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README.md
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---
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license: cc-by-nc-4.0
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base_model: facebook/mms-1b-all
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_6_1
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metrics:
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- wer
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model-index:
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- name: wav2vec2-large-mms-1b-turkish-colab
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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_6_1
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type: common_voice_6_1
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config: tr
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split: test
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args: tr
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metrics:
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- name: Wer
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type: wer
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value: 0.21938514962720865
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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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# wav2vec2-large-mms-1b-turkish-colab
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This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_6_1 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1555
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- Wer: 0.2194
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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: 0.001
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- train_batch_size: 32
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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: 100
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- num_epochs: 4
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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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| 4.6821 | 0.92 | 100 | 0.1833 | 0.2524 |
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| 0.2846 | 1.83 | 200 | 0.1667 | 0.2338 |
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| 0.268 | 2.75 | 300 | 0.1602 | 0.2231 |
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| 0.243 | 3.67 | 400 | 0.1555 | 0.2194 |
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### Framework versions
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- Transformers 4.31.0.dev0
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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