---
license: apache-2.0
tags:
- generated_from_trainer
base_model: openai/whisper-small
datasets:
- verba_lex_voice
metrics:
- wer
model-index:
- name: verbalex-zh
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: verba_lex_voice
      type: verba_lex_voice
      config: zh
      split: test
      args: zh
    metrics:
    - type: wer
      value: 4.670558798999166
      name: Wer
---

<!-- 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. -->

# verbalex-zh

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the verba_lex_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1147
- Wer: 4.6706

## 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: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.0025        | 5.0505  | 1000 | 0.1035          | 8.5071 |
| 0.0002        | 10.1010 | 2000 | 0.1130          | 4.7540 |
| 0.0002        | 15.1515 | 3000 | 0.1147          | 4.6706 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.19.1