---
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