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Audio8
/
GPA

Text-to-Speech
Transformers
ONNX
Safetensors
English
Chinese
qwen3
text-generation
automatic-speech-recognition
voice-conversion
speech
audio
custom_code
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use Audio8/GPA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Audio8/GPA with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-to-speech", model="Audio8/GPA", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Audio8/GPA", trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained("Audio8/GPA", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
GPA
8.02 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
chua's picture
chua
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  • BiCodec
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  • figures
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  • glm-4-voice-tokenizer
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  • gpa_0_3b
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  • .gitattributes
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  • README.md
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  • added_tokens.json
    1 MB
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  • chat_template.jinja
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  • config.json
    1.5 kB
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  • config.json.bak
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  • configuration_qwen3.py
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  • generation_config.json
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  • merges.txt
    1.67 MB
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  • model.safetensors
    625 MB
    xet
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  • modeling_qwen3.py
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  • special_tokens_map.json
    616 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    5.41 MB
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  • vocab.json
    2.78 MB
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