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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-tiny
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tags:
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- generated_from_trainer
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datasets:
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- deepdml/igbo-dict-expansion-16khz
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- deepdml/igbo-dict-16khz
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metrics:
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- wer
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model-index:
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- name: Whisper Tiny 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: google/fleurs
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type: deepdml/igbo-dict-expansion-16khz
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config: ig_ng
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split: test
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args: ig_ng
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metrics:
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- name: Wer
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type: wer
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value: 89.15504591613625
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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 Tiny ig
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the google/fleurs dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.8423
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- Wer: 89.1550
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- Cer: 51.2543
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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: 128
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- eval_batch_size: 128
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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_ratio: 0.04
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- training_steps: 5000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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|:-------------:|:-------:|:----:|:---------------:|:-------:|:-------:|
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| 0.2126 | 2.0344 | 1000 | 3.7178 | 86.3321 | 49.8998 |
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| 0.1179 | 4.0688 | 2000 | 2.8735 | 87.9452 | 52.9006 |
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| 0.0729 | 7.0204 | 3000 | 2.6541 | 86.7208 | 50.7179 |
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| 0.0519 | 9.0548 | 4000 | 2.7895 | 89.4806 | 51.4953 |
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| 0.0331 | 12.0064 | 5000 | 2.8423 | 89.1550 | 51.2543 |
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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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