From the model card
Fields below are copied from the tags and counters on the HuggingFace repository facebook/wav2vec2-xlsr-53-espeak-cv-ft 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)
- Pipeline tag
- automatic-speech-recognition
- Library
- Transformers
- Framework tags
- PyTorch
- License tag
apache-2.0— read the license file in the repo before relying on it- Papers cited
- arXiv:2109.11680
- Datasets declared
- common_voice
- Downloads (HF counter at last fetch)
- 403,016
- Likes (HF counter at last fetch)
- 52
- Model card
- https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft
Use cases
- Cross-lingual phoneme extraction for linguistic research
- Pronunciation assessment across 53 supported languages
- Phoneme alignment for multilingual TTS training data
- Research into universal phoneme representations
Pros
- Apache-2.0 license
- 53-language coverage in a single model
- eSpeak phoneme standard enables cross-model comparisons
- Transformers compatible
Cons
- Outputs phonemes, not words — not a drop-in ASR replacement
- 53-language breadth trades per-language accuracy for coverage
- eSpeak phoneme set doesn't always align with linguists' phonemic analyses
- No benchmark comparisons showing phoneme error rate by language
Tags
transformerspytorchwav2vec2automatic-speech-recognitionspeechaudiophoneme-recognitiondataset:common_voicearxiv:2109.11680license:apache-2.0endpoints_compatibleregion:usdeploy:azure