andrewbawitlung/whisper-small-mizonal3-E5-lus-v2026.06

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Disclaimer / Notice: Details for these are in Peer Review and publications of the paper will be made available soon for more details.

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whisper-small-mizonal3-E5-lus-v2026.06

This model is a fine-tuned version of 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: 21.0562
• —Cer: 7.2973
• —Real Time Factor: 0.0263

Quick Inference

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-E5-lus-v2026.06")
model = WhisperForConditionalGeneration.from_pretrained("andrewbawitlung/whisper-small-mizonal3-E5-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 augmentations.

All Models in this Family

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 0.0003
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: OptimizerNames.ADAMWTORCHFUSED
• —lrschedulertype: SchedulerType.LINEAR
• —num_epochs: 8

Training results