metadata
language:
- ca
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Large-V2 Catalan
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_13_0 ca
type: mozilla-foundation/common_voice_13_0
config: ca
split: test
args: ca
metrics:
- name: Wer
type: wer
value: 4.671620462989425
Whisper Large-V2 Catalan
This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_13_0 ca dataset. It achieves the following results on the evaluation set:
- Loss: 0.1494
- Wer: 4.6716
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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1072 | 1.02 | 1000 | 0.1637 | 7.0329 |
| 0.0239 | 3.02 | 2000 | 0.1784 | 7.0277 |
| 0.0507 | 5.02 | 3000 | 0.1754 | 6.5773 |
| 0.0571 | 7.02 | 4000 | 0.1620 | 6.5047 |
| 0.0193 | 9.02 | 5000 | 0.1821 | 6.4887 |
| 0.0625 | 11.02 | 6000 | 0.1443 | 6.7585 |
| 0.0752 | 13.02 | 7000 | 0.1653 | 5.9097 |
| 0.0359 | 15.02 | 8000 | 0.1406 | 5.8760 |
| 0.0565 | 17.01 | 9000 | 0.1496 | 5.9680 |
| 0.0196 | 19.01 | 10000 | 0.1788 | 5.2746 |
| 0.0215 | 21.01 | 11000 | 0.1539 | 5.3895 |
| 0.0178 | 23.01 | 12000 | 0.1800 | 5.3764 |
| 0.0114 | 25.01 | 13000 | 0.1709 | 5.2078 |
| 0.0123 | 27.01 | 14000 | 0.1827 | 5.2003 |
| 0.0337 | 29.01 | 15000 | 0.1553 | 5.3655 |
| 0.0108 | 31.01 | 16000 | 0.1476 | 4.9151 |
| 0.0194 | 33.01 | 17000 | 0.1396 | 4.8477 |
| 0.0472 | 35.0 | 18000 | 0.1202 | 4.8717 |
| 0.0401 | 37.0 | 19000 | 0.1494 | 4.6716 |
| 0.0127 | 39.0 | 20000 | 0.1187 | 4.7276 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3