metadata
library_name: transformers
language:
- dv
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
model-index:
- name: Whisper Small - DV - Marlhex
results: []
metrics:
- wer
Whisper Small - DV - Marlhex
This model is a fine-tuned version of openai/whisper-small on the Common Voice 13 dataset.
Model description
Whisper fine tunned for dv language (Maldivan language, Divehi) from Maldives
Intended uses & limitations
part of the AI portfolio to show to companies some of the work I've done in the Audio pilar.
Training and evaluation data
WER normalized.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 5
- training_steps: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| No log | 0.0326 | 10 | 1.9577 | 100.0 | 100.0 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
Next Steps
- Looking forward to training more languages that require more GB of storage, but my setup is limited.