Instructions to use khazarai/Cardiology-TTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use khazarai/Cardiology-TTS with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/csm-1b") model = PeftModel.from_pretrained(base_model, "khazarai/Cardiology-TTS") - Transformers
How to use khazarai/Cardiology-TTS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="khazarai/Cardiology-TTS")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("khazarai/Cardiology-TTS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from khazarai/Cardiology-TTS: direct link, hf CLI and curl.
- Browser
- Download file 271 Bytes
-
https://huggingface.co/khazarai/Cardiology-TTS/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://khazarai/Cardiology-TTS/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/khazarai/Cardiology-TTS/resolve/main/preprocessor_config.json
271 Bytes
| { | |
| "chunk_length_s": null, | |
| "feature_extractor_type": "EncodecFeatureExtractor", | |
| "feature_size": 1, | |
| "overlap": null, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "CsmProcessor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 24000 | |
| } | |