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:
- 971d361879b74b6858cabcc205d00b5fc87820e45943a72396671b81c601dd60
- Size of remote file:
- 1.22 GB
- SHA256:
- 065f42f7ab6148b66f43e9be0d01ac336343dfe16161350c37b71a87c3e1981b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.