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README.md
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---
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language:
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- sw
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license: apache-2.0
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tags:
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- hf-asr-leaderboard
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_13_0
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metrics:
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- wer
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model-index:
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- name: Whisper Small Swahili - Badili
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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: Common Voice 13.0
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type: mozilla-foundation/common_voice_13_0
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config: sw
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split: test
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args: 'config: sw, split: test'
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metrics:
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- name: Wer
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type: wer
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value: 98.40119332745073
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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 Swahili - Badili
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 13.0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4329
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- Wer: 98.4012
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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: 8
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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: 12000
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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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| 0.3563 | 0.35 | 1000 | 0.4938 | 100.5715 |
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| 0.2853 | 0.69 | 2000 | 0.4143 | 100.7007 |
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| 0.1612 | 1.04 | 3000 | 0.3910 | 100.9748 |
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| 0.1399 | 1.38 | 4000 | 0.3762 | 98.4989 |
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| 0.1657 | 1.73 | 5000 | 0.3700 | 90.3357 |
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| 0.0818 | 2.08 | 6000 | 0.3775 | 98.0493 |
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| 0.0749 | 2.42 | 7000 | 0.3768 | 97.9936 |
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| 0.0637 | 2.77 | 8000 | 0.3822 | 92.9440 |
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| 0.0355 | 3.11 | 9000 | 0.4036 | 93.8979 |
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| 0.0299 | 3.46 | 10000 | 0.4141 | 97.9695 |
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| 0.0277 | 3.8 | 11000 | 0.4175 | 98.2961 |
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| 0.0147 | 4.15 | 12000 | 0.4329 | 98.4012 |
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### Framework versions
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- Transformers 4.29.0.dev0
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- Pytorch 2.0.0+cu117
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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