Whisper Large v3 Turbo Mizonal3 E3 Lus v2026.06

--- language: - lus license: apache-2.0 pipeline_tag: automatic-speech-recognition base_model: openai/whisper-large-v3-turbo tags: - generated_from_trainer datasets: - andrewbawitlung/MiZonal-v3.0 metrics: - wer - cer model-index: - name: whisper-large-v3-turbo-mizonal3-E3-lus-v2026.06 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: MiZonal v3.0 type: andrewbawitlung/MiZonal-v3.0 config: default split: test metrics: - name: Wer type: wer value: 14.1847 - name: Cer type: cer value: 2.7327 - name: Real Time Factor type: rtf value: 0.0546 ---  > **Disclaimer / Notice:** Details for these are in Peer Review and publications of the paper will be made available soon for more details. # whisper-large-v3-turbo-mizonal3-E3-lus-v2026.06 This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the **MiZonal v3.0** dataset. It achieves the following results on the evaluation set: - Wer: 14.1847 - Cer: 2.7327 - Real Time Factor: 0.0546 ## Quick Inference ```python import torch import librosa from transformers import WhisperProcessor, WhisperForConditionalGeneration device = "cuda" if torch.cuda.is_available() else "cpu" processor = WhisperProcessor.from_pretrained("andrewbawitlung/whisper-large-v3-turbo-mizonal3-E3-lus-v2026.06") model = WhisperForConditionalGeneration.from_pretrained("andrewbawitlung/whisper-large-v3-turbo-mizonal3-E3-lus-v2026.06").to(device) audio, sr = librosa.load("your_audio.wav", sr=16000) input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features.to(device) with torch.no_grad(): predicted_ids = model.generate(input_features, max_new_tokens=256) transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] print(transcription) ``` ## Model description ### Experiment Configurations This repository is part of a series of experiments. The different configurations are: - **E1 (Baseline):** Standard training configuration. - **E2 (Noise):** Training with background noise augmentation. - **E3 (Speed):** Training with speed perturbation augmentation. - **E4 (SpecAug):** Training with SpecAugment (time and frequency masking). - **E5 (Combined):** Training with a combination of all...

Source context: 9 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo andrewbawitlung/whisper-large-v3-turbo-mizonal3-E3-lus-v2026.06

Source context: 39 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo andrewbawitlung/whisper-large-v3-turbo-mizonal3-E3-lus-v2026.06