Automatic Speech Recognition
PEFT
TensorBoard
Safetensors
Chinese
wft
whisper
audio
speech
Generated from Trainer
Eval Results (legacy)
Instructions to use JacobLinCool/whisper-large-v3-turbo-common_voice_16_1-zh-TW-pissa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JacobLinCool/whisper-large-v3-turbo-common_voice_16_1-zh-TW-pissa with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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---
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library_name: peft
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language:
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- zh
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license: mit
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base_model: openai/whisper-large-v3-turbo
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tags:
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- wft
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- whisper
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- automatic-speech-recognition
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- audio
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- speech
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- generated_from_trainer
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datasets:
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- JacobLinCool/mozilla-foundation-common_voice_16_1-zh-TW-preprocessed
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metrics:
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- wer
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model-index:
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- name: whisper-large-v3-turbo-common_voice_16_1-zh-TW-pissa
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results:
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- task:
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type: automatic-speech-recognition
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name: Automatic Speech Recognition
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dataset:
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name: JacobLinCool/mozilla-foundation-common_voice_16_1-zh-TW-preprocessed
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type: JacobLinCool/mozilla-foundation-common_voice_16_1-zh-TW-preprocessed
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metrics:
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- type: wer
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value: 63.665594855305464
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name: Wer
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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-v3-turbo-common_voice_16_1-zh-TW-pissa
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This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the JacobLinCool/mozilla-foundation-common_voice_16_1-zh-TW-preprocessed dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5133
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- Wer: 63.6656
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- Cer: 23.5752
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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: 0.0005
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|
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| No log | 0 | 0 | 2.7520 | 77.6125 | 20.7783 |
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| 7.6982 | 0.9987 | 377 | 0.8744 | 87.9421 | 41.2804 |
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| 5.1677 | 2.0 | 755 | 0.7499 | 82.5965 | 36.6407 |
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| 3.3647 | 2.9987 | 1132 | 0.6433 | 76.8087 | 31.6068 |
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| 3.4711 | 4.0 | 1510 | 0.6397 | 76.2460 | 30.2862 |
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| 1.5694 | 4.9987 | 1887 | 0.5779 | 71.5434 | 27.5471 |
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| 0.7951 | 6.0 | 2265 | 0.5664 | 71.3223 | 27.0600 |
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| 0.4709 | 6.9987 | 2642 | 0.5492 | 68.8706 | 26.0131 |
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| 0.116 | 8.0 | 3020 | 0.5427 | 66.7605 | 24.8104 |
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| 0.0512 | 8.9987 | 3397 | 0.5298 | 66.1375 | 24.8632 |
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| 0.0273 | 9.9868 | 3770 | 0.5133 | 63.6656 | 23.5752 |
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### Framework versions
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- PEFT 0.13.2
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- Transformers 4.46.0
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- Pytorch 2.4.0
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- Datasets 3.0.2
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- Tokenizers 0.20.1
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