transformers safetensors wav2vec2-bert automatic-speech-recognition generated_from_trainer dataset:common_voice_16_0 base_model:Watarungurunnn/w2v-bert-2.0-japanese-CV16.0 model-index endpoints_compatible region:us
---
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
base_model: Watarungurunnn/w2v-bert-2.0-japanese-CV16.0
model-index:
- name: w2v-bert-2.0-japanese-CV16.0
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_16_0
      type: common_voice_16_0
      config: ja
      split: validation
      args: ja
    metrics:
    - type: wer
      value: 32.61876963445312
      name: Wer
---

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# w2v-bert-2.0-japanese-CV16.0

This model is a fine-tuned version of [Watarungurunnn/w2v-bert-2.0-japanese-CV16.0](https://huggingface.co/Watarungurunnn/w2v-bert-2.0-japanese-CV16.0) on the common_voice_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5149
- Wer: 32.6188

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1205        | 1.69  | 500  | 1.3602          | 36.4355 |
| 0.2116        | 3.39  | 1000 | 1.4580          | 35.1067 |
| 0.1054        | 5.08  | 1500 | 1.4180          | 34.6457 |
| 0.0661        | 6.78  | 2000 | 1.4557          | 32.3889 |
| 0.0208        | 8.47  | 2500 | 1.5149          | 32.6188 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
