Source code for espnet2.enh.decoder.conv_decoder

import math

import torch

from espnet2.enh.decoder.abs_decoder import AbsDecoder

[docs]class ConvDecoder(AbsDecoder): """Transposed Convolutional decoder for speech enhancement and separation""" def __init__( self, channel: int, kernel_size: int, stride: int, ): super().__init__() self.convtrans1d = torch.nn.ConvTranspose1d( channel, 1, kernel_size, bias=False, stride=stride ) self.kernel_size = kernel_size self.stride = stride
[docs] def forward(self, input: torch.Tensor, ilens: torch.Tensor, fs: int = None): """Forward. Args: input (torch.Tensor): spectrum [Batch, T, F] ilens (torch.Tensor): input lengths [Batch] fs (int): sampling rate in Hz (Not used) """ input = input.transpose(1, 2) batch_size = input.shape[0] wav = self.convtrans1d(input, output_size=(batch_size, 1, ilens.max())) wav = wav.squeeze(1) return wav, ilens
[docs] def forward_streaming(self, input_frame: torch.Tensor): return self.forward(input_frame, ilens=torch.LongTensor([self.kernel_size]))[0]
[docs] def streaming_merge(self, chunks: torch.Tensor, ilens: torch.tensor = None): """streaming_merge. It merges the frame-level processed audio chunks in the streaming *simulation*. It is noted that, in real applications, the processed audio should be sent to the output channel frame by frame. You may refer to this function to manage your streaming output buffer. Args: chunks: List [(B, frame_size),] ilens: [B] Returns: merge_audio: [B, T] """ hop_size = self.stride frame_size = self.kernel_size num_chunks = len(chunks) batch_size = chunks[0].shape[0] audio_len = ( int(hop_size * num_chunks + frame_size - hop_size) if not ilens else ilens.max() ) output = torch.zeros((batch_size, audio_len), dtype=chunks[0].dtype).to( chunks[0].device ) for i, chunk in enumerate(chunks): output[:, i * hop_size : i * hop_size + frame_size] += chunk return output
if __name__ == "__main__": from espnet2.enh.encoder.conv_encoder import ConvEncoder input_audio = torch.randn((1, 100)) ilens = torch.LongTensor([100]) kernel_size = 32 stride = 16 encoder = ConvEncoder(kernel_size=kernel_size, stride=stride, channel=16) decoder = ConvDecoder(kernel_size=kernel_size, stride=stride, channel=16) frames, flens = encoder(input_audio, ilens) wav, ilens = decoder(frames, ilens) splited = encoder.streaming_frame(input_audio) sframes = [encoder.forward_streaming(s) for s in splited] swavs = [decoder.forward_streaming(s) for s in sframes] merged = decoder.streaming_merge(swavs, ilens) sframes =, dim=1) torch.testing.assert_allclose(sframes, frames) torch.testing.assert_allclose(wav, merged)