_gpu, model_path, onnx=False)
for f_name, tran in zip(file_names, trans):
if "." not in f_name:
f_name += ".wav"
audio_path = f"./raw/{f_name}"
run_clip(raw_audio_path=audio_path, svc_model=model, key=tran, acc=accelerate, use_crepe=False,
spk_id=spk_id, auto_key=auto_key, project_name=project_name, format=wav_format)
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