Instructions to use AveroLabs/Sol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AveroLabs/Sol with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="AveroLabs/Sol")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("AveroLabs/Sol") model = AutoModelForTextToSpectrogram.from_pretrained("AveroLabs/Sol", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sol_meta.json from AveroLabs/Sol: direct link, hf CLI and curl.
- Browser
- Download file 386 Bytes
-
https://huggingface.co/AveroLabs/Sol/resolve/main/sol_meta.json
- Command line
-
hf download hf://AveroLabs/Sol/sol_meta.json
-
curl -L -o sol_meta.json https://huggingface.co/AveroLabs/Sol/resolve/main/sol_meta.json
386 Bytes
| { | |
| "name": "Sol", | |
| "base_model": "C:\\Users\\donov\\Downloads\\Sol\\assets\\microsoft\\speecht5_tts", | |
| "dataset": "openslr/librispeech_asr/clean/validation", | |
| "speaker_id": 2277, | |
| "num_samples": 87, | |
| "max_steps": 80, | |
| "sampling_rate": 16000, | |
| "learning_rate": 2e-06, | |
| "note": "light fine-tune; speaker embedding is a raw x-vector (norm ~320), do not unit-normalise" | |
| } |