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Transformer-based Speech Recognition

Speech recognition is the task of transcribing spoken language into text. It is often tackled with deep learning models that take in variable-length sequences of audio data and output a sequence of text tokens. Transformer models can be adapted for this task by processing the audio data with a feature extractor and then passing the resulting feature sequence through a Transformer encoder.

A common feature extraction method used for speech recognition is Mel Frequency Cepstral Coefficients (MFCCs), which captures the frequency components of the audio signal in a compact form. We can use the torchaudio library in PyTorch to extract MFCC features from audio files:

python
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import torchaudio

def extract_mfcc(audio_path):
    waveform, sample_rate = torchaudio.load(audio_path)
    mfcc_transform = torchaudio.transforms.MFCC(sample_rate=sample_rate)
    mfcc = mfcc_transform(waveform)
    return mfcc

This function loads an audio file using torchaudio.load() and extracts MFCC features using the torchaudio.transforms.MFCC transform. The resulting feature sequence has shape (num_channels, num_frames, num_coefficients), where num_channels is the number of audio channels (usually 1), num_frames is the number of frames in the audio signal, and num_coefficients is the number of MFCC coefficients per frame.

We can then pass the MFCC feature sequence through a Transformer encoder using a similar approach as in the image recognition example. However, since the feature sequence has variable length, we need to pad the sequence to a fixed length before passing it through the Transformer. We can use PyTorch's pad_sequence() function to do this:

python
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import torch.nn.functional as F

def pad_collate(batch):
    features = [item[0] for item in batch]
    targets = [item[1] for item in batch]
    features_padded = F.pad_sequence(features, batch_first=True)
    return features_padded, targets

This function takes in a batch of (features, targets) pairs, where features is an MFCC feature sequence and targets is the corresponding text transcription. It pads the feature sequences to a fixed length using F.pad_sequence(), and returns a tuple of (features_padded, targets).

We can then define a Transformer-based speech recognition model using the nn.TransformerEncoder class in PyTorch:

python
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import torch.nn as nn

class TransformerSpeechRecognizer(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, num_heads, dropout):
        super().__init__()
        self.embedding = nn.Linear(input_dim, hidden_dim)
        self.transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=hidden_dim,
                nhead=num_heads,
                dropout=dropout
            ),
            num_layers=num_layers
        )
        self.output_layer = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        x = self.embedding(x)
        x = x.permute(1, 0, 2)
        x = self.transformer(x)
        x = x.permute(1, 0, 2)
        x = self.output_layer(x)
        return x

This model takes in an MFCC feature sequence of shape (batch_size, num_frames, num_coefficients) and passes it through a linear embedding layer, a Transformer encoder with num_layers layers and num_heads attention heads per layer, and a linear output layer that predicts a sequence of text tokens.



Next: Transformer-based Video Processing

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