oddadmix / Whisperv3-tunisian-codeswitch

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Total runs: 5.3K
24-hour runs: 0
7-day runs: 8
30-day runs: 8
Model's Last Updated: August 03 2026
automatic-speech-recognition

Introduction of Whisperv3-tunisian-codeswitch

Model Details of Whisperv3-tunisian-codeswitch

NADI 2026 Subtask 1.3 — Tunisian Code-Switched ASR (faruk-v7)

Whisper-large-v3 full fine-tune for Tunisian Arabic ↔ French/English code-switched ASR (NADI 2026 shared task, subtask 1.3).

  • Recipe: fresh full-FT from tun-asr-aug-v4 on FARUKxAUTO/tunisian-asr-cleaned (46K, dense Tunisian↔French code-switch) + NADI TEDx train replay, 2 epochs , --spec_augment , paged_adamw_8bit , lr 5e-6 (a single longer cosine schedule — 2 epochs is the sweet spot).
  • Results: validation WER 17.26% / CER 6.91%; blind test WER 15.22 (🥉).
  • Decode greedy, language="ar" , task="transcribe" . Submit raw (scorer applies clean_transcription ).
from transformers import WhisperForConditionalGeneration, WhisperProcessor
import torch
m = WhisperForConditionalGeneration.from_pretrained("oddadmix/nadi2026-subtask1.3-tunisian-codeswitch-asr-faruk-v7", torch_dtype=torch.bfloat16).cuda().eval()
p = WhisperProcessor.from_pretrained("oddadmix/nadi2026-subtask1.3-tunisian-codeswitch-asr-faruk-v7")
# feats = p(audio, sampling_rate=16000, return_tensors="pt").input_features.cuda().to(torch.bfloat16)
# ids = m.generate(feats, language="ar", task="transcribe", max_new_tokens=256)
# print(p.batch_decode(ids, skip_special_tokens=True))

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5.3K
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huggingface.co

Total runs: 17
Run Growth: 1
Growth Rate: 5.88%
Updated:July 18 2026
huggingface.co

Total runs: 17
Run Growth: 0
Growth Rate: 0.00%
Updated:July 18 2026