Instructions to use atharva27/orpheus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use atharva27/orpheus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="atharva27/orpheus")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("atharva27/orpheus") model = AutoModelForCausalLM.from_pretrained("atharva27/orpheus") - Notebooks
- Google Colab
- Kaggle
Create requirements.txt
Browse files- requirements.txt +7 -0
requirements.txt
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transformers # for model and tokenizer
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torch # PyTorch for running the model
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numpy # for audio data handling
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livekit == 1.0.6 # Use a compatible version of livekit
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torchaudio # for audio processing with PyTorch
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pydub # for audio file manipulation (optional, depending on use case)
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livekit-agents>=0.12.17
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