Update README.md

README.md

@@ -34,57 +34,7 @@ This version was finetuned by twobob from ![this dataset](https://www.kaggle.com
 ## Usage
 
 This model can be used with:
-1. the [`stable-audio-tools`](https://github.com/Stability-AI/stable-audio-tools) library
-2. the [`diffusers`](https://huggingface.co/docs/diffusers/main/en/index) library
-
-
-### Using with `stable-audio-tools`
-
-This model is made to be used with the [`stable-audio-tools`](https://github.com/Stability-AI/stable-audio-tools) library for inference, for example:
-
-```python
-import torch
-import torchaudio
-from einops import rearrange
-from stable_audio_tools import get_pretrained_model
-from stable_audio_tools.inference.generation import generate_diffusion_cond
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Download model
-model, model_config = get_pretrained_model("PsiPi/audio")
-sample_rate = model_config["sample_rate"]
-sample_size = model_config["sample_size"]
-
-model = model.to(device)
-
-# Set up text and timing conditioning
-conditioning = [{
-    "prompt": "specialKay vocal, A cappella, 120 BPM twobob house vocalisations feat. Special Kay",
-    "seconds_start": 0, 
-    "seconds_total": 30
-}]
-
-# Generate stereo audio
-output = generate_diffusion_cond(
-    model,
-    steps=100,
-    cfg_scale=7,
-    conditioning=conditioning,
-    sample_size=sample_size,
-    sigma_min=0.3,
-    sigma_max=500,
-    sampler_type="dpmpp-3m-sde",
-    device=device
-)
-
-# Rearrange audio batch to a single sequence
-output = rearrange(output, "b d n -> d (b n)")
-
-# Peak normalize, clip, convert to int16, and save to file
-output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
-torchaudio.save("output.wav", output, sample_rate)
-```
+1. the [`diffusers`](https://huggingface.co/docs/diffusers/main/en/index) library
 
 ## Using with `diffusers`