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notebooks/.ipynb_checkpoints/use_small_models_on_cpu-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6a682b61",
+   "metadata": {},
+   "source": [
+    "# Benchmarking small models on CPU\n",
+    " - We can enable small models with the `SUNO_USE_SMALL_MODELS` environment variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9500dd93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
+    "os.environ[\"SUNO_USE_SMALL_MODELS\"] = \"1\"\n",
+    "\n",
+    "from IPython.display import Audio\n",
+    "import numpy as np\n",
+    "\n",
+    "from bark import generate_audio, preload_models, SAMPLE_RATE\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4e3454b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No GPU being used. Careful, inference might be very slow!\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 5.52 s, sys: 2.34 s, total: 7.86 s\n",
+      "Wall time: 4.33 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "preload_models()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f6024e5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.89it/s]\n",
+      "100%|██████████████████████████████████████████████████████████| 15/15 [00:43<00:00,  2.90s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "took 62s to generate 6s of audio\n"
+     ]
+    }
+   ],
+   "source": [
+    "t0 = time.time()\n",
+    "text = \"In the light of the moon, a little egg lay on a leaf\"\n",
+    "audio_array = generate_audio(text)\n",
+    "generation_duration_s = time.time() - t0\n",
+    "audio_duration_s = audio_array.shape[0] / SAMPLE_RATE\n",
+    "\n",
+    "print(f\"took {generation_duration_s:.0f}s to generate {audio_duration_s:.0f}s of audio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2dcce86c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.cpu_count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3046eddb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

requirements.txt

@@ -1,3 +1,4 @@
+devtools
 transformers
 diffusers
 ffmpeg