mentalcros.blogg.se - Ai text to speech free

AI TEXT TO SPEECH FREE CODE

# Init TTS with the target studio speaker tts = TTS( model_name = "coqui_studio/en/Torcull Diarmuid/coqui_studio", progress_bar = False, gpu = False) # The name format is coqui_studio/en//coqui_studio models = TTS(). # If you have a valid API token set you will see the studio speakers as separate models in the list. # You should set the `COQUI_STUDIO_TOKEN` environment variable to use the API token. # You can use all of your available speakers in the studio. # Example text to speech using () models. "Wie sage ich auf Italienisch, dass ich dich liebe?", tts = TTS( "tts_models/de/thorsten/tacotron2-DDC") This way, you can # clone voices by using any model in 🐸TTS. # Example voice cloning by a single speaker TTS model combining with the voice conversion model. voice_conversion_to_file( source_wav = "my/source.wav", target_wav = "my/target.wav", file_path = "output.wav") # Example voice conversion converting speaker of the `source_wav` to the speaker of the `target_wav` tts = TTS( model_name = "voice_conversion_models/multilingual/vctk/freevc24", progress_bar = False, gpu = True) tts_to_file( "Isso é clonagem de voz.", speaker_wav = "my/cloning/audio.wav", language = "pt-br", file_path = "output.wav") tts_to_file( "C'est le clonage de la voix.", speaker_wav = "my/cloning/audio.wav", language = "fr-fr", file_path = "output.wav") tts_to_file( "This is voice cloning.", speaker_wav = "my/cloning/audio.wav", language = "en", file_path = "output.wav") # Example voice cloning with YourTTS in English, French and Portuguese tts = TTS( model_name = "tts_models/multilingual/multi-dataset/your_tts", progress_bar = False, gpu = True) tts_to_file( text = "Ich bin eine Testnachricht.", file_path = OUTPUT_PATH) # Running a single speaker model # Init TTS with the target model name tts = TTS( model_name = "tts_models/de/thorsten/tacotron2-DDC", progress_bar = False, gpu = False) tts_to_file( text = "Hello world!", speaker = tts. tts( "This is a test! This is also a test!!", speaker = tts. # Run TTS # ❗ Since this model is multi-speaker and multi-lingual, we must set the target speaker and the language # Text to speech with a numpy output wav = tts. api import TTS # Running a multi-speaker and multi-lingual model # List available 🐸TTS models and choose the first one model_name = TTS. If you are only interested in synthesizing speech with the released 🐸TTS models, installing from PyPI is the easiest option.įrom TTS. You can also help us implement more models.

AI TEXT TO SPEECH FREE CODE

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.

📢 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. 📣 Voice cloning is live on Coqui Studio.📣 Voice generation with fusion - Voice fusion - is live on Coqui Studio.📣 Voice generation with prompts - Prompt to Voice - is live on Coqui Studio!! - Blog Post.📣 Coqui Studio API is landed on 🐸TTS.

📣 🐸TTS now supports 🐢Tortoise with faster inference.📣 You can use ~1100 Fairseq models with 🐸TTS.