metadata
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- librispeech_asr
metrics:
- wer
model-index:
- name: Whisper-Small En-10h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: librispeech
type: librispeech_asr
config: default
split: None
args: 'config: en, split: test-clean'
metrics:
- name: Wer
type: wer
value: 3.9809209319390937
Whisper-Small En-10h
This model is a fine-tuned version of openai/whisper-small on the librispeech dataset. It achieves the following results on the evaluation set:
- Loss: 0.1307
- Wer: 3.9809
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-07
- 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: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.525 | 0.5556 | 100 | 0.7431 | 3.4571 |
| 0.382 | 1.1111 | 200 | 0.5645 | 3.4836 |
| 0.1704 | 1.6667 | 300 | 0.2111 | 4.0237 |
| 0.0953 | 2.2222 | 400 | 0.1527 | 4.1114 |
| 0.0904 | 2.7778 | 500 | 0.1404 | 4.0400 |
| 0.0784 | 3.3333 | 600 | 0.1355 | 4.0482 |
| 0.0793 | 3.8889 | 700 | 0.1331 | 3.9768 |
| 0.0776 | 4.4444 | 800 | 0.1318 | 3.9646 |
| 0.0629 | 5.0 | 900 | 0.1310 | 3.9830 |
| 0.0746 | 5.5556 | 1000 | 0.1307 | 3.9809 |
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1