metadata
language:
- eu
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Base Basque
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_16_1 eu
type: mozilla-foundation/common_voice_16_1
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 16.17652806002814
Whisper Base Basque
This model is a fine-tuned version of openai/whisper-base on the mozilla-foundation/common_voice_16_1 eu dataset. It achieves the following results on the evaluation set:
- Loss: 0.5038
- Wer: 16.1765
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: 2.5e-05
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 40000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0225 | 10.0 | 1000 | 0.3059 | 19.0812 |
| 0.0037 | 20.0 | 2000 | 0.3530 | 18.4618 |
| 0.0012 | 30.0 | 3000 | 0.3724 | 17.9332 |
| 0.0004 | 40.0 | 4000 | 0.4025 | 17.8951 |
| 0.0002 | 50.0 | 5000 | 0.4245 | 17.8951 |
| 0.0001 | 60.0 | 6000 | 0.4459 | 17.9772 |
| 0.0001 | 70.0 | 7000 | 0.4665 | 18.0163 |
| 0.0 | 80.0 | 8000 | 0.4882 | 18.1081 |
| 0.0003 | 90.0 | 9000 | 0.3803 | 16.3807 |
| 0.0001 | 100.0 | 10000 | 0.4047 | 16.2293 |
| 0.0001 | 110.0 | 11000 | 0.4207 | 16.2420 |
| 0.0001 | 120.0 | 12000 | 0.4353 | 16.2879 |
| 0.0 | 130.0 | 13000 | 0.4502 | 16.3700 |
| 0.0 | 140.0 | 14000 | 0.4653 | 16.5087 |
| 0.0 | 150.0 | 15000 | 0.4805 | 16.4393 |
| 0.0 | 160.0 | 16000 | 0.4964 | 16.4941 |
| 0.0 | 170.0 | 17000 | 0.5128 | 16.5107 |
| 0.0 | 180.0 | 18000 | 0.5285 | 16.6377 |
| 0.0 | 190.0 | 19000 | 0.5457 | 16.6572 |
| 0.0102 | 200.0 | 20000 | 0.4229 | 18.1902 |
| 0.0 | 210.0 | 21000 | 0.4498 | 16.2117 |
| 0.0 | 220.0 | 22000 | 0.4646 | 16.2146 |
| 0.0 | 230.0 | 23000 | 0.4754 | 16.1961 |
| 0.0 | 240.0 | 24000 | 0.4853 | 16.1863 |
| 0.0 | 250.0 | 25000 | 0.4946 | 16.1912 |
| 0.0 | 260.0 | 26000 | 0.5038 | 16.1765 |
| 0.0 | 270.0 | 27000 | 0.5133 | 16.2215 |
| 0.0 | 280.0 | 28000 | 0.5228 | 16.2224 |
| 0.0 | 290.0 | 29000 | 0.5326 | 16.2557 |
| 0.0 | 300.0 | 30000 | 0.5427 | 16.2420 |
| 0.0 | 310.0 | 31000 | 0.5525 | 16.2635 |
| 0.0 | 320.0 | 32000 | 0.5624 | 16.2957 |
| 0.0 | 330.0 | 33000 | 0.5706 | 16.3299 |
| 0.0 | 340.0 | 34000 | 0.5798 | 16.3534 |
| 0.0 | 350.0 | 35000 | 0.5880 | 16.3495 |
| 0.0 | 360.0 | 36000 | 0.5948 | 16.3622 |
| 0.0 | 370.0 | 37000 | 0.6005 | 16.3934 |
| 0.0 | 380.0 | 38000 | 0.6045 | 16.3876 |
| 0.0 | 390.0 | 39000 | 0.6074 | 16.4325 |
| 0.0 | 400.0 | 40000 | 0.6085 | 16.4315 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1