infer.py

# --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)