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import io
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import logging
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import time
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from pathlib import Path
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import librosa
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import numpy as np
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import soundfile
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import os
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useCUDA = True
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if not useCUDA:
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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from inference import infer_tool
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from inference import slicer
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from inference.infer_tool import Svc
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logging.getLogger('numba').setLevel(logging.WARNING)
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chunks_dict = infer_tool.read_temp("inference/chunks_temp.json")
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# model_path = "logs/32k/G_112000.pth"
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model_path = "/dev/shm/G_120000.pth"
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config_path = "configs/config.json"
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svc_model = Svc(model_path, config_path)
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infer_tool.mkdir(["raw", "results"])
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# 支持多个wav文件，放在raw文件夹下
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clean_names = ["18"]
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trans = [10] # 音高调整，支持正负（半音）
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spk_list = ['JP_Hoshino'] # 每次同时合成多语者音色
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slice_db = -40 # 默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50
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wav_format = 'flac' # 音频输出格式
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infer_tool.fill_a_to_b(trans, clean_names)
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for clean_name, tran in zip(clean_names, trans):
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raw_audio_path = f"raw/{clean_name}"
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if "." not in raw_audio_path:
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raw_audio_path += ".wav"
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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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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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for spk in spk_list:
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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 * svc_model.target_sample))
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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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_audio = np.zeros(length)
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else:
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out_audio, out_sr = svc_model.infer(spk, tran, raw_path)
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_audio = out_audio.cpu().numpy()
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audio.extend(list(_audio))
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res_path = f'./results/{clean_name}_{tran}key_{spk}.{wav_format}'
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soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format)
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