Add minimal usage snippets (Kotlin + Python)

README.md

@@ -44,6 +44,47 @@ So the spectral front-end is computed on the **CPU** (the Whisper/Kokoro pattern
 
 Mono **32 kHz**, padded/truncated to 10 s (320000 samples), values in [-1, 1]. Compute the log-mel as above → [1,1,1001,64]. The output 527 sigmoid probabilities are per-class (multi-label); take the top-K as tags.
 
+## Minimal usage
+
+**Android (Kotlin, CompiledModel GPU)**
+
+```kotlin
+// staged into filesDir by an install script (162 MB — too big for assets)
+val model = CompiledModel.create(File(ctx.filesDir, "cnn14_audioset_fp16.tflite").absolutePath,
+    CompiledModel.Options(Accelerator.GPU), null)
+val inputs = model.createInputBuffers(); val outputs = model.createOutputBuffers()
+inputs[0].writeFloat(logmel)          // [1,1,1001,64] host log-mel (see Python below)
+model.run(inputs, outputs)
+val probs = outputs[0].readFloat()    // [527] sigmoid, multi-label -> top-K tags
+```
+
+**Python (desktop verification)**
+
+```python
+import numpy as np, soundfile as sf
+from ai_edge_litert.interpreter import Interpreter
+
+SR, NFFT, HOP, NMEL, CLIP = 32000, 1024, 320, 64, 320000
+wav, _ = sf.read("clip_32k.wav", dtype="float32")               # mono 32 kHz
+x = np.zeros(CLIP, np.float32); n = min(len(wav), CLIP); x[:n] = wav[:n]
+
+# torchlibrosa-exact log-mel: center reflect-pad, periodic Hann, |rFFT|^2, mel, 10*log10
+pad = np.pad(x, NFFT // 2, mode="reflect")
+win = 0.5 - 0.5 * np.cos(2 * np.pi * np.arange(NFFT) / NFFT)
+frames = 1 + CLIP // HOP                                        # 1001
+power = np.stack([np.abs(np.fft.rfft(pad[t*HOP:t*HOP+NFFT] * win))**2 for t in range(frames)])
+fb = np.fromfile("mel_basis.bin", np.float32).reshape(NMEL, 513)
+logmel = (10.0 * np.log10(np.maximum(power @ fb.T, 1e-10))).astype(np.float32)
+
+it = Interpreter(model_path="cnn14_audioset_fp16.tflite"); it.allocate_tensors()
+it.set_tensor(it.get_input_details()[0]["index"], logmel[None, None]); it.invoke()
+probs = it.get_tensor(it.get_output_details()[0]["index"])[0]   # [527]
+
+labels = open("audioset_labels.txt").read().splitlines()
+for i in probs.argsort()[::-1][:5]:
+    print(f"{probs[i]:.3f}  {labels[i]}")
+```
+
 ## License
 
 Code [Apache-2.0](https://github.com/qiuqiangkong/audioset_tagging_cnn/blob/master/LICENSE); weights `Cnn14_mAP=0.431.pth` [CC-BY-4.0](https://zenodo.org/record/3987831) (Zenodo). AudioSet ontology © Google, [CC-BY-4.0](https://research.google.com/audioset/). Upstream: [qiuqiangkong/audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn).