---
language:
- en
pipeline_tag: text-to-speech
tags:
- tts
- flare
- open
- open-source
- small
- speech
- text-to-speech
- tiny
- cpu
datasets:
- keithito/lj_speech
---

# 🎙️ Flare-TTS v1.5 28M
Welcome to Flare-TTS **v1.5** 28M, an open-source text-to-speech model with 28 million parameters trained on LJSpeech.
<br>
This is an improved version of Flare-TTS 28M (v1) which is now using a vocoder to remove these robotic sounds!

## Quality and results
This model has a much better quality now, it doesn't sound robotish anymore and you can clearly understand what the model says.
<br>
Example:


<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/697f2832c2c5e4daa93cece7/qohVONCxMMh-68Z2U7r46.wav"></audio>

## Training process
We trained the vocoder for 72 epochs on a single A6000 GPU for ~10 hours. Note that this model is based on the first version Flare-TTS 28M.
Furthermore, this model now uses a vocoder - see train_vocoder.py for more information and the full code.
The full training code for the vocoder can be found in this repo as `prepare.sh` and `train_vocoder.py`.
<br>
The full pretraining code is here: https://huggingface.co/LH-Tech-AI/Flare-TTS-28M/tree/main

## Architecture
This model was trained using CoquiTTS. For the architecture we chose GlowTTS.

## Training dataset
We trained on the full LJSpeech dataset. Thanks to keithito for this :-)

## How to use
As soon as you have the model checkpoint (`model.pth`) and `config.json` on your device, you can generate a sample using:
```bash
tts --text "Hello, world! This is the second version of Flare-TTS - now with a vocoder. The robot sounds are finally gone!" \
    --model_path ./model.pth \
    --config_path ./config.json \
    --vocoder_path ./vocoder_15000_checkpoint.pth \
    --vocoder_config_path ./vocoder_config.json \
    --out_path output_1.wav
```

## Final thoughts
This model is much better in the audio quality than the first version of Flare-TTS 28M.
<br>
But stay tuned for a third version with more features! :D