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Sunbird
/
spark-tts-salt

Text-to-Speech
Transformers
Safetensors
qwen2
text-generation
unsloth
trl
sft
tts
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use Sunbird/spark-tts-salt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Sunbird/spark-tts-salt with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-to-speech", model="Sunbird/spark-tts-salt")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Sunbird/spark-tts-salt")
    model = AutoModelForCausalLM.from_pretrained("Sunbird/spark-tts-salt")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • Unsloth Studio

    How to use Sunbird/spark-tts-salt 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 Sunbird/spark-tts-salt 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 Sunbird/spark-tts-salt to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for Sunbird/spark-tts-salt to start chatting
    Load model with FastModel
    pip install unsloth
    from unsloth import FastModel
    model, tokenizer = FastModel.from_pretrained(
        model_name="Sunbird/spark-tts-salt",
        max_seq_length=2048,
    )

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