|
--- |
|
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 |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# Whisper Small English |
|
|
|
This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/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 |
|
|
