python/examples/experimental/whylogs_Audio_examples.ipynb
{
"cells": [
{
"attachments": {},
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"source": [
">### 🚩 *Create a free WhyLabs account to get more value out of whylogs!*<br> \n",
">*Did you know you can store, visualize, and monitor whylogs profiles with the [WhyLabs Observability Platform](https://whylabs.ai/whylogs-free-signup?utm_source=whylogs-Github&utm_medium=whylogs-example&utm_campaign=Audio)? Sign up for a [free WhyLabs account](https://whylabs.ai/whylogs-free-signup?utm_source=whylogs-Github&utm_medium=whylogs-example&utm_campaign=Audio) to leverage the power of whylogs and WhyLabs together!*"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Extracting and Monitoring Audio Samples"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/whylabs/whylogs/blob/mainline/python/examples/experimental/whylogs_Audio_examples.ipynb)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "R8JfJ7PgytuK"
},
"source": [
"This notebook demonstrates how to extract features from audio samples for the purpose of monitoring for drift/quality. \n",
"\n",
"* The audio sample data is taken from Kaggle\n",
"* The feature extraction is done using pyAudioAnalysis (https://github.com/tyiannak/pyAudioAnalysis/wiki/3.-Feature-Extraction) \n",
"* The logging of features is done by whylogs (https://github.com/whylabs/whylogs) \n",
"\n",
"For a full list of features that we extract and in-depth explanation of the pyAudioAnalysis set up, please refer to this blog post by the pyAudioAnalysis author: https://medium.com/behavioral-signals-ai/intro-to-audio-analysis-recognizing-sounds-using-machine-learning-20fd646a0ec5"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TlnPId8BDQQx",
"outputId": "d8e90727-b274-4db6-8f90-b7fcd0254c55"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"# Note: you may need to restart the kernel to use updated packages.\n",
"%pip install whylogs pyAudioAnalysis eyed3 pydub"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "MWTwAYftyUkL"
},
"source": [
"**We will use Kaggle to get audio samples from a public Audio MNIST dataset.**\n",
"\n",
"To set up Kaggle in Colab: https://www.kaggle.com/general/74235 "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 91
},
"id": "Sqbvnzpz2q4Z",
"outputId": "329c1007-ceaf-4c83-8cf3-87173fc85612"
},
"outputs": [],
"source": [
"! pip install -q kaggle\n",
"from google.colab import files\n",
"files.upload()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Chy903zH6-FR",
"outputId": "afc9a9e0-9026-47a7-eedb-43931f9a7a22"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading free-spoken-digit-dataset-fsdd.zip to /content\n",
"\r 0% 0.00/15.5M [00:00<?, ?B/s]\r 32% 5.00M/15.5M [00:00<00:00, 34.9MB/s]\n",
"\r100% 15.5M/15.5M [00:00<00:00, 67.7MB/s]\n"
]
}
],
"source": [
"! kaggle datasets download -d joserzapata/free-spoken-digit-dataset-fsdd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZuIBKryp73nX",
"outputId": "43541f14-41b8-4157-8fc1-e1c8fc68e882"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"replace ./.gitignore? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n"
]
}
],
"source": [
"!unzip -q /content/free-spoken-digit-dataset-fsdd.zip -d ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4XehuqoyE-ho"
},
"outputs": [],
"source": [
"# This cell is optional and specific to the Kaggle dataset we are using\n",
"# The dataset contains audio samples of the format [digit]_speaker_sampleNum.wav\n",
"# Example: 0_lucas_21.wav is the speaker Lucas saying digit ZERO, 21st sample \n",
"# There are 500 samples total per speaker (50 for each digit between 0 and 9)\n",
"# We will organize the data by speaker for demo purposes, all samples\n",
"# from a specific speaker will be in a dedicated directory \n",
"import os\n",
"import shutil\n",
"\n",
"speakers = [\"theo\", \"george\", \"jackson\", \"lucas\", \"nicolas\", \"yweweler\"]\n",
"for speaker in speakers:\n",
" path = os.path.join(\"/content/recordings\", speaker) \n",
" os.mkdir(path) \n",
"\n",
"for filename in os.listdir(\"/content/recordings\"):\n",
" parts = filename.split(\"_\")\n",
" if(len(parts) > 1):\n",
" shutil.move(os.path.join(\"/content/recordings/\", filename), \"/content/recordings/\"+parts[1])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nRDM_qTaKVvm",
"outputId": "64d73993-d750-4ecf-ca1a-3324415a43f2"
},
"outputs": [],
"source": [
"# Set up the WhyLabs environment \n",
"# The model type can be set as \"Other\"\n",
"# Documentation: https://docs.whylabs.ai/docs/whylabs-onboarding \n",
"\n",
"import getpass\n",
"import os\n",
"\n",
"# set your org-id here - should be something like \"org-xxxx\"\n",
"print(\"Enter your WhyLabs Org ID\") \n",
"os.environ[\"WHYLABS_DEFAULT_ORG_ID\"] = input()\n",
"\n",
"# set your datased_id (or model_id) here - should be something like \"model-xxxx\"\n",
"print(\"Enter your WhyLabs Dataset ID\")\n",
"os.environ[\"WHYLABS_DEFAULT_DATASET_ID\"] = input()\n",
"\n",
"\n",
"# set your API key here\n",
"print(\"Enter your WhyLabs API key\")\n",
"os.environ[\"WHYLABS_API_KEY\"] = getpass.getpass()\n",
"print(\"Using API Key ID: \", os.environ[\"WHYLABS_API_KEY\"][0:10])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yLD0HovF826g"
},
"outputs": [],
"source": [
"# Set up the audio feature extraction step with pyAudioAnalysis \n",
"# https://medium.com/behavioral-signals-ai/intro-to-audio-analysis-recognizing-sounds-using-machine-learning-20fd646a0ec5\n",
"\n",
"from pyAudioAnalysis import MidTermFeatures as aF\n",
"import os\n",
"import numpy as np\n",
"import plotly.graph_objs as go \n",
"import plotly\n",
"import pandas as pd\n",
"\n",
"# This example will focus on just one speaker, but can be set up with\n",
"# multiple directories \n",
"dirs = [\"/content/recordings/george\"] \n",
"\n",
"# The feature extraction has the following parameters: \n",
"# the mid-term window size (mw)\n",
"# the mid-term step size (ms)\n",
"# the short-term window size (sw)\n",
"# and the short-term step size (ss)\n",
"# These windows can be configured depending on the side of each audio sample\n",
"m_win, m_step, s_win, s_step = 0.5, 0.5, 0.1, 0.05 \n",
"# segment-level feature extraction:\n",
"features = [] \n",
"for d in dirs: # get feature matrix for each directory \n",
" f, files, fn = aF.directory_feature_extraction(d, m_win, m_step, \n",
" s_win, s_step) \n",
" features.append(f)\n",
"# (each element of the features list contains a \n",
"# (samples x segment features) = (N x 138) feature matrix)\n",
"\n",
"# Create a dataframe from the resulting features \n",
"df = pd.DataFrame(f, columns = fn)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_SB3iZeTLYQs",
"outputId": "8397a0be-5d49-41fa-d5dc-b075833aedf0"
},
"outputs": [],
"source": [
"# Now that we have all features extracted, we have a dataframe where features\n",
"# are columns and samples are rows. \n",
"# We will use whylogs to profile the features dataframe to extract key statistics\n",
"# that we will tracking using the WhyLabs monitoring platform\n",
"# whylogs documentation: https://github.com/whylabs/whylogs \n",
"\n",
"import whylogs as why\n",
"from datetime import datetime, timedelta, timezone\n",
"\n",
"# We are setting the date of profiling for easier navigation in the \n",
"# WhyLabs platform \n",
"current_date = datetime.now(timezone.utc)\n",
"profile = why.log(df).profile()\n",
"profile.set_dataset_timestamp(current_date)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6_TKduvNLcBP",
"outputId": "532744a5-c2df-49e2-97d5-4756e500fff5"
},
"outputs": [
{
"data": {
"text/plain": [
"(True, 'OK')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# As a final step, we write the resulting feature profile to the WhyLabs platform\n",
"# To see the result, log into https://hub.whylabsapp.com/ and navigate to the\n",
"# model you specified in the \"Set up the WhyLabs environment\" step \n",
"from whylogs.api.writer.whylabs import WhyLabsWriter\n",
"\n",
"# Write the log to the WhyLabs platform \n",
"writer = WhyLabsWriter()\n",
"writer.write(file=profile.view())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ccz0_Zq_Llo-",
"outputId": "8c7490bb-16df-4a2b-949f-a9a9eea0f06f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Feature extraction complexity ratio: 19.2 x realtime\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:whylogs.core.dataset_profile:No timezone set in the datetime_timestamp object. Default to local timezone\n"
]
},
{
"data": {
"text/plain": [
"(True, 'OK')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Optional: Extract features from samples generated by another speaker\n",
"# Submit profiles to the WhyLabs platform to compare feature differences\n",
"# between two speakers \n",
"#https://medium.com/behavioral-signals-ai/intro-to-audio-analysis-recognizing-sounds-using-machine-learning-20fd646a0ec5\n",
"\n",
"dirs = [\"/content/recordings/lucas\"] \n",
"\n",
"m_win, m_step, s_win, s_step = 0.5, 0.5, 0.1, 0.05 \n",
"# segment-level feature extraction:\n",
"features = [] \n",
"for d in dirs: # get feature matrix for each directory\n",
" f, files, fn = aF.directory_feature_extraction(d, m_win, m_step, \n",
" s_win, s_step) \n",
" features.append(f)\n",
"# (each element of the features list contains a \n",
"# (samples x segment features) = (10 x 138) feature matrix)\n",
"\n",
"df_lucas = pd.DataFrame(f, columns = fn)\n",
"yesterday_date = datetime.now() - timedelta(days=1)\n",
"profile = why.log(df_lucas).profile()\n",
"profile.set_dataset_timestamp(yesterday_date)\n",
"writer = WhyLabsWriter()\n",
"writer.write(file=profile.view())\n"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
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"name": "python3"
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