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README.md
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---
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language:
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- ig
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license: apache-2.0
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base_model: openai/whisper-base
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tags:
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- generated_from_trainer
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datasets:
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- deepdml/igbo-dict-16khz
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- deepdml/igbo-dict-expansion-16khz
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metrics:
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- wer
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model-index:
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- name: Whisper Base ig
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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 17.0
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type: deepdml/igbo-dict-16khz
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config: ig
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split: test
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args: ig
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metrics:
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- name: Wer
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type: wer
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value: 333.33333333333337
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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 Base ig
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 17.0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.0006
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- Wer: 333.3333
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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: 64
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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: 5000
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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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| 0.238 | 1.0346 | 1000 | 2.6604 | 55.5556 |
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| 0.1268 | 2.0692 | 2000 | 2.6675 | 50.0 |
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| 0.0561 | 3.1038 | 3000 | 2.8636 | 325.0 |
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| 0.0436 | 4.1384 | 4000 | 2.9398 | 58.3333 |
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| 0.021 | 6.0076 | 5000 | 3.0006 | 333.3333 |
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### Framework versions
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- Transformers 4.42.0.dev0
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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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