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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: CER |
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type: cer |
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value: 10.27 |
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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). This has a CER of 10.27 on Common Voice 16 (yue) test set (without punctuations). |
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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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## Code Example |
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``` |
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from transformers import pipeline |
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bert_asr = pipeline( |
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"automatic-speech-recognition", model="alvanlii/wav2vec2-BERT-cantonese", device="cuda" |
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) |
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text = pipe(file)["text"] |
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``` |
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or |
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``` |
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import torch |
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import soundfile as sf |
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from transformers import AutoModelForCTC, Wav2Vec2BertProcessor |
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model_name = "alvanlii/wav2vec2-BERT-cantonese" |
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asr_model = AutoModelForCTC.from_pretrained(model_name).to(device) |
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processor = Wav2Vec2BertProcessor.from_pretrained(model_name) |
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audio_input, _ = sf.read(file) |
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inputs = processor([audio_input], sampling_rate=16_000).input_features |
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features = torch.tensor(inputs) |
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with torch.no_grad(): |
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logits = asr_model(features).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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predictions = processor.batch_decode(predicted_ids, skip_special_tokens=True) |
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``` |
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## Training Hyperparameters |
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- learning_rate: 5e-5 |
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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: 1500 |
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