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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-xls-r-300m |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: XLS-R_Synthesis_GN_v1 |
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results: [] |
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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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# XLS-R_Synthesis_GN_v1 |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5071 |
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- Wer: 0.3125 |
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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: 0.0001 |
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- train_batch_size: 18 |
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- eval_batch_size: 9 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 72 |
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- total_eval_batch_size: 18 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: polynomial |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 100.0 |
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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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| 5.5691 | 1.0 | 1252 | 2.9844 | 1.0 | |
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| 1.7927 | 2.0 | 2505 | 0.5051 | 0.5816 | |
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| 0.5062 | 3.0 | 3757 | 0.3025 | 0.3928 | |
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| 0.3566 | 4.0 | 5010 | 0.2515 | 0.3208 | |
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| 0.2883 | 5.0 | 6262 | 0.2237 | 0.2811 | |
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| 0.2474 | 6.0 | 7515 | 0.2055 | 0.2538 | |
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| 0.223 | 7.0 | 8767 | 0.1929 | 0.2394 | |
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| 0.2068 | 8.0 | 10020 | 0.1957 | 0.2354 | |
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| 0.2043 | 9.0 | 11272 | 0.2222 | 0.2486 | |
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| 0.3093 | 10.0 | 12525 | 0.3168 | 0.3350 | |
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| 0.2501 | 11.0 | 13777 | 0.2099 | 0.2458 | |
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| 0.3961 | 12.0 | 15030 | 0.5071 | 0.3125 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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