Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: whisper-meidum-ko-normalized-1273h
+  results: []
+---
+
+# whisper-small-ko-normalized-1273h
+
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-medium) on a custom dataset for improving Korean speech recognition.
+It achieves the following results on the evaluation set:
+- Loss: 0.1254
+- Wer: 0.0551
+
+## Model description
+
+The model was trained to transcript the Korean audio sources into text.
+
+## Intended uses & limitations
+
+This model was trained to extend the performance of the original whisper model for Korean transcription task.
+
+## Training and evaluation data
+
+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". 
+Following indicates the hours information for each dastset.
+
+|dataset name| train_split | validation_split|
+|---|---|---|
+|Instruction Audio Set|910|105|
+|Noisy Conversation Audio Set|363|76|
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Wer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|
+| 0.0588        | 1.0   | 8775  | 0.1225          | 0.0604 |
+| 0.0287        | 2.0   | 17550 | 0.1186          | 0.0567 |
+| 0.0148        | 3.0   | 26325 | 0.1254          | 0.0551 |
+
+
+### Framework versions
+
+- Transformers 4.28.0.dev0
+- Pytorch 1.13.1+cu117
+- Datasets 2.11.0
+- Tokenizers 0.13.2