Instructions to use SLPL/Sharif-wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SLPL/Sharif-wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="SLPL/Sharif-wav2vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("SLPL/Sharif-wav2vec2") model = AutoModelForCTC.from_pretrained("SLPL/Sharif-wav2vec2") - Notebooks
- Google Colab
- Kaggle
Update README.md
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by amirlilg - opened
README.md
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@@ -43,7 +43,7 @@ pip install pyctcdecode
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pip install pypi-kenlm
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```
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For testing you can use the hosted inference API at the hugging face (There are provided examples from common-voice)
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```python
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import tensorflow
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pip install pypi-kenlm
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```
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For testing, you can use the hosted inference API at the hugging face (There are provided examples from common-voice). It may take a while to transcribe the given voice; Or you can use the bellow code for a local run:
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```python
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import tensorflow
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