|
--- |
|
license: apache-2.0 |
|
base_model: openai/whisper-small |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- facebook/voxpopuli |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: WhisperForSpokenNER-end2end |
|
results: |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: facebook/voxpopuli de+es+fr+nl |
|
type: facebook/voxpopuli |
|
config: de+es+fr_nl |
|
split: None |
|
metrics: |
|
- name: Wer |
|
type: wer |
|
value: 0.08582479210984335 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# WhisperForSpokenNER-end2end |
|
|
|
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the facebook/voxpopuli de+es+fr+nl dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2755 |
|
- Combined Wer: 0.1491 |
|
- F1 Score: 0.7163 |
|
- Label F1: 0.8200 |
|
- Wer: 0.0858 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 64 |
|
- eval_batch_size: 64 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2 |
|
- total_train_batch_size: 128 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_steps: 500 |
|
- training_steps: 5000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Combined Wer | F1 Score | Label F1 | Wer | |
|
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:------:| |
|
| 0.3252 | 0.1 | 500 | 0.3396 | 0.1918 | 0.6148 | 0.7578 | 0.1193 | |
|
| 0.2729 | 0.2 | 1000 | 0.3158 | 0.1730 | 0.6449 | 0.7907 | 0.1058 | |
|
| 0.2369 | 0.3 | 1500 | 0.2971 | 0.1736 | 0.6917 | 0.8083 | 0.1067 | |
|
| 0.1967 | 0.4 | 2000 | 0.2823 | 0.1634 | 0.6915 | 0.8095 | 0.0999 | |
|
| 0.1623 | 0.5 | 2500 | 0.2804 | 0.1693 | 0.7088 | 0.8249 | 0.1052 | |
|
| 0.1146 | 1.02 | 3000 | 0.2820 | 0.1593 | 0.7012 | 0.8106 | 0.0951 | |
|
| 0.0938 | 1.12 | 3500 | 0.2792 | 0.1500 | 0.7205 | 0.8238 | 0.0875 | |
|
| 0.1001 | 1.22 | 4000 | 0.2750 | 0.1549 | 0.7072 | 0.8061 | 0.0928 | |
|
| 0.0848 | 1.32 | 4500 | 0.2741 | 0.1471 | 0.7243 | 0.8318 | 0.0860 | |
|
| 0.0649 | 1.42 | 5000 | 0.2745 | 0.1468 | 0.7304 | 0.8350 | 0.0858 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.0.dev0 |
|
- Pytorch 2.1.0 |
|
- Datasets 2.14.6 |
|
- Tokenizers 0.14.1 |
|
|