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---
language:
- multilingual
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
base_model: openai/whisper-large-v3
model-index:
- name: Whisper large-v3 nan-tw
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 16.0
type: mozilla-foundation/common_voice_16_0
config: nan-tw
split: test
args: 'config: nan-tw, split: test'
metrics:
- type: wer
value: 280.9248554913295
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. -->
# Whisper large-v3 nan-tw
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the Common Voice 16.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0601
- Wer: 280.9249
## 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: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 0.2485 | 3.05 | 1000 | 0.9971 | 538.5505 |
| 0.0154 | 6.1 | 2000 | 1.0482 | 1460.5158 |
| 0.0024 | 9.15 | 3000 | 1.0330 | 261.3161 |
| 0.0014 | 12.2 | 4000 | 1.0554 | 300.3112 |
| 0.0003 | 15.24 | 5000 | 1.0601 | 280.9249 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|