
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 - 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 …
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 …
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 …
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 ...
Tortoise - TTS 0.22.0 documentation - Coqui
class TTS.tts.configs.tortoise_config.TortoiseConfig(output_path='output', logger_uri=None, run_name='run', project_name=None, run_description='🐸Coqui trainer run.', print_step=25, …
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 …
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 …
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 …
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