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---
language:
- ja
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: whisper-large-v3-japanese-4k-steps
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: ja
split: None
args: 'config: ja, split: test'
metrics:
- name: Wer
type: wer
value: 1821.4909443725744
---
<!-- 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-large-v3-japanese-4k-steps
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 16.1 dataset. I followed a post by Sanchit Gandhi, https://huggingface.co/blog/fine-tune-whisper
It took 24 hours using an A100 on Google Colab to complete 4000 steps using the Common Voice 16.1 dataset. Training loss dropped over epochs but validation loss increased, so textbook overfitting. Furthermore, WER increased. It achieves the following results on the evaluation set:
- Loss: 0.4057
- Wer: 18.2149
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 0.1374 | 1.02 | 1000 | 0.3618 | 11.983182 |
| 0.0508 | 2.04 | 2000 | 0.3658 | 17.554657 |
| 0.0206 | 3.05 | 3000 | 0.3904 | 21.087484 |
| 0.0066 | 4.07 | 4000 | 0.4057 | 18.214909 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|