---
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: 0.1088
- Wer: 15.5669
- Avg Precision Exact: 0.8757
- Avg Recall Exact: 0.8740
- Avg F1 Exact: 0.8742
- Avg Precision Letter Shift: 0.8953
- Avg Recall Letter Shift: 0.8939
- Avg F1 Letter Shift: 0.8939
- Avg Precision Word Level: 0.8982
- Avg Recall Word Level: 0.8977
- Avg F1 Word Level: 0.8973
- Avg Precision Word Shift: 0.9445
- Avg Recall Word Shift: 0.9462
- Avg F1 Word Shift: 0.9446
- 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 |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|
| 0.1548        | 0.0517 | 1000 | 0.1831          | 26.0896 | 0.7735              | 0.7794           | 0.7755       | 0.8024                     | 0.8089                  | 0.8047              | 0.8073                   | 0.8148                | 0.8101            | 0.8872                   | 0.8993                | 0.8921            | 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               | 0.0769                   | 0.0909                | 0.0833            |
| 0.0967        | 0.1033 | 2000 | 0.1297          | 18.6035 | 0.8415              | 0.8449           | 0.8425       | 0.8634                     | 0.8672                  | 0.8646              | 0.8676                   | 0.8714                | 0.8688            | 0.9293                   | 0.9355                | 0.9315            | 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            |
| 0.0618        | 0.1550 | 3000 | 0.1088          | 15.5669 | 0.8757              | 0.8740           | 0.8742       | 0.8953                     | 0.8939                  | 0.8939              | 0.8982                   | 0.8977                | 0.8973            | 0.9445                   | 0.9462                | 0.9446            | 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