metadata
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-minds14-us-vickymm
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: minds14-us (whisper-tiny)
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.7485242030696576
whisper-tiny-minds14-us-vickymm
This model is a fine-tuned version of openai/whisper-tiny on the minds14-us (whisper-tiny) dataset. It achieves the following results on the evaluation set:
- Loss: 2.4049
- Wer: 0.7485
- Wer Ortho: 0.7569
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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Wer Ortho |
|---|---|---|---|---|---|
| 1.1149 | 1.79 | 100 | 0.5379 | 0.4097 | 0.4176 |
| 0.1705 | 3.57 | 200 | 0.7637 | 0.5762 | 0.5836 |
| 0.166 | 5.36 | 300 | 1.2479 | 0.5384 | 0.5416 |
| 0.2409 | 7.14 | 400 | 1.5261 | 0.6765 | 0.6619 |
| 0.2773 | 8.93 | 500 | 1.8106 | 0.7863 | 0.7816 |
| 0.2715 | 10.71 | 600 | 2.0421 | 0.7739 | 0.7841 |
| 0.2434 | 12.5 | 700 | 2.2664 | 0.7456 | 0.7514 |
| 0.1979 | 14.29 | 800 | 2.1956 | 0.6983 | 0.7039 |
| 0.1843 | 16.07 | 900 | 2.3711 | 0.8182 | 0.8229 |
| 0.1555 | 17.86 | 1000 | 2.4049 | 0.7485 | 0.7569 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0