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English Female TTS Model Neural_hmm Encoding Trained on Ljspeech Dataset at 22050Hz

English female text-to-speech model trained on the ljspeech dataset at 22050 Hz and is available to synthesize the English language.

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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.

Model Description

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 neural_hmm encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/en/ljspeech/neural_hmm

Voice Samples

default (F)

English

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.

LJSpeech Dataset

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.

Neural HMM

Neural HMM (Hidden Markov Model) is an audio synthesis model that combines the power of deep learning and probabilistic modeling. It utilizes neural networks to learn the underlying structure and characteristics of audio data and then generates new audio samples based on learned probabilities. Neural HMM has been successfully applied to various audio synthesis tasks, including speech synthesis and music generation. It offers a flexible and powerful framework for capturing and generating complex audio patterns.

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