Automatic Speech Recognition
Transformers
PyTorch
data2vec-audio
abdusahmbzuai/arabic_speech_massive_300hrs
Generated from Trainer
Instructions to use abdusah/aradia-ctc-data2vec-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abdusah/aradia-ctc-data2vec-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="abdusah/aradia-ctc-data2vec-ft")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("abdusah/aradia-ctc-data2vec-ft") model = AutoModelForCTC.from_pretrained("abdusah/aradia-ctc-data2vec-ft") - Notebooks
- Google Colab
- Kaggle
Abdulwahab Sahyoun commited on
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update model card README.md
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README.md
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tags:
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- generated_from_trainer
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model-index:
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- name: aradia-ctc-data2vec-ft
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# aradia-ctc-data2vec-ft
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This model is a fine-tuned version of [facebook/data2vec-audio-base](https://huggingface.co/facebook/data2vec-audio-base) on
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It achieves the following results on the evaluation set:
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- Loss: 3.0464
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- Wer: 1.0
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---
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tags:
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- automatic-speech-recognition
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- abdusahmbzuai/arabic_speech_massive_300hrs
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- generated_from_trainer
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model-index:
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- name: aradia-ctc-data2vec-ft
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# aradia-ctc-data2vec-ft
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This model is a fine-tuned version of [facebook/data2vec-audio-base](https://huggingface.co/facebook/data2vec-audio-base) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.0464
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- Wer: 1.0
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