82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
import io
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from pathlib import Path
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import numpy as np
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import soundfile
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from infer_tools import infer_tool
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from infer_tools import slicer
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from infer_tools.infer_tool import Svc
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from utils.hparams import hparams
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def run_clip(raw_audio_path, svc_model, key, acc, use_crepe, spk_id=0, auto_key=False, out_path=None, slice_db=-40,
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**kwargs):
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print(f'code version:2023-02-18')
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clean_name = Path(raw_audio_path).name.split(".")[0]
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infer_tool.format_wav(raw_audio_path)
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wav_path = Path(raw_audio_path).with_suffix('.wav')
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key = svc_model.evaluate_key(wav_path, key, auto_key)
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chunks = slicer.cut(wav_path, db_thresh=slice_db)
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audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
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count = 0
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f0_tst, f0_pred, audio = [], [], []
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for (slice_tag, data) in audio_data:
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print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
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length = int(np.ceil(len(data) / audio_sr * hparams['audio_sample_rate']))
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raw_path = io.BytesIO()
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soundfile.write(raw_path, data, audio_sr, format="wav")
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raw_path.seek(0)
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if slice_tag:
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print('jump empty segment')
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_f0_tst, _f0_pred, _audio = (
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np.zeros(int(np.ceil(length / hparams['hop_size']))),
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np.zeros(int(np.ceil(length / hparams['hop_size']))),
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np.zeros(length))
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else:
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_f0_tst, _f0_pred, _audio = svc_model.infer(raw_path, spk_id=spk_id, key=key, acc=acc, use_crepe=use_crepe)
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fix_audio = np.zeros(length)
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fix_audio[:] = np.mean(_audio)
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fix_audio[:len(_audio)] = _audio[0 if len(_audio) < len(fix_audio) else len(_audio) - len(fix_audio):]
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f0_tst.extend(_f0_tst)
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f0_pred.extend(_f0_pred)
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audio.extend(list(fix_audio))
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count += 1
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if out_path is None:
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out_path = f'./results/{clean_name}_{key}key_{project_name}_{hparams["residual_channels"]}_{hparams["residual_layers"]}_{int(step / 1000)}k_{accelerate}x.{kwargs["format"]}'
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soundfile.write(out_path, audio, hparams["audio_sample_rate"], 'PCM_16', format=out_path.split('.')[-1])
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return np.array(f0_tst), np.array(f0_pred), audio
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if __name__ == '__main__':
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# 工程文件夹名，训练时用的那个
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project_name = "fox_cn"
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model_path = f'./checkpoints/{project_name}/model_ckpt_steps_370000.ckpt'
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config_path = f'./checkpoints/{project_name}/config.yaml'
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# 支持多个wav/ogg文件，放在raw文件夹下，带扩展名
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file_names = ["逍遥仙"]
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spk_id = 0
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# 自适应变调（仅支持单人模型）
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auto_key = False
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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
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# 加速倍数
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accelerate = 20
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hubert_gpu = True
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wav_format = 'flac'
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step = int(model_path.split("_")[-1].split(".")[0])
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# 下面不动
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infer_tool.mkdir(["./raw", "./results"])
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infer_tool.fill_a_to_b(trans, file_names)
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model = Svc(project_name, config_path, hubert_gpu, model_path, onnx=False)
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for f_name, tran in zip(file_names, trans):
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if "." not in f_name:
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f_name += ".wav"
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audio_path = f"./raw/{f_name}"
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run_clip(raw_audio_path=audio_path, svc_model=model, key=tran, acc=accelerate, use_crepe=False,
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spk_id=spk_id, auto_key=auto_key, project_name=project_name, format=wav_format)
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