metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small Albanian - Sumitesh
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_17_0
config: sq
split: None
args: 'config: sq, split: test'
metrics:
- name: Wer
type: wer
value: 52.63324873096447
language:
- sq
pipeline_tag: automatic-speech-recognition
Whisper Small Alb - Sumitesh
This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.2013
- Wer: 52.6332
Model description
This is a speech to text model finetuned over Whisper model by OpenAI.
Intended uses & limitations
This is free to use for learning or commercial purposes. I don't plan to monetize this ever or make it private. My goal is to make whisper more localized which is why i have this trained this model and made it public for everyone.
Training and evaluation data
This model is trained on common_voice_17 dataset. It is an open source multilingual dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.005 | 15.1515 | 1000 | 0.9955 | 53.7437 |
| 0.0003 | 30.3030 | 2000 | 1.1066 | 52.5698 |
| 0.0001 | 45.4545 | 3000 | 1.1585 | 52.8553 |
| 0.0001 | 60.6061 | 4000 | 1.1889 | 52.7284 |
| 0.0001 | 75.7576 | 5000 | 1.2013 | 52.6332 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1