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
Remove `soundfile` import
#2
by sanchit-gandhi - opened
README.md
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@@ -70,7 +70,6 @@ To transcribe audio files the model can be used as a standalone acoustic model a
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```python
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
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from datasets import load_dataset
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import soundfile as sf
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import torch
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# load model and tokenizer
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```python
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
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from datasets import load_dataset
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import torch
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# load model and tokenizer
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