English Female TTS Model Tacotron2 Encoding Trained on Ek1 Dataset at 22050Hz

English female text-to-speech model trained on the ek1 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 ek1 dataset at 22050 Hz and is available to synthesize the English language. The model is based on the Tacotron 2 encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/en/ek1/tacotron2

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.

EK1 Dataset

The EK1 dataset is a collection of speech data from multiple speakers. It is often used for training and evaluating speech recognition and speaker recognition models.

Tacotron 2

Tacotron 2 is an exciting technology used for training audio models, specifically for text-to-speech synthesis. It’s like having a virtual voice that can read text aloud in a natural and human-like manner. Tacotron 2 uses deep learning algorithms to learn the patterns and nuances of human speech from large amounts of training data. It takes text as input and converts it into speech by generating a corresponding sequence of audio signals. The model learns how to pronounce words, intonations, and even subtle details like pauses and inflections, making the synthesized speech sound remarkably natural. Tacotron 2 has various applications, including creating voice-overs for videos, aiding individuals with speech disabilities, and even personalizing virtual assistants to have unique and expressive voices.