espnet conformer的数据预处理代码
时间: 2023-08-06 18:05:57 浏览: 45
ESPNet Conformer是一种语音识别模型,它主要基于卷积神经网络和自注意力机制,具有高效、准确、可扩展等优点。其数据预处理代码如下:
```python
import kaldiio
import numpy as np
import torch
from torch.utils.data import Dataset
def process_feats(feats, cmvn, delta_order, input_dim, no_cuda=False):
if no_cuda:
device = torch.device('cpu')
else:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
feats = np.append(feats, np.zeros((delta_order, input_dim)), axis=0)
feats = np.append(np.zeros((delta_order, input_dim)), feats, axis=0)
feats = np.concatenate(
[feats[idx:idx - delta_order - 1 or None:-1] for idx in range(delta_order + 1)],
axis=-1)
feats = (feats - cmvn[0]) / cmvn[1]
feats = torch.FloatTensor(feats).to(device)
return feats
class ConformerDataset(Dataset):
def __init__(self, feats_scp, cmvn_path, delta_order, no_cuda=False):
self.feats_scp = feats_scp
self.delta_order = delta_order
self.cmvn = kaldiio.load_mat(cmvn_path)
self.no_cuda = no_cuda
def __len__(self):
return len(self.feats_scp)
def __getitem__(self, idx):
key, feats_path = self.feats_scp[idx]
feats = kaldiio.load_mat(feats_path)
feats = process_feats(feats, self.cmvn, self.delta_order, feats.shape[-1], self.no_cuda)
return key, feats
```
这段代码中定义了一个`ConformerDataset`类,用于加载输入特征,并对其进行预处理。其中,`process_feats`函数用于对输入特征`feats`进行预处理,包括计算delta特征、进行归一化处理等。在`ConformerDataset`类的`__init__`函数中,会读取对应的特征文件路径和CMVN文件路径,并在`__getitem__`函数中调用`process_feats`函数,最后返回处理好的特征数据。