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---
library_name: transformers
language:
- yue
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small Canontese X v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 16.1 and 17.0
type: mozilla-foundation/common_voice_17_0
config: yue
split: None
args: 'config: zh-HK, split: test'
metrics:
- name: Wer
type: wer
value: 54.825384904243336
---
<!-- 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 Small Canontese X v2
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.1 and 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2264
- Wer: 54.8254
## 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: 4
- 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 |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2578 | 0.6954 | 1000 | 0.2680 | 61.4345 |
| 0.0892 | 1.3908 | 2000 | 0.2376 | 57.3789 |
| 0.0295 | 2.0862 | 3000 | 0.2264 | 54.8254 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|