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---
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
should probably proofread and complete it, then remove this comment. -->
[<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
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