Instructions to use Dupaja/speecht5_tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dupaja/speecht5_tts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Dupaja/speecht5_tts")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("Dupaja/speecht5_tts") model = AutoModelForTextToSpectrogram.from_pretrained("Dupaja/speecht5_tts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add handling for fractions and hopefully other number uses
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Increase max length to 400
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Added function to convert digits to written numbers, for handling with SpeechT5
279f06b -
Update handler.py
1e92b35 -
Rewrite to handle larger text
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Update handler.py
3b62ecd -
Update handler.py
4fd4826 -
Added device map
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Update handler.py
617d2a4 -
Update handler.py
9cf2190 -
Attempt to speed up
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Revert direct file send.
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Update handler.py
910abc8 -
Try sending wav file directly
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Update handler.py
be0ef80 -
Remove unused file write
2349229 -
Add trust_remote_code=True to dataset
9594309 -
Attempt to reduce latency by moving more to init
9277202
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Update handler.py
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Update to personal repos
5e7a614 -
Updating using SpeechT5 Article
https://huggingface.co/blog/speecht59f940c9 -
Update handler.py
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Fixes
74cf751 -
Create handler.py
aaf0168