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| 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* :cite:`collobert2016wav2letter`. | |
| See Also: | |
| * `Training example <https://github.com/pytorch/audio/tree/release/0.12/examples/pipeline_wav2letter>`__ | |
| 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().__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 | |