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
Download processor_config.json from daksh-neo/MOSS-TTS: direct link, hf CLI and curl.
- Browser
- Download file 145 Bytes
-
https://huggingface.co/daksh-neo/MOSS-TTS/resolve/main/processor_config.json
- Command line
-
hf download hf://daksh-neo/MOSS-TTS/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/daksh-neo/MOSS-TTS/resolve/main/processor_config.json
145 Bytes
| { | |
| "processor_class": "MossTTSDelayProcessor", | |
| "auto_map": { | |
| "AutoProcessor": "processing_moss_tts.MossTTSDelayProcessor" | |
| } | |
| } | |