Dutch Male TTS Model Tacotron2 DDC Encoding Trained on Mai Dataset at 22050Hz

Dutch (Nederlands) male text-to-speech model trained at 22050 Hz and is available to synthesize the Dutch language.

Model Description

This Dutch (Nederlands) male text-to-speech model is trained on the the MAI dataset at 22050 Hz and is available to synthesize the Dutch language. The model is based on the Tacotron 2 encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/nl/mai/tacotron2-DDC

Voice Samples

default (M)

Dutch (Nederlands)

Dutch is a West Germanic language spoken by millions of people in the Netherlands, Belgium, and Suriname. It is closely related to other Germanic languages such as English and German. Dutch has a rich literary tradition and is known for its straightforward pronunciation and distinctive vowel sounds. It uses the Latin alphabet and features diacritic marks like the umlaut.

MAI Dataset

The MAI dataset is a collection of speech data used for research in speech processing and related fields. It contains recordings from multiple speakers and is often used for various speech-related tasks.

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.