metadata
language:
- et
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium et
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: ERR2020, Common Voice 11.0, FLEURS
type: mozilla-foundation/common_voice_11_0
config: et
split: test
args: et
metrics:
- name: Wer
type: wer
value: 29.720322799236126
Whisper Medium et
This model is a fine-tuned version of openai/whisper-medium on the ERR2020, Common Voice 11.0, FLEURS dataset. It achieves the following results on the evaluation set:
- Loss: 0.4288
- Wer: 29.7203
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-06
- 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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4018 | 0.1 | 500 | 0.5518 | 39.3951 |
| 0.2654 | 0.2 | 1000 | 0.4611 | 34.3929 |
| 0.2121 | 0.3 | 1500 | 0.4346 | 32.0582 |
| 0.1752 | 0.4 | 2000 | 0.4247 | 31.1926 |
| 0.1337 | 0.5 | 2500 | 0.4216 | 30.3364 |
| 0.1281 | 0.6 | 3000 | 0.4219 | 30.0745 |
| 0.1127 | 0.7 | 3500 | 0.4252 | 29.7388 |
| 0.1254 | 0.8 | 4000 | 0.4276 | 29.8928 |
| 0.1035 | 0.9 | 4500 | 0.4292 | 29.7634 |
| 0.1114 | 1.0 | 5000 | 0.4288 | 29.7203 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2