Text-to-Speech
Transformers
ONNX
Safetensors
English
Chinese
qwen3
text-generation
automatic-speech-recognition
voice-conversion
speech
audio
custom_code
text-generation-inference
Instructions to use AutoArk-AI/GPA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AutoArk-AI/GPA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="AutoArk-AI/GPA", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AutoArk-AI/GPA", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("AutoArk-AI/GPA", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
GPA_TTS: add fp16&fp32 decoder option
Browse files
GPA_TTS/GPA_TTS_INT8/model/spark_detokenizer_fp16.onnx
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size 1815092
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GPA_TTS/GPA_TTS_INT8/model/spark_detokenizer_fp16.onnx.data
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size 192517440
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GPA_TTS/GPA_TTS_INT8/model/spark_detokenizer_fp32.onnx
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oid sha256:c6ef5e628fed19a845f63acc73052c2fbf1300c51f4dfb58522d3dbe5c4ec346
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size 1724019
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GPA_TTS/GPA_TTS_INT8/model/spark_detokenizer_fp32.onnx.data
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oid sha256:0e437cd54f749ca6df49590eb36a2d336e85608a2d999129a6ae351eb05f01a5
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size 385286144
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