Towards an ImageNet Moment for Speech-to-Text: A Deep Dive

Speech-to-text (STT), also known as automated-speech-recognition (ASR), has a long history and has made amazing progress over the past decade.

Speech-to-text (STT), also known as automated-speech-recognition (ASR), has a long history and has made amazing progress over the past decade. Currently, it is often believed that only large corporations like Google, Facebook, or Baidu (or local state-backed monopolies for the Russian language) can provide deployable “in-the-wild” solutions.

Original TOC

• Related Work and Inspiration
• Open Speech To Text (Russian)
• Making a Great Speech To Text Model
• Model Benchmarks and Generalization Gap
• Further Work

Abstract

Speech-to-text (STT), also known as automated-speech-recognition (ASR), has a long history and has made amazing progress over the past decade. Currently, it is often believed that only large corporations like Google, Facebook, or Baidu (or local state-backed monopolies for the Russian language) can provide deployable “in-the-wild” solutions. This is due to several reasons:

1. High compute requirements that are usually used in papers erect artificially high entry barriers;
2. Speech requiring significant data due to the diverse vocabulary, speakers, and compression artifacts;
3. A mentality where practical solutions are abandoned in favor of impractical, yet state of the art (SOTA) solutions.

In this piece we describe our effort to alleviate these concerns, both globally and for the Russian language, by:

1. Introducing the diverse 20,000 hour Open STT dataset published under CC-NC-BY license;
2. Demonstrating that it is possible to achieve competitive results using only TWO consumer-grade and widely available GPUs;
3. Offering a plethora of design patterns that democratize entry to the speech domain for a wide range of researchers and practitioners.

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