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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
logging.getLogger('numba').setLevel(logging.WARNING)
chunks_dict = infer_tool.read_temp("inference/chunks_temp.json")
model_path = "logs/48k/G_280000.pth"
config_path = "configs/config.json"
svc_model = Svc(model_path, config_path)
infer_tool.mkdir(["raw", "results"])
# 支持多个wav文件，放在raw文件夹下
clean_names = ["深海少女part1"]
trans = [0] # 音高调整，支持正负（半音）
spk_list = ['Umi'] # 每次同时合成多语者音色
slice_db = -50 # 默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50
wav_format = 'wav' # 音频输出格式
infer_tool.fill_a_to_b(trans, clean_names)
for clean_name, tran in zip(clean_names, trans):
raw_audio_path = f"raw/{clean_name}"
if "." not in raw_audio_path:
raw_audio_path += ".wav"
infer_tool.format_wav(raw_audio_path)
wav_path = Path(raw_audio_path).with_suffix('.wav')
audio, sr = librosa.load(wav_path, mono=True, sr=None)
wav_hash = infer_tool.get_md5(audio)
if wav_hash in chunks_dict.keys():
print("load chunks from temp")
chunks = chunks_dict[wav_hash]["chunks"]
else:
chunks = slicer.cut(wav_path, db_thresh=slice_db)
print(chunks)
chunks_dict[wav_hash] = {"chunks": chunks, "time": int(time.time())}
infer_tool.write_temp("inference/chunks_temp.json", chunks_dict)
audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
for spk in spk_list:
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))
res_path = f'./results/{clean_name}_{tran}key_{spk}.{wav_format}'
soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format)
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