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import os
import librosa
import torch
import torch.nn as nn
from academicodec.models.hificodec.vqvae import VQVAE
class VqvaeTester(nn.Module):
def __init__(self, config_path, model_path, sample_rate=24000):
super().__init__()
self.vqvae = VQVAE(config_path, model_path, with_encoder=True)
self.sample_rate = sample_rate
@torch.no_grad()
def forward(self, wav_path):
# 单声道
# wav.shape (T, ), 按照模型的 sr 读取
wav, sr = librosa.load(wav_path, sr=self.sample_rate)
fid = os.path.basename(wav_path)[:-4]
wav = torch.tensor(wav).unsqueeze(0)
wav = wav.cuda()
# vq_codes is acoustic token
vq_codes = self.vqvae.encode(wav)
syn = self.vqvae(vq_codes)
return fid, syn
@torch.no_grad()
def vq(self, wav_path):
wav, sr = librosa.load(wav_path, sr=self.sample_rate)
fid = os.path.basename(wav_path)[:-4]
wav = torch.tensor(wav).unsqueeze(0)
wav = wav.cuda()
# vq_codes is acoustic token
vq_codes = self.vqvae.encode(wav)
return fid, vq_codes
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