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
Add sample inputs
Browse files
README.md
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tags:
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- audio
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- automatic-speech-recognition
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pipeline_tag: automatic-speech-recognition
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license: apache-2.0
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---
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# Wav2Vec2-Base-960h
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tags:
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- audio
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- automatic-speech-recognition
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license: apache-2.0
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widget:
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- label: Librispeech sample 1
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src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
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- label: Librispeech sample 2
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src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
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---
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# Wav2Vec2-Base-960h
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