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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": 5,
"id": "9500dd93",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (1816758531.py, line 9)",
"output_type": "error",
"traceback": [
"\u001b[0;36m Cell \u001b[0;32mIn[5], line 9\u001b[0;36m\u001b[0m\n\u001b[0;31m from '../bark' import generate_audio, preload_models, SAMPLE_RATE\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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