metadata
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: openai/whisper-large
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: gl
split: validation
args: gl
metrics:
- name: Wer
type: wer
value: 6.500536091031715
openai/whisper-large
This model is a fine-tuned version of openai/whisper-large on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3219
- Wer: 6.5005
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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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 |
|---|---|---|---|---|
| 0.0476 | 5.83 | 1000 | 0.1829 | 6.3535 |
| 0.008 | 11.66 | 2000 | 0.2224 | 6.2705 |
| 0.0043 | 17.49 | 3000 | 0.2360 | 6.3397 |
| 0.0029 | 23.32 | 4000 | 0.2544 | 6.5386 |
| 0.0036 | 29.15 | 5000 | 0.2552 | 6.6977 |
| 0.0026 | 34.99 | 6000 | 0.2737 | 6.8568 |
| 0.0009 | 40.82 | 7000 | 0.2734 | 6.6320 |
| 0.0009 | 46.65 | 8000 | 0.2769 | 6.8187 |
| 0.0006 | 52.48 | 9000 | 0.2832 | 6.6164 |
| 0.0013 | 58.31 | 10000 | 0.2883 | 7.0176 |
| 0.0005 | 64.14 | 11000 | 0.2972 | 6.8983 |
| 0.0006 | 69.97 | 12000 | 0.2964 | 6.6735 |
| 0.0003 | 75.8 | 13000 | 0.3042 | 6.7392 |
| 0.0002 | 81.63 | 14000 | 0.3084 | 6.7426 |
| 0.0001 | 87.46 | 15000 | 0.3145 | 6.6631 |
| 0.0002 | 93.29 | 16000 | 0.3091 | 6.6666 |
| 0.0001 | 99.13 | 17000 | 0.3170 | 6.8758 |
| 0.0002 | 104.96 | 18000 | 0.3223 | 6.6337 |
| 0.0 | 110.79 | 19000 | 0.3219 | 6.4971 |
| 0.0001 | 116.62 | 20000 | 0.3219 | 6.5005 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1