metadata
language:
- lt
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-lt-finetune
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: 'null'
split: None
args: 'config: lt, split: test'
metrics:
- name: Wer
type: wer
value: 28.115930505307517
whisper-lt-finetune
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3634
- Wer: 28.1159
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: 5e-05
- train_batch_size: 16
- 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: 250
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.399 | 0.9 | 500 | 0.4877 | 49.1790 |
| 0.1925 | 1.8 | 1000 | 0.4019 | 39.1325 |
| 0.0734 | 2.7 | 1500 | 0.3989 | 37.5581 |
| 0.0324 | 3.6 | 2000 | 0.3947 | 32.9662 |
| 0.0053 | 5.4 | 3000 | 0.3708 | 29.2808 |
| 0.0007 | 7.19 | 4000 | 0.3634 | 28.1159 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2