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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# SpeechT5 TTS technical
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the custom dataset.
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It achieves the following results on the evaluation set:
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# SpeechT5 TTS technical train2
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the custom dataset.
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It achieves the following results on the evaluation set:
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