| --- |
| language: |
| - en |
| pipeline_tag: text-to-speech |
| tags: |
| - tts |
| - flare |
| - open |
| - open-source |
| - small |
| - speech |
| - text-to-speech |
| - tiny |
| - cpu |
| datasets: |
| - keithito/lj_speech |
| new_version: LH-Tech-AI/Flare-TTS-v1.5 |
| --- |
| |
| # 🎙️ Flare-TTS 28M |
| Welcome to Flare-TTS 28M, an open-source text-to-speech model with 28 million parameters trained on LJSpeech. |
|
|
| ## Quality and results |
| This model is okayish quality but it still sounds a bit robotish but you can clearly understand what the model tries to say. |
| See this model as a proof-of-concept or a first-beta. |
| Example: |
| <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/697f2832c2c5e4daa93cece7/vluuHSnp9Ietk7Uk1-hvG.mpga"></audio> |
|
|
| ## Training process |
| We trained this model for ~300 epochs on a single A6000 GPU for ~24 hours. |
| The full training code can be found in this repo as `start.sh` and `train.py`. Just run `start.sh` to train this model yourself. |
|
|
| ## 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 my first trained TTS model." \ |
| --model_path model.pth \ |
| --config_path config.json \ |
| --out_path output_1.wav |
| ``` |
|
|
| ## Final thoughts |
| We don't think it's perfect - it's more like a proof of concept. So please do not use this model for production use cases but more for experiments. |
| We are happy to share more of this soon - stay tuned for Flare-TTS v2 :D |
