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--- |
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language: |
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- zh |
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license: apache-2.0 |
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datasets: |
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- mozilla-foundation/common_voice_16_0 |
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model-index: |
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- name: Wav2Vec2-BERT - Alvin |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: mozilla-foundation/common_voice_16_0 yue |
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type: mozilla-foundation/common_voice_16_0 |
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config: yue |
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split: test |
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args: yue |
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metrics: |
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- name: Normalized CER |
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type: cer |
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value: Pending |
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--- |
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# Wav2Vec2-BERT - Alvin |
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0). |
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## Training and evaluation data |
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For training, three datasets were used: |
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- Common Voice 16 `zh-HK` and `yue` Train Set |
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- 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. |
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- 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 |
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## Training Hyperparameters |
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- learning_rate: 1e-4 |
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- train_batch_size: 4 (on 1 3090) |
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- eval_batch_size: 1 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 32x4=128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_warmup_steps: 500 |
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- num_train_epochs: 8 |
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## Training Results |
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| Training Loss | Epoch | Step | Validation Loss | Normalized CER | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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