metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-finetuned-minds14
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.30460448642266824
whisper-tiny-finetuned-minds14
This model is a fine-tuned version of openai/whisper-tiny on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5352
- Wer Ortho: 0.3029
- Wer: 0.3046
Model description
I have made it for audio corse Unit 5 Hands-on. Here is some additional info https://outleys.site/en/development/AI/hugface-unit-5-excercise-guide/
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 50
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 1.1444 | 0.8850 | 100 | 0.4740 | 0.3411 | 0.3388 |
| 0.2788 | 1.7699 | 200 | 0.4633 | 0.2986 | 0.3017 |
| 0.1377 | 2.6549 | 300 | 0.4969 | 0.3048 | 0.3052 |
| 0.0561 | 3.5398 | 400 | 0.5145 | 0.3017 | 0.3034 |
| 0.0177 | 4.4248 | 500 | 0.5241 | 0.3091 | 0.3117 |
| 0.01 | 5.3097 | 600 | 0.5352 | 0.3029 | 0.3046 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3