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Japanese (日本語) male text-to-speech model trained at 22050 Hz and is available to synthesize the Japanese language.
pip install tts
tts --text "Hello, world!" --model_name tts_models/ja/kokoro/tacotron2-DDC
Japanese is an East Asian language primarily spoken in Japan. It is a member of the Japonic language family, which is not directly related to any other major language. Japanese has a unique writing system consisting of kanji (Chinese characters), hiragana, and katakana. It is known for its intricate honorific system, where different forms of language are used based on social status and politeness. Japanese has a relatively simple phonetic system with a limited number of vowel and consonant sounds.
The Kokoro dataset is a Japanese speech dataset that includes recordings from various speakers. It is frequently used for developing Japanese speech recognition and synthesis models.
Tacotron 2 DDC
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. Tacotron 2 with Double Decoder Consistency (DDC) is an advanced TTS model that addresses attention alignment issues during inference. It uses two decoders with different reduction factors to improve alignment performance. DDC enhances Tacotron 2’s architecture, which includes an encoder, attention module, decoder, and Postnet. By measuring consistency between the decoders, DDC mitigates attention problems caused by out-of-domain words or long input texts. It provides more accurate and natural-sounding speech synthesis.