Text-to-Speech
Transformers
Safetensors
GGUF
llama
text-generation
speech-synthesis
multilingual
indic
orpheus
lora
low-latency
zero-shot
emotions
discrete-audio-tokens
text-generation-inference
Instructions to use kenpath/svara-tts-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kenpath/svara-tts-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="kenpath/svara-tts-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kenpath/svara-tts-v1") model = AutoModelForCausalLM.from_pretrained("kenpath/svara-tts-v1") - Notebooks
- Google Colab
- Kaggle
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## 🙏 Acknowledgments
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- **Canopy Labs — Orpheus:** foundational ideas & open release
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Release: https://canopylabs.ai/releases/orpheus_can_speak_any_language
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- **SPIRE Lab, IISc Bangalore** — **SYSPIN** (multilingual studio) and **SPICOR** (Indian English)
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## 🙏 Acknowledgments
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This work was developed by **[Kenpath Technologies](https://kenpath.ai/)** for the open-source community. We also thank RunPod for the startup credits that supported our GPU compute.
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- **Canopy Labs — Orpheus:** foundational ideas & open release
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Release: https://canopylabs.ai/releases/orpheus_can_speak_any_language
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- **SPIRE Lab, IISc Bangalore** — **SYSPIN** (multilingual studio) and **SPICOR** (Indian English)
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