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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-meidum-ko-normalized-1273h |
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results: [] |
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--- |
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# whisper-medium-ko-normalized-1273h |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on a custom dataset for improving Korean speech recognition. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1254 |
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- Wer: 0.0551 |
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## Model description |
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The model was a fine-tuned version of `openai/whisper-medium` transcript the Korean audio sources into text. |
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It was trained on GCP's `a2-highgpu-1g` (a100-40G) for 26 hours with about $90. |
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## Intended uses & limitations |
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This model was trained to extend the performance of the original whisper model for Korean transcription task. |
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## Training and evaluation data |
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I downloaded all data from AI-HUB (https://aihub.or.kr/). Two datasets, in particular, caught my attention: "Instruction Audio Set" and "Noisy Conversation Audio Set". |
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Following indicates the hours information for each dastset. |
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|dataset name| train_split (hours) | validation_split (hours)| |
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|---|---|---| |
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|Instruction Audio Set|910|105| |
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|Noisy Conversation Audio Set|363|76| |
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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: 24 |
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- eval_batch_size: 32 |
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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: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 0.0588 | 1.0 | 8775 | 0.1225 | 0.0604 | |
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| 0.0287 | 2.0 | 17550 | 0.1186 | 0.0567 | |
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| 0.0148 | 3.0 | 26325 | 0.1254 | 0.0551 | |
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### Framework versions |
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- Transformers 4.28.0.dev0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.2 |
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## Evaluation Result for the dataset `google/fleurs` |
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The trained model is evaluated on the `test` split of subset `ko_kr` from the dataset `google/fleurs`. |
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Please note that the model was not trained on the `train` split from the dataset. |
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|model|Wer| |
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|---|---| |
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|openai/whisper|0.2469| |
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|this model|0.2189| |
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