---
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: 0.1099
- Wer: 16.2686
- Avg Precision Exact: 0.8645
- Avg Recall Exact: 0.8618
- Avg F1 Exact: 0.8626
- Avg Precision Letter Shift: 0.8838
- Avg Recall Letter Shift: 0.8813
- Avg F1 Letter Shift: 0.8819
- Avg Precision Word Level: 0.8869
- Avg Recall Word Level: 0.8850
- Avg F1 Word Level: 0.8853
- Avg Precision Word Shift: 0.9412
- Avg Recall Word Shift: 0.9408
- Avg F1 Word Shift: 0.9403
- 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: 0.1429
- Recall Min Word Shift: 0.0909
- F1 Min Word Shift: 0.1111

## 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 |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|
| 0.1552        | 0.0517 | 1000 | 0.1843          | 26.9266 | 0.7790              | 0.7668           | 0.7718       | 0.8099                     | 0.7974                  | 0.8025              | 0.8153                   | 0.8044                | 0.8088            | 0.8935                   | 0.8895                | 0.8902            | 0.8462                 | 0.8462              | 0.8462          | 1.0                 | 1.0              | 1.0          | 0.0                 | 0.0              | 0.0          | 0.0                        | 0.0                     | 0.0                 | 0.0                      | 0.0                   | 0.0               | 0.1429                   | 0.0909                | 0.1111            |
| 0.0973        | 0.1033 | 2000 | 0.1317          | 19.9251 | 0.8323              | 0.8338           | 0.8323       | 0.8540                     | 0.8558                  | 0.8542              | 0.8583                   | 0.8605                | 0.8587            | 0.9204                   | 0.9258                | 0.9223            | 0.9091                 | 0.9091              | 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               | 0.0                      | 0.0                   | 0.0               |
| 0.0625        | 0.1550 | 3000 | 0.1099          | 16.2686 | 0.8645              | 0.8618           | 0.8626       | 0.8838                     | 0.8813                  | 0.8819              | 0.8869                   | 0.8850                | 0.8853            | 0.9412                   | 0.9408                | 0.9403            | 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               | 0.1429                   | 0.0909                | 0.1111            |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.20.0
- Tokenizers 0.19.1