Create README.md

README.md

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+---
+language:
+- zh
+license: apache-2.0
+datasets:
+- mozilla-foundation/common_voice_16_0
+model-index:
+- name: Wav2Vec2-BERT - Alvin
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: mozilla-foundation/common_voice_16_0 yue
+      type: mozilla-foundation/common_voice_16_0
+      config: yue
+      split: test
+      args: yue
+    metrics:
+    - name: Normalized CER
+      type: cer
+      value: Pending
+---
+
+
+# Wav2Vec2-BERT - Alvin
+
+This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0).  
+
+## Training and evaluation data
+For training, three datasets were used:
+- Common Voice 16 `zh-HK` and `yue` Train Set
+- CantoMap: Winterstein, Grégoire, Tang, Carmen and Lai, Regine (2020) "CantoMap: a Hong Kong Cantonese MapTask Corpus", in Proceedings of The 12th Language Resources and Evaluation Conference, Marseille: European Language Resources Association, p. 2899-2906.
+- Cantonse-ASR: Yu, Tiezheng, Frieske, Rita, Xu, Peng, Cahyawijaya, Samuel, Yiu, Cheuk Tung, Lovenia, Holy, Dai, Wenliang, Barezi, Elham, Chen, Qifeng, Ma, Xiaojuan, Shi, Bertram, Fung, Pascale (2022) "Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset", 2022. Link: https://arxiv.org/pdf/2201.02419.pdf
+
+
+## Training Hyperparameters
+- learning_rate: 1e-4
+- train_batch_size: 4 (on 1 3090)
+- eval_batch_size: 1
+- gradient_accumulation_steps: 32
+- total_train_batch_size: 32x4=128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_warmup_steps: 500
+- num_train_epochs: 8
+
+## Training Results
+
+| Training Loss | Epoch | Step | Validation Loss | Normalized CER    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+|