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: 4.148066613669255
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: 0.1610
- Wer: 4.1481
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 |
|---|---|---|---|---|
| 0.5827 | 5.1282 | 100 | 0.7468 | 3.4509 |
| 0.3801 | 10.2564 | 200 | 0.5781 | 3.4856 |
| 0.1166 | 15.3846 | 300 | 0.2330 | 3.8872 |
| 0.0469 | 20.5128 | 400 | 0.1750 | 4.1053 |
| 0.0249 | 25.6410 | 500 | 0.1637 | 4.1277 |
| 0.0173 | 30.7692 | 600 | 0.1609 | 4.1297 |
| 0.0119 | 35.8974 | 700 | 0.1604 | 4.1358 |
| 0.0087 | 41.0256 | 800 | 0.1607 | 4.1501 |
| 0.0074 | 46.1538 | 900 | 0.1609 | 4.1460 |
| 0.0071 | 51.2821 | 1000 | 0.1610 | 4.1481 |
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1