README.md

---
license: apache-2.0
base_model: pinot/wav2vec2-xls-r-300m-ja-cv-14_4
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: cv-14-rakugo
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.1156558533145275
---

<!-- 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. -->

# cv-14-rakugo

This model is a fine-tuned version of [pinot/wav2vec2-xls-r-300m-ja-cv-14_4](https://huggingface.co/pinot/wav2vec2-xls-r-300m-ja-cv-14_4) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3970
- Wer: 0.1157

## 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: 0.1
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.94  | 2    | 2.2324          | 0.2962 |
| No log        | 1.88  | 4    | 0.8925          | 0.2073 |
| No log        | 2.82  | 6    | 0.3568          | 0.1269 |
| No log        | 3.76  | 8    | 0.3970          | 0.1157 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0