Upload upload.ipynb

upload.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b13595e3-ccbe-452d-b1da-056316b377a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import musdb\n",
+    "import datasets\n",
+    "import torch\n",
+    "import torchaudio\n",
+    "import tempfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f94aecdc-640d-4a0d-b16e-bf0f07637d80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "components = [\n",
+    "    'mixture',\n",
+    "    'drums',\n",
+    "    'bass',\n",
+    "    'other',\n",
+    "    'vocals'\n",
+    "]\n",
+    "fs = 44100\n",
+    "features=datasets.Features(\n",
+    "    {\n",
+    "        \"name\": datasets.Value(\"string\"),\n",
+    "        **{\n",
+    "            comp: datasets.Audio(\n",
+    "                sampling_rate=fs, mono=False\n",
+    "            )\n",
+    "            for comp in components\n",
+    "        }\n",
+    "    }\n",
+    ")         "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1dbac3bd-0ec3-4b0f-8f46-2659ebff0049",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mus_test = musdb.DB(\n",
+    "    root=\"musdb18hq\",\n",
+    "    subsets = \"test\",\n",
+    "    is_wav = True,\n",
+    "    download=False\n",
+    ")               "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "eaeb97d6-b381-48a9-87f7-a1a5127ed05b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def encode(audio):\n",
+    "    audio = torch.tensor(audio).to(torch.float32).permute((1,0))\n",
+    "    with tempfile.NamedTemporaryFile() as f:\n",
+    "        torchaudio.save(f.name, audio, sample_rate=fs, format=\"wav\")\n",
+    "        bytes = f.file.read(-1)\n",
+    "    encoded = datasets.Audio(\n",
+    "        sampling_rate=fs,\n",
+    "        mono=False,\n",
+    "        decode=False\n",
+    "    ).encode_example(value={\"path\" : None, \"bytes\": bytes})\n",
+    "    return encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d59e2e48-5b97-4941-80e0-83630d647447",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n",
+      "14\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab1203173acb42f2b02dc7647361ea27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/2 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e947a6f2024b457cbe06781e9cc4436b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing dataset shards to the dataset hub:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a61a2b49619448dfa51f2e743e482160",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f5c9fa126f94f1b9b6256956d40bd80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "name = []\n",
+    "mixture = []\n",
+    "drums = []\n",
+    "bass = []\n",
+    "other = []\n",
+    "vocals = []\n",
+    "for i_track,track in enumerate(mus_test[0:15]):\n",
+    "    print(i_track)\n",
+    "    name.append(track.name)\n",
+    "    mixture.append(encode(track.audio))\n",
+    "    drums.append(encode(track.targets['drums'].audio))\n",
+    "    bass.append(encode(track.targets['bass'].audio))\n",
+    "    other.append(encode(track.targets['other'].audio))\n",
+    "    vocals.append(encode(track.targets['vocals'].audio))   \n",
+    "dataset = datasets.Dataset.from_dict(\n",
+    "    {\n",
+    "        'name': name,\n",
+    "        'mixture': mixture,\n",
+    "        'drums': drums,\n",
+    "        'bass': bass,\n",
+    "        'other': other,\n",
+    "        'vocals': vocals \n",
+    "    },\n",
+    "    features=features\n",
+    ")\n",
+    "dataset.push_to_hub(\"danjacobellis/musdb\", split='test')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}