Instructions to use btoscano/speecht5_tts_minds14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use btoscano/speecht5_tts_minds14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="btoscano/speecht5_tts_minds14")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("btoscano/speecht5_tts_minds14") model = AutoModelForTextToSpectrogram.from_pretrained("btoscano/speecht5_tts_minds14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +2 -2
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
README.md
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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:
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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:
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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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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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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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- gradient_accumulation_steps: 2
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- total_train_batch_size: 16
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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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pytorch_model.bin
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training_args.bin
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