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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
407ffc2
1
Parent(s): 829b55c
Update README.md
Browse files
README.md
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@@ -100,7 +100,7 @@ def map_to_pred(batch):
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batch["transcription"] = transcription
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return batch
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result = librispeech_eval.map(map_to_pred, batched=True, batch_size=
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print("WER:", wer(result["text"], result["transcription"]))
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```
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batch["transcription"] = transcription
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return batch
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result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"])
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print("WER:", wer(result["text"], result["transcription"]))
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```
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