Text-to-Speech
Transformers
Safetensors
moss_tts_delay
feature-extraction
audio
cpu-optimized
custom_code
Instructions to use daksh-neo/MOSS-TTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daksh-neo/MOSS-TTS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="daksh-neo/MOSS-TTS", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("daksh-neo/MOSS-TTS", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Remove requirements.txt; add pipeline_tag to README
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Remove __pycache__ compiled files and standalone scripts (benchmark.py, optimize_tts.py)
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Upload folder using huggingface_hub
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Initial model upload with complete configuration and weights
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Emergency fallback upload to daksh-neo namespace
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initial commit
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