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---
language:
- ja
license: other
tags:
- whisper-event
- generated_from_trainer
datasets:
- Elite35P-Server/EliteVoiceProject
metrics:
- wer
model-index:
- name: Whisper Small Japanese Elite
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Elite35P-Server/EliteVoiceProject youtube
type: Elite35P-Server/EliteVoiceProject
config: youtube
split: test
args: youtube
metrics:
- name: Wer
type: wer
value: 31.536388140161726
---
<!-- 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 Small Japanese Elite
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Elite35P-Server/EliteVoiceProject youtube dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1596
- Wer: 31.5364
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 100
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0003 | 52.0 | 1000 | 0.8053 | 28.8410 |
| 0.0 | 105.0 | 2000 | 0.8636 | 28.5714 |
| 0.0 | 157.0 | 3000 | 0.9056 | 28.0323 |
| 0.0 | 210.0 | 4000 | 0.9414 | 28.8410 |
| 0.0 | 263.0 | 5000 | 0.9842 | 31.2668 |
| 0.0 | 315.0 | 6000 | 1.0223 | 31.2668 |
| 0.0 | 368.0 | 7000 | 1.0677 | 31.2668 |
| 0.0 | 421.0 | 8000 | 1.1079 | 31.2668 |
| 0.0 | 473.0 | 9000 | 1.1468 | 31.5364 |
| 0.0 | 526.0 | 10000 | 1.1596 | 31.5364 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|