73 lines
3.2 KiB
Python
73 lines
3.2 KiB
Python
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from torch import nn, Tensor
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__all__ = [
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"Wav2Letter",
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]
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class Wav2Letter(nn.Module):
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r"""Wav2Letter model architecture from *Wav2Letter: an End-to-End ConvNet-based Speech
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Recognition System* :cite:`collobert2016wav2letter`.
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See Also:
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* `Training example <https://github.com/pytorch/audio/tree/release/0.12/examples/pipeline_wav2letter>`__
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Args:
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num_classes (int, optional): Number of classes to be classified. (Default: ``40``)
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input_type (str, optional): Wav2Letter can use as input: ``waveform``, ``power_spectrum``
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or ``mfcc`` (Default: ``waveform``).
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num_features (int, optional): Number of input features that the network will receive (Default: ``1``).
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"""
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def __init__(self, num_classes: int = 40, input_type: str = "waveform", num_features: int = 1) -> None:
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super().__init__()
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acoustic_num_features = 250 if input_type == "waveform" else num_features
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acoustic_model = nn.Sequential(
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nn.Conv1d(in_channels=acoustic_num_features, out_channels=250, kernel_size=48, stride=2, padding=23),
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nn.ReLU(inplace=True),
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nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
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nn.ReLU(inplace=True),
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nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
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nn.ReLU(inplace=True),
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nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
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nn.ReLU(inplace=True),
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nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
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nn.ReLU(inplace=True),
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nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
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nn.ReLU(inplace=True),
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nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
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nn.ReLU(inplace=True),
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nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
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nn.ReLU(inplace=True),
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nn.Conv1d(in_channels=250, out_channels=2000, kernel_size=32, stride=1, padding=16),
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nn.ReLU(inplace=True),
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nn.Conv1d(in_channels=2000, out_channels=2000, kernel_size=1, stride=1, padding=0),
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nn.ReLU(inplace=True),
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nn.Conv1d(in_channels=2000, out_channels=num_classes, kernel_size=1, stride=1, padding=0),
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nn.ReLU(inplace=True),
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)
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if input_type == "waveform":
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waveform_model = nn.Sequential(
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nn.Conv1d(in_channels=num_features, out_channels=250, kernel_size=250, stride=160, padding=45),
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nn.ReLU(inplace=True),
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)
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self.acoustic_model = nn.Sequential(waveform_model, acoustic_model)
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if input_type in ["power_spectrum", "mfcc"]:
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self.acoustic_model = acoustic_model
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def forward(self, x: Tensor) -> Tensor:
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r"""
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Args:
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x (torch.Tensor): Tensor of dimension (batch_size, num_features, input_length).
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Returns:
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Tensor: Predictor tensor of dimension (batch_size, number_of_classes, input_length).
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"""
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x = self.acoustic_model(x)
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x = nn.functional.log_softmax(x, dim=1)
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return x
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