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update model card README.md
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README.md
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---
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language:
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- ln_cd
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- google/fleurs
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metrics:
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- wer
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model-index:
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- name: Whisper Base Lingala - BrainTheos
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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: Fleurs
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type: google/fleurs
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config: ln_cd
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split: validation
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args: ln_cd
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metrics:
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- name: Wer
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type: wer
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value: 25.050916496945007
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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 Lingala - BrainTheos
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Fleurs dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7265
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- Wer: 25.0509
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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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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|
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| 0.0081 | 21.0 | 1000 | 0.6218 | 29.8710 |
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| 0.0016 | 42.01 | 2000 | 0.6865 | 25.1188 |
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| 0.0009 | 63.01 | 3000 | 0.7152 | 24.9151 |
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| 0.0007 | 85.0 | 4000 | 0.7265 | 25.0509 |
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### Framework versions
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- Transformers 4.30.0.dev0
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- Pytorch 2.0.0+cu118
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- Datasets 2.12.1.dev0
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- Tokenizers 0.13.3
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