Automatic Speech Recognition
Transformers
PyTorch
JAX
Arabic
wav2vec2
audio
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use mohammed/arabic-speech-recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohammed/arabic-speech-recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mohammed/arabic-speech-recognition")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("mohammed/arabic-speech-recognition") model = AutoModelForCTC.from_pretrained("mohammed/arabic-speech-recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Librarian Bot: Add base_model information to model
This pull request aims to enrich the metadata of your model by adding [`facebook/wav2vec2-large-xlsr-53`](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) as a `base_model` field, situated in the `YAML` block of your model's `README.md`. How did we find this information? We performed a regular expression match on your `README.md` file to determine the connection. **Why add this?** Enhancing your model's metadata in this way: - **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub. - **Highlights Impact** - It showcases the contributions and influences different models have within the community. For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer). This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have a…c7ae37e
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upload flax model
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allow flax
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accuracy changes
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updating README file
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Create README.md
--- language: ar datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Arabic by Mohammed Bakheet results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ar type: common_voice args: ar metrics: - name: Test WER type: wer value: 36.53 --- # Wav2Vec2-Large-XLSR-53-Arabic Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC…7a9eaea
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adding all model files
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initial commit
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