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 = "open-aqua" model_path = f'./checkpoints/{project_name}/model_ckpt_steps_90000.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)