transformer语音识别代码

以下是一个使用Transformer进行语音识别的Python代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchaudio
from torch.utils.data import DataLoader
from torch.utils.data import Dataset

# Define the dataset for loading the audio data
class AudioDataset(Dataset):
    def __init__(self, file_paths):
        self.file_paths = file_paths

    def __len__(self):
        return len(self.file_paths)

    def __getitem__(self, idx):
        waveform, sample_rate = torchaudio.load(self.file_paths[idx])
        return waveform, sample_rate

# Define the Transformer model
class TransformerModel(nn.Module):
    def __init__(self, input_dim, output_dim, d_model, nhead, num_layers, dim_feedforward, dropout):
        super(TransformerModel, self).__init__()
        self.input_proj = nn.Linear(input_dim, d_model)
        encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
        self.output_proj = nn.Linear(d_model, output_dim)

    def forward(self, src):
        src = self.input_proj(src)
        src = src.permute(1, 0, 2)
        output = self.transformer_encoder(src)
        output = output.permute(1, 0, 2)
        output = self.output_proj(output)
        return output

# Set the hyperparameters
input_dim = 1
output_dim = 29 # Number of phonemes in English language
d_model = 512
nhead = 8
num_layers = 6
dim_feedforward = 2048
dropout = 0.1
lr = 0.0001
batch_size = 32
epochs = 10

# Load the audio dataset and create the dataloader
file_paths = ["audio1.wav", "audio2.wav", ...]
audio_dataset = AudioDataset(file_paths)
audio_dataloader = DataLoader(audio_dataset, batch_size=batch_size, shuffle=True)

# Initialize the Transformer model and the optimizer
model = TransformerModel(input_dim, output_dim, d_model, nhead, num_layers, dim_feedforward, dropout)
optimizer = optim.Adam(model.parameters(), lr=lr)

# Define the loss function
criterion = nn.CrossEntropyLoss()

# Train the Transformer model
for epoch in range(epochs):
    running_loss = 0.0
    for i, data in enumerate(audio_dataloader, 0):
        inputs, labels = data
        inputs = inputs.squeeze().transpose(0, 1) # Shape: (seq_len, batch_size, input_dim)
        labels = labels.squeeze() - 1 # Subtract 1 to convert phoneme index from 1-based to 0-based
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs.view(-1, output_dim), labels.view(-1))
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print("Epoch %d loss: %.3f" % (epoch+1, running_loss / len(audio_dataloader)))

请注意，此示例是基于一个简单的英语语音识别任务，使用TIMIT数据集。在实际使用时，你需要根据你的数据集和任务进行适当的修改。