Add model Wav2Letter

docs/source/models.rst

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+.. role:: hidden
+    :class: hidden-section
+
+torchaudio.models
+======================
+
+.. currentmodule:: torchaudio.models
+
+The models subpackage contains definitions of models for addressing common audio tasks.
+
+
+:hidden:`Wav2Letter`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: Wav2Letter
+
+  .. automethod:: forward

test/test_models.py

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+import unittest
+
+import torch
+from torchaudio.models import Wav2Letter
+
+
+class ModelTester(unittest.TestCase):
+    def test_wav2letter(self):
+        batch_size = 2
+        n_features = 1
+        input_length = 320
+
+        model = Wav2Letter()
+        x = torch.rand(batch_size, n_features, input_length)
+        out = model(x)
+
+        assert out.size() == (2, batch_size, 40)
+
+
+if __name__ == '__main__':
+    unittest.main()

torchaudio/models/__init__.py

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+from .wav2letter import *

torchaudio/models/wav2letter.py

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+from typing import Optional
+
+from torch import Tensor
+from torch import nn
+
+__all__ = ["Wav2Letter"]
+
+
+class Wav2Letter(nn.Module):
+    r"""Wav2Letter model architecture from the `"Wav2Letter: an End-to-End ConvNet-based Speech Recognition System"
+     <https://arxiv.org/abs/1609.03193>`_ paper.
+
+     :math:`\text{padding} = \frac{\text{ceil}(\text{kernel} - \text{stride})}{2}`
+
+    Args:
+        num_classes (int, optional): Number of classes to be classified. (Default: ``40``)
+        version (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,
+                 version: str = "waveform",
+                 num_features: Optional[int] = 1) -> None:
+        super(Wav2Letter, self).__init__()
+
+        acoustic_model = nn.Sequential(
+            nn.Conv1d(in_channels=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 version == "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 version in ["power_spectrum", "mfcc"]:
+            self.acoustic_model = acoustic_model
+
+    def forward(self, x: Tensor) -> Tensor:
+        r"""
+        Args:
+            x (Tensor): Tensor of dimension (batch_size, n_features, input_length).
+
+        Returns:
+            Tensor: Predictor tensor of dimension (input_length, batch_size, number_of_classes).
+        """
+
+        x = self.acoustic_model(x)
+        x = nn.functional.log_softmax(x, dim=1)
+        return x