added style transfer example

notebooks/test_model.ipynb

@@ -128,7 +128,7 @@
    "id": "80e5b5fa",
    "metadata": {},
    "source": [
-    "Try playing around with `start_steps`. Values closer to zero will produce new samples, while values closer to `steps` will produce samples more faithful to the original. You can also try generatring variations of a `slice` of an `audio_file` instead of passing in a `raw_audio`, which results in a kind of style transfer."
+    "Try playing around with `start_steps`. Values closer to zero will produce new samples, while values closer to `steps` will produce samples more faithful to the original."
    ]
   },
   {
@@ -156,7 +156,7 @@
    "outputs": [],
    "source": [
     "start_steps = 500  #@param {type:\"slider\", min:0, max:1000, step:10}\n",
-    "loop = AudioDiffusion.loop_it(audio, sample_rate, loops=1)\n",
+    "track = AudioDiffusion.loop_it(audio, sample_rate, loops=1)\n",
     "for variation in range(12):\n",
     "    image2, (\n",
     "        sample_rate, audio2\n",
@@ -166,8 +166,56 @@
     "        steps=1000)\n",
     "    display(image2)\n",
     "    display(Audio(audio2, rate=sample_rate))\n",
-    "    loop = np.concatenate([loop, AudioDiffusion.loop_it(audio2, sample_rate, loops=1)])\n",
-    "display(Audio(loop, rate=sample_rate))"
+    "    track = np.concatenate([track, AudioDiffusion.loop_it(audio2, sample_rate, loops=1)])\n",
+    "display(Audio(track, rate=sample_rate))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15a39884",
+   "metadata": {},
+   "source": [
+    "### Remix (style transfer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "116bd8c3",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can start from another audio altogether, resulting in a kind of style transfer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2a9c85c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "start_steps = 700  #@param {type:\"slider\", min:0, max:1000, step:10}\n",
+    "audio_file = \"track.mp3\"  #@param {type:\"string\"}\n",
+    "audio_diffusion.mel.load_audio(audio_file)\n",
+    "track = np.array([])\n",
+    "generator = torch.Generator().manual_seed(seed)\n",
+    "seed = generator.seed()\n",
+    "for slice in range(audio_diffusion.mel.get_number_of_slices()):\n",
+    "    generator.manual_seed(seed)\n",
+    "    audio = audio_diffusion.mel.get_audio_slice(slice)\n",
+    "    _, (\n",
+    "        sample_rate, audio2\n",
+    "    ) = audio_diffusion.generate_spectrogram_and_audio_from_audio(\n",
+    "        audio_file=audio_file,\n",
+    "        slice=slice,\n",
+    "        start_step=start_steps,\n",
+    "        steps=1000,\n",
+    "        generator=generator)\n",
+    "    display(Audio(audio, rate=sample_rate))\n",
+    "    display(Audio(audio2, rate=sample_rate))\n",
+    "    track = np.concatenate([track, audio2])\n",
+    "display(Audio(track, rate=sample_rate))"
    ]
   },
   {