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Introducing Indic Parler-TTS: Open-Source Text-to-Speech for Over a Billion Indic Speakers! 🌏 In collaboration with Hugging Face, we are excited to release Indic Parler-TTS, a state-of-the-art open-source text-to-speech system designed to bring accessible and high-quality speech technology to India’s diverse linguistic community. Supporting 20 of the 22 scheduled Indian... show more
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For those who want to know what the training data is, please take a look at this:
🇮🇳/ acc
@huggingface that sounds pretty cool! more voices for diverse languages, right?
@huggingface That's great news!
@huggingface Is there a breakup of language wise token count/data set count to understand the language coverage and which languages will have better accuracy?
@huggingface Kickass! Thank you so much. Looks so good.
@huggingface @huggingface, that's a dope initiative! Bringing voice tech to such a diverse audience is crucial. Wonder how it'll impact accessibility in those communities?
@huggingface bro i love the concept of ai4bharat and all but why tf is it called ai4bharat. like bro ai4bharat sounds like a discount brand of ai. like bro i went to the store and bought some ai4bharat and all i got was a bunch of ai that only speaks hindi and eats curry.
@huggingface Does not support garbage Chinese language?
![Today we launched Gemini 3.1 Flash TTS, our most expressive and controllable text-to-speech model yet. This launch [excitement] includes audio tags! 🗣🏷 Audio tags [explanatory] are a seamless way to guide vocal style, pace, and delivery using natural language commands embedded directly in your text. Want a different tempo or tone? [amazement] Just tag the audio to steer the AI-speech output! The model supports 70+ languages (24 of which are high-quality evaluated languages, including: Japanese, Hindi, and Arabic). Watch the audio tags in action in the demo below ↓](https://image.24vids.com/tw-2044447560384102592/amplify_video_thumb/2044447449708990464/img/DpiMl_0pWyVPDKFC.jpg)
![Google presents AudioPaLM: A Large Language Model That Can Speak and Listen paper page: introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt.](https://image.24vids.com/tw-1672048076469698561/ext_tw_video_thumb/1672047839378300928/pu/img/MuoOBsAyUd-HAI7D.jpg)
