Whisper Small Myanmar

--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-myanmar results: [] datasets: - chuuhtetnaing/myanmar-speech-dataset-openslr-80 language: - my pipeline_tag: automatic-speech-recognition library_name: transformers --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-myanmar This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [chuuhtetnaing/myanmar-speech-dataset-openslr-80](https://huggingface.co/datasets/chuuhtetnaing/myanmar-speech-dataset-openslr-80) dataset. It achieves the following results on the evaluation set: - Loss: 0.1904 - Wer: 49.0650 ## Usage ```python from datasets import Audio, load_dataset from transformers import pipeline # Load a sample audio dataset = load_dataset("chuuhtetnaing/myanmar-speech-dataset-openslr-80") dataset = dataset.cast_column("audio", Audio(sampling_rate=16000)) test_dataset = dataset['test'] input_speech = test_dataset[42]['audio'] pipe = pipeline(model='chuuhtetnaing/whisper-small-myanmar') output = pipe(input_speech, generate_kwargs={"language": "myanmar", "task": "transcribe"}) print(output['text']) # ကျွန်မ ပြည်ပ မှာ ပညာသင် တော့ စာမေးပွဲ ကို တပတ်တခါ စစ်တယ် ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2566 | 1.0 | 36 | 0.8893 | 215.0045 | | 0.8862 | 2.0 | 72 | 0.6243 | 388.6465 | | 0.3546 | 3.0 | 108 | 0.2046 | 316.8744 | | 0.1839 | 4.0 | 144 | 0.1695 | 81.3001 | | 0.1198 | 5.0 | 180 | 0.1385 | 63.8914 | | 0.0969 | 6.0 | 216 | 0.1583 | 66.0285 | | 0.084 | 7.0 | 252 | 0.1539 | 70.6589 | | 0.0628 | 8.0 | 288 | 0.1603 | 61.3090 | | 0.0565 | 9.0 | 324 | 0.1424 | 60.3295 | | 0.0355 | 10.0 | 360 | 0.1457 | 58.1478 | | 0.0299 | 11.0 | 396 | 0.1547 | 57.7916 | | 0.0183 | 12....