metadata
base_model: openai/whisper-large-v3
datasets:
- google/fleurs
library_name: transformers
license: apache-2.0
metrics:
- wer
model-index:
- name: whisper-large-v3-Urdu-Version1
results: []
language:
- ur
pipeline_tag: automatic-speech-recognition
whisper-large-v3-Urdu-Version1
This model is a fine-tuned version of openai/whisper-large-v3 on the fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.3244
- Wer: 20.6725
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 8
- 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: 1000
- training_steps: 20000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3271 | 6.7340 | 2000 | 0.3375 | 21.5842 |
| 0.3107 | 13.4680 | 4000 | 0.3244 | 20.9093 |
| 0.2797 | 20.2020 | 6000 | 0.3205 | 20.8383 |
| 0.2639 | 26.9360 | 8000 | 0.3202 | 20.5778 |
| 0.2529 | 33.6700 | 10000 | 0.3216 | 20.7909 |
| 0.26 | 40.4040 | 12000 | 0.3230 | 20.6843 |
| 0.2485 | 47.1380 | 14000 | 0.3244 | 20.6725 |
Framework versions
- PEFT 0.12.1.dev0
- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1