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--- |
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language: |
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- en |
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license: mit |
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base_model: microsoft/speecht5_tts |
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tags: |
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- . |
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- generated_from_trainer |
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datasets: |
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- speecht5_imda_nsc_p1_p3 |
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model-index: |
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- name: Speech T5 TTS English |
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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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# Speech T5 TTS English |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the IMDA National Speech Corpus dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.3551 |
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- eval_runtime: 236.1173 |
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- eval_samples_per_second: 66.962 |
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- eval_steps_per_second: 2.096 |
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- epoch: 5.97 |
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- step: 40002 |
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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: 16 |
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- eval_batch_size: 32 |
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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_steps: 500 |
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- num_epochs: 10.0 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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