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--- |
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library_name: transformers |
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language: |
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- yo |
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license: mit |
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base_model: microsoft/speecht5_tts |
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tags: |
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- Nigeria |
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- generated_from_trainer |
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datasets: |
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- ccibeekeoc42/naija_tts_concatenated |
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model-index: |
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- name: SpeechT5 TTS Igb0 Yoruba |
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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 TTS Igb0 Yoruba |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the naija_tts_concatenated dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4370 |
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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: 1e-05 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 12 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 6000 |
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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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| 0.5354 | 0.1339 | 500 | 0.4752 | |
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| 0.5157 | 0.2679 | 1000 | 0.4604 | |
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| 0.5028 | 0.4018 | 1500 | 0.4537 | |
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| 0.4872 | 0.5358 | 2000 | 0.4489 | |
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| 0.4892 | 0.6697 | 2500 | 0.4453 | |
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| 0.4813 | 0.8036 | 3000 | 0.4436 | |
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| 0.483 | 0.9376 | 3500 | 0.4421 | |
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| 0.4763 | 1.0715 | 4000 | 0.4401 | |
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| 0.4786 | 1.2055 | 4500 | 0.4400 | |
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| 0.481 | 1.3394 | 5000 | 0.4379 | |
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| 0.4811 | 1.4733 | 5500 | 0.4393 | |
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| 0.4846 | 1.6073 | 6000 | 0.4370 | |
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### Framework versions |
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- Transformers 4.48.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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