# --coding:utf-8-- import os from encoder.utils import convert_audio import torchaudio import torch from decoder.pretrained import WavTokenizer import time import logging device1=torch.device('cuda:0') device2=torch.device('cpu') input_path = "./WavTokenizer/data/infer/lirbitts_testclean" out_folder = './WavTokenizer/result/infer' # os.system("rm -r %s"%(out_folder)) # os.system("mkdir -p %s"%(out_folder)) # ll="libritts_testclean500_large" ll="wavtokenizer_smalldata_frame40_3s_nq1_code4096_dim512_kmeans200_attn_testclean_epoch34" tmptmp=out_folder+"/"+ll os.system("rm -r %s"%(tmptmp)) os.system("mkdir -p %s"%(tmptmp)) # 自己数据模型加载 config_path = "./WavTokenizer/configs/wavtokenizer_smalldata_frame40_3s_nq1_code4096_dim512_kmeans200_attn.yaml" model_path = "./WavTokenizer/result/train/wavtokenizer_smalldata_frame40_3s_nq1_code4096_dim512_kmeans200_attn/lightning_logs/version_3/checkpoints/wavtokenizer_checkpoint_epoch=24_step=137150_val_loss=5.6731.ckpt" wavtokenizer = WavTokenizer.from_pretrained0802(config_path, model_path) wavtokenizer = wavtokenizer.to(device1) # wavtokenizer = wavtokenizer.to(device2) with open(input_path,'r') as fin: x=fin.readlines() x = [i.strip() for i in x] # 完成一些加速处理 features_all=[] for i in range(len(x)): wav, sr = torchaudio.load(x[i]) # print("***:",x[i]) # wav = convert_audio(wav, sr, 24000, 1) # (1,131040) bandwidth_id = torch.tensor([0]) wav=wav.to(device1) print(i) features,discrete_code= wavtokenizer.encode_infer(wav, bandwidth_id=bandwidth_id) features_all.append(features.cpu()) wavtokenizer = wavtokenizer.to(device2) for i in range(len(x)): bandwidth_id = torch.tensor([0]) print(i) audio_out = wavtokenizer.decode(features_all[i], bandwidth_id=bandwidth_id) # print(i,time.time()) # breakpoint() # (1, 131200) audio_path = out_folder + '/' + ll + '/' + x[i].split('/')[-1] # os.makedirs(out_folder + '/' + ll, exist_ok=True) torchaudio.save(audio_path, audio_out, sample_rate=24000, encoding='PCM_S', bits_per_sample=16)