Text-to-Speech
Transformers
Safetensors
Chinese
qwen2
text-generation
unsloth
text-generation-inference
Instructions to use Juicesyo/Spark-TTS-Encore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Juicesyo/Spark-TTS-Encore with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Juicesyo/Spark-TTS-Encore")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Juicesyo/Spark-TTS-Encore") model = AutoModelForCausalLM.from_pretrained("Juicesyo/Spark-TTS-Encore") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Juicesyo/Spark-TTS-Encore with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Juicesyo/Spark-TTS-Encore to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Juicesyo/Spark-TTS-Encore to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Juicesyo/Spark-TTS-Encore to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Juicesyo/Spark-TTS-Encore", max_seq_length=2048, )