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--- |
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base_model: facebook/mms-1b-all |
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datasets: |
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- common_voice_17_0 |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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metrics: |
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- wer |
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- bleu |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: wav2vec2-mms-1b-CV17.0 |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: common_voice_17_0 |
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type: common_voice_17_0 |
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config: ta |
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split: test[:5%]+test[20%:25%]+test[60%:65%]+test[90%:] |
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args: ta |
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metrics: |
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- type: wer |
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value: 0.39458150446496143 |
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name: Wer |
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- type: bleu |
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value: 0.3894086227361947 |
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name: Bleu |
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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-mms-1b-CV17.0 |
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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_17_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2560 |
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- Wer: 0.3946 |
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- Cer: 0.0667 |
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- Bleu: 0.3894 |
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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: 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_ratio: 0.15 |
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- training_steps: 5000 |
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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 | Cer | Bleu | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:| |
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| 5.2362 | 0.3509 | 500 | 0.3039 | 0.4233 | 0.0716 | 0.3579 | |
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| 0.2003 | 0.7018 | 1000 | 0.2726 | 0.4072 | 0.0694 | 0.3806 | |
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| 0.1821 | 1.0526 | 1500 | 0.2632 | 0.3993 | 0.0677 | 0.3890 | |
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| 0.1792 | 1.4035 | 2000 | 0.2602 | 0.4011 | 0.0677 | 0.3850 | |
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| 0.1764 | 1.7544 | 2500 | 0.2572 | 0.3984 | 0.0674 | 0.3850 | |
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| 0.1771 | 2.1053 | 3000 | 0.2570 | 0.3955 | 0.0672 | 0.3918 | |
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| 0.1777 | 2.4561 | 3500 | 0.2562 | 0.3970 | 0.0670 | 0.3866 | |
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| 0.1721 | 2.8070 | 4000 | 0.2547 | 0.3975 | 0.0669 | 0.3841 | |
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| 0.177 | 3.1579 | 4500 | 0.2560 | 0.3949 | 0.0668 | 0.3910 | |
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| 0.1765 | 3.5088 | 5000 | 0.2560 | 0.3946 | 0.0667 | 0.3894 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
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