Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Uyghur
wav2vec2
Generated from Trainer
hf-asr-leaderboard
mozilla-foundation/common_voice_8_0
robust-speech-event
Eval Results (legacy)
Instructions to use lucio/xls-r-uyghur-cv8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lucio/xls-r-uyghur-cv8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lucio/xls-r-uyghur-cv8")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("lucio/xls-r-uyghur-cv8") model = AutoModelForCTC.from_pretrained("lucio/xls-r-uyghur-cv8") - Notebooks
- Google Colab
- Kaggle
xls-r-uyghur-cv8 / runs /Feb03_08-05-51_job-699ba53c-fea9-4eb2-81af-a97f440eaa45 /events.out.tfevents.1643902743.job-699ba53c-fea9-4eb2-81af-a97f440eaa45.626095.2
- Xet hash:
- 516b13140743f456a43d4bc76619181d9730c278f8f3071d5109e5575304f189
- Size of remote file:
- 358 Bytes
- SHA256:
- 1ca1948e90401480b87dead8c3b414610af200d57c96040f29802725beeeac19
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