---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
- cer
model-index:
- name: hubert-large-japanese-asr
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Reazonspeech
      type: custom
      args: ja
    metrics:
    - name: Test WER
      type: wer
      value: 40.5197
    - name: Test CER
      type: cer
      value: 23.220979
  - 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: 22.705487
    - name: Test CER
      type: cer
      value: 9.39939
datasets:
- reazon-research/reazonspeech
- mozilla-foundation/common_voice_11_0
language:
- ja
---


<!-- 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. -->

# hubert-large-asr

This model is a fine-tuned version of [rinna/japanese-hubert-large](https://huggingface.co/rinna/japanese-hubert-large) ASR. Initially fine-tuned on the [Reazonspeech(small) dataset](https://huggingface.co/datasets/reazon-research/reazonspeech), it was subsequently further fine-tuned 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

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