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 tokenizer.json from daksh-neo/MOSS-TTS: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/daksh-neo/MOSS-TTS/resolve/main/tokenizer.json
- Command line
-
hf download hf://daksh-neo/MOSS-TTS/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/daksh-neo/MOSS-TTS/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 9b974365098c70d34cc326c43a6c7420e8d48205a1fb471d7caa41b194dc212d
- Size of remote file:
- 11.4 MB
- SHA256:
- cb3c8fa82993d515469c2800cc455bff4aaa3c4fed9da1f2b0c0668c304f335a
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