Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- whisper-small
+- asr
+- zh-TW
+datasets:
+- mozilla-foundation/common_voice_11_0
+model-index:
+- name: Whisper Small TW
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: mozilla-foundation/common_voice_11_0
+      type: mozilla-foundation/common_voice_11_0
+      config: zh-TW
+      split: test
+    metrics:
+    - type: wer
+      value: 9.78
+      name: WER
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Whisper Medium TW 
+
+This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 dataset.
+
+## Training and evaluation data
+
+Training:
+- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation)
+
+Evaluation:
+- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (test)
+
+## Training procedure
+
+- Datasets were augmented using [audiomentations](https://github.com/iver56/audiomentations) via PitchShift, TimeStretch, Gain, AddGaussianNoise transformations at `p=0.3`.
+- A space is added between each Chinese character, as demonstrated in the original paper. Effectively, WER == CER in this case.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- gradient_accumulation_steps: 1
+- optimizer: Adam
+- generation_max_length: 225
+- warmup_steps: 500
+- max_steps: 2400
+- fp16: True
+- evaluation_strategy: "steps"
+
+### Framework versions
+
+- Transformers 4.27.1
+- Pytorch 2.0.1+cu120
+- Datasets 2.13.1