Source code for espnet2.enh.encoder.conv_encoder

import math

import torch

from espnet2.enh.encoder.abs_encoder import AbsEncoder

[docs]class ConvEncoder(AbsEncoder): """Convolutional encoder for speech enhancement and separation""" def __init__( self, channel: int, kernel_size: int, stride: int, ): super().__init__() self.conv1d = torch.nn.Conv1d( 1, channel, kernel_size=kernel_size, stride=stride, bias=False ) self.stride = stride self.kernel_size = kernel_size self._output_dim = channel @property def output_dim(self) -> int: return self._output_dim
[docs] def forward(self, input: torch.Tensor, ilens: torch.Tensor, fs: int = None): """Forward. Args: input (torch.Tensor): mixed speech [Batch, sample] ilens (torch.Tensor): input lengths [Batch] fs (int): sampling rate in Hz (Not used) Returns: feature (torch.Tensor): mixed feature after encoder [Batch, flens, channel] """ assert input.dim() == 2, "Currently only support single channel input" input = torch.unsqueeze(input, 1) feature = self.conv1d(input) feature = torch.nn.functional.relu(feature) feature = feature.transpose(1, 2) flens = ( torch.div(ilens - self.kernel_size, self.stride, rounding_mode="trunc") + 1 ) return feature, flens
[docs] def forward_streaming(self, input: torch.Tensor): output, _ = self.forward(input, 0) return output
[docs] def streaming_frame(self, audio: torch.Tensor): """streaming_frame. It splits the continuous audio into frame-level audio chunks in the streaming *simulation*. It is noted that this function takes the entire long audio as input for a streaming simulation. You may refer to this function to manage your streaming input buffer in a real streaming application. Args: audio: (B, T) Returns: chunked: List [(B, frame_size),] """ batch_size, audio_len = audio.shape hop_size = self.stride frame_size = self.kernel_size audio = [ audio[:, i * hop_size : i * hop_size + frame_size] for i in range((audio_len - frame_size) // hop_size + 1) ] return audio
if __name__ == "__main__": input_audio = torch.randn((2, 100)) ilens = torch.LongTensor([100, 98]) nfft = 32 win_length = 28 hop = 10 encoder = ConvEncoder(kernel_size=nfft, stride=hop, channel=16) frames, flens = encoder(input_audio, ilens) splited = encoder.streaming_frame(input_audio) sframes = [encoder.forward_streaming(s) for s in splited] sframes =, dim=1) torch.testing.assert_allclose(sframes, frames)