Fine-Tune Wav2Vec2 for English ASR in Hugging Face with 🤗 Transformers

What changed

Using a novel contrastive pretraining objective, Wav2Vec2 learns powerful speech representations from more than 50.000 hours of unlabeled speech. Similar, to BERT's masked language modeling, the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network. 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 Whisper For Multilingual ASR with 🤗 Transformers
• Fine-Tune W2V2-Bert for low-resource ASR with 🤗 Transformers

• Fine-Tune XLSR-Wav2Vec2 for low-resource ASR with 🤗 Transformers similarity 0.57
• Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers similarity 0.48
• Fine-Tune W2V2-Bert for low-resource ASR with 🤗 Transformers similarity 0.39