metadata

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: 0.6459627329192547

cv-14-rakugo

This model is a fine-tuned version of pinot/wav2vec2-xls-r-300m-ja-cv-14_4 on the audiofolder dataset. It achieves the following results on the evaluation set:

Loss: 5.1127
Wer: 0.6460

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: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.59	1	12.0211	0.3851
No log	1.78	3	12.0211	0.3851
No log	2.96	5	5.0175	0.4161
No log	3.56	6	2.7911	0.5093
No log	4.74	8	2.4989	0.5093
No log	5.93	10	4.0524	0.4907
No log	6.52	11	3.6691	0.4720
No log	7.7	13	4.6285	0.5280
No log	8.89	15	4.5016	0.5652
No log	9.48	16	4.4013	0.5776
No log	10.67	18	4.3718	0.6025
No log	11.85	20	5.1127	0.6460

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0