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--- |
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language: |
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- en |
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license: mit |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-small-singlish-122k |
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results: |
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- task: |
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type: automatic-speech-recognition |
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dataset: |
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name: NSC |
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type: NSC |
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metrics: |
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- name: WER |
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type: WER |
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value: 9.69 |
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--- |
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# Whisper-small-singlish-122k. |
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This model is a [openai/whisper-small](https://huggingface.co/openai/whisper-small), fine-tuned on a subset (122k samples) of the [National Speech Corpus](https://www.imda.gov.sg/how-we-can-help/national-speech-corpus). |
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The following results on the evaluation set (43,788k samples) are reported: |
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- Loss: 0.171377 |
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- WER: 9.69 |
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## Model Details |
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### Model Description |
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- **Developed by:** [jensenlwt](https://huggingface.co/jensenlwt) |
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- **Model type:** automatic-speech-recognition |
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- **License:** MIT |
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- **Finetuned from model:** [openai/whisper-small](https://huggingface.co/openai/whisper-small) |
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## Uses |
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The model is intended as exploration exercise to develop better ASR model for Singapore English (singlish). |
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The recommended audio usage for testing should be: |
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1. Involves local Singapore slang, dialect, names, and terms etc. |
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2. Involves Singaporean accent. |
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### Direct Use |
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To use the model in an application, you can make use of `transformers`: |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline("automatic-speech-recognition", model="jensenlwt/whisper-small-singlish-122k") |
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``` |
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### Out-of-Scope Use |
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- Long form audio |
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- Broken Singlish (typically from older generation) |
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- Poor quality audio (audio samples are recorded in a controlled environment) |
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- Conversation (as the model is not trained on conversation) |
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## Training Details |
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### Training Data |
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We made use of the [National Speech Corpus](https://www.imda.gov.sg/how-we-can-help/national-speech-corpus) for training. |
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In specific, we made use of **Part 2** – which is a series of audio samples of prompted read speech recordings that involves local named entities, slang, and dialect. |
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To train, I make used of the first 300 transcripts in the corpus, which is around 122k samples from ~161 speakers. |
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### Training Procedure |
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The model is fine-tuned with occasional interruptions to adjust batch size to maximise GPU utilisation. |
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In addition, I also end training early if eval_loss does not decrease in two evaluation steps as per previous training experience. |
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#### Training Hyperparameters |
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The following hyperparameters are used: |
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- **batch_size**: 128 |
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- **gradient_accumulation_steps**: 1 |
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- **learning_rate**: 1e-5 |
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- **warmup_steps**: 500 |
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- **max_steps**: 5000 |
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- **fp16**: true |
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- **eval_batch_size**: 32 |
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- **eval_step**: 500 |
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- **max_grad_norm**: 1.0 |
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- **generation_max_length**: 225 |
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#### Training Results |
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| Steps | Epoch | Train Loss | Eval Loss | WER | |
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|:-----:|:--------:|:----------:|:---------:|:------------------:| |
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| 500 | 0.654450 | 0.7418 | 0.3889 | 17.968250 | |
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| 1000 | 1.308901 | 0.2831 | 0.2519 | 11.880948 | |
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| 1500 | 1.963351 | 0.1960 | 0.2038 | 9.948440 | |
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| 2000 | 2.617801 | 0.1236 | 0.1872 | 9.420248 | |
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| 2500 | 3.272251 | 0.0970 | 0.1791 | 8.539280 | |
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| 3000 | 3.926702 | 0.0728 | 0.1714 | 8.207827 | |
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| 3500 | 4.581152 | 0.0484 | 0.1741 | 8.145801 | |
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| 4000 | 5.235602 | 0.0401 | 0.1773 | 8.138047 | |
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The model with the lowest evaluation loss is used as the final checkpoint. |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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To test the model, I made use of the last 100 transcripts (held-out test set) in the corpus, which is around 43k samples. |
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### Results |
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| Model | WER | |
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|:----------------------------:|:-----:| |
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| fine-tuned-122k-whisper-small| 9.69% | |
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#### Summary |
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## Technical Specifications |
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### Model Architecture and Objective |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
