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---
language:
- ja
license: other
tags:
- whisper-event
- generated_from_trainer
datasets:
- Elite35P-Server/EliteVoiceProject
metrics:
- wer
model-index:
- name: Whisper Base Japanese Elite
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Elite35P-Server/EliteVoiceProject twitter
type: Elite35P-Server/EliteVoiceProject
config: twitter
split: test
args: twitter
metrics:
- name: Wer
type: wer
value: 11.585365853658537
---
<!-- 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 Base Japanese Elite
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Elite35P-Server/EliteVoiceProject twitter dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1459
- Wer: 11.5854
## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0009 | 29.01 | 1000 | 0.1459 | 11.5854 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|