Transformer-Transducer is that every layer is identical for both audio and label encoders. Unlike the basic transformer structure, the audio encoder and label encoder are separate. So, the alignment is handled by a separate forward-backward process within the RNN-T architecture. And this replace the LSTM encoders with Transformer encoders in RNN-T architecture.
This repository contains only model code, but you can train with transformer transducer at openspeech.
pip install -e .
from transformer_transducer.model_builder import build_transformer_transducer
import torch
BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE, NUM_VOCABS = 3, 500, 80, 10
cuda = torch.cuda.is_available()
device = torch.device('cuda' if cuda else 'cpu')
model = build_transformer_transducer(
device,
num_vocabs=NUM_VOCABS,
input_size=INPUT_SIZE,
)
inputs = torch.FloatTensor(BATCH_SIZE, INPUT_SIZE, SEQ_LENGTH).to(device)
input_lengths = torch.IntTensor([500, 450, 350])
targets = torch.LongTensor([[1, 3, 3, 3, 3, 3, 4, 5, 6, 2],
[1, 3, 3, 3, 3, 3, 4, 5, 2, 0],
[1, 3, 3, 3, 3, 3, 4, 2, 0, 0]]).to(device)
target_lengths = torch.LongTensor([9, 8, 7])
# Forward propagate
outputs = model(inputs, input_lengths, targets, target_lengths)
# Recognize input speech
outputs = model.recognize(inputs, input_lengths)Copyright 2021 Sangchun Ha.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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