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---
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language:
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- my
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license: apache-2.0
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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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datasets:
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- malaysia-ai/malay-conversational-speech-corpus
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metrics:
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- wer
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model-index:
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- name: Whisper small Malay (4 batch size) - Gab
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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: malay-conversational-speech-corpus
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type: malaysia-ai/malay-conversational-speech-corpus
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args: 'config: malay, split: test'
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metrics:
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- name: Wer
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type: wer
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value: 27.394540942928042
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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 Malay (4 batch size) - Gab
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the malay-conversational-speech-corpus dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7126
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- Wer: 27.3945
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 16
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- eval_batch_size: 4
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- seed: 42
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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: 4000
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-------:|:----:|:---------------:|:-------:|
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| 0.0217 | 6.1728 | 1000 | 0.5993 | 28.8586 |
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| 0.0013 | 12.3457 | 2000 | 0.6816 | 28.0397 |
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| 0.0003 | 18.5185 | 3000 | 0.7018 | 27.8660 |
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| 0.0002 | 24.6914 | 4000 | 0.7126 | 27.3945 |
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### Framework versions
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- Transformers 4.41.1
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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