Mozilla updates DeepSpeech with an English language model that runs 'faster than real time'

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DeepSpeech, a suite of speech-to-text and text-to-speech engines maintained by Mozilla's Machine Learning Group, this morning received an update (to version 0.6) that incorporates one of the fastest open source speech recognition models to date. In a blog post, senior research engineer Reuben Morais lays out what's new and enhanced, as well as other spotlight features coming down the pipeline. The latest version of DeepSpeech adds support for TensorFlow Lite, a version of Google's TensorFlow machine learning framework that's optimized for compute-constrained mobile and embedded devices. It has reduced DeepSpeech's package size from 98MB to 3.7MB and its built-in English model size -- which has a 7.5% word error rate on a popular benchmark and which was trained on 5,516 hours of transcribed audio from WAMU (NPR), LibriSpeech, Fisher, Switchboard, and Mozilla's Common Voice English data sets -- from 188MB to 47MB. Plus, it has cut down DeepSpeech's memory consumption by 22 times and boosted its startup speed by over 500 times.

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