Create README

README.md

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+---
+language: 
+  - en
+tags:
+- AudioClassification
+datasets:
+- marsyas/gtzan
+metrics:
+- accuracy
+---
+
+# Audio Classification
+
+This repo contains code and notes for [this tutorial](https://huggingface.co/learn/audio-course/chapter4/fine-tuning).
+
+## Dataset
+
+[GTZAN](https://huggingface.co/datasets/marsyas/gtzan) is used.
+
+## Usage
+
+```shell
+export HUGGINGFACE_TOKEN=<your_token>
+python main.py
+```
+
+## Performance
+
+Acc: 0.81 (default setting)
+
+## Notes
+
+1. 🤗 Datasets support `train_test_split()` method to split the dataset.
+
+2. `feature_extractor` can not handle resampling
+    - To resample, one can use `dataset.map()`
+```python
+from datasets import Audio
+
+gtzan = gtzan.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
+```
+
+3. `feature_extractor` do the normalization and returns `input_values` and `attention_mask`.
+
+4. `.map()` support batched preprocess.
+
+5. Why `AutoModelForAudioClassification.from_pretrained` takes `label2id` and `id2label`?