taphuynh/whisper-turbo-ml-en-codeswitch-fullft-2607.29.1-fp16-ct2

CTranslate2 conversion. This repo is `taphuynh/whisper-turbo-ml-en-codeswitch-fullft-2607.29.1-fp16` converted to CTranslate2 (float16) for use with faster-whisper. ``python from faster_whisper import WhisperModel model = WhisperModel("taphuynh/whisper-turbo-ml-en-codeswitch-fullft-2607.29.1-fp16-ct2", compute_type="float16") ` Converted with ct2-transformers-converter`. Original model card below.

taphuynh/whisper-turbo-ml-en-codeswitch-fullft-2607.29.1

Fine-tuned from `openai/whisper-large-v3-turbo` with arca-tuner-lite (prune_finetune_ml_en_codeswitch).

• —Base model: openai/whisper-large-v3-turbo
• —Recipe: full fine-tune
• —Language(s): ml, en
• —Run tags: whisper-turbo, malayalam-english, code-switch, pruned, vocab-prune, full-ft
• —Run group: ml-en-cs-fullft

Evaluation

Metrics on the held-out eval split, on the best checkpoint (the one this repo contains — training used early stopping / load_best_model_at_end):

Usage

from transformers import pipeline

asr = pipeline("automatic-speech-recognition", model="taphuynh/whisper-turbo-ml-en-codeswitch-fullft-2607.29.1")
print(asr("audio.wav")["text"])

Training data

• —taphuynh/arcaai-medical-malayalam-english
• —thennal/indic_tts_ml
• —thennal/ulca_ml
• —thennal/GMaSC
• —vrclc/imasc_slr
• —smcproject/MSC
• —taphuynh/MayoClinic_00001

Training procedure

The exact resolved configuration and environment are in run_card.json in this repo.

Notes & limitations

• —Fine-tuned on domain-specific speech; expect the usual Whisper failure modes (hallucination on silence/noise, degradation far out of domain).
• —Full weights are included; load directly with transformers.
• —Not a medical device and not for clinical decision-making.