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Vishalshendge3198
/
spark_tts_finetune

Text-to-Speech
Transformers
Safetensors
PEFT
German
qwen2
text-generation
german
emotional
spark-tts
lora
unsloth
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use Vishalshendge3198/spark_tts_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Vishalshendge3198/spark_tts_finetune with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-to-speech", model="Vishalshendge3198/spark_tts_finetune")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Vishalshendge3198/spark_tts_finetune")
    model = AutoModelForCausalLM.from_pretrained("Vishalshendge3198/spark_tts_finetune", device_map="auto")
  • PEFT

    How to use Vishalshendge3198/spark_tts_finetune with PEFT:

    Task type is invalid.
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • Unsloth Studio

    How to use Vishalshendge3198/spark_tts_finetune 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 Vishalshendge3198/spark_tts_finetune 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 Vishalshendge3198/spark_tts_finetune to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for Vishalshendge3198/spark_tts_finetune to start chatting
    Load model with FastModel
    pip install unsloth
    from unsloth import FastModel
    model, tokenizer = FastModel.from_pretrained(
        model_name="Vishalshendge3198/spark_tts_finetune",
        max_seq_length=2048,
    )

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  • .gitattributes
    1.57 kB
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  • README.md
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  • added_tokens.json
    522 kB
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  • chat_template.jinja
    2.56 kB
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  • config.json
    1.35 kB
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  • merges.txt
    1.67 MB
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  • model.safetensors
    2.03 GB
    xet
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  • special_tokens_map.json
    644 Bytes
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  • tokenizer.json
    14.1 MB
    xet
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  • tokenizer_config.json
    2.69 MB
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  • vocab.json
    2.78 MB
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