Instructions to use BHOSAI/SARA_TTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BHOSAI/SARA_TTS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="BHOSAI/SARA_TTS")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("BHOSAI/SARA_TTS") model = AutoModelForTextToWaveform.from_pretrained("BHOSAI/SARA_TTS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Baku Higher Oil School Research and Development Center on AI introduce their new Text-to-Speech model in collaboration with PRODATA. Model is based on VITS architecture, referenced to Meta MMS on Azerbaijani.
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Meta MMS model has good performance in naturalness of the speech while it was not robust to the change in the input tokens. Intonation varied according to the input tokens.
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Our team has built speech and text pairs from public sources and combined them with 2-3 sentences to create continuous speech in the input.
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Baku Higher Oil School Research and Development Center on AI introduce their new Text-to-Speech model in collaboration with PRODATA. Model is based on VITS architecture, referenced to Meta MMS on Azerbaijani.
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(c) Image has been generated by using Microsoft AI Image Generator!
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Meta MMS model has good performance in naturalness of the speech while it was not robust to the change in the input tokens. Intonation varied according to the input tokens.
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Our team has built speech and text pairs from public sources and combined them with 2-3 sentences to create continuous speech in the input.
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