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--- |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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base_model: mms-meta/mms-zeroshot-300m |
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tags: |
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- automatic-speech-recognition |
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- BembaSpeech |
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- mms |
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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: mms-zeroshot-300m-bembaspeech-model |
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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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# mms-zeroshot-300m-bembaspeech-model |
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This model is a fine-tuned version of [mms-meta/mms-zeroshot-300m](https://huggingface.co./mms-meta/mms-zeroshot-300m) on the BEMBASPEECH - BEM dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2038 |
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- Wer: 0.4007 |
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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: 8 |
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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: 30.0 |
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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 | 0.1405 | 200 | 2.2803 | 1.0 | |
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| No log | 0.2811 | 400 | 0.2945 | 0.5101 | |
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| 2.741 | 0.4216 | 600 | 0.2641 | 0.4834 | |
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| 2.741 | 0.5622 | 800 | 0.2611 | 0.4743 | |
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| 0.5962 | 0.7027 | 1000 | 0.2608 | 0.4831 | |
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| 0.5962 | 0.8433 | 1200 | 0.2477 | 0.4563 | |
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| 0.5962 | 0.9838 | 1400 | 0.2407 | 0.4567 | |
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| 0.536 | 1.1244 | 1600 | 0.2343 | 0.4412 | |
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| 0.536 | 1.2649 | 1800 | 0.2307 | 0.4423 | |
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| 0.5221 | 1.4055 | 2000 | 0.2252 | 0.4348 | |
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| 0.5221 | 1.5460 | 2200 | 0.2228 | 0.4326 | |
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| 0.5221 | 1.6866 | 2400 | 0.2162 | 0.4253 | |
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| 0.5027 | 1.8271 | 2600 | 0.2200 | 0.4188 | |
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| 0.5027 | 1.9677 | 2800 | 0.2131 | 0.4142 | |
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| 0.4818 | 2.1082 | 3000 | 0.2281 | 0.4281 | |
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| 0.4818 | 2.2488 | 3200 | 0.2178 | 0.4147 | |
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| 0.4818 | 2.3893 | 3400 | 0.2123 | 0.4155 | |
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| 0.4619 | 2.5299 | 3600 | 0.2142 | 0.4079 | |
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| 0.4619 | 2.6704 | 3800 | 0.2156 | 0.4011 | |
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| 0.464 | 2.8110 | 4000 | 0.2072 | 0.4009 | |
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| 0.464 | 2.9515 | 4200 | 0.2128 | 0.4013 | |
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| 0.464 | 3.0921 | 4400 | 0.2056 | 0.3982 | |
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| 0.4464 | 3.2326 | 4600 | 0.2038 | 0.4007 | |
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| 0.4464 | 3.3732 | 4800 | 0.2089 | 0.3987 | |
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| 0.4418 | 3.5137 | 5000 | 0.2043 | 0.4009 | |
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| 0.4418 | 3.6543 | 5200 | 0.2050 | 0.3964 | |
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### Framework versions |
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- Transformers 4.46.0.dev0 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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