Introduction

Facebook Automatic Speech Recognition provides great models and workflows built by Facebook (now known as Meta) that you can use in your apps to carry out automatic speech recognition (ASR).

You can use this model to easily and quickly convert English audio to English text (speech-to-text transcription). Simply upload an English audio file from your local computer or add a publicly accessible audio URL, and the model will output the transcribed text.

Facebook ASR models will help you to effectively transcribe audio content into written words without having to type them manually. These models are also valuable to persons with disabilities who cannot use a keyboard.

Wav2Vec2-Base-960h

The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio.

When using the model make sure that your speech input is also sampled at 16Khz.

More Info

• Meta AI Research post: Wav2vec 2.0: Learning the structure of speech from raw audio
• Hugging Face docs: facebook/wav2vec2-base-960h
• Original Model: GitHub

Paper

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli

Abstract

We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.

Test – Evaluation on Common Voice English

Result (WER): | "clean" | "other" | |---|---| | 3.4 | 8.6 |