metadata
base_model: nadsoft/hamsa_small
tags:
- whisper-event
- generated_from_trainer
datasets:
- nadsoft/QASR-Speech-Resource
metrics:
- wer
model-index:
- name: hamsa-small-finetuned-qasr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: nadsoft/QASR-Speech-Resource default
type: nadsoft/QASR-Speech-Resource
metrics:
- name: Wer
type: wer
value: 22.587152044424403
hamsa-small-finetuned-qasr
This model is a fine-tuned version of nadsoft/hamsa_small on the nadsoft/QASR-Speech-Resource default dataset. It achieves the following results on the evaluation set:
- Loss: 0.2831
- Wer: 22.5872
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: 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: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3207 | 0.03 | 2500 | 0.3458 | 24.4928 |
| 0.3264 | 0.05 | 5000 | 0.3316 | 23.4082 |
| 0.3223 | 0.08 | 7500 | 0.3239 | 25.1050 |
| 0.3121 | 0.1 | 10000 | 0.3143 | 23.4557 |
| 0.3103 | 0.13 | 12500 | 0.3079 | 23.4296 |
| 0.3041 | 0.15 | 15000 | 0.3033 | 23.2113 |
| 0.3077 | 0.18 | 17500 | 0.2998 | 21.7091 |
| 0.2867 | 0.2 | 20000 | 0.2943 | 20.1761 |
| 0.265 | 0.23 | 22500 | 0.2921 | 21.6522 |
| 0.3096 | 0.25 | 25000 | 0.2894 | 22.1505 |
| 0.2813 | 0.28 | 27500 | 0.2863 | 22.4993 |
| 0.2805 | 0.3 | 30000 | 0.2832 | 21.0114 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0