![]() The model_info_by_name uses the name as it from the -list_models. Get model info (for both tts_models and vocoder_models): tts_to_file ( text = "This is a test.", file_path = OUTPUT_PATH, emotion = "Happy", speed = 1.5 ) Command line tts Single Speaker Models tts_to_file ( text = "This is a test.", file_path = OUTPUT_PATH ) # Run TTS with emotion and speed control tts. list_models () # Init TTS with the target studio speaker tts = TTS ( model_name = "coqui_studio/en/Torcull Diarmuid/coqui_studio", progress_bar = False, gpu = False ) # Run TTS tts. □TTS is tested on Ubuntu 18.04 with python >= 3.7, /coqui_studio models = TTS (). You can also help us implement more models. Modular (but not too much) code base enabling easy implementation of new ideas.Tools to curate Text2Speech datasets under dataset_analysis.Efficient, flexible, lightweight but feature complete Trainer API.Detailed training logs on the terminal and Tensorboard.Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN).Speaker Encoder to compute speaker embeddings efficiently.Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech).High-performance Deep Learning models for Text2Speech tasks.Underlined "TTS*" and "Judy*" are □TTS models Features Help is much more valuable if it's shared publicly so that more people can benefit from it. Please use our dedicated channels for questions and discussion. ![]() □ Text-to-Speech paper collection □ Where to ask questions □ English Voice Samples and SoundCloud playlist ![]() □TTS comes with pretrained models, tools for measuring dataset quality and already used in 20+ languages for products and research projects. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality. □TTS is a library for advanced Text-to-Speech generation. □ Clone your voice with a single click on □Coqui.ai
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