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--- |
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language: |
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- tr |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- common_voice |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-common_voice-tr-output |
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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 - TR |
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type: common_voice |
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config: tr |
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split: test |
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args: 'Config: tr, Training split: train+validation, Eval split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.32427739761005003 |
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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-common_voice-tr-output |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3776 |
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- Wer: 0.3243 |
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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: 0.0003 |
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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: 2 |
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- total_train_batch_size: 32 |
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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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- num_epochs: 20.0 |
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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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| No log | 0.92 | 100 | 3.6020 | 1.0 | |
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| No log | 1.83 | 200 | 2.9971 | 0.9999 | |
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| No log | 2.75 | 300 | 0.9174 | 0.7772 | |
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| No log | 3.67 | 400 | 0.5668 | 0.6356 | |
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| 3.1619 | 4.59 | 500 | 0.4949 | 0.5256 | |
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| 3.1619 | 5.5 | 600 | 0.4516 | 0.4744 | |
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| 3.1619 | 6.42 | 700 | 0.4291 | 0.4575 | |
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| 3.1619 | 7.34 | 800 | 0.4330 | 0.4273 | |
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| 3.1619 | 8.26 | 900 | 0.4016 | 0.4145 | |
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| 0.2261 | 9.17 | 1000 | 0.4214 | 0.4005 | |
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| 0.2261 | 10.09 | 1100 | 0.4093 | 0.3946 | |
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| 0.2261 | 11.01 | 1200 | 0.4051 | 0.3917 | |
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| 0.2261 | 11.93 | 1300 | 0.3908 | 0.3719 | |
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| 0.2261 | 12.84 | 1400 | 0.3850 | 0.3603 | |
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| 0.1119 | 13.76 | 1500 | 0.3967 | 0.3645 | |
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| 0.1119 | 14.68 | 1600 | 0.3821 | 0.3526 | |
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| 0.1119 | 15.6 | 1700 | 0.3919 | 0.3519 | |
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| 0.1119 | 16.51 | 1800 | 0.3763 | 0.3366 | |
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| 0.1119 | 17.43 | 1900 | 0.3682 | 0.3349 | |
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| 0.074 | 18.35 | 2000 | 0.3753 | 0.3323 | |
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| 0.074 | 19.27 | 2100 | 0.3753 | 0.3267 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 1.12.1+cu102 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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