Instructions to use omarimc/dst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omarimc/dst with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="omarimc/dst")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("omarimc/dst") model = AutoModelForTextToWaveform.from_pretrained("omarimc/dst", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download generation_config.json from omarimc/dst: direct link, hf CLI and curl.
- Browser
- Download file 224 Bytes
-
https://huggingface.co/omarimc/dst/resolve/main/generation_config.json
- Command line
-
hf download hf://omarimc/dst/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/omarimc/dst/resolve/main/generation_config.json
224 Bytes
| { | |
| "_from_model_config": true, | |
| "bos_token_id": 2048, | |
| "decoder_start_token_id": 2048, | |
| "do_sample": true, | |
| "guidance_scale": 3.0, | |
| "max_length": 1500, | |
| "pad_token_id": 2048, | |
| "transformers_version": "4.31.0.dev0" | |
| } | |