metadata
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- openai/whisper-small
metrics:
- wer
model-index:
- name: Whisper Small fine tuned with comms
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: BrainHack ASR Test Two
type: openai/whisper-small
metrics:
- name: Wer
type: wer
value: 0.03260869565217391
Whisper Small fine tuned with comms
This model is a fine-tuned version of openai/whisper-small on the BrainHack ASR Test Two dataset. It achieves the following results on the evaluation set:
- Loss: 0.2146
- Wer: 0.0326
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 10
- training_steps: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0059 | 13.3333 | 20 | 0.1427 | 0.0380 |
| 0.0003 | 26.6667 | 40 | 0.2099 | 0.0380 |
| 0.0001 | 40.0 | 60 | 0.2171 | 0.0326 |
| 0.0001 | 53.3333 | 80 | 0.2154 | 0.0326 |
| 0.0001 | 66.6667 | 100 | 0.2146 | 0.0326 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1