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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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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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model-index: |
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- name: Whisper Small zh-HK - 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_11_0 zh-HK |
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type: mozilla-foundation/common_voice_11_0 |
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config: zh-HK |
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split: test |
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args: zh-HK |
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metrics: |
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- name: Normalized CER |
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type: cer |
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value: 7.766 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Large V2 zh-HK - Alvin (Weights not uploaded yet) |
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. This is trained with PEFT LoRA+BNB INT8. |
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To use the model, use the following code. It should be able to inference with less than 16GB VRAM. |
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``` |
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from peft import PeftModel, PeftConfig |
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from transformers import WhisperForConditionalGeneration, Seq2SeqTrainer, WhisperTokenizer, WhisperProcessor |
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peft_model_id = "alvanlii/whisper-largev2-cantonese-peft-lora" |
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peft_config = PeftConfig.from_pretrained(peft_model_id) |
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model = WhisperForConditionalGeneration.from_pretrained( |
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peft_config.base_model_name_or_path, load_in_8bit=True, device_map="auto" |
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) |
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model = PeftModel.from_pretrained(model, peft_model_id) |
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task = "transcribe" |
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tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, task=task) |
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processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, task=task) |
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feature_extractor = processor.feature_extractor |
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forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task=task) |
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pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor) |
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audio = # load audio here |
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text = pipe(audio, generate_kwargs={"forced_decoder_ids": forced_decoder_ids}, max_new_tokens=255)["text"] |
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``` |
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## Training and evaluation data |
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For training, three datasets were used: |
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- Common Voice 11 Canto 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-3 |
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- train_batch_size: 60 (on 1 3090 GPU) |
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- eval_batch_size: 10 |
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- gradient_accumulation_steps: 1 |
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- total_train_batch_size: 60x1x1=60 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 12000 |
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- augmentation: SpecAugment |
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## Training Results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| <TBA> | 0.55 | 2000 | <TBA> | |
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| <TBA> | 1.11 | 4000 | <TBA> | |
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| <TBA> | 1.66 | 6000 | <TBA> | |
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| <TBA> | 2.22 | 8000 | <TBA> | |
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| <TBA> | 2.77 | 10000 | <TBA> | |
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| <TBA> | 3.32 | 12000 | <TBA> | |
