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fursund/pyannote-audio-coreml.ipynb

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pyannote-audio-coreml.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"mount_file_id": "1XfDFuuxr_7zzkMMwfPNlF8YjcVY1GQkG",
"authorship_tag": "ABX9TyOCFvSHtwpuoc1XQ2yQ4vUj",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/fursund/39c897d25f583686fe2626c56b48ffa3/pyannote-audio-coreml.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"!pip install -qq torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 torchtext==0.13.1\n",
"!pip install -qq pyannote.audio\n",
"!pip install -qq coremltools"
],
"metadata": {
"id": "Vs9avrwO11Nl",
"outputId": "ea491ef6-3636-4c7d-a660-1b0dbdb460d3",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m776.3/776.3 MB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m365.3/365.3 KB\u001b[0m \u001b[31m32.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m210.6/210.6 KB\u001b[0m \u001b[31m23.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for julius (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
]
},
{
"cell_type": "code",
"source": [
"!wget -q https://www2.cs.uic.edu/~i101/SoundFiles/gettysburg10.wav"
],
"metadata": {
"id": "_0t7j-mPTrQh"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"DEMO_FILE = {'uri': 'gettysburg10', 'audio': 'gettysburg10.wav'}\n",
"\n",
"import torch\n",
"\n",
"import pyannote.audio\n",
"from pyannote.audio import Inference\n",
"from pyannote.audio import Model\n",
"import coremltools as ct\n",
"\n",
"print(torch.__version__)\n",
"\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')\n",
"\n",
"model = Model.from_pretrained(\"/content/drive/MyDrive/pytorch_model.bin\")\n",
"#model = Model.from_pretrained(\"pyannote/segmentation\",\n",
"# use_auth_token=\"INPUT_AUTH_TOKEN_HERE\")\n",
"\n",
"\n",
"inference = Inference(model, step=2.5, use_auth_token=False)\n",
"# inference.model\n",
"output = inference(DEMO_FILE)\n",
"\n",
"waveform, sample_rate = inference.model.audio(DEMO_FILE)\n",
"chunks = waveform[None]\n",
"model_input = chunks.to(inference.device)\n",
"print(inference.model);\n",
"traced_model = torch.jit.trace(inference.model, model_input)\n",
"print(traced_model)\n",
"traced_model.eval()\n",
"\n",
"# out = traced_model(model_input)\n",
"print(\"Starting CoreML conversion\")\n",
"export_model = ct.convert(\n",
" traced_model,\n",
" convert_to=\"mlprogram\",\n",
" source=\"pytorch\",\n",
" inputs=[ct.TensorType(name=\"audio\", shape=model_input.shape)],\n",
" compute_precision=ct.precision.FLOAT16\n",
")\n",
"\n",
"# Save the converted model.\n",
"export_model.save(\"newmodel.mlpackage\")\n",
"\n",
"print(output)"
],
"metadata": {
"id": "W57A_b1d1_UQ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 407
},
"outputId": "a32aea2d-306e-4ea8-a89f-1509fee05624"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1.12.1+cu102\n"
]
},
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-7f48c99ed3e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# model = Model.from_pretrained(\"/content/drive/MyDrive/pytorch_model.bin\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m return _mount(\n\u001b[0m\u001b[1;32m 102\u001b[0m \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m case = d.expect([\n\u001b[0m\u001b[1;32m 244\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pexpect/spawnbase.py\u001b[0m in \u001b[0;36mexpect\u001b[0;34m(self, pattern, timeout, searchwindowsize, async_, **kw)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 342\u001b[0m \u001b[0mcompiled_pattern_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile_pattern_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 343\u001b[0;31m return self.expect_list(compiled_pattern_list,\n\u001b[0m\u001b[1;32m 344\u001b[0m timeout, searchwindowsize, async_)\n\u001b[1;32m 345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pexpect/spawnbase.py\u001b[0m in \u001b[0;36mexpect_list\u001b[0;34m(self, pattern_list, timeout, searchwindowsize, async_, **kw)\u001b[0m\n\u001b[1;32m 370\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mexpect_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 371\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 372\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mexp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpect_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 374\u001b[0m def expect_exact(self, pattern_list, timeout=-1, searchwindowsize=-1,\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pexpect/expect.py\u001b[0m in \u001b[0;36mexpect_loop\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0mincoming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspawn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_nonblocking\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspawn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaxread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspawn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelayafterread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspawn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelayafterread\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 172\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mincoming\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;31m# Keep reading until exception or return.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "tanbgHXMQzig"
},
"execution_count": null,
"outputs": []
}
]
}
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