metadata

language:
  - zh
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - whucedar/datasets_stt_2
metrics:
  - wer
model-index:
  - name: zh-CN-model-medium-3 - whucedar
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: zh-CN
          type: whucedar/datasets_stt_2
          args: 'config: zh, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 92.0589784096893

zh-CN-model-medium-3 - whucedar

This model is a fine-tuned version of openai/whisper-medium on the zh-CN dataset. It achieves the following results on the evaluation set:

Loss: 0.2745
Wer: 92.0590

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: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.2079	0.8306	1000	0.2799	78.5571
0.0942	1.6611	2000	0.2712	76.6719
0.0291	2.4917	3000	0.2717	85.0026
0.0069	3.3223	4000	0.2745	92.0590

Framework versions

Transformers 4.42.3
Pytorch 2.3.1+cu118
Datasets 2.20.0
Tokenizers 0.19.1