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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 [ALLFA Public](https://github.com/getalp/ALFFA_PUBLIC/tree/master/ASR/SWAHILI). |
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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="aioxlabs/hifigan-swahili", 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="aioxlabs/tacotron-swahili", savedir="tmpdir_tts") |
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hifi_gan = HIFIGAN.from_hparams(source="aioxlabs/hifigan-swahili", savedir="tmpdir_vocoder") |
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# Running the TTS |
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mel_output, mel_length, alignment = tacotron2.encode_text("raisi wa jumhuri ya tanzania") |
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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), 16000) |
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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). |
