|
--- |
|
language: |
|
- he |
|
base_model: ivrit-ai/whisper-v2-pd1-e1 |
|
tags: |
|
- hf-asr-leaderboard |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: he-cantillation |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# he-cantillation |
|
|
|
This model is a fine-tuned version of [ivrit-ai/whisper-v2-pd1-e1](https://huggingface.co/ivrit-ai/whisper-v2-pd1-e1) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1078 |
|
- Wer: 15.5291 |
|
- Avg Precision Exact: 0.8760 |
|
- Avg Recall Exact: 0.8758 |
|
- Avg F1 Exact: 0.8753 |
|
- Avg Precision Letter Shift: 0.8960 |
|
- Avg Recall Letter Shift: 0.8959 |
|
- Avg F1 Letter Shift: 0.8953 |
|
- Avg Precision Word Level: 0.8987 |
|
- Avg Recall Word Level: 0.8993 |
|
- Avg F1 Word Level: 0.8984 |
|
- Avg Precision Word Shift: 0.9450 |
|
- Avg Recall Word Shift: 0.9466 |
|
- Avg F1 Word Shift: 0.9451 |
|
- Precision Median Exact: 0.9231 |
|
- Recall Median Exact: 0.9231 |
|
- F1 Median Exact: 0.9286 |
|
- Precision Max Exact: 1.0 |
|
- Recall Max Exact: 1.0 |
|
- F1 Max Exact: 1.0 |
|
- Precision Min Exact: 0.0 |
|
- Recall Min Exact: 0.0 |
|
- F1 Min Exact: 0.0 |
|
- Precision Min Letter Shift: 0.0 |
|
- Recall Min Letter Shift: 0.0 |
|
- F1 Min Letter Shift: 0.0 |
|
- Precision Min Word Level: 0.0 |
|
- Recall Min Word Level: 0.0 |
|
- F1 Min Word Level: 0.0 |
|
- Precision Min Word Shift: 0.0769 |
|
- Recall Min Word Shift: 0.0909 |
|
- F1 Min Word Shift: 0.0833 |
|
|
|
## 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: 8 |
|
- 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: 10 |
|
- training_steps: 3000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | Avg Precision Exact | Avg Recall Exact | Avg F1 Exact | Avg Precision Letter Shift | Avg Recall Letter Shift | Avg F1 Letter Shift | Avg Precision Word Level | Avg Recall Word Level | Avg F1 Word Level | Avg Precision Word Shift | Avg Recall Word Shift | Avg F1 Word Shift | Precision Median Exact | Recall Median Exact | F1 Median Exact | Precision Max Exact | Recall Max Exact | F1 Max Exact | Precision Min Exact | Recall Min Exact | F1 Min Exact | Precision Min Letter Shift | Recall Min Letter Shift | F1 Min Letter Shift | Precision Min Word Level | Recall Min Word Level | F1 Min Word Level | Precision Min Word Shift | Recall Min Word Shift | F1 Min Word Shift | |
|
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:| |
|
| 0.1518 | 0.0517 | 1000 | 0.1823 | 26.0329 | 0.7790 | 0.7842 | 0.7807 | 0.8094 | 0.8153 | 0.8114 | 0.8144 | 0.8207 | 0.8167 | 0.8953 | 0.9056 | 0.8994 | 0.8462 | 0.8571 | 0.8571 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
|
| 0.0993 | 0.1033 | 2000 | 0.1316 | 18.8804 | 0.8477 | 0.8540 | 0.8501 | 0.8691 | 0.8757 | 0.8716 | 0.8734 | 0.8799 | 0.8759 | 0.9264 | 0.9342 | 0.9294 | 0.9091 | 0.9167 | 0.9091 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0769 | 0.0909 | 0.0833 | |
|
| 0.0625 | 0.1550 | 3000 | 0.1078 | 15.5291 | 0.8760 | 0.8758 | 0.8753 | 0.8960 | 0.8959 | 0.8953 | 0.8987 | 0.8993 | 0.8984 | 0.9450 | 0.9466 | 0.9451 | 0.9231 | 0.9231 | 0.9286 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0769 | 0.0909 | 0.0833 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.2 |
|
- Pytorch 2.2.1 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
