Text-to-Speech
Transformers
Safetensors
Arabic
English
qwen3
text-generation
tts
expressive-speech
paralinguistic
voice-cloning
multilingual
text-generation-inference
Instructions to use audarai/tts-pro-xpression-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use audarai/tts-pro-xpression-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="audarai/tts-pro-xpression-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("audarai/tts-pro-xpression-v2") model = AutoModelForCausalLM.from_pretrained("audarai/tts-pro-xpression-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ecc03e828e031ebc06beee99717281b22bced8313f15ce3aee72d46c927a031
- Size of remote file:
- 24.1 MB
- SHA256:
- 23bf4b6867c6fe6f893bbf55851048dabc67275f53c57466c8af57fb13134584
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