Instructions to use mmcgovern574/speecht5_finetuned_voxpopuli_nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmcgovern574/speecht5_finetuned_voxpopuli_nl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="mmcgovern574/speecht5_finetuned_voxpopuli_nl")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("mmcgovern574/speecht5_finetuned_voxpopuli_nl") model = AutoModelForTextToSpectrogram.from_pretrained("mmcgovern574/speecht5_finetuned_voxpopuli_nl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 6758936
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_nl
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results: []
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# speecht5_finetuned_voxpopuli_nl
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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: 0.4604
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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_nl
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results: []
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# speecht5_finetuned_voxpopuli_nl
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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: 0.4604
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