doc: update readme and samples

README.md

@@ -1,6 +1,6 @@
 # ✨ Supervoice Enhance [BETA]
 
-Enhancing diffusion neural network for a single speaker speech.
+Enhancing diffusion neural network for a single speaker speech based on Speech Flow architecture. [Evaluation notebook](/eval.ipynb).
 
 > [!IMPORTANT]  
 > Network was trained using 5s intrevals, but it can work with any length of audio with slightly reduced quality.
@@ -12,19 +12,32 @@ Enhancing diffusion neural network for a single speaker speech.
 * 🤹‍♂️ Can work with unknown languages
 
 # Usage
+
 Supervoice Enhance consists of multiple networks, but they are all loaded using a single command and published using Torch Hub, so you can use it as follows:
 
 ```python
 import torch
 import torchaudio
 
-# Load audio
+# Load model
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-enhance = torch.hub.load(repo_or_dir='ex3ndr/supervoice-enhance', model='enhance')
-enhance.to(device)
-enhance.eval()
+model = torch.hub.load(repo_or_dir='ex3ndr/supervoice-enhance', model='enhance')
+model.to(device)
+model.eval()
+
+# Load audio
+def load_mono_audio(path):
+    audio, sr = torchaudio.load(path)
+    if sr != model.sample_rate:
+        audio = torchaudio.transforms.Resample(sr, model.sample_rate)(audio)
+        sr = model.sample_rate
+    if audio.shape[0] > 1:
+        audio = audio.mean(dim=0, keepdim=True)
+    return audio[0]
+audio = load_mono_audio("./eval/eval_2.wav")
 
 # Enhance
+enhanced = model.enhance(audio, steps = 8) # 8 is optimal, 32 is higer quality but sometimes it halluciante
 
 ```