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| import io | |
| import logging | |
| import time | |
| from pathlib import Path | |
| import librosa | |
| import numpy as np | |
| import soundfile | |
| from inference import infer_tool | |
| from inference import slicer | |
| from inference.infer_tool import Svc | |
| import uuid | |
| logging.getLogger('numba').setLevel(logging.WARNING) | |
| # chunks_dict = infer_tool.read_temp("inference/chunks_temp.json") | |
| infer_tool.mkdir(["./results"]) | |
| model_path = "vits_models/Haruhi_54000.pth" | |
| config_path = "configs/config.json" | |
| svc_model = Svc(model_path, config_path) | |
| def set_model_path(path): | |
| global model_path | |
| model_path = path | |
| def infer_to(spk, tran, voice): | |
| slice_db = -40 | |
| wav_format = 'wav' | |
| # audio_file = io.BytesIO(voice) | |
| audio_file = voice | |
| chunks = slicer.cut(audio_file, db_thresh=slice_db) | |
| # audio_file = io.BytesIO(voice) | |
| audio_data, audio_sr = slicer.chunks2audio(audio_file, chunks) | |
| 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)) | |
| raw_path = io.BytesIO() | |
| soundfile.write(raw_path, data, audio_sr, format="wav") | |
| raw_path.seek(0) | |
| if slice_tag: | |
| print('jump empty segment') | |
| _audio = np.zeros(length) | |
| else: | |
| out_audio, out_sr = svc_model.infer(spk, tran, raw_path) | |
| _audio = out_audio.cpu().numpy() | |
| audio.extend(list(_audio)) | |
| infer_tool.mkdir(["./vits_results"]) | |
| res_path = f'./vits_results/{tran}key_{spk}_{str(uuid.uuid4())}.{wav_format}' | |
| soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format) | |
| return res_path |