Text-to-Speech
Transformers
Safetensors
qwen3
text-generation
speech
tts
voice
text-generation-inference
Instructions to use SPRINGLab/Indic-Mio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SPRINGLab/Indic-Mio with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="SPRINGLab/Indic-Mio")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SPRINGLab/Indic-Mio") model = AutoModelForCausalLM.from_pretrained("SPRINGLab/Indic-Mio") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cdb83036a72c32859805955bc53f1c34d1afc81d53d397748dc8ea40ec4be48
- Size of remote file:
- 639 kB
- SHA256:
- 1b331f7ea55e3ded2ba9c0d8b69466d773bd28a375fcff2524a85ba1a0054402
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.