metadata
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Medium en
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: en
split: test
args: en
metrics:
- name: Wer
type: wer
value: 26.482731293299587
Whisper Medium en
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6272
- Wer: 26.4827
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: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3844 | 0.2 | 1000 | 0.6787 | 28.9037 |
| 0.3104 | 0.4 | 2000 | 0.6485 | 27.1148 |
| 0.3125 | 0.6 | 3000 | 0.6359 | 26.4310 |
| 0.2607 | 0.8 | 4000 | 0.6310 | 26.3389 |
| 0.2683 | 1.0 | 5000 | 0.6272 | 26.4827 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1