license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small English
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs en_us
type: google/fleurs
config: en_us
split: test
args: en_us
metrics:
- type: wer
value: 7.990755655157924
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: en
split: test
metrics:
- type: wer
value: 18.21
name: WER
Whisper Small English
This model is a fine-tuned version of openai/whisper-small.en on the google/fleurs en_us dataset. It achieves the following results on the evaluation set:
- Loss: 0.6007
- Wer: 7.9908
Model description
This model was created as part of the Whisper Fine-Tune Event. This is my first attempt at fine-tuning the Whisper neural network. Honestly, it's my second time ever trying anything related to training a neural network, and my first time was pretty bad (but I did get a lot of rather funny images out of it, so perhaps it wasn't entirely fruitless?), and it seems like the WER only went up after step 2000, so... I'm not sure if I did a good job or if I just wasted GPU cycles, but maybe I can try again and get a better score?
I'm learning.
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: 32
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0005 | 24.0 | 1000 | 0.5092 | 7.5566 |
| 0.0002 | 48.01 | 2000 | 0.5528 | 7.7526 |
| 0.0001 | 73.0 | 3000 | 0.5785 | 7.8507 |
| 0.0001 | 97.0 | 4000 | 0.5936 | 7.9908 |
| 0.0001 | 121.01 | 5000 | 0.6007 | 7.9908 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2