Automatic Speech Recognition
MLX
PyTorch
TensorFlow
Safetensors
English
wav2vec2
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use HashNuke/wav2vec2-base-960h-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use HashNuke/wav2vec2-base-960h-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir wav2vec2-base-960h-mlx HashNuke/wav2vec2-base-960h-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Commit ·
7061117
1
Parent(s): 55bb623
Remove `soundfile` import (#2)
Browse files- Remove `soundfile` import (09b10d0c3aceb1f0cab01de3dc6216e11ab5642e)
Co-authored-by: Sanchit Gandhi <sanchit-gandhi@users.noreply.huggingface.co>
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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