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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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base_model: openai/whisper-small |
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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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type: automatic-speech-recognition |
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name: 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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- type: cer |
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value: 10.11 |
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name: Normalized CER |
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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 Small zh-HK - Alvin |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. This version has a lower CER (by 1%) compared to the previous one. |
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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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## Using the Model |
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``` |
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import librosa |
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import torch |
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from transformers import WhisperForConditionalGeneration, WhisperProcessor |
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y, sr = librosa.load('audio.mp3', sr=16000) |
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MODEL_NAME = "alvanlii/whisper-small-cantonese" |
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processor = WhisperProcessor.from_pretrained(MODEL_NAME) |
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model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME) |
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model.config.forced_decoder_ids = None |
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model.config.suppress_tokens = [] |
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model.config.use_cache = False |
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processed_in = processor(y, sampling_rate=sr, return_tensors="pt") |
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gout = model.generate( |
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input_features=processed_in.input_features, |
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output_scores=True, return_dict_in_generate=True |
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) |
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transcription = processor.batch_decode(gout.sequences, skip_special_tokens=True)[0] |
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print(transcription) |
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``` |
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- Alternatively, you can use huggingface pipelines |
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``` |
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from transformers import pipeline |
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MODEL_NAME = "alvanlii/whisper-small-cantonese" |
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lang = "zh" |
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pipe = pipeline( |
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task="automatic-speech-recognition", |
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model=MODEL_NAME, |
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chunk_length_s=30, |
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device=device, |
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) |
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pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe") |
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text = pipe(file)["text"] |
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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: 25 (on 2 GPUs) |
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- eval_batch_size: 8 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 25x2x2=100 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 14000 |
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- mixed_precision_training: Native AMP |
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- augmentation: SpecAugment |
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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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| 0.4610 | 0.55 | 2000 | 0.3106 | 13.08 | |
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| 0.3441 | 1.11 | 4000 | 0.2875 | 11.79 | |
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| 0.3466 | 1.66 | 6000 | 0.2820 | 11.44 | |
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| 0.2539 | 2.22 | 8000 | 0.2777 | 10.59 | |
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| 0.2312 | 2.77 | 10000 | 0.2822 | 10.60 | |
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| 0.1639 | 3.32 | 12000 | 0.2859 | 10.17 | |
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| 0.1569 | 3.88 | 14000 | 0.2866 | 10.11 | |
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