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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/common_voice_19_0_zh-TW |
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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_19_0-zh-TW-lora |
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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/common_voice_19_0_zh-TW |
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type: JacobLinCool/common_voice_19_0_zh-TW |
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metrics: |
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- type: wer |
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value: 32.55535607420706 |
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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_19_0-zh-TW-lora |
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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/common_voice_19_0_zh-TW dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1786 |
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- Wer: 32.5554 |
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- Cer: 8.6009 |
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- Decode Runtime: 90.9833 |
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- Wer Runtime: 0.1257 |
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- Cer Runtime: 0.1534 |
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## Model description |
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This is an open-source Traditional Chinese (Taiwan) automatic speech recognition (ASR) model. |
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## Intended uses & limitations |
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This model is designed to be a prompt-free ASR model for Traditional Chinese. Due to its inherited language identification (LID) system from Whisper, which supports other Chinese language variants under the same language token (`zh`), we expect that performance may degrade when transcribing Simplified Chinese. |
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The model is free to use under the MIT license. |
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## Training and evaluation data |
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This model was trained on the [Common Voice Corpus 19.0 Chinese (Taiwan) Subset](https://huggingface.co/datasets/JacobLinCool/common_voice_19_0_zh-TW), containing about 50k training examples (44 hours) and 5k test examples (5 hours). This dataset is four times larger than the combination of training and validation set (`train+validation`) of [mozilla-foundation/common_voice_16_1](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1), which includes about 12k examples. |
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## Training procedure |
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[Tensorboard](https://huggingface.co/JacobLinCool/whisper-large-v3-turbo-common_voice_19_0-zh-TW-lora/tensorboard) |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 4 |
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- eval_batch_size: 32 |
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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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- lr_scheduler_warmup_steps: 50 |
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- training_steps: 5000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Decode Runtime | Wer Runtime | Cer Runtime | |
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|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:--------------:|:-----------:|:-----------:| |
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| No log | 0 | 0 | 2.7208 | 76.5011 | 20.4851 | 89.4916 | 0.1213 | 0.1639 | |
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| 1.1832 | 0.1 | 500 | 0.1939 | 39.9561 | 10.8721 | 90.0926 | 0.1222 | 0.1555 | |
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| 1.5179 | 0.2 | 1000 | 0.1774 | 37.6621 | 9.9322 | 89.8657 | 0.1225 | 0.1545 | |
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| 0.6179 | 0.3 | 1500 | 0.1796 | 36.2657 | 9.8325 | 90.2480 | 0.1198 | 0.1573 | |
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| 0.3626 | 1.0912 | 2000 | 0.1846 | 36.2258 | 9.7801 | 90.3306 | 0.1196 | 0.1539 | |
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| 0.1311 | 1.1912 | 2500 | 0.1776 | 34.8095 | 9.3214 | 90.3124 | 0.1286 | 0.1610 | |
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| 0.1263 | 1.2912 | 3000 | 0.1763 | 36.1261 | 9.3563 | 90.4271 | 0.1330 | 0.1650 | |
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| 0.2194 | 2.0825 | 3500 | 0.1891 | 34.6898 | 9.3114 | 91.1932 | 0.1320 | 0.1643 | |
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| 0.1127 | 2.1825 | 4000 | 0.1838 | 34.0714 | 9.1095 | 90.2416 | 0.1196 | 0.1529 | |
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| 0.3792 | 2.2824 | 4500 | 0.1786 | 33.1339 | 8.7679 | 90.9144 | 0.1310 | 0.1550 | |
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| 0.0606 | 3.0737 | 5000 | 0.1786 | 32.5554 | 8.6009 | 90.9833 | 0.1257 | 0.1534 | |
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### Framework versions |
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- PEFT 0.13.2 |
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- Transformers 4.46.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.2 |
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- Tokenizers 0.20.1 |
