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-10m
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: 3.7424325811777654
Whisper-Small En-10m
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: 0.3635
- Wer: 3.7424
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 |
|---|---|---|---|---|
| 0.482 | 33.3333 | 100 | 0.7436 | 3.4183 |
| 0.2402 | 66.6667 | 200 | 0.5833 | 3.4448 |
| 0.0135 | 100.0 | 300 | 0.3881 | 3.5834 |
| 0.0029 | 133.3333 | 400 | 0.3731 | 3.6324 |
| 0.0019 | 166.6667 | 500 | 0.3685 | 3.6568 |
| 0.0014 | 200.0 | 600 | 0.3663 | 3.6854 |
| 0.0012 | 233.3333 | 700 | 0.3649 | 3.7098 |
| 0.0011 | 266.6667 | 800 | 0.3641 | 3.7241 |
| 0.001 | 300.0 | 900 | 0.3637 | 3.7465 |
| 0.001 | 333.3333 | 1000 | 0.3635 | 3.7424 |
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1