---
language:
- zh
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Medium Zh - Kimas
  results: []
---

<!-- 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. -->

# Whisper Medium Zh - Kimas

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0635
- Wer: 100.0

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer      |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1461        | 0.28  | 1000  | 0.1406          | 100.0    |
| 0.0803        | 0.57  | 2000  | 0.1181          | 100.0    |
| 0.0715        | 0.85  | 3000  | 0.1039          | 100.0    |
| 0.0255        | 1.14  | 4000  | 0.0925          | 100.0207 |
| 0.0199        | 1.42  | 5000  | 0.0810          | 100.0    |
| 0.027         | 1.7   | 6000  | 0.0767          | 100.0207 |
| 0.0328        | 1.99  | 7000  | 0.0706          | 100.0    |
| 0.0026        | 2.27  | 8000  | 0.0700          | 100.0    |
| 0.0082        | 2.56  | 9000  | 0.0646          | 100.0    |
| 0.0099        | 2.84  | 10000 | 0.0635          | 100.0    |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 1.12.1
- Datasets 2.14.6
- Tokenizers 0.14.1