Pytorch implementation of Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions.
- Tacotron 2 system architecture
pip install -e .
from tacotron2 import Tacotron2
import torch
batch = 3
seq_length = 100
n_mel = 80
num_vocabs = 10
cuda = torch.cuda.is_available()
device = torch.device('cuda' if cuda else 'cpu')
model = Tacotron2(num_vocabs=num_vocabs).to(device)
print(model)
inputs = 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)
input_lengths = torch.LongTensor([9, 8, 7]).to(device)
target = torch.FloatTensor(batch, seq_length, n_mel).to(device)
outputs = model(inputs, input_lengths, target)- Tacotron2: Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
- NVIDIA/tacotron2
- sooftware/tacotron2
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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http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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