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---
language:
- he
license: apache-2.0
base_model: openai/whisper-large-v2
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 [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0963
- Wer: 7.5207
- Avg Precision Exact: 0.9345
- Avg Recall Exact: 0.9347
- Avg F1 Exact: 0.9343
- Avg Precision Letter Shift: 0.9448
- Avg Recall Letter Shift: 0.9451
- Avg F1 Letter Shift: 0.9447
- Avg Precision Word Level: 0.9467
- Avg Recall Word Level: 0.9470
- Avg F1 Word Level: 0.9466
- Avg Precision Word Shift: 0.9749
- Avg Recall Word Shift: 0.9755
- Avg F1 Word Shift: 0.9748
- Precision Median Exact: 1.0
- Recall Median Exact: 1.0
- F1 Median Exact: 1.0
- 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: 0.1333
- Recall Min Word Shift: 0.1111
- F1 Min Word Shift: 0.125

## 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: 500
- training_steps: 80000
- 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|
| No log        | 0.0001 | 1     | 5.8251          | 118.7797 | 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                    | 0.0                 | 0.0             | 0                   | 0                | 0            | 0                   | 0                | 0            | 0                          | 0                       | 0                   | 0                        | 0                     | 0                 | 0                        | 0                     | 0                 |
| 0.0424        | 0.5167 | 10000 | 0.1027          | 13.0873  | 0.8913              | 0.8931           | 0.8917       | 0.9070                     | 0.9088                  | 0.9074              | 0.9103                   | 0.9117                | 0.9105            | 0.9544                   | 0.9564                | 0.9548            | 0.9412                 | 1.0                 | 0.9565          | 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                   | 0.125                 | 0.1333            |
| 0.0139        | 1.0334 | 20000 | 0.0944          | 10.6171  | 0.9121              | 0.9101           | 0.9107       | 0.9250                     | 0.9231                  | 0.9236              | 0.9271                   | 0.9256                | 0.9260            | 0.9644                   | 0.9639                | 0.9637            | 1.0                    | 1.0                 | 1.0             | 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               |
| 0.0085        | 1.5501 | 30000 | 0.0906          | 9.6951   | 0.9196              | 0.9190           | 0.9189       | 0.9328                     | 0.9324                  | 0.9322              | 0.9353                   | 0.9351                | 0.9348            | 0.9679                   | 0.9687                | 0.9679            | 1.0                    | 1.0                 | 1.0             | 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               |
| 0.0038        | 2.0668 | 40000 | 0.0954          | 9.0311   | 0.9217              | 0.9201           | 0.9206       | 0.9331                     | 0.9316                  | 0.9320              | 0.9352                   | 0.9335                | 0.9340            | 0.9696                   | 0.9696                | 0.9692            | 1.0                    | 1.0                 | 1.0             | 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                   | 0.1111                | 0.125             |
| 0.0033        | 2.5834 | 50000 | 0.0927          | 8.4962   | 0.9258              | 0.9256           | 0.9254       | 0.9371                     | 0.9370                  | 0.9367              | 0.9391                   | 0.9392                | 0.9388            | 0.9718                   | 0.9729                | 0.9719            | 1.0                    | 1.0                 | 1.0             | 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                   | 0.125                 | 0.1333            |
| 0.0025        | 3.1001 | 60000 | 0.0928          | 7.9801   | 0.9305              | 0.9299           | 0.9298       | 0.9413                     | 0.9407                  | 0.9407              | 0.9435                   | 0.9429                | 0.9429            | 0.9744                   | 0.9747                | 0.9742            | 1.0                    | 1.0                 | 1.0             | 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                   | 0.1111                | 0.125             |
| 0.0012        | 3.6168 | 70000 | 0.0960          | 7.9738   | 0.9312              | 0.9305           | 0.9306       | 0.9421                     | 0.9415                  | 0.9416              | 0.9441                   | 0.9437                | 0.9436            | 0.9737                   | 0.9739                | 0.9734            | 1.0                    | 1.0                 | 1.0             | 1.0                 | 1.0              | 1.0          | 0.0                 | 0.0              | 0.0          | 0.0                        | 0.0                     | 0.0                 | 0.0                      | 0.0                   | 0.0               | 0.1333                   | 0.1111                | 0.125             |
| 0.0002        | 4.1335 | 80000 | 0.0963          | 7.5207   | 0.9345              | 0.9347           | 0.9343       | 0.9448                     | 0.9451                  | 0.9447              | 0.9467                   | 0.9470                | 0.9466            | 0.9749                   | 0.9755                | 0.9748            | 1.0                    | 1.0                 | 1.0             | 1.0                 | 1.0              | 1.0          | 0.0                 | 0.0              | 0.0          | 0.0                        | 0.0                     | 0.0                 | 0.0                      | 0.0                   | 0.0               | 0.1333                   | 0.1111                | 0.125             |


### Framework versions

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