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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/speecht5_tts |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_17_0 |
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model-index: |
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- name: SpeechT5-Hausa-9 |
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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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# SpeechT5-Hausa-9 |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) 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: 0.4650 |
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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: 4 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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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: 100 |
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- training_steps: 2000 |
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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 | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 0.6131 | 1.8476 | 100 | 0.5543 | |
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| 0.5611 | 3.6952 | 200 | 0.5054 | |
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| 0.5362 | 5.5427 | 300 | 0.4950 | |
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| 0.5222 | 7.3903 | 400 | 0.4837 | |
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| 0.5067 | 9.2379 | 500 | 0.4770 | |
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| 0.5028 | 11.0855 | 600 | 0.4733 | |
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| 0.4926 | 12.9330 | 700 | 0.4685 | |
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| 0.4833 | 14.7806 | 800 | 0.4640 | |
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| 0.4776 | 16.6282 | 900 | 0.4606 | |
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| 0.4769 | 18.4758 | 1000 | 0.4582 | |
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| 0.4715 | 20.3233 | 1100 | 0.4651 | |
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| 0.462 | 22.1709 | 1200 | 0.4612 | |
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| 0.4579 | 24.0185 | 1300 | 0.4616 | |
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| 0.455 | 25.8661 | 1400 | 0.4585 | |
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| 0.4551 | 27.7136 | 1500 | 0.4646 | |
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| 0.4516 | 29.5612 | 1600 | 0.4644 | |
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| 0.4471 | 31.4088 | 1700 | 0.4636 | |
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| 0.4427 | 33.2564 | 1800 | 0.4645 | |
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| 0.4494 | 35.1039 | 1900 | 0.4653 | |
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| 0.4357 | 36.9515 | 2000 | 0.4650 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.19.1 |
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