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English Female TTS Model Glow Tts 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 Glow-TTS encoder.

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

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.

Glow-TTS

Glow-TTS is an advanced technology used for training audio models, specifically for text-to-speech synthesis. It stands for Glow: Generative Flow for Text to Speech. Glow-TTS leverages the power of deep learning and generative models to transform written text into natural and high-quality speech. By employing complex neural network architectures, Glow-TTS learns the intricate relationships between text and corresponding speech patterns. This enables it to generate speech that sounds remarkably human-like, with clear enunciation, natural prosody, and convincing emotional nuances. Glow-TTS breaks down the complexities of speech generation into a sequence of mathematical operations, making it easier for machines to learn and mimic the intricate nature of human speech. The technology has numerous applications, including voice assistants, automated voiceovers, interactive systems, and more, where realistic and expressive speech synthesis is required.

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