---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper_JP
  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. -->

# Whisper_JP

This model is a a Phoneme Level Speech Recognition network, originally a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on a
mixture of Different datasets.

It achieves the following results on the evaluation set:
- Loss: 0.2186
- Wer: 21.6707

## 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: 24
- eval_batch_size: 8
- 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: 6000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2101        | 0.8058 | 1000 | 0.2090          | 30.1840 |
| 0.1369        | 1.6116 | 2000 | 0.1837          | 27.6756 |
| 0.0838        | 2.4174 | 3000 | 0.1829          | 26.4036 |
| 0.0454        | 3.2232 | 4000 | 0.1922          | 20.9549 |
| 0.0434        | 4.0290 | 5000 | 0.2072          | 20.8898 |
| 0.021         | 4.8348 | 6000 | 0.2186          | 21.6707 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.4.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1