cstr/wav2vec2-large-xlsr-53-arabic-GGUF

Wav2Vec2 Large Xlsr 53 Arabic -- GGUF

GGUF conversions and quantisations of jonatasgrosman/wav2vec2-large-xlsr-53-arabic for use with CrispStrobe/CrispASR.

Available variants

File	Quant	Size	Notes
wav2vec2-large-xlsr-53-arabic-f16.gguf	F16	~627 MB	Reference conversion
wav2vec2-large-xlsr-53-arabic-q4_k.gguf	Q4_K	~212 MB	Default auto-download target
wav2vec2-large-xlsr-53-arabic-q8_0.gguf	Q8_0	~356 MB	Higher precision

Model details

• Architecture: Wav2Vec2ForCTC
• Hidden size: 1024
• Attention heads: 16
• Transformer layers: 24
• CTC vocabulary: 51 tokens
• Language: Arabic
• Base model: jonatasgrosman/wav2vec2-large-xlsr-53-arabic

Usage with CrispASR

crispasr --backend wav2vec2 -m wav2vec2-large-xlsr-53-arabic-q4_k.gguf -f audio.wav -l ar

# Auto-download via registry alias once published:
crispasr --backend wav2vec2 -m auto --auto-download -l ar -f audio.wav

Provenance

Converted from the upstream Hugging Face checkpoint with models/convert-wav2vec2-to-gguf.py, then quantized with build-ninja-compile/bin/crispasr-quantize.

License

Apache-2.0 — same as the upstream model card.

Provenance and EU AI Act Art. 53 note

• Upstream model: jonatasgrosman/wav2vec2-large-xlsr-53-arabic — published by jonatasgrosman.
• Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.