Whisper Large v3 Turbo Mizonal3 E4 Lus v2026.06
> Disclaimer / Notice: Details for these are in Peer Review and publications of the paper will be made available soon for more details.
Source du modèle
- Type
- Text model
- Taille du fichier
- 3,01 GB
- Modèle de base
- openai/whisper-large-v3-turbo
- Version
- vmodel
- Mis à jour
- 19 oct. 2026
- Téléchargements
- 10
- Source
- Hugging Face
Description de la source
Mizo Automatic Speech Recognition (ASR) Models v3.0
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-E4-lus-v2026.06
This model is a fine-tuned version of openai/whisper-large-v3-turbo on the MiZonal v3.0 dataset.
It achieves the following results on the evaluation set:
- Wer: 15.2744
- Cer: 2.9739
- Real Time Factor: 0.0546
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-large-v3-turbo-mizonal3-E4-lus-v2026.06")
model = WhisperForConditionalGeneration.from_pretrained("andrewbawitlung/whisper-large-v3-turbo-mizonal3-E4-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
| Experiment | Hugging Face Repository |
|---|---|
| E1 (Baseline) | andrewbawitlung/whisper-large-v3-turbo-mizonal3-E1-lus-v2026.06 |
| E2 (Noise) | andrewbawitlung/whisper-large-v3-turbo-mizonal3-E2-lus-v2026.06 |
| E3 (Speed) | andrewbawitlung/whisper-large-v3-turbo-mizonal3-E3-lus-v2026.06 |
| E4 (SpecAug) | andrewbawitlung/whisper-large-v3-turbo-mizonal3-E4-lus-v2026.06 |
| E5 (Combined) | andrewbawitlung/whisper-large-v3-turbo-mizonal3-E5-lus-v2026.06 |
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: OptimizerNames.ADAMW_TORCH_FUSED
- lr_scheduler_type: SchedulerType.LINEAR
- num_epochs: 8
Training results
| step | epoch | train_loss | eval_loss | eval_wer | eval_cer | learning_rate | grad_norm |
|---|---|---|---|---|---|---|---|
| 250 | 0.9107 | 0.5478 | 0.4402 | 30.4953 | 7.0045 | 4.98e-06 | 9.9833 |
| 500 | 1.8197 | 0.3494 | 0.3157 | 22.3995 | 4.7993 | 9.98e-06 | 8.1049 |
| 750 | 2.7286 | 0.2393 | 0.2534 | 19.2079 | 3.9317 | 8.54e-06 | 6.5961 |
| 1000 | 3.6375 | 0.1636 | 0.2337 | 16.6196 | 3.4139 | 7.06e-06 | 6.5331 |
| 1250 | 4.5464 | 0.1076 | 0.2236 | 15.5687 | 3.1418 | 5.59e-06 | 4.9694 |
| 1500 | 5.4554 | 0.0743 | 0.2219 | 17.9722 | 5.5450 | 4.12e-06 | 4.4744 |
| 1750 | 6.3643 | 0.0506 | 0.2183 | 14.6735 | 2.9262 | 2.65e-06 | 3.7141 |
| 2000 | 7.2732 | 0.0339 | 0.2199 | 13.8465 | 2.7654 | 1.18e-06 | 2.8581 |
Sources
1 source
Sources
1 sourceVérifié 19 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- 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-E4-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: 15.2744 - name: Cer type: cer value: 2.9739 - 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-E4-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: 15.2744 - Cer: 2.9739 - 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-E4-lus-v2026.06") model = WhisperForConditionalGeneration.from_pretrained("andrewbawitlung/whisper-large-v3-turbo-mizonal3-E4-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: 10 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo andrewbawitlung/whisper-large-v3-turbo-mizonal3-E4-lus-v2026.06
Source context: 42 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo andrewbawitlung/whisper-large-v3-turbo-mizonal3-E4-lus-v2026.06
