metadata
language:
- multilingual
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: >-
basic_train_basic_test 1000 similar params: per_device_train_batch_size=8,
# bylo 16 a pod tim 1 gradient_accumulation_steps=2, warmup_steps=300,
max_steps=3000
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: xbilek25/basic_train_set_en_last3cs_1000
type: mozilla-foundation/common_voice_11_0
args: 'config: csen, split: train'
metrics:
- name: Wer
type: wer
value: 15.800293685756243
basic_train_basic_test 1000 similar params: per_device_train_batch_size=8, # bylo 16 a pod tim 1 gradient_accumulation_steps=2, warmup_steps=300, max_steps=3000
This model is a fine-tuned version of openai/whisper-small on the xbilek25/basic_train_set_en_last3cs_1000 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1368
- Wer: 15.8003
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0021 | 9.01 | 600 | 0.1419 | 16.5590 |
| 0.0002 | 19.0 | 1200 | 0.1322 | 15.3842 |
| 0.0001 | 28.02 | 1800 | 0.1351 | 15.2912 |
| 0.0001 | 38.01 | 2400 | 0.1362 | 15.8639 |
| 0.0001 | 47.02 | 3000 | 0.1368 | 15.8003 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2