| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071 |
- from torch import nn, Tensor
- __all__ = [
- "Wav2Letter",
- ]
- class Wav2Letter(nn.Module):
- r"""Wav2Letter model architecture from *Wav2Letter: an End-to-End ConvNet-based Speech
- Recognition System* [:footcite:`collobert2016wav2letter`].
- :math:`\text{padding} = \frac{\text{ceil}(\text{kernel} - \text{stride})}{2}`
- Args:
- num_classes (int, optional): Number of classes to be classified. (Default: ``40``)
- input_type (str, optional): Wav2Letter can use as input: ``waveform``, ``power_spectrum``
- or ``mfcc`` (Default: ``waveform``).
- num_features (int, optional): Number of input features that the network will receive (Default: ``1``).
- """
- def __init__(self, num_classes: int = 40, input_type: str = "waveform", num_features: int = 1) -> None:
- super(Wav2Letter, self).__init__()
- acoustic_num_features = 250 if input_type == "waveform" else num_features
- acoustic_model = nn.Sequential(
- nn.Conv1d(in_channels=acoustic_num_features, out_channels=250, kernel_size=48, stride=2, padding=23),
- nn.ReLU(inplace=True),
- nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
- nn.ReLU(inplace=True),
- nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
- nn.ReLU(inplace=True),
- nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
- nn.ReLU(inplace=True),
- nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
- nn.ReLU(inplace=True),
- nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
- nn.ReLU(inplace=True),
- nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
- nn.ReLU(inplace=True),
- nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
- nn.ReLU(inplace=True),
- nn.Conv1d(in_channels=250, out_channels=2000, kernel_size=32, stride=1, padding=16),
- nn.ReLU(inplace=True),
- nn.Conv1d(in_channels=2000, out_channels=2000, kernel_size=1, stride=1, padding=0),
- nn.ReLU(inplace=True),
- nn.Conv1d(in_channels=2000, out_channels=num_classes, kernel_size=1, stride=1, padding=0),
- nn.ReLU(inplace=True),
- )
- if input_type == "waveform":
- waveform_model = nn.Sequential(
- nn.Conv1d(in_channels=num_features, out_channels=250, kernel_size=250, stride=160, padding=45),
- nn.ReLU(inplace=True),
- )
- self.acoustic_model = nn.Sequential(waveform_model, acoustic_model)
- if input_type in ["power_spectrum", "mfcc"]:
- self.acoustic_model = acoustic_model
- def forward(self, x: Tensor) -> Tensor:
- r"""
- Args:
- x (torch.Tensor): Tensor of dimension (batch_size, num_features, input_length).
- Returns:
- Tensor: Predictor tensor of dimension (batch_size, number_of_classes, input_length).
- """
- x = self.acoustic_model(x)
- x = nn.functional.log_softmax(x, dim=1)
- return x
|
