Whisper Small Mizonal3 E1 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.
Fonte do modelo
- Tipo
- Text model
- Tamanho do arquivo
- 922 MB
- Modelo base
- openai/whisper-small
- Versão
- vmodel
- Atualizado
- 19 de out. de 2026
- Fonte
- Hugging Face
Descrição da fonte
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-small-mizonal3-E1-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: 20.9090
- Cer: 6.6145
- Real Time Factor: 0.0256
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-E1-lus-v2026.06")
model = WhisperForConditionalGeneration.from_pretrained("andrewbawitlung/whisper-small-mizonal3-E1-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-small-mizonal3-E1-lus-v2026.06 |
| E2 (Noise) | andrewbawitlung/whisper-small-mizonal3-E2-lus-v2026.06 |
| E3 (Speed) | andrewbawitlung/whisper-small-mizonal3-E3-lus-v2026.06 |
| E4 (SpecAug) | andrewbawitlung/whisper-small-mizonal3-E4-lus-v2026.06 |
| E5 (Combined) | andrewbawitlung/whisper-small-mizonal3-E5-lus-v2026.06 |
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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 | 0.91 | 0.5595 | 0.5548 | 61.74 | 28.44 | 1.49e-04 | 5.02 |
| 500 | 1.82 | 0.6523 | 0.7330 | 39.35 | 15.07 | 2.99e-04 | 8.09 |
| 750 | 2.73 | 0.5007 | 0.6280 | 37.52 | 17.94 | 2.56e-04 | 6.30 |
| 1000 | 3.64 | 0.3050 | 0.5721 | 29.66 | 11.26 | 2.12e-04 | 3.05 |
| 1250 | 4.55 | 0.1731 | 0.5281 | 27.26 | 10.38 | 1.68e-04 | 3.45 |
| 1500 | 5.46 | 0.0961 | 0.4887 | 23.05 | 7.51 | 1.24e-04 | 2.42 |
| 1750 | 6.36 | 0.0464 | 0.4911 | 22.28 | 7.40 | 7.96e-05 | 2.54 |
| 2000 | 7.27 | 0.0174 | 0.4611 | 19.28 | 5.81 | 3.55e-05 | 0.62 |
Fontes
1 fonte
Fontes
1 fonteVerificado 19 de out.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- 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-E1-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: 20.9090 - name: Cer type: cer value: 6.6145 - name: Real Time Factor type: rtf value: 0.0256 ---  > **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-E1-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: 20.9090 - Cer: 6.6145 - Real Time Factor: 0.0256 ## 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-E1-lus-v2026.06") model = WhisperForConditionalGeneration.from_pretrained("andrewbawitlung/whisper-small-mizonal3-E1-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...
Source context: 44 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo andrewbawitlung/whisper-small-mizonal3-E1-lus-v2026.06
