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---
language:
- zh
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- thomas0104/nan_tw_soap_opera
metrics:
- wer
base_model: openai/whisper-large-v2
model-index:
- name: openai/whisper-large-v2
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: thomas0104/nan_tw_soap_opera nan-tw
type: thomas0104/nan_tw_soap_opera
config: nan-tw
split: test
metrics:
- type: cer
value: 63.42
name: Cer
---
<!-- 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. -->
# openai/large_v2_nan_tw_so_short_30s
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the thomas0104/nan_tw_soap_opera nan-tw dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3322
- Wer: 343.5629
- Cer: 63.42
## 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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 1.1133 | 1.0 | 1000 | 1.3322 | 343.5629 | 416.4573 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|