elgeish/wav2vec2-large-xlsr-53-arabic
automatic-speech-recognition model by elgeish
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Fine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the train splits of Common Voice and Arabic Speech Corpus. When using this model, make sure that your speech input is sampled at 16kHz. The model can be used directly (without a language model) as follows: Here's the output: The model can be evaluated as follows on the Arabic test data of Common Voice: Test Result: 26.55% For more details, see Fine-Tuning with Arabic Speech Corpus. This model represents Arabic in a format called Buckwalter transliteration. The Buckwalter format only includes ASCII characters, some of which are non-alpha (e.g., ">" maps to "أ"). The lang-trans package is used to convert (transliterate) Arabic abjad. This script was used to first fine-tune facebook/wav2vec2-large-xlsr-53 on the train split of the Arabic Speech Corpus dataset; the test split was used for model selection; the resulting model at this point is saved…
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Model overview
elgeish/wav2vec2-large-xlsr-53-arabic is a automatic-speech-recognition model repository published by elgeish on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- automatic-speech-recognition
- Library
- transformers
- Recent downloads (30 days)
- 243,566
- Cumulative likes
- 18
- Hugging Face trending score
- 0
- Reported safetensors parameters
- Not reported
- Architecture
- Wav2Vec2ForCTC
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2022-03-02T23:29:05.000Z
- Last modified
- 2022-06-04T23:37:05.000Z
Compatibility and lineage
Reported languages: ar
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-06T06:40:55.269Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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