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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_11_0 |
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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_11_0 |
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type: common_voice_11_0 |
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config: tr |
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split: test[:10] |
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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: 0.42857142857142855 |
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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_11_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4348 |
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- Wer: 0.4286 |
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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: 1 |
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- num_epochs: 1 |
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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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| 1.3297 | 0.12 | 100 | 0.5375 | 0.4571 | |
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| 0.2726 | 0.25 | 200 | 0.5256 | 0.4714 | |
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| 0.265 | 0.37 | 300 | 0.4696 | 0.4571 | |
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| 0.263 | 0.49 | 400 | 0.4405 | 0.4286 | |
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| 0.2574 | 0.61 | 500 | 0.4363 | 0.4143 | |
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| 0.2517 | 0.74 | 600 | 0.4592 | 0.4286 | |
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| 0.2454 | 0.86 | 700 | 0.4445 | 0.4143 | |
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| 0.2425 | 0.98 | 800 | 0.4348 | 0.4286 | |
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### Framework versions |
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- Transformers 4.37.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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