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: 16.551919937539925
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: 0.4981
- Wer: 16.5519
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: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5019 | 0.2222 | 100 | 0.4649 | 17.3540 |
| 0.4235 | 0.4444 | 200 | 0.4257 | 16.7932 |
| 0.4364 | 0.6667 | 300 | 0.4184 | 16.5164 |
| 0.4106 | 0.8889 | 400 | 0.4043 | 15.6434 |
| 0.2338 | 1.1111 | 500 | 0.4064 | 15.7286 |
| 0.2286 | 1.3333 | 600 | 0.4066 | 15.9699 |
| 0.2185 | 1.5556 | 700 | 0.4058 | 15.7428 |
| 0.212 | 1.7778 | 800 | 0.3999 | 15.6079 |
| 0.2308 | 2.0 | 900 | 0.3991 | 17.2617 |
| 0.0983 | 2.2222 | 1000 | 0.4233 | 15.9415 |
| 0.1183 | 2.4444 | 1100 | 0.4286 | 16.0409 |
| 0.1003 | 2.6667 | 1200 | 0.4304 | 16.0764 |
| 0.1005 | 2.8889 | 1300 | 0.4332 | 15.7641 |
| 0.048 | 3.1111 | 1400 | 0.4636 | 16.3248 |
| 0.0475 | 3.3333 | 1500 | 0.4684 | 16.2041 |
| 0.0516 | 3.5556 | 1600 | 0.4679 | 16.2254 |
| 0.058 | 3.7778 | 1700 | 0.4691 | 16.2538 |
| 0.0457 | 4.0 | 1800 | 0.4693 | 16.2041 |
| 0.028 | 4.2222 | 1900 | 0.4940 | 16.4880 |
| 0.0235 | 4.4444 | 2000 | 0.4981 | 16.5519 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.0