Text-to-Speech
Transformers
Safetensors
English
Hindi
llama
text-generation
tts
hindi
english
audio
speech
india
text-generation-inference
Instructions to use maya-research/Veena with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maya-research/Veena with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="maya-research/Veena")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("maya-research/Veena") model = AutoModelForCausalLM.from_pretrained("maya-research/Veena") - Notebooks
- Google Colab
- Kaggle
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# Veena - Text to Speech for Indian Languages
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Veena is a state-of-the-art neural text-to-speech (TTS) model
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## Model Overview
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**Veena** is a 3B parameter autoregressive transformer model based on the Llama architecture. It is designed to synthesize high-quality speech from text in Hindi and English, including code-mixed scenarios. The model outputs audio at a 24kHz sampling rate using the SNAC neural codec.
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# Veena - Text to Speech for Indian Languages
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Veena is a state-of-the-art neural text-to-speech (TTS) model developed by Maya Research, designed for English and Indian languages. Built on a Llama architecture backbone, Veena generates natural, expressive speech with emotional tone, remarkable quality, and ultra-low latency. It represents the foundation of our voice intelligence work, bringing human-like voice to the two most spoken languages in the world.
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## Model Overview
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**Veena** is a 3B parameter autoregressive transformer model based on the Llama architecture. It is designed to synthesize high-quality speech from text in Hindi and English, including code-mixed scenarios. The model outputs audio at a 24kHz sampling rate using the SNAC neural codec.
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