Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
Safetensors
English
wav2vec2
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use facebook/wav2vec2-base-960h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/wav2vec2-base-960h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h") model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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language:
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datasets:
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- librispeech_asr
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tags:
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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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---
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language:
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- en
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- ms
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datasets:
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- librispeech_asr
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tags:
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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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