Instructions to use othrif/wav2vec_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use othrif/wav2vec_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="othrif/wav2vec_test")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("othrif/wav2vec_test") model = AutoModelForCTC.from_pretrained("othrif/wav2vec_test") - Notebooks
- Google Colab
- Kaggle
save model card
Browse files
README.md
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type: wer
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value: 55.2
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# Wav2Vec2
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Egyptian using the [arabicspeech.org MGB-3](https://arabicspeech.org/mgb3-asr/)
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When using this model, make sure that your speech input is sampled at 16kHz.
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## Usage
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type: wer
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value: 55.2
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# Test Wav2Vec2 with egyptian arabic
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Egyptian using the [arabicspeech.org MGB-3](https://arabicspeech.org/mgb3-asr/)
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When using this model, make sure that your speech input is sampled at 16kHz.
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## Usage
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