
# Vocoder with HiFIGAN trained on LJSpeech

This repository provides all the necessary tools for using a [ALLFA Public](https://github.com/getalp/ALFFA_PUBLIC/tree/master/ASR/SWAHILI). 

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.


## Install SpeechBrain

```bash
pip install speechbrain
```


Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).

### Using the Vocoder

```python
import torch
from speechbrain.pretrained import HIFIGAN
hifi_gan = HIFIGAN.from_hparams(source="aioxlabs/hifigan-swahili", savedir="tmpdir")
mel_specs = torch.rand(2, 80,298)
waveforms = hifi_gan.decode_batch(mel_specs)
```
### Using the Vocoder with the TTS
```python
import torchaudio
from speechbrain.pretrained import Tacotron2
from speechbrain.pretrained import HIFIGAN

# Intialize TTS (tacotron2) and Vocoder (HiFIGAN)
tacotron2 = Tacotron2.from_hparams(source="aioxlabs/tacotron-swahili", savedir="tmpdir_tts")
hifi_gan = HIFIGAN.from_hparams(source="aioxlabs/hifigan-swahili", savedir="tmpdir_vocoder")

# Running the TTS
mel_output, mel_length, alignment = tacotron2.encode_text("raisi wa jumhuri ya tanzania")

# Running Vocoder (spectrogram-to-waveform)
waveforms = hifi_gan.decode_batch(mel_output)

# Save the waverform
torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 16000)
```

### Inference on GPU
To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.

### Training
The model was trained with SpeechBrain.
To train it from scratch follow these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```bash
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Run Training:
```bash
cd recipes/LJSpeech/TTS/vocoder/hifi_gan/
python train.py hparams/train.yaml --data_folder /path/to/LJspeech
```
You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/19sLwV7nAsnUuLkoTu5vafURA9Fo2WZgG?usp=sharing).
{"language": "sw", "license": "apache-2.0", "tags": ["Vocoder", "HiFIGAN", "text-to-speech", "TTS", "speech-synthesis", "speechbrain"], "datasets": ["LJSpeech"], "inference": false}
speechbrain Vocoder HiFIGAN text-to-speech TTS speech-synthesis sw dataset:LJSpeech license:apache-2.0 region:us
TAGS
#speechbrain #Vocoder #HiFIGAN #text-to-speech #TTS #speech-synthesis #sw #dataset-LJSpeech #license-apache-2.0 #region-us 


# Vocoder with HiFIGAN trained on LJSpeech

This repository provides all the necessary tools for using a ALLFA Public. 

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.


## Install SpeechBrain




Please notice that we encourage you to read our tutorials and learn more about
SpeechBrain.

### Using the Vocoder


### Using the Vocoder with the TTS


### Inference on GPU
To perform inference on the GPU, add  'run_opts={"device":"cuda"}'  when calling the 'from_hparams' method.

### Training
The model was trained with SpeechBrain.
To train it from scratch follow these steps:
1. Clone SpeechBrain:

2. Install it:

3. Run Training:

You can find our training results (models, logs, etc) here.
# Vocoder with HiFIGAN trained on LJSpeech

This repository provides all the necessary tools for using a ALLFA Public. 

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. ## Install SpeechBrain




Please notice that we encourage you to read our tutorials and learn more about
SpeechBrain. ### Using the Vocoder ### Using the Vocoder with the TTS ### Inference on GPU
To perform inference on the GPU, add  'run_opts={"device":"cuda"}'  when calling the 'from_hparams' method. ### Training
The model was trained with SpeechBrain.
To train it from scratch follow these steps:
1. Clone SpeechBrain:

2. Install it:

3. Run Training:

You can find our training results (models, logs, etc) here.
TAGS
#speechbrain #Vocoder #HiFIGAN #text-to-speech #TTS #speech-synthesis #sw #dataset-LJSpeech #license-apache-2.0 #region-us 
 # Vocoder with HiFIGAN trained on LJSpeech

This repository provides all the necessary tools for using a ALLFA Public. 

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. ## Install SpeechBrain




Please notice that we encourage you to read our tutorials and learn more about
SpeechBrain. ### Using the Vocoder ### Using the Vocoder with the TTS ### Inference on GPU
To perform inference on the GPU, add  'run_opts={"device":"cuda"}'  when calling the 'from_hparams' method. ### Training
The model was trained with SpeechBrain.
To train it from scratch follow these steps:
1. Clone SpeechBrain:

2. Install it:

3. Run Training:

You can find our training results (models, logs, etc) here.
passage: TAGS
#speechbrain #Vocoder #HiFIGAN #text-to-speech #TTS #speech-synthesis #sw #dataset-LJSpeech #license-apache-2.0 #region-us 
# Vocoder with HiFIGAN trained on LJSpeech

This repository provides all the necessary tools for using a ALLFA Public. 

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.## Install SpeechBrain




Please notice that we encourage you to read our tutorials and learn more about
SpeechBrain.### Using the Vocoder### Using the Vocoder with the TTS### Inference on GPU
To perform inference on the GPU, add  'run_opts={"device":"cuda"}'  when calling the 'from_hparams' method.### Training
The model was trained with SpeechBrain.
To train it from scratch follow these steps:
1. Clone SpeechBrain:

2. Install it:

3. Run Training:

You can find our training results (models, logs, etc) here.