Whisper Large v3 Turbo Mizonal3 E2 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.
Fuente del modelo
- Tipo
- Text model
- Tamaño del archivo
- 3,01 GB
- Modelo base
- openai/whisper-large-v3-turbo
- Versión
- vmodel
- Actualizado
- 19 oct 2026
- Descargas
- 7
- Fuente
- Hugging Face
Descripción de la fuente
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-E2-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: 14.6167
- Cer: 2.8214
- Real Time Factor: 0.0560
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-E2-lus-v2026.06")
model = WhisperForConditionalGeneration.from_pretrained("andrewbawitlung/whisper-large-v3-turbo-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.
- 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 | 0.4554 | 0.5879 | 0.4359 | 32.0230 | 7.0116 | 4.98e-06 | 11.2265 |
| 500 | 0.9107 | 0.3496 | 0.3265 | 22.7498 | 4.9071 | 9.98e-06 | 8.8566 |
| 750 | 1.3661 | 0.2324 | 0.2632 | 19.2858 | 3.9599 | 9.36e-06 | 7.9487 |
| 1000 | 1.8215 | 0.1762 | 0.2435 | 17.4954 | 3.6383 | 8.72e-06 | 5.7156 |
| 1250 | 2.2769 | 0.0953 | 0.2509 | 17.7873 | 4.6455 | 8.08e-06 | 4.9855 |
| 1500 | 2.7322 | 0.0857 | 0.2428 | 18.3711 | 5.0095 | 7.43e-06 | 4.0912 |
| 1750 | 3.1876 | 0.0474 | 0.2399 | 16.2304 | 3.3503 | 6.79e-06 | 2.3282 |
| 2000 | 3.6430 | 0.0421 | 0.2514 | 15.9774 | 4.0147 | 6.15e-06 | 2.2509 |
| 2250 | 4.0984 | 0.0224 | 0.2566 | 14.7903 | 3.1241 | 5.51e-06 | 1.3668 |
| 2500 | 4.5537 | 0.0202 | 0.2679 | 15.4520 | 3.3097 | 4.86e-06 | 2.0542 |
| 2750 | 5.0091 | 0.0164 | 0.2702 | 16.7072 | 5.0944 | 4.22e-06 | 1.1179 |
| 3000 | 5.4645 | 0.0107 | 0.2733 | 16.6780 | 5.0908 | 3.58e-06 | 2.6542 |
| 3250 | 5.9199 | 0.0076 | 0.2848 | 13.9729 | 3.0322 | 2.94e-06 | 0.2975 |
| 3500 | 6.3752 | 0.0039 | 0.2819 | 15.7731 | 5.0785 | 2.29e-06 | 0.1877 |
| 3750 | 6.8306 | 0.0024 | 0.2863 | 13.7297 | 2.8979 | 1.65e-06 | 0.3065 |
| 4000 | 7.2860 | 0.0015 | 0.2860 | 13.7297 | 2.8697 | 1.01e-06 | 0.2391 |
| 4250 | 7.7413 | 0.0010 | 0.2887 | 14.8779 | 4.2497 | 3.67e-07 | 0.0548 |
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 19 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- 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-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: 14.6167 - name: Cer type: cer value: 2.8214 - name: Real Time Factor type: rtf value: 0.0560 ---  > **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-E2-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: 14.6167 - Cer: 2.8214 - Real Time Factor: 0.0560 ## 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-E2-lus-v2026.06") model = WhisperForConditionalGeneration.from_pretrained("andrewbawitlung/whisper-large-v3-turbo-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. - **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: 7 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo andrewbawitlung/whisper-large-v3-turbo-mizonal3-E2-lus-v2026.06
Source context: 39 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo andrewbawitlung/whisper-large-v3-turbo-mizonal3-E2-lus-v2026.06
