license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- wer
- cer
model-index:
- name: hubert-base-ser
results: []
datasets:
- reazon-research/reazonspeech
- mozilla-foundation/common_voice_11_0
language:
- ja
hubert-large-asr
This model is a fine-tuned version of rinna/japanese-hubert-large ASR. Initially fine-tuned on the Reazonspeech(small) dataset, it was subsequently further fine-tuned on the common_voice_11_0 dataset for ASR tasks.
Acknowledgments
This model's fine-tuning approach was inspired by and references the training methodology used in vumichien/wav2vec2-large-xlsr-japanese-hiragana.
Training procedure
The model was fine-tuned in two main stages, first on the Reazonspeech dataset, followed by the common_voice_11_0 dataset. Details of the training steps and results are as follows:
Training on Reazonspeech
The initial fine-tuning on the Reazonspeech(small) dataset was carried out with the following performance metrics:
| Step | Training Loss | Validation Loss | WER |
|---|---|---|---|
| 1000 | 12.29880 | 3.610288 | 1.00000 |
| 2000 | 3.601800 | 3.505306 | 1.00000 |
| 3000 | 2.80300 | 1.948012 | 0.722361 |
| 4000 | 1.961500 | 1.545842 | 0.558738 |
| 5000 | 1.712000 | 1.420027 | 0.509049 |
| 6000 | 1.565500 | 1.235171 | 0.466279 |
| 7000 | 1.504900 | 1.160565 | 0.461829 |
| 8000 | 1.409800 | 1.088012 | 0.427435 |
| 9000 | 1.358800 | 1.097211 | 0.409861 |
| 10000 | 1.318600 | 1.062294 | 0.403694 |
| 11000 | 1.258500 | 1.026783 | 0.385464 |
| 12000 | 1.245100 | 1.024860 | 0.379845 |
| 13000 | 1.217700 | 0.985201 | 0.375634 |
| 14000 | 1.187900 | 0.977686 | 0.367163 |
| 15000 | 1.168100 | 0.978529 | 0.363656 |
| 16000 | 1.135800 | 0.965668 | 0.363942 |
| 17000 | 1.140600 | 0.953237 | 0.360912 |
Training on common_voice_11_0
After fine-tuning on Reazonspeech, further fine-tuning was performed on the common_voice_11_0 dataset, leading to the following results:
| Step | Training Loss | Validation Loss | WER |
|---|---|---|---|
| 1000 | 1.08950 | 0.49275 | 0.302035 |
| 2000 | 0.86100 | 0.45113 | 0.266950 |
| 3000 | 0.76240 | 0.442281 | 0.244981 |
| 4000 | 0.70170 | 0.411666 | 0.234287 |
| 5000 | 0.66400 | 0.411769 | 0.227942 |
| 6000 | 0.63810 | 0.413067 | 0.225690 |
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-4
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 10
- lr_scheduler_type: linear
Test results
The final model was evaluated as follows:
On Reazonspeech: WER: 40.519700% CER: 23.220979%
On common_voice_11_0: WER: 22.705487% CER: 9.399390%
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu118
- Datasets 2.17.1