Create README.md

README.md

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+---
+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 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