---
base_model: rinna/japanese-wav2vec2-base
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-kanji-base-unigram
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/tokudai-nishimura-lab/Emoto-ASR-training-log/runs/icxw1p2j)
# wav2vec2-kanji-base-unigram

This model is a fine-tuned version of [rinna/japanese-wav2vec2-base](https://huggingface.co./rinna/japanese-wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6841
- Cer: 0.4426
- Wer: 1.0

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 2
- 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: cosine
- lr_scheduler_warmup_steps: 77380
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Cer    | Wer   |
|:-------------:|:-----:|:------:|:---------------:|:------:|:-----:|
| 5.0463        | 1.0   | 19348  | 7.1017          | 0.9448 | 0.999 |
| 2.3465        | 2.0   | 38696  | 4.1177          | 0.6757 | 1.0   |
| 1.6622        | 3.0   | 58044  | 4.1171          | 0.6136 | 1.0   |
| 1.478         | 4.0   | 77392  | 2.8624          | 0.5836 | 1.0   |
| 1.3639        | 5.0   | 96740  | 3.2231          | 0.5709 | 1.0   |
| 1.2924        | 6.0   | 116088 | 3.6346          | 0.5807 | 1.0   |
| 1.2191        | 7.0   | 135436 | 2.4840          | 0.5444 | 1.0   |
| 1.1705        | 8.0   | 154784 | 2.5931          | 0.5403 | 1.0   |
| 1.117         | 9.0   | 174132 | 2.4750          | 0.5263 | 1.0   |
| 1.0689        | 10.0  | 193480 | 2.1332          | 0.5131 | 1.0   |
| 1.0371        | 11.0  | 212828 | 2.0070          | 0.4903 | 1.0   |
| 0.9922        | 12.0  | 232176 | 2.0196          | 0.4847 | 1.0   |
| 0.948         | 13.0  | 251524 | 1.7148          | 0.4597 | 1.0   |
| 0.9143        | 14.0  | 270872 | 1.5651          | 0.4506 | 1.0   |
| 0.8841        | 15.0  | 290220 | 1.7433          | 0.4530 | 1.0   |
| 0.8593        | 16.0  | 309568 | 1.7191          | 0.4528 | 1.0   |
| 0.8388        | 17.0  | 328916 | 1.6598          | 0.4455 | 1.0   |
| 0.8304        | 18.0  | 348264 | 1.6671          | 0.4434 | 1.0   |
| 0.8219        | 19.0  | 367612 | 1.6866          | 0.4426 | 1.0   |
| 0.8184        | 20.0  | 386960 | 1.6841          | 0.4426 | 1.0   |


### Framework versions

- Transformers 4.42.0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1