--- 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: ## 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