Text-to-Speech
Transformers
TensorBoard
Safetensors
Spanish
speecht5
text-to-audio
Generated from Trainer
Instructions to use neopolita/speecht5_finetuned_voxpopuli_es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neopolita/speecht5_finetuned_voxpopuli_es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="neopolita/speecht5_finetuned_voxpopuli_es")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("neopolita/speecht5_finetuned_voxpopuli_es") model = AutoModelForTextToSpectrogram.from_pretrained("neopolita/speecht5_finetuned_voxpopuli_es") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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tags:
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- generated_from_trainer
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datasets:
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- voxpopuli
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model-index:
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- name: speecht5_finetuned_voxpopuli_es
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results: []
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# speecht5_finetuned_voxpopuli_es
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4936
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tags:
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- generated_from_trainer
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datasets:
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- facebook/voxpopuli
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model-index:
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- name: speecht5_finetuned_voxpopuli_es
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results: []
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# speecht5_finetuned_voxpopuli_es
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4936
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