metadata
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-et-ERR2020-v2
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: et
split: test
metrics:
- type: wer
value: 17.4
name: WER
whisper-large-et-ERR2020-v2
This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2915
- Wer: 13.8640
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: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2158 | 0.1 | 1000 | 0.3205 | 23.8154 |
| 0.0897 | 0.2 | 2000 | 0.2961 | 18.3340 |
| 0.0785 | 0.3 | 3000 | 0.2839 | 17.5230 |
| 0.0653 | 0.4 | 4000 | 0.2847 | 17.8752 |
| 0.0541 | 0.5 | 5000 | 0.2906 | 15.2645 |
| 0.0566 | 0.6 | 6000 | 0.2845 | 15.2081 |
| 0.051 | 0.7 | 7000 | 0.2888 | 14.4668 |
| 0.049 | 1.03 | 8000 | 0.2927 | 15.3130 |
| 0.044 | 1.13 | 9000 | 0.2915 | 13.8640 |
| 0.0379 | 1.23 | 10000 | 0.2913 | 16.5773 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+rocm5.1.1
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2