metadata
library_name: transformers
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Ori vi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 15.282853384008185
Whisper Small Ori vi
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4093
- Wer: 15.2829
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: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 1300
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4964 | 0.2222 | 100 | 0.4513 | 18.4622 |
| 0.4607 | 0.4444 | 200 | 0.4251 | 18.1479 |
| 0.4223 | 0.6667 | 300 | 0.4054 | 17.3732 |
| 0.4186 | 0.8889 | 400 | 0.3998 | 17.2562 |
| 0.2411 | 1.1111 | 500 | 0.3978 | 16.8616 |
| 0.2425 | 1.3333 | 600 | 0.3946 | 16.8396 |
| 0.2194 | 1.5556 | 700 | 0.3926 | 14.8882 |
| 0.238 | 1.7778 | 800 | 0.3905 | 16.5034 |
| 0.2323 | 2.0 | 900 | 0.3904 | 15.2755 |
| 0.1294 | 2.2222 | 1000 | 0.4076 | 15.0709 |
| 0.1139 | 2.4444 | 1100 | 0.4080 | 15.3706 |
| 0.1108 | 2.6667 | 1200 | 0.4102 | 15.1732 |
| 0.1197 | 2.8889 | 1300 | 0.4093 | 15.2829 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.0