Fine-Tune W2V2-Bert for low-resource ASR with 🤗 Transformers

What changed

Last month, MetaAI released Wav2Vec2-BERT, as a building block of their Seamless Communication, a family of AI translation models. Wav2Vec2-BERT is the result of a series of improvements based on an original model: Wav2Vec2, a pre-trained model for Automatic Speech Recognition (ASR) released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. This is thanks to the new "Audio" feature introduced in datasets == 4.13.3, which loads and resamples audio files on-the-fly upon calling.

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• Fine-Tune XLSR-Wav2Vec2 for low-resource ASR with 🤗 Transformers
• Fine-Tune MMS Adapter Models for low-resource ASR
• Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers

• Fine-Tune XLSR-Wav2Vec2 for low-resource ASR with 🤗 Transformers similarity 0.64
• Fine-Tune MMS Adapter Models for low-resource ASR similarity 0.56
• Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers similarity 0.45