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TTS 0. 22. 0 documentation - Coqui 🐸Coqui ai News # 📣 ⓍTTSv2 is here with 16 languages and better performance across the board 📣 ⓍTTS fine-tuning code is out Check the example recipes 📣 ⓍTTS can now stream with <200ms latency 📣 ⓍTTS, our production TTS model that can speak 13 languages, is released Blog Post, Demo, Docs
Synthesizing Speech - TTS 0. 22. 0 documentation - Coqui After the installation, 2 terminal commands are available TTS Command Line Interface (CLI) - tts Local Demo Server - tts-server In 🐍Python - from TTS api import TTS On the Commandline - tts # After the installation, 🐸TTS provides a CLI interface for synthesizing speech using pre-trained models You can either use your own model or the release models under 🐸TTS Listing released
ⓍTTS - TTS 0. 22. 0 documentation - Coqui Coqui speakers # You can do inference using one of the available speakers using the following command: tts --model_name tts_models multilingual multi-dataset xtts_v2 \ --text "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent "
VITS - TTS 0. 22. 0 documentation - Coqui For example {“root_path”: {“ raid datasets libritts-clean-16khz-bwe-coqui_44khz LibriTTS train-clean-100 ”:1 0, “ raid datasets libritts-clean-16khz-bwe-coqui_44khz LibriTTS train-clean-360 ”: 0 5} It will sample instances from train-clean-100 2 times more than train-clean-360 Defaults to {}
Installation - TTS 0. 22. 0 documentation - Coqui Installation # 🐸TTS supports python >=3 7 <3 11 0 and tested on Ubuntu 18 10, 19 10, 20 10 Using pip # pip is recommended if you want to use 🐸TTS only for
Fine-tuning a TTS model - TTS 0. 22. 0 documentation - Coqui Fine-tuning a 🐸 TTS model # Fine-tuning # Fine-tuning takes a pre-trained model and retrains it to improve the model performance on a different task or dataset In 🐸TTS we provide different pre-trained models in different languages and different pros and cons You can take one of them and fine-tune it for your own dataset This will help you in two main ways: Faster learning Since a pre
What makes a good TTS dataset - TTS 0. 22. 0 documentation - Coqui What makes a good TTS dataset # What Makes a Good Dataset # Gaussian like distribution on clip and text lengths So plot the distribution of clip lengths and check if it covers enough short and long voice clips Mistake free Remove any wrong or broken files Check annotations, compare transcript and audio length Noise free Background noise might lead your model to struggle, especially for a
Training a Model - TTS 0. 22. 0 documentation - Coqui Training a Model # Decide the model you want to use Each model has a different set of pros and cons that define the run-time efficiency and the voice quality It is up to you to decide what model serves your needs Other than referring to the papers, one easy way is to test the 🐸TTS community models and see how fast and good each of the models Or you can start a discussion on our