---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
- cer
model-index:
- name: hubert-base-japanese-asr
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice
      args: ja
    metrics:
    - name: Test WER
      type: wer
      value: 27.511982
    - name: Test CER
      type: cer
      value: 11.699897
datasets:
- mozilla-foundation/common_voice_11_0
language:
- ja
---

# hubert-large-asr

This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the [common_voice_11_0 dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/ja) 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](https://huggingface.co/vumichien/wav2vec2-large-xlsr-japanese-hiragana).

## Training Procedure

Fine-tuning on the common_voice_11_0 dataset led to the following results:

| Step  | Training Loss | Validation Loss | WER    |
|-------|---------------|-----------------|--------|
| 1000  | 2.505600      | 1.009531        | 0.614952|
| 2000  | 1.186900      | 0.752440        | 0.422948|
| 3000  | 0.947700      | 0.658266        | 0.358543|
| 4000  | 0.817700      | 0.656034        | 0.356308|
| 5000  | 0.741300      | 0.623420        | 0.314537|
| 6000  | 0.694700      | 0.624534        | 0.294018|
| 7000  | 0.653400      | 0.603341        | 0.286735|
| 8000  | 0.616200      | 0.606606        | 0.285132|
| 9000  | 0.594800      | 0.596215        | 0.277422|
| 10000 | 0.590500      | 0.603380        | 0.274949|

### Training hyperparameters

The training hyperparameters remained consistent throughout the fine-tuning process:

- learning_rate: 1e-4
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 30
- lr_scheduler_type: linear

### Test results
The final model was evaluated as follows:

On common_voice_11_0:
- WER: 27.511982%
- CER: 11.699897%
### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu118
- Datasets 2.17.1