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---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- Chinese_english
metrics:
- wer
model-index:
- name: Whisper tiny Chinese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Chinese English
type: Chinese_english
args: 'config: default, split: test'
metrics:
- name: Wer
type: wer
value: 18.379666401906274
---
<!-- 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 tiny Chinese
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Chinese English dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5120
- Wer: 18.3797
## 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: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0001 | 1.6667 | 500 | 0.4646 | 17.5060 |
| 0.0 | 3.3333 | 1000 | 0.5031 | 18.0937 |
| 0.0 | 5.0 | 1500 | 0.5120 | 18.3797 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|