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
| license: cc-by-sa-4.0 | |
| language: | |
| - az | |
| base_model: facebook/mms-tts | |
| pipeline_tag: text-to-speech | |
| library_name: transformers | |
| <h1> Voice of SARA </h1> | |
| 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. | |
|  | |
| (c) Image has been generated by using Microsoft AI Image Generator! | |
| 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. | |
| 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. | |
| Thanks to Kavsar Huseynova, Elvin Mammadov, Qurban Quliyev and PRODATA for the the contributions to this project! | |
| All rights are reserved! | |
| Note: Team and collaborators are not responsible about the contents of the generated voices by different individuals! |