few fixes for collab

webbook/resources/exploring-performance.ipynb

@@ -49,12 +49,9 @@
     "## Installing (if not) and importing compiam to the project\n",
     "import importlib.util\n",
     "%pip install -U compiam==0.4.1  # Install latest version of compiam\n",
-    "if importlib.util.find_spec('essentia') is None:\n",
-    "    %pip install essentia\n",
-    "if importlib.util.find_spec('torch') is None:\n",
-    "    %pip install \"torch==1.13\"\n",
-    "if importlib.util.find_spec('tensorflow') is None:\n",
-    "    %pip install \"tensorflow==2.15.0\" \"keras<3\"\n",
+    "%pip install essentia\n",
+    "%pip install \"torch==1.13\"\n",
+    "%pip install \"tensorflow==2.15.0\" \"keras<3\"\n",
     "\n",
     "import compiam\n",
     "import essentia.standard as estd\n",
@@ -77,6 +74,22 @@
     "AUDIO_PATH = os.path.join(\"..\", \"audio\", \"demos\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*NOTE:* If working on Collab, please uncomment and run this cell below, otherwise, don't!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#AUDIO_PATH = \"./\"  ## Run if working on collab!"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -632,7 +645,21 @@
     "\n",
     "There are several models in the literature to address this problem, most of them based on deep learning architectures, some of them provide pre-trained weights such as Demucs {cite}`demucs` or Spleeter {cite}`spleeter`, the latter is broadly used in Carnatic Music computational research works. However, thes systems have two problems: (1) the training data of these models does normally not include Carnatic Music examples, therefore there are instruments and practices which are completely unseed by these models, and (2) these models have a restricted set of target elements, namely (_vocals_, _bass_, _drums_, and _other_), which does not fit to Carnatic Music arrangements at all. \n",
     "\n",
-    "To address problem (1), there have been few attemps on trying to use the multi-stem data presented above to develop Carnatic-tailored source separation systmes, although the available multi-stem recordings are collected from mixing consoles in live performances, and therefore the individual tracks are noisy (have background leakage from the rest of the sources). We can test one example of these systems here: {cite}`plaja_separation_2023`."
+    "To address problem (1), there have been few attemps on trying to use the multi-stem data presented above to develop Carnatic-tailored source separation systmes, although the available multi-stem recordings are collected from mixing consoles in live performances, and therefore the individual tracks are noisy (have background leakage from the rest of the sources). We can test one example of these systems here: {cite}``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "remove-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Make sure proper TF version is installed\n",
+    "%pip install \"tensorflow==2.15.0\" \"keras<3\""
    ]
   },
   {
@@ -741,6 +768,20 @@
     "The f0 pitch track of the predominant vocal melody has proved useful for range of computational analysis tasks in Indian Art Music. We can extract this for our performance using Melodia {cite}`salamon_pitch_2012`, a broadly used knowledge-based method. We will also test a recently published DL model to achieve the same goal: FTA-Net model, which has been trained specifically for the Carnatic Music use case and included in compIAM as well {cite}`plaja_pitch_2023`."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "remove-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Make sure proper TF version is installed\n",
+    "%pip install \"tensorflow==2.15.0\" \"keras<3\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,