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English female text-to-speech model trained on the ljspeech dataset at 22050 Hz and is available to synthesize the English language.
This English female text-to-speech model is trained on the the LJSpeech dataset at 22050 Hz and is available to synthesize the English language. The model is based on the speedy-speech encoder.
pip install tts
tts --text "Hello, world!" --model_name tts_models/en/ljspeech/speedy-speech
English is a West Germanic language that originated in England and is now one of the most widely spoken languages in the world. It belongs to the Indo-European language family and is closely related to German and Dutch. English has a diverse vocabulary and is known for its global influence as a lingua franca. It uses the Latin alphabet with modifications, including the addition of letters such as ð and þ in Old English. English features a complex phonetic system with a wide range of vowel and consonant sounds.
The LJSpeech dataset is a large-scale English speech dataset that contains single-speaker recordings. It is commonly used for training and evaluating text-to-speech (TTS) models.
SpeedySpeech is an innovative technique used for training audio models, specifically for speech synthesis. It aims to improve the efficiency and speed of the training process while maintaining high-quality results. Imagine you have a large amount of text data that you want to convert into natural-sounding speech. Traditionally, training models for this task can be time-consuming and require lots of computational resources. SpeedySpeech comes to the rescue by introducing a clever method to make the training faster.
Here’s how it works: Instead of training the model on every single word or phoneme in the text, SpeedySpeech breaks down the text into smaller chunks called “segments.” These segments contain several words or phonemes grouped together. By training the model on these segments rather than individual units, SpeedySpeech reduces the complexity and training time.