Instructions to use pranavdaware/speecht5_tts_technical_train2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pranavdaware/speecht5_tts_technical_train2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="pranavdaware/speecht5_tts_technical_train2")# Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("pranavdaware/speecht5_tts_technical_train2") model = AutoModelForTextToSpectrogram.from_pretrained("pranavdaware/speecht5_tts_technical_train2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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It achieves the following results on the evaluation set:
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- Loss: 0.3763
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## Model description
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More information needed
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It achieves the following results on the evaluation set:
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- Loss: 0.3763
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SAMPLE TEXT : "hello ,few technical terms i used while fine tuning are API and REST and CUDA and TTS."
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<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/66f64964584cae45b5494560/JYJmDNPHnBRLuvqGTJQSu.wav"></audio>
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## Model description
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More information needed
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