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---
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
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