metadata
language:
- eu
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Basque
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_13_0 eu
type: mozilla-foundation/common_voice_13_0
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 13.179958686054519
Whisper Small Basque
This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_13_0 eu dataset. It achieves the following results on the evaluation set:
- Loss: 0.2201
- Wer: 13.1800
Model description
More information needed
Intended uses & limitations
If you need to use this model with whisper.cpp, you can download the ggml file: ggml-medium-eu.bin
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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 7000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4203 | 0.14 | 1000 | 0.4128 | 28.2656 |
| 0.2693 | 0.29 | 2000 | 0.3240 | 22.0523 |
| 0.2228 | 0.43 | 3000 | 0.2737 | 18.1437 |
| 0.1002 | 1.1 | 4000 | 0.2554 | 16.3534 |
| 0.0863 | 1.24 | 5000 | 0.2351 | 14.7880 |
| 0.0636 | 1.39 | 6000 | 0.2251 | 13.5971 |
| 0.0271 | 2.06 | 7000 | 0.2201 | 13.1800 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2