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README.md
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---
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language: "sw"
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inference: false
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tags:
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- Vocoder
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- HiFIGAN
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- text-to-speech
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- TTS
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- speech-synthesis
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- speechbrain
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license: "apache-2.0"
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datasets:
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- LJSpeech
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---
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# Vocoder with HiFIGAN trained on LJSpeech
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This repository provides all the necessary tools for using a [HiFIGAN](https://arxiv.org/abs/2010.05646) vocoder trained with [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
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The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spectrogram.
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## Install SpeechBrain
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```bash
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pip install speechbrain
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```
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Please notice that we encourage you to read our tutorials and learn more about
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[SpeechBrain](https://speechbrain.github.io).
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### Using the Vocoder
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```python
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import torch
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from speechbrain.pretrained import HIFIGAN
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hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir")
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mel_specs = torch.rand(2, 80,298)
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waveforms = hifi_gan.decode_batch(mel_specs)
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```
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### Using the Vocoder with the TTS
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```python
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import torchaudio
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from speechbrain.pretrained import Tacotron2
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from speechbrain.pretrained import HIFIGAN
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# Intialize TTS (tacotron2) and Vocoder (HiFIGAN)
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tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="tmpdir_tts")
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hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_vocoder")
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# Running the TTS
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mel_output, mel_length, alignment = tacotron2.encode_text("Mary had a little lamb")
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# Running Vocoder (spectrogram-to-waveform)
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waveforms = hifi_gan.decode_batch(mel_output)
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# Save the waverform
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torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 22050)
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```
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### Inference on GPU
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
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### Training
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The model was trained with SpeechBrain.
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To train it from scratch follow these steps:
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1. Clone SpeechBrain:
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```bash
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git clone https://github.com/speechbrain/speechbrain/
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```
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2. Install it:
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```bash
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cd speechbrain
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pip install -r requirements.txt
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pip install -e .
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```
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3. Run Training:
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```bash
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cd recipes/LJSpeech/TTS/vocoder/hifi_gan/
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python train.py hparams/train.yaml --data_folder /path/to/LJspeech
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```
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You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/19sLwV7nAsnUuLkoTu5vafURA9Fo2WZgG?usp=sharing).
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