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README.md
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---
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language:
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- zh
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_13_0
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metrics:
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- wer
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model-index:
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- name: Whisper large zh - seiching
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice 13
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type: mozilla-foundation/common_voice_13_0
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config: zh-TW
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split: test
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args: zh-TW
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metrics:
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- name: Wer
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type: wer
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value: 39.92812936713915
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Whisper large zh - seiching
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This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the Common Voice 13 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2457
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- Wer Ortho: 40.3316
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- Wer: 39.9281
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant_with_warmup
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- lr_scheduler_warmup_steps: 50
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- training_steps: 4000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
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| 0.0361 | 0.69 | 500 | 0.1989 | 38.3627 | 37.9517 |
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| 0.0105 | 1.38 | 1000 | 0.2217 | 39.0259 | 38.9100 |
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| 0.0208 | 2.06 | 1500 | 0.2299 | 39.6891 | 39.3292 |
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| 0.0091 | 2.75 | 2000 | 0.2264 | 39.8964 | 39.4091 |
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| 0.0153 | 3.44 | 2500 | 0.2363 | 39.8135 | 39.3891 |
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| 0.0191 | 4.13 | 3000 | 0.2415 | 40.1865 | 40.0080 |
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| 0.0061 | 4.81 | 3500 | 0.2542 | 41.1813 | 39.9281 |
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| 0.0107 | 5.5 | 4000 | 0.2457 | 40.3316 | 39.9281 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 1.13.1+cu117
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- Datasets 2.13.2
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- Tokenizers 0.13.3
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