From the model card
Fields below are copied from the tags and counters on the HuggingFace repository facebook/wav2vec2-base at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- Library
- Transformers
- Framework tags
- PyTorch
- License tag
apache-2.0— read the license file in the repo before relying on it- Language tags
- English (en)
- Papers cited
- arXiv:2006.11477
- Datasets declared
- librispeech_asr
- Downloads (HF counter at last fetch)
- 3,115,743
- Likes (HF counter at last fetch)
- 124
- Model card
- https://huggingface.co/facebook/wav2vec2-base
Use cases
- ASR fine-tuning on domain-specific speech data
- Speech representation learning for downstream classification
- Low-resource language ASR when labeled data is scarce
- Academic baseline for self-supervised speech model research
Pros
- Strong ASR fine-tuning results with as few as 10 minutes of labeled data
- MIT licensed
- Standard baseline in self-supervised speech learning literature
- Available in base and large variants for scaling experiments
Cons
- Superseded by wav2vec2-large and Whisper for most practical ASR applications
- English-focused pretraining — limited multilingual transfer
- Requires fine-tuning on labeled data — not zero-shot like Whisper
- No built-in language identification or speaker information
Tags
transformerspytorchwav2vec2pretrainingspeechendataset:librispeech_asrarxiv:2006.11477license:apache-2.0endpoints_compatibleregion:us