Whisper Small Mizonal3 E2 Lus v2026.06

--- language: - lus license: apache-2.0 pipeline_tag: automatic-speech-recognition base_model: openai/whisper-small tags: - generated_from_trainer datasets: - andrewbawitlung/MiZonal-v3.0 metrics: - wer - cer model-index: - name: whisper-small-mizonal3-E2-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: 22.3422 - name: Cer type: cer value: 8.0155 - name: Real Time Factor type: rtf value: 0.0255 ---  > **Disclaimer / Notice:** Details for these are in Peer Review and publications of the paper will be made available soon for more details. <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-mizonal3-E2-lus-v2026.06 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the **MiZonal v3.0** dataset. Note: ~1 hour of conversational speech was added to this dataset version. It achieves the following results on the evaluation set: - Wer: 22.3422 - Cer: 8.0155 - Real Time Factor: 0.0255 ## 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-small-mizonal3-E2-lus-v2026.06") model = WhisperForConditionalGeneration.from_pretrained("andrewbawitlung/whisper-small-mizonal3-E2-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...