---
library_name: transformers
language:
- he
license: apache-2.0
base_model: openai/whisper-tiny
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-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9273
- Wer: 71.8937
- Avg Precision Exact: 0.1966
- Avg Recall Exact: 0.2113
- Avg F1 Exact: 0.2010
- Avg Precision Letter Shift: 0.2269
- Avg Recall Letter Shift: 0.2498
- Avg F1 Letter Shift: 0.2336
- Avg Precision Word Level: 0.2459
- Avg Recall Word Level: 0.2741
- Avg F1 Word Level: 0.2538
- Avg Precision Word Shift: 0.4328
- Avg Recall Word Shift: 0.5013
- Avg F1 Word Shift: 0.4529
- Precision Median Exact: 0.0638
- Recall Median Exact: 0.0870
- F1 Median Exact: 0.0723
- 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: 0.0
- Recall Min Word Shift: 0.0
- F1 Min Word Shift: 0.0

## 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: 16
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 100000
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|
| 0.0506        | 3.5549  | 30000 | 0.7696          | 74.5620 | 0.1888              | 0.1999           | 0.1925       | 0.2219                     | 0.2382                  | 0.2266              | 0.2426                   | 0.2636                | 0.2485            | 0.4348                   | 0.4847                | 0.4502            | 0.0714                 | 0.0854              | 0.0755          | 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               |
| 0.0214        | 7.1098  | 60000 | 0.8339          | 70.3940 | 0.2052              | 0.2197           | 0.2098       | 0.2364                     | 0.2572                  | 0.2429              | 0.2575                   | 0.2825                | 0.2647            | 0.4499                   | 0.5098                | 0.4678            | 0.0741                 | 0.0930              | 0.08            | 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               |
| 0.0062        | 10.6648 | 90000 | 0.9273          | 71.8937 | 0.1966              | 0.2113           | 0.2010       | 0.2269                     | 0.2498                  | 0.2336              | 0.2459                   | 0.2741                | 0.2538            | 0.4328                   | 0.5013                | 0.4529            | 0.0638                 | 0.0870              | 0.0723          | 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               |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu126
- Datasets 2.12.0
- Tokenizers 0.20.1