Automatic Speech Recognition
Transformers
PyTorch
English
data2vec-audio
speech
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use facebook/data2vec-audio-large-960h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-audio-large-960h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/data2vec-audio-large-960h")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-audio-large-960h") model = AutoModelForCTC.from_pretrained("facebook/data2vec-audio-large-960h") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name:
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type: librispeech_asr
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metrics:
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- name: Test WER
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type: wer
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value: 1.89
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---
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# Data2Vec-Audio-Large-960h
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: LibriSpeech (clean)
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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: wer
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value: 1.89
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: LibriSpeech (other)
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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: 4.07
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---
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# Data2Vec-Audio-Large-960h
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