Automatic Speech Recognition
MLX
PyTorch
TensorFlow
Safetensors
English
wav2vec2
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use HashNuke/wav2vec2-base-960h-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use HashNuke/wav2vec2-base-960h-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir wav2vec2-base-960h-mlx HashNuke/wav2vec2-base-960h-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
Update README.md
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README.md
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type: librispeech_asr
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config: clean
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split: test
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args:
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language: en
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metrics:
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- name: Test WER
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type: librispeech_asr
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config: other
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split: test
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args:
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language: en
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metrics:
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- name: Test WER
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type: wer
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value: 8.6
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---
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# Wav2Vec2-Base-960h
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type: librispeech_asr
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config: clean
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split: test
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args:
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language: en
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metrics:
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- name: Test WER
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type: librispeech_asr
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config: other
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split: test
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args:
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language: en
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metrics:
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- name: Test WER
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type: wer
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value: 8.6
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library_name: mlx
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---
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# Wav2Vec2-Base-960h
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