实现敂擸端擹端的CTC声擳语擵识别模型。python

要实现 CTC（Connectionist Temporal Classification）声学语音识别模型，可以使用 PyTorch 框架和 Librosa 库进行开发。以下是一个基本的 CTC 模型的实现代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F
import librosa

class CTCModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(CTCModel, self).__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.lstm1 = nn.LSTM(input_dim, hidden_dim, bidirectional=True, batch_first=True)
        self.lstm2 = nn.LSTM(hidden_dim*2, hidden_dim, bidirectional=True, batch_first=True)
        self.fc = nn.Linear(hidden_dim*2, output_dim)

    def forward(self, x):
        # x shape: (batch, seq_len, input_dim)
        x, _ = self.lstm1(x)
        x, _ = self.lstm2(x)
        x = self.fc(x)
        x = F.log_softmax(x, dim=-1)
        return x

# 定义训练函数
def train(model, optimizer, criterion, train_loader, device):
    model.train()
    total_loss = 0
    for i, (inputs, targets, input_lengths, target_lengths) in enumerate(train_loader):
        inputs, targets = inputs.to(device), targets.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        outputs = outputs.transpose(0, 1) # (seq_len, batch, output_dim)
        input_lengths = input_lengths.cpu().numpy()
        target_lengths = target_lengths.cpu().numpy()
        loss = criterion(outputs, targets, input_lengths, target_lengths)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    return total_loss / len(train_loader)

# 定义测试函数
def test(model, criterion, test_loader, device):
    model.eval()
    total_loss = 0
    with torch.no_grad():
        for i, (inputs, targets, input_lengths, target_lengths) in enumerate(test_loader):
            inputs, targets = inputs.to(device), targets.to(device)
            outputs = model(inputs)
            outputs = outputs.transpose(0, 1) # (seq_len, batch, output_dim)
            input_lengths = input_lengths.cpu().numpy()
            target_lengths = target_lengths.cpu().numpy()
            loss = criterion(outputs, targets, input_lengths, target_lengths)
            total_loss += loss.item()
    return total_loss / len(test_loader)

# 定义 CTC 损失函数
class CTCLoss(nn.Module):
    def __init__(self):
        super(CTCLoss, self).__init__()
        self.ctc_loss = nn.CTCLoss()

    def forward(self, outputs, targets, input_lengths, target_lengths):
        batch_size = outputs.size(1)
        log_probs = outputs.permute(1, 0, 2)
        log_probs_lens = torch.full((batch_size,), log_probs.size(0), dtype=torch.int32)
        targets_lens = target_lengths
        targets = targets.T
        loss = self.ctc_loss(log_probs, targets, log_probs_lens, targets_lens)
        return loss

# 加载数据集
def load_dataset(audio_files, transcripts, sample_rate, window_size, window_stride, window, batch_size):
    audio_transforms = nn.Sequential(
        lambda x: librosa.util.normalize(x),
        lambda x: librosa.feature.mfcc(x, sr=sample_rate, n_mfcc=40, n_fft=int(sample_rate*window_size), hop_length=int(sample_rate*window_stride)),
        lambda x: (x - x.mean(axis=1, keepdims=True)) / x.std(axis=1, keepdims=True)
    )
    text_transforms = nn.Sequential(
        lambda x: [char2idx[c] for c in x],
        torch.LongTensor
    )
    dataset = SpeechDataset(audio_files, transcripts, audio_transforms, text_transforms)
    loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, collate_fn=collate_fn)
    return loader

# 定义数据集类和数据处理函数
class SpeechDataset(torch.utils.data.Dataset):
    def __init__(self, audio_files, transcripts, audio_transforms=None, text_transforms=None):
        self.audio_files = audio_files
        self.transcripts = transcripts
        self.audio_transforms = audio_transforms
        self.text_transforms = text_transforms

    def __getitem__(self, index):
        audio_file = self.audio_files[index]
        transcript = self.transcripts[index]
        audio, sample_rate = librosa.load(audio_file, sr=None)
        audio = torch.from_numpy(audio).float()
        if self.audio_transforms is not None:
            audio = self.audio_transforms(audio)
        if self.text_transforms is not None:
            transcript = self.text_transforms(transcript)
        input_length = audio.size(1)
        target_length = len(transcript)
        return audio, transcript, input_length, target_length

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

def collate_fn(batch):
    audios = [item[0] for item in batch]
    transcripts = [item[1] for item in batch]
    input_lengths = torch.LongTensor([item[2] for item in batch])
    target_lengths = torch.LongTensor([item[3] for item in batch])
    max_input_length = max(input_lengths)
    max_target_length = max(target_lengths)
    padded_audios = torch.zeros(len(audios), max_input_length, audios[0].size(0))
    padded_transcripts = torch.zeros(len(transcripts), max_target_length, dtype=torch.long)
    for i, audio in enumerate(audios):
        padded_audios[i, :audio.size(1), :] = audio
    for i, transcript in enumerate(transcripts):
        padded_transcripts[i, :len(transcript)] = transcript
    return padded_audios, padded_transcripts, input_lengths, target_lengths

在上述代码中，`CTCModel` 是一个基本的 CTC 模型，包含两个 LSTM 层和一个全连接层。`train` 函数和 `test` 函数分别用于训练和测试模型。`CTCLoss` 是一个 CTC 损失函数类。`load_dataset` 函数用于加载训练和测试数据集，其中 `SpeechDataset` 类和 `collate_fn` 函数用于处理数据。

在使用时，可以先定义训练集和测试集的音频文件路径和文本数据，然后调用 `load_dataset` 函数加载数据集。接着定义模型、训练参数、优化器和损失函数，并开始训练模型。