From ccf6d3f1b0af0c45d4740509a23b135761e5007f Mon Sep 17 00:00:00 2001
From: Simon Leglaive <simon.leglaive@centralesupelec.fr>
Date: Wed, 27 Nov 2024 13:45:27 +0100
Subject: [PATCH] update after testing on MyDocker
---
baseline/0-setup.ipynb | 238 --------
baseline/1-preliminaries.ipynb | 343 +++++++----
baseline/2-feature-extraction.ipynb | 28 +-
baseline/3-baseline-dev.ipynb | 570 ++++++++++--------
baseline/paths.py | 2 +-
.../final_results_validation.txt | 12 -
.../hyper_params.json | 10 -
.../baseline_2024-09-03-12h38m17s/log.txt | 192 ------
.../output_predictions_test.csv | 275 ---------
.../output_predictions_validation.csv | 444 --------------
.../final_results_test-checkpoint.txt | 12 +
.../final_results_validation-checkpoint.txt | 12 +
.../best_model.pth | Bin 288576802 -> 288576802 bytes
.../final_results_validation.txt | 12 +
.../hyper_params.json | 10 +
.../baseline_2024-11-27-12h35m18s/log.txt | 192 ++++++
.../loss.pdf | Bin 8679 -> 8663 bytes
.../output_predictions_test.csv | 275 +++++++++
.../output_predictions_validation.csv | 444 ++++++++++++++
readme.md | 51 +-
requirements.txt | 21 +-
setup.ipynb | 417 +++++++++++++
22 files changed, 1990 insertions(+), 1570 deletions(-)
delete mode 100755 baseline/0-setup.ipynb
delete mode 100755 data/output/baseline_2024-09-03-12h38m17s/final_results_validation.txt
delete mode 100755 data/output/baseline_2024-09-03-12h38m17s/hyper_params.json
delete mode 100755 data/output/baseline_2024-09-03-12h38m17s/log.txt
delete mode 100755 data/output/baseline_2024-09-03-12h38m17s/output_predictions_test.csv
delete mode 100755 data/output/baseline_2024-09-03-12h38m17s/output_predictions_validation.csv
create mode 100644 data/output/baseline_2024-11-27-12h35m18s/.ipynb_checkpoints/final_results_test-checkpoint.txt
create mode 100644 data/output/baseline_2024-11-27-12h35m18s/.ipynb_checkpoints/final_results_validation-checkpoint.txt
rename data/output/{baseline_2024-09-03-12h38m17s => baseline_2024-11-27-12h35m18s}/best_model.pth (99%)
mode change 100755 => 100644
create mode 100644 data/output/baseline_2024-11-27-12h35m18s/final_results_validation.txt
create mode 100644 data/output/baseline_2024-11-27-12h35m18s/hyper_params.json
create mode 100644 data/output/baseline_2024-11-27-12h35m18s/log.txt
rename data/output/{baseline_2024-09-03-12h38m17s => baseline_2024-11-27-12h35m18s}/loss.pdf (72%)
mode change 100755 => 100644
create mode 100644 data/output/baseline_2024-11-27-12h35m18s/output_predictions_test.csv
create mode 100644 data/output/baseline_2024-11-27-12h35m18s/output_predictions_validation.csv
create mode 100755 setup.ipynb
diff --git a/baseline/0-setup.ipynb b/baseline/0-setup.ipynb
deleted file mode 100755
index f95de1f..0000000
--- a/baseline/0-setup.ipynb
+++ /dev/null
@@ -1,238 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hAyWzMHRd_bW"
- },
- "source": [
- "# Environment setup\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Install Python libraries"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We first pip-install the Python libraries listed in ```requirements.txt```."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "vscode": {
- "languageId": "plaintext"
- }
- },
- "outputs": [],
- "source": [
- "!pip install -r requirements.txt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Download data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We will download the [SONYC-UST dataset](https://zenodo.org/record/2590742#.XIkTPBNKjuM). The dataset was released along with the following paper (it's probably a good idea to have a look at it):\n",
- "\n",
- "> Cartwright, M., Mendez, A.E.M., Cramer, J., Lostanlen, V., Dove, G., Wu, H., Salamon, J., Nov, O., Bello, J.P. SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network. In Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE) , 2019. [PDF](https://dcase.community/documents/workshop2019/proceedings/DCASE2019Workshop_Cartwright_4.pdf)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "root_path = \"/content/drive/My Drive/Enseignement/2023-2024/NumVi/IA-DL/UST_project\"\n",
- "ust_data_path = os.path.join(root_path, \"data/ust-data/sonyc-ust\")\n",
- "os.makedirs(ust_data_path, exist_ok=True) # create a folder to store the data\n",
- "os.makedirs(os.path.join(ust_data_path, 'audio-dev'), exist_ok=True) # create a folder to store the development data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "--2024-02-21 13:37:19-- https://zenodo.org/record/3338310/files/annotations.csv\n",
- "Resolving zenodo.org (zenodo.org)... 188.184.103.159, 188.185.79.172, 188.184.98.238, ...\n",
- "Connecting to zenodo.org (zenodo.org)|188.184.103.159|:443... connected.\n",
- "HTTP request sent, awaiting response... 301 MOVED PERMANENTLY\n",
- "Location: /records/3338310/files/annotations.csv [following]\n",
- "--2024-02-21 13:37:20-- https://zenodo.org/records/3338310/files/annotations.csv\n",
- "Reusing existing connection to zenodo.org:443.\n",
- "HTTP request sent, awaiting response... 200 OK\n",
- "Length: 3163972 (3.0M) [text/plain]\n",
- "Saving to: ‘annotations.csv’\n",
- "\n",
- "annotations.csv 100%[===================>] 3.02M 4.17MB/s in 0.7s \n",
- "\n",
- "2024-02-21 13:37:21 (4.17 MB/s) - ‘annotations.csv’ saved [3163972/3163972]\n",
- "\n",
- "--2024-02-21 13:37:21-- https://zenodo.org/record/3338310/files/audio-dev.tar.gz\n",
- "Resolving zenodo.org (zenodo.org)... 188.184.103.159, 188.185.79.172, 188.184.98.238, ...\n",
- "Connecting to zenodo.org (zenodo.org)|188.184.103.159|:443... connected.\n",
- "HTTP request sent, awaiting response... 301 MOVED PERMANENTLY\n",
- "Location: /records/3338310/files/audio-dev.tar.gz [following]\n",
- "--2024-02-21 13:37:21-- https://zenodo.org/records/3338310/files/audio-dev.tar.gz\n",
- "Reusing existing connection to zenodo.org:443.\n",
- "HTTP request sent, awaiting response... 200 OK\n",
- "Length: 1892542516 (1.8G) [application/octet-stream]\n",
- "Saving to: ‘audio-dev.tar.gz’\n",
- "\n",
- "audio-dev.tar.gz 100%[===================>] 1.76G 4.84MB/s in 6m 37s \n",
- "\n",
- "2024-02-21 13:43:59 (4.54 MB/s) - ‘audio-dev.tar.gz’ saved [1892542516/1892542516]\n",
- "\n",
- "--2024-02-21 13:43:59-- https://zenodo.org/record/3338310/files/audio-eval.tar.gz\n",
- "Resolving zenodo.org (zenodo.org)... 188.184.103.159, 188.184.98.238, 188.185.79.172, ...\n",
- "Connecting to zenodo.org (zenodo.org)|188.184.103.159|:443... connected.\n",
- "HTTP request sent, awaiting response... 301 MOVED PERMANENTLY\n",
- "Location: /records/3338310/files/audio-eval.tar.gz [following]\n",
- "--2024-02-21 13:44:00-- https://zenodo.org/records/3338310/files/audio-eval.tar.gz\n",
- "Reusing existing connection to zenodo.org:443.\n",
- "HTTP request sent, awaiting response... 200 OK\n",
- "Length: 180759660 (172M) [application/octet-stream]\n",
- "Saving to: ‘audio-eval.tar.gz’\n",
- "\n",
- "audio-eval.tar.gz 100%[===================>] 172.39M 20.4MB/s in 8.7s \n",
- "\n",
- "2024-02-21 13:44:09 (19.7 MB/s) - ‘audio-eval.tar.gz’ saved [180759660/180759660]\n",
- "\n",
- "--2024-02-21 13:44:09-- https://zenodo.org/record/3338310/files/dcase-ust-taxonomy.yaml\n",
- "Resolving zenodo.org (zenodo.org)... 188.184.103.159, 188.184.98.238, 188.185.79.172, ...\n",
- "Connecting to zenodo.org (zenodo.org)|188.184.103.159|:443... connected.\n",
- "HTTP request sent, awaiting response... 301 MOVED PERMANENTLY\n",
- "Location: /records/3338310/files/dcase-ust-taxonomy.yaml [following]\n",
- "--2024-02-21 13:44:09-- https://zenodo.org/records/3338310/files/dcase-ust-taxonomy.yaml\n",
- "Reusing existing connection to zenodo.org:443.\n",
- "HTTP request sent, awaiting response... 200 OK\n",
- "Length: 1081 (1.1K) [application/octet-stream]\n",
- "Saving to: ‘dcase-ust-taxonomy.yaml’\n",
- "\n",
- "dcase-ust-taxonomy. 100%[===================>] 1.06K 5.24KB/s in 0.2s \n",
- "\n",
- "2024-02-21 13:44:10 (5.24 KB/s) - ‘dcase-ust-taxonomy.yaml’ saved [1081/1081]\n",
- "\n",
- "--2024-02-21 13:44:10-- https://zenodo.org/record/3338310/files/README.md\n",
- "Resolving zenodo.org (zenodo.org)... 188.184.103.159, 188.184.98.238, 188.185.79.172, ...\n",
- "Connecting to zenodo.org (zenodo.org)|188.184.103.159|:443... connected.\n",
- "HTTP request sent, awaiting response... 301 MOVED PERMANENTLY\n",
- "Location: /records/3338310/files/README.md [following]\n",
- "--2024-02-21 13:44:11-- https://zenodo.org/records/3338310/files/README.md\n",
- "Reusing existing connection to zenodo.org:443.\n",
- "HTTP request sent, awaiting response... 200 OK\n",
- "Length: 8716 (8.5K) [application/octet-stream]\n",
- "Saving to: ‘README.md’\n",
- "\n",
- "README.md 100%[===================>] 8.51K --.-KB/s in 0.004s \n",
- "\n",
- "2024-02-21 13:44:11 (2.26 MB/s) - ‘README.md’ saved [8716/8716]\n",
- "\n"
- ]
- }
- ],
- "source": [
- "os.chdir(ust_data_path)\n",
- "!wget https://zenodo.org/record/3338310/files/annotations.csv\n",
- "!wget https://zenodo.org/record/3338310/files/audio-dev.tar.gz\n",
- "!wget https://zenodo.org/record/3338310/files/audio-eval.tar.gz\n",
- "!wget https://zenodo.org/record/3338310/files/dcase-ust-taxonomy.yaml\n",
- "!wget https://zenodo.org/record/3338310/files/README.md\n",
- "os.chdir(\"audio-dev\")\n",
- "!tar xf ../audio-dev.tar.gz\n",
- "os.chdir(\"..\")\n",
- "!rm audio-dev.tar.gz\n",
- "!tar xf audio-eval.tar.gz\n",
- "!rm audio-eval.tar.gz\n",
- "os.chdir(root_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You should end-up with the following file structure:"
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "```\n",
- "data\n",
- "+-- ust-data\n",
- "| +-- sonyc-ust\n",
- "| | +-- audio-dev\n",
- "| | | +-- train\n",
- "| | | | +-- 01_000006.wav\n",
- "| | | | +-- ...\n",
- "| | | +-- validate\n",
- "| | | | +-- 00_000066.wav\n",
- "| | | | +-- ...\n",
- "| | +-- audio-eval\n",
- "| | | +-- 00_010346.wav\n",
- "| | | +-- ...\n",
- "| | +-- annotations.csv\n",
- "| | +-- dcase-ust-taxonomy.yaml\n",
- "| | +-- README.md\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "vscode": {
- "languageId": "plaintext"
- }
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "colab": {
- "provenance": []
- },
- "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": "3.10.14"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/baseline/1-preliminaries.ipynb b/baseline/1-preliminaries.ipynb
index 9fc9431..d5d65c7 100755
--- a/baseline/1-preliminaries.ipynb
+++ b/baseline/1-preliminaries.ipynb
@@ -22,13 +22,24 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {
- "id": "HwMtwyCmMlOz"
+ "collapsed": true,
+ "id": "HwMtwyCmMlOz",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
"import os\n",
- "from paths import (root_path, ust_data_dir, dataset_dir, annotation_file, \n",
- " taxonomy_file, log_mel_spec_dir)"
+ "\n",
+ "from paths import (\n",
+ " annotation_file,\n",
+ " dataset_dir,\n",
+ " log_mel_spec_dir,\n",
+ " root_path,\n",
+ " taxonomy_file,\n",
+ " ust_data_dir,\n",
+ ")"
]
},
{
@@ -231,14 +242,18 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {
- "id": "ravumD6HQZsV"
+ "collapsed": true,
+ "id": "ravumD6HQZsV",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
- "import pandas as pd\n",
+ "import numpy as np\n",
"import oyaml as yaml\n",
- "from utils import get_file_targets, get_subset_split\n",
- "import numpy as np\n"
+ "import pandas as pd\n",
+ "from utils import get_file_targets, get_subset_split"
]
},
{
@@ -249,6 +264,7 @@
"base_uri": "https://localhost:8080/",
"height": 290
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 1022,
"status": "ok",
@@ -260,6 +276,9 @@
"user_tz": -60
},
"id": "kuBzTd3pNhXT",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "a64e6285-8677-43cd-b162-7775cc9b3718"
},
"outputs": [
@@ -314,12 +333,12 @@
" <td>0</td>\n",
" <td>00_000066.wav</td>\n",
" <td>95</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
" <td>...</td>\n",
" <td>far</td>\n",
" <td>far</td>\n",
@@ -338,12 +357,12 @@
" <td>0</td>\n",
" <td>00_000066.wav</td>\n",
" <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
" <td>...</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
@@ -362,12 +381,12 @@
" <td>0</td>\n",
" <td>00_000066.wav</td>\n",
" <td>-5</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
" <td>...</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
@@ -386,12 +405,12 @@
" <td>0</td>\n",
" <td>00_000066.wav</td>\n",
" <td>-2</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
" <td>...</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
@@ -410,12 +429,12 @@
" <td>0</td>\n",
" <td>00_000066.wav</td>\n",
" <td>-3</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
+ " <td>-1.0</td>\n",
" <td>...</td>\n",
" <td>-1</td>\n",
" <td>-1</td>\n",
@@ -442,32 +461,32 @@
"9276 validate 0 00_000066.wav -3 \n",
"\n",
" 1-1_small-sounding-engine_presence 1-2_medium-sounding-engine_presence \\\n",
- "0 1 1 \n",
- "9708 -1 -1 \n",
- "8515 -1 -1 \n",
- "9277 -1 -1 \n",
- "9276 -1 -1 \n",
+ "0 1.0 1.0 \n",
+ "9708 -1.0 -1.0 \n",
+ "8515 -1.0 -1.0 \n",
+ "9277 -1.0 -1.0 \n",
+ "9276 -1.0 -1.0 \n",
"\n",
" 1-3_large-sounding-engine_presence \\\n",
- "0 1 \n",
- "9708 -1 \n",
- "8515 -1 \n",
- "9277 -1 \n",
- "9276 -1 \n",
+ "0 1.0 \n",
+ "9708 -1.0 \n",
+ "8515 -1.0 \n",
+ "9277 -1.0 \n",
+ "9276 -1.0 \n",
"\n",
" 1-X_engine-of-uncertain-size_presence 2-1_rock-drill_presence \\\n",
- "0 1 1 \n",
- "9708 -1 0 \n",
- "8515 -1 -1 \n",
- "9277 -1 -1 \n",
- "9276 -1 -1 \n",
+ "0 1.0 1.0 \n",
+ "9708 -1.0 0.0 \n",
+ "8515 -1.0 -1.0 \n",
+ "9277 -1.0 -1.0 \n",
+ "9276 -1.0 -1.0 \n",
"\n",
" 2-2_jackhammer_presence ... 7-X_other-unknown-human-voice_proximity \\\n",
- "0 1 ... far \n",
- "9708 0 ... -1 \n",
- "8515 -1 ... -1 \n",
- "9277 -1 ... -1 \n",
- "9276 -1 ... -1 \n",
+ "0 1.0 ... far \n",
+ "9708 0.0 ... -1 \n",
+ "8515 -1.0 ... -1 \n",
+ "9277 -1.0 ... -1 \n",
+ "9276 -1.0 ... -1 \n",
"\n",
" 8-1_dog-barking-whining_proximity 1_engine_presence \\\n",
"0 far 1 \n",
@@ -507,7 +526,7 @@
],
"source": [
"# Create a Pandas DataFrame from the annotation CSV file\n",
- "annotation_data = pd.read_csv(annotation_file).sort_values('audio_filename')\n",
+ "annotation_data = pd.read_csv(annotation_file).sort_values(\"audio_filename\")\n",
"\n",
"# You can view the top rows of the frame with\n",
"annotation_data.head()"
@@ -517,12 +536,16 @@
"cell_type": "code",
"execution_count": 4,
"metadata": {
- "id": "Oi3FJQPYPeuW"
+ "collapsed": true,
+ "id": "Oi3FJQPYPeuW",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
"# List of all audio files\n",
- "file_list = annotation_data['audio_filename'].unique().tolist()"
+ "file_list = annotation_data[\"audio_filename\"].unique().tolist()"
]
},
{
@@ -532,6 +555,7 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 542,
"status": "ok",
@@ -543,6 +567,9 @@
"user_tz": -60
},
"id": "OWge7-ewPndg",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "5ebb9fff-e0db-4d20-d659-eceaa6191405"
},
"outputs": [
@@ -556,11 +583,11 @@
],
"source": [
"# Load taxonomy\n",
- "with open(taxonomy_file, 'r') as f:\n",
+ "with open(taxonomy_file, \"r\") as f:\n",
" taxonomy = yaml.load(f, Loader=yaml.Loader)\n",
"\n",
"# get list of labels from taxonomy\n",
- "labels = [\"_\".join([str(k), v]) for k,v in taxonomy['coarse'].items()]\n",
+ "labels = [\"_\".join([str(k), v]) for k, v in taxonomy[\"coarse\"].items()]\n",
"\n",
"# number of classes\n",
"n_classes = len(labels)\n",
@@ -572,7 +599,11 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {
- "id": "tK74heqaVuXm"
+ "collapsed": true,
+ "id": "tK74heqaVuXm",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -599,6 +630,7 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 6,
"status": "ok",
@@ -610,6 +642,9 @@
"user_tz": -60
},
"id": "4T-SZlUMTMJq",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "08fda22f-cfc3-4bae-de54-5804e719bcac"
},
"outputs": [
@@ -641,24 +676,23 @@
}
],
"source": [
- "train_proportions = np.sum(target_list[train_file_idxs,:],\n",
- " axis=0)/len(train_file_idxs)\n",
+ "train_proportions = np.sum(target_list[train_file_idxs, :], axis=0) / len(\n",
+ " train_file_idxs\n",
+ ")\n",
"\n",
- "val_proportions = np.sum(target_list[val_file_idxs,:],\n",
- " axis=0)/len(val_file_idxs)\n",
+ "val_proportions = np.sum(target_list[val_file_idxs, :], axis=0) / len(val_file_idxs)\n",
"\n",
- "test_proportions = np.sum(target_list[test_file_idxs,:],\n",
- " axis=0)/len(test_file_idxs)\n",
+ "test_proportions = np.sum(target_list[test_file_idxs, :], axis=0) / len(test_file_idxs)\n",
"\n",
- "print('Distribution of classes in the training set:')\n",
+ "print(\"Distribution of classes in the training set:\")\n",
"for idx, label in enumerate(labels):\n",
- " print(label+': {:.2%}'.format(train_proportions[idx]))\n",
+ " print(label + \": {:.2%}\".format(train_proportions[idx]))\n",
"\n",
- "print('\\n')\n",
+ "print(\"\\n\")\n",
"\n",
- "print('Distribution of classes in the validation set:')\n",
+ "print(\"Distribution of classes in the validation set:\")\n",
"for idx, label in enumerate(labels):\n",
- " print(label+': {:.2%}'.format(val_proportions[idx]))"
+ " print(label + \": {:.2%}\".format(val_proportions[idx]))"
]
},
{
@@ -693,7 +727,11 @@
"cell_type": "code",
"execution_count": 8,
"metadata": {
- "id": "-CcRl9LSjSSn"
+ "collapsed": true,
+ "id": "-CcRl9LSjSSn",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -702,8 +740,7 @@
"# get a file in the training set\n",
"\n",
"training_file_list = [file_list[ind] for ind in train_file_idxs]\n",
- "audio_file = os.path.join(dataset_dir, 'audio-dev/train',\n",
- " training_file_list[10])\n",
+ "audio_file = os.path.join(dataset_dir, \"audio-dev/train\", training_file_list[10])\n",
"x, sr = librosa.load(audio_file, mono=True, sr=None)"
]
},
@@ -723,6 +760,7 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 10,
"status": "ok",
@@ -734,6 +772,9 @@
"user_tz": -60
},
"id": "P8_Um_CW7Oj5",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "c1480263-2105-4b07-a006-5a628f9f140d"
},
"outputs": [
@@ -755,7 +796,11 @@
"cell_type": "code",
"execution_count": 10,
"metadata": {
- "id": "bOQBGd5VuPIJ"
+ "collapsed": true,
+ "id": "bOQBGd5VuPIJ",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -790,7 +835,11 @@
"cell_type": "code",
"execution_count": 11,
"metadata": {
- "id": "gC1CCZyS7Oj-"
+ "collapsed": true,
+ "id": "gC1CCZyS7Oj-",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -805,6 +854,7 @@
"base_uri": "https://localhost:8080/",
"height": 351
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 462,
"status": "ok",
@@ -816,6 +866,9 @@
"user_tz": -60
},
"id": "Nt7LJHK-7OkC",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "ec34834f-1a69-4c96-e793-ce799dbf7b6c"
},
"outputs": [
@@ -841,13 +894,13 @@
}
],
"source": [
- "time_axis = np.arange(0,x.shape[0]/sr, 1/sr)\n",
+ "time_axis = np.arange(0, x.shape[0] / sr, 1 / sr)\n",
"\n",
"plt.figure(figsize=(7, 3))\n",
"plt.plot(time_axis, x)\n",
- "plt.title('waveform')\n",
- "plt.ylabel('amplitude')\n",
- "plt.xlabel('time (s)')"
+ "plt.title(\"waveform\")\n",
+ "plt.ylabel(\"amplitude\")\n",
+ "plt.xlabel(\"time (s)\")"
]
},
{
@@ -876,6 +929,7 @@
"base_uri": "https://localhost:8080/",
"height": 74
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 805,
"status": "ok",
@@ -887,6 +941,9 @@
"user_tz": -60
},
"id": "gJOWAg7V7OkM",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "8131faae-50ee-4055-a619-5773291347cc"
},
"outputs": [
@@ -911,7 +968,8 @@
],
"source": [
"import IPython.display as ipd\n",
- "ipd.Audio(x, rate=sr) # load a local WAV file"
+ "\n",
+ "ipd.Audio(x, rate=sr) # load a local WAV file"
]
},
{
@@ -936,13 +994,17 @@
"cell_type": "code",
"execution_count": 14,
"metadata": {
- "id": "FjuUDR3X7Oka"
+ "collapsed": true,
+ "id": "FjuUDR3X7Oka",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
"import soundfile as sf\n",
"\n",
- "sf.write('example.wav', x, sr)"
+ "sf.write(\"example.wav\", x, sr)"
]
},
{
@@ -981,6 +1043,7 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 21,
"status": "ok",
@@ -992,6 +1055,9 @@
"user_tz": -60
},
"id": "lDaQxv5Utzzx",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "7dc25cf5-6554-4d09-f708-d6f8fe8cc13b"
},
"outputs": [
@@ -1012,15 +1078,16 @@
"\n",
"num_samples = x.shape[0]\n",
"\n",
- "num_frames = 1 + int(np.floor((num_samples - window_length_samples) /\n",
- " hop_length_samples))\n",
+ "num_frames = 1 + int(\n",
+ " np.floor((num_samples - window_length_samples) / hop_length_samples)\n",
+ ")\n",
"\n",
"\n",
"X_frames = np.zeros((window_length_samples, num_frames))\n",
"for n in np.arange(num_frames):\n",
- " beg = n*hop_length_samples\n",
- " end = beg + window_length_samples\n",
- " X_frames[:,n] = x[beg:end]\n",
+ " beg = n * hop_length_samples\n",
+ " end = beg + window_length_samples\n",
+ " X_frames[:, n] = x[beg:end]\n",
"\n",
"print(X_frames.shape)\n",
"\n",
@@ -1058,6 +1125,7 @@
"base_uri": "https://localhost:8080/",
"height": 509
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 17,
"status": "ok",
@@ -1069,6 +1137,9 @@
"user_tz": -60
},
"id": "7tqzLgSNs-SG",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "61eef60e-ce36-4121-bad2-28b643cd2dfc"
},
"outputs": [
@@ -1101,17 +1172,18 @@
}
],
"source": [
- "window = .5 - (0.5 * np.cos(2 * np.pi / window_length_samples *\n",
- " np.arange(window_length_samples))) # \"periodic\" Hann\n",
+ "window = 0.5 - (\n",
+ " 0.5 * np.cos(2 * np.pi / window_length_samples * np.arange(window_length_samples))\n",
+ ") # \"periodic\" Hann\n",
"\n",
- "X_windowed_frames = X_frames * window[:,np.newaxis]\n",
+ "X_windowed_frames = X_frames * window[:, np.newaxis]\n",
"\n",
"plt.figure()\n",
"plt.plot(window)\n",
"\n",
"print(X_windowed_frames.shape)\n",
- "plt.title('analysis window')\n",
- "plt.xlabel('samples')"
+ "plt.title(\"analysis window\")\n",
+ "plt.xlabel(\"samples\")"
]
},
{
@@ -1140,6 +1212,7 @@
"base_uri": "https://localhost:8080/",
"height": 659
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 1362,
"status": "ok",
@@ -1151,6 +1224,9 @@
"user_tz": -60
},
"id": "iLZYNkMTwYew",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "a0d3cad6-0d16-4474-ba18-8cf73f5c1989"
},
"outputs": [
@@ -1185,9 +1261,13 @@
"\n",
"\n",
"plt.figure(figsize=(14, 7))\n",
- "librosa.display.specshow(librosa.amplitude_to_db(X_spec), sr=sr,\n",
- " hop_length=hop_length_samples, x_axis='time',\n",
- " y_axis='hz')\n",
+ "librosa.display.specshow(\n",
+ " librosa.amplitude_to_db(X_spec),\n",
+ " sr=sr,\n",
+ " hop_length=hop_length_samples,\n",
+ " x_axis=\"time\",\n",
+ " y_axis=\"hz\",\n",
+ ")\n",
"\n",
"# This is basically equivalent to:\n",
"# librosa.display.specshow(20*np.log10(X_spec), sr=sr,\n",
@@ -1196,9 +1276,9 @@
"# plt.clim(-60,25)\n",
"\n",
"plt.colorbar()\n",
- "plt.title('dB-scaled spectrogram')\n",
- "plt.xlabel('time (s)')\n",
- "plt.ylabel('frequency (Hz)')"
+ "plt.title(\"dB-scaled spectrogram\")\n",
+ "plt.xlabel(\"time (s)\")\n",
+ "plt.ylabel(\"frequency (Hz)\")"
]
},
{
@@ -1227,6 +1307,7 @@
"base_uri": "https://localhost:8080/",
"height": 525
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 847,
"status": "ok",
@@ -1238,6 +1319,9 @@
"user_tz": -60
},
"id": "iKFTcCdgtVgy",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "20a7b85c-7cbc-4b8e-a59f-f3fda1a6c724"
},
"outputs": [
@@ -1277,23 +1361,24 @@
"num_mel_bins = vggish_params.NUM_MEL_BINS\n",
"\n",
"\n",
- "\n",
- "spec_to_mel_mat = mel_features.spectrogram_to_mel_matrix(num_mel_bins=num_mel_bins,\n",
- " num_spectrogram_bins=X_spec.shape[0],\n",
- " audio_sample_rate=sr,\n",
- " lower_edge_hertz=lower_edge_hertz,\n",
- " upper_edge_hertz=upper_edge_hertz)\n",
+ "spec_to_mel_mat = mel_features.spectrogram_to_mel_matrix(\n",
+ " num_mel_bins=num_mel_bins,\n",
+ " num_spectrogram_bins=X_spec.shape[0],\n",
+ " audio_sample_rate=sr,\n",
+ " lower_edge_hertz=lower_edge_hertz,\n",
+ " upper_edge_hertz=upper_edge_hertz,\n",
+ ")\n",
"\n",
"print(spec_to_mel_mat.T.shape)\n",
"\n",
"plt.figure(figsize=(14, 7))\n",
- "plt.imshow(spec_to_mel_mat.T, origin='lower')\n",
- "plt.colorbar(orientation='horizontal')\n",
- "plt.set_cmap('magma')\n",
+ "plt.imshow(spec_to_mel_mat.T, origin=\"lower\")\n",
+ "plt.colorbar(orientation=\"horizontal\")\n",
+ "plt.set_cmap(\"magma\")\n",
"\n",
- "plt.title('Mel filterbank matrix')\n",
- "plt.xlabel('DFT-frequency bins')\n",
- "plt.ylabel('Mel-frequency bins')"
+ "plt.title(\"Mel filterbank matrix\")\n",
+ "plt.xlabel(\"DFT-frequency bins\")\n",
+ "plt.ylabel(\"Mel-frequency bins\")"
]
},
{
@@ -1316,12 +1401,13 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 659
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 20,
"status": "ok",
@@ -1333,6 +1419,9 @@
"user_tz": -60
},
"id": "7vxRS74O_XmR",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "2c836735-5a09-4eeb-c0af-cced95f62eaf"
},
"outputs": [
@@ -1342,7 +1431,7 @@
"Text(0, 0.5, 'Mel-frequency bins')"
]
},
- "execution_count": 19,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
@@ -1362,15 +1451,19 @@
"\n",
"\n",
"plt.figure(figsize=(14, 7))\n",
- "librosa.display.specshow(librosa.amplitude_to_db(X_mel_spec), sr=sr,\n",
- " hop_length=hop_length_samples, x_axis='time')\n",
- "plt.set_cmap('magma')\n",
+ "librosa.display.specshow(\n",
+ " librosa.amplitude_to_db(X_mel_spec),\n",
+ " sr=sr,\n",
+ " hop_length=hop_length_samples,\n",
+ " x_axis=\"time\",\n",
+ ")\n",
+ "plt.set_cmap(\"magma\")\n",
"plt.colorbar()\n",
"\n",
- "plt.title('dB-scaled Mel-spectrogram')\n",
- "plt.xlabel('time (s)')\n",
- "plt.yticks(np.arange(0,num_mel_bins,10))\n",
- "plt.ylabel('Mel-frequency bins')\n"
+ "plt.title(\"dB-scaled Mel-spectrogram\")\n",
+ "plt.xlabel(\"time (s)\")\n",
+ "plt.yticks(np.arange(0, num_mel_bins, 10))\n",
+ "plt.ylabel(\"Mel-frequency bins\")"
]
},
{
@@ -1411,9 +1504,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "version": "3.12.3"
}
},
"nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
}
diff --git a/baseline/2-feature-extraction.ipynb b/baseline/2-feature-extraction.ipynb
index f8ff3e4..97e1ed5 100755
--- a/baseline/2-feature-extraction.ipynb
+++ b/baseline/2-feature-extraction.ipynb
@@ -11,9 +11,13 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {
- "id": "HwMtwyCmMlOz"
+ "collapsed": true,
+ "id": "HwMtwyCmMlOz",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -43,12 +47,13 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 391
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 19487,
"status": "error",
@@ -60,9 +65,20 @@
"user_tz": -60
},
"id": "y40jVUlHNj8A",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "f1828644-8aad-46b6-e881-e8e593d2cb45"
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(3068/3068) 46_010968.wav\n"
+ ]
+ }
+ ],
"source": [
"import librosa\n",
"import os\n",
@@ -175,9 +191,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "version": "3.12.3"
}
},
"nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
}
diff --git a/baseline/3-baseline-dev.ipynb b/baseline/3-baseline-dev.ipynb
index 44104ab..8928e6f 100755
--- a/baseline/3-baseline-dev.ipynb
+++ b/baseline/3-baseline-dev.ipynb
@@ -32,9 +32,13 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 1,
"metadata": {
+ "collapsed": true,
"id": "9BfpoUwfxE0-",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"scrolled": true
},
"outputs": [],
@@ -63,11 +67,12 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 9,
"status": "ok",
@@ -79,6 +84,9 @@
"user_tz": -60
},
"id": "HwMtwyCmMlOz",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "d40c9238-8ef4-48c7-8b2a-e850b65f5923"
},
"outputs": [
@@ -86,7 +94,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "baseline_2024-09-03-12h38m17s\n"
+ "baseline_2024-11-27-12h35m18s\n"
]
}
],
@@ -119,8 +127,13 @@
},
{
"cell_type": "code",
- "execution_count": 37,
- "metadata": {},
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -155,9 +168,13 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 4,
"metadata": {
- "id": "jTSLmdEEwuNY"
+ "collapsed": true,
+ "id": "jTSLmdEEwuNY",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -169,9 +186,13 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 5,
"metadata": {
- "id": "JSomLphkY8xT"
+ "collapsed": true,
+ "id": "JSomLphkY8xT",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -211,9 +232,13 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 6,
"metadata": {
- "id": "4eK-7Mwrw-AW"
+ "collapsed": true,
+ "id": "4eK-7Mwrw-AW",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -251,9 +276,13 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 7,
"metadata": {
- "id": "bAtHMZuxfxAI"
+ "collapsed": true,
+ "id": "bAtHMZuxfxAI",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -277,9 +306,13 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 8,
"metadata": {
- "id": "Fy6Eo8KgRV0q"
+ "collapsed": true,
+ "id": "Fy6Eo8KgRV0q",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -340,8 +373,13 @@
},
{
"cell_type": "code",
- "execution_count": 43,
- "metadata": {},
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -402,9 +440,13 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 10,
"metadata": {
- "id": "AWUeUvMpyfSM"
+ "collapsed": true,
+ "id": "AWUeUvMpyfSM",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -548,9 +590,13 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 11,
"metadata": {
- "id": "o-nRAMdHKpMe"
+ "collapsed": true,
+ "id": "o-nRAMdHKpMe",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": [
@@ -569,16 +615,20 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 12,
"metadata": {
- "id": "pLwfnP0HUDSF"
+ "collapsed": true,
+ "id": "pLwfnP0HUDSF",
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_5569/1253985691.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+ "/tmp/ipykernel_11819/1253985691.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
" pretrained_state_dict = torch.load(vggish_weights_file)\n"
]
}
@@ -607,11 +657,12 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 57176,
"status": "ok",
@@ -623,6 +674,9 @@
"user_tz": -60
},
"id": "KaSf8Rx50JCN",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "1f9960ee-4d31-4112-ca5f-26d3859097c2"
},
"outputs": [
@@ -673,11 +727,12 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 19,
"status": "ok",
@@ -689,6 +744,9 @@
"user_tz": -60
},
"id": "kUZ2XdfPMC4v",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "4374a773-0445-4a18-8963-d6f24ccc26d9"
},
"outputs": [
@@ -746,11 +804,12 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 58050,
"status": "ok",
@@ -762,6 +821,9 @@
"user_tz": -60
},
"id": "87wiAJBTMSpw",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "10808da7-b28b-4dd0-ae55-b3c4d21b246c"
},
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@@ -818,11 +880,12 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 9,
"status": "ok",
@@ -834,6 +897,9 @@
"user_tz": -60
},
"id": "u5f7EkT5NaeE",
+ "jupyter": {
+ "outputs_hidden": true
+ },
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},
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@@ -928,11 +994,12 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 453524,
"status": "ok",
@@ -944,6 +1011,9 @@
"user_tz": -60
},
"id": "-imciSMAMakA",
+ "jupyter": {
+ "outputs_hidden": true
+ },
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},
"outputs": [
@@ -959,28 +1029,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 19/19 [00:09<00:00, 2.06it/s]\n"
+ "100%|██████████| 19/19 [00:03<00:00, 5.10it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.5116179110850744\n"
+ "Training loss: 0.5116179013495646\n"
]
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{
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"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.44it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 5.74it/s]\n"
]
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{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.43115173095235976\n",
+ "Validation loss: 0.4311517137302502\n",
"\n",
"Epoch 2/100\n",
"\n"
@@ -990,28 +1060,28 @@
"name": "stderr",
"output_type": "stream",
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+ "100%|██████████| 19/19 [00:03<00:00, 5.59it/s]\n"
]
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{
"name": "stdout",
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"text": [
- "Training loss: 0.4549653892160121\n"
+ "Training loss: 0.4549653902428042\n"
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{
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"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 5.93it/s]\n"
]
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{
"name": "stdout",
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"text": [
- "Validation loss: 0.3879189138756918\n",
+ "Validation loss: 0.38791888804252744\n",
"\n",
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"\n"
@@ -1021,28 +1091,28 @@
"name": "stderr",
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+ "100%|██████████| 19/19 [00:03<00:00, 5.62it/s]\n"
]
},
{
"name": "stdout",
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"text": [
- "Training loss: 0.44149337766627866\n"
+ "Training loss: 0.4414933697181476\n"
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{
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"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.41it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 8.26it/s]\n"
]
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"name": "stdout",
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"text": [
- "Validation loss: 0.3982476512008004\n",
+ "Validation loss: 0.39824765516995847\n",
"\n",
"Epoch 4/100\n",
"\n"
@@ -1052,28 +1122,28 @@
"name": "stderr",
"output_type": "stream",
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- "100%|██████████| 19/19 [00:08<00:00, 2.17it/s]\n"
+ "100%|██████████| 19/19 [00:02<00:00, 6.37it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.4375808220602309\n"
+ "Training loss: 0.43758081881506095\n"
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{
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"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 6.60it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.3893466655745194\n",
+ "Validation loss: 0.3893466821238903\n",
"\n",
"Epoch 5/100\n",
"\n"
@@ -1083,28 +1153,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 19/19 [00:08<00:00, 2.16it/s]\n"
+ "100%|██████████| 19/19 [00:03<00:00, 5.61it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.4336092874426783\n"
+ "Training loss: 0.43360928582009334\n"
]
},
{
"name": "stderr",
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"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.48it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 6.38it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.38681010340191174\n",
+ "Validation loss: 0.3868101153093859\n",
"\n",
"Epoch 6/100\n",
"\n"
@@ -1114,28 +1184,28 @@
"name": "stderr",
"output_type": "stream",
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+ "100%|██████████| 19/19 [00:03<00:00, 5.62it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.42993238319847643\n"
+ "Training loss: 0.4299323833632702\n"
]
},
{
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"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 8.14it/s]\n"
]
},
{
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"output_type": "stream",
"text": [
- "Validation loss: 0.3777852108866044\n",
+ "Validation loss: 0.37778522279407856\n",
"\n",
"Epoch 7/100\n",
"\n"
@@ -1145,28 +1215,28 @@
"name": "stderr",
"output_type": "stream",
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]
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{
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"text": [
- "Training loss: 0.42765030432183504\n"
+ "Training loss: 0.42765029399053234\n"
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+ "100%|██████████| 4/4 [00:00<00:00, 5.83it/s]\n"
]
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{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.38583240626089726\n",
+ "Validation loss: 0.38583244003237777\n",
"\n",
"Epoch 8/100\n",
"\n"
@@ -1176,28 +1246,28 @@
"name": "stderr",
"output_type": "stream",
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]
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{
"name": "stdout",
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"text": [
- "Training loss: 0.4253459416111192\n"
+ "Training loss: 0.4253459379349501\n"
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{
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]
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"text": [
- "Validation loss: 0.3898058629870146\n",
+ "Validation loss: 0.3898058835055435\n",
"\n",
"Epoch 9/100\n",
"\n"
@@ -1207,28 +1277,28 @@
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"output_type": "stream",
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{
"name": "stdout",
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"text": [
- "Training loss: 0.4240761897833689\n"
+ "Training loss: 0.4240761812394449\n"
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]
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"text": [
- "Validation loss: 0.3810898496792494\n",
+ "Validation loss: 0.3810898741669364\n",
"\n",
"Epoch 10/100\n",
"\n"
@@ -1238,28 +1308,28 @@
"name": "stderr",
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"text": [
- "Training loss: 0.4219647049523576\n"
+ "Training loss: 0.42196470776652845\n"
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"text": [
- "Validation loss: 0.36943493921385395\n",
+ "Validation loss: 0.36943496834343764\n",
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"Epoch 11/100\n",
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@@ -1269,28 +1339,28 @@
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{
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- "Training loss: 0.4196228338068772\n"
+ "Training loss: 0.41962283175329307\n"
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"text": [
- "Validation loss: 0.3632496683511454\n",
+ "Validation loss: 0.36324972264115485\n",
"\n",
"Epoch 12/100\n",
"\n"
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- "Training loss: 0.41839490296586124\n"
+ "Training loss: 0.41839488938938857\n"
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"text": [
- "Validation loss: 0.36096879624620787\n",
+ "Validation loss: 0.36096882934494967\n",
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"Epoch 13/100\n",
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{
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- "Training loss: 0.417352295439074\n"
+ "Training loss: 0.41735228202739505\n"
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"text": [
- "Validation loss: 0.3615072171790336\n",
+ "Validation loss: 0.36150727940735916\n",
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"Epoch 14/100\n",
"\n"
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{
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- "Training loss: 0.4171233050674137\n"
+ "Training loss: 0.4171233040406216\n"
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"text": [
- "Validation loss: 0.36007951499377094\n",
+ "Validation loss: 0.36007953948145793\n",
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"Epoch 15/100\n",
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- "Training loss: 0.41568095691150325\n"
+ "Training loss: 0.4156809535015395\n"
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"text": [
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+ "Validation loss: 0.35970411518626505\n",
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+ "Training loss: 0.41340482170304965\n"
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+ "Validation loss: 0.36264635841141435\n",
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"Epoch 17/100\n",
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+ "Training loss: 0.4135770821180916\n"
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"text": [
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+ "Validation loss: 0.3659793619512166\n",
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"Epoch 18/100\n",
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+ "Training loss: 0.413315953839743\n"
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- "Validation loss: 0.35784488075473775\n",
+ "Validation loss: 0.3578449250882151\n",
"\n",
"Epoch 41/100\n",
"\n"
@@ -2199,28 +2269,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 19/19 [00:09<00:00, 2.02it/s]\n"
+ "100%|██████████| 19/19 [00:03<00:00, 6.19it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.4005504657947373\n"
+ "Training loss: 0.4005504395037902\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.33it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 7.97it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.3680538980471092\n",
+ "Validation loss: 0.36805393841142847\n",
"\n",
"Epoch 42/100\n",
"\n"
@@ -2230,28 +2300,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 19/19 [00:08<00:00, 2.11it/s]\n"
+ "100%|██████████| 19/19 [00:03<00:00, 6.31it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.4016602750485727\n"
+ "Training loss: 0.40166026683423633\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 8.08it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.3618762901485908\n",
+ "Validation loss: 0.3618763219018553\n",
"\n",
"Epoch 43/100\n",
"\n"
@@ -2261,28 +2331,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 19/19 [00:08<00:00, 2.28it/s]\n"
+ "100%|██████████| 19/19 [00:03<00:00, 6.32it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.4004241489111642\n"
+ "Training loss: 0.4004241353346915\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.28it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 7.47it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.3617446670144729\n",
+ "Validation loss: 0.3617447285700598\n",
"\n",
"Epoch 44/100\n",
"\n"
@@ -2292,28 +2362,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 19/19 [00:09<00:00, 2.10it/s]\n"
+ "100%|██████████| 19/19 [00:02<00:00, 6.73it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.4005723379485813\n"
+ "Training loss: 0.4005723364907901\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.35it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 6.41it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.3552824944997749\n",
+ "Validation loss: 0.3552825348640941\n",
"\n",
"Epoch 45/100\n",
"\n"
@@ -2323,28 +2393,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 19/19 [00:08<00:00, 2.12it/s]\n"
+ "100%|██████████| 19/19 [00:02<00:00, 6.37it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.3997899769900049\n"
+ "Training loss: 0.39978997245183756\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.26it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 8.20it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.34941496240904313\n",
+ "Validation loss: 0.349415019995472\n",
"\n",
"Epoch 46/100\n",
"\n"
@@ -2354,28 +2424,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 19/19 [00:08<00:00, 2.25it/s]\n"
+ "100%|██████████| 19/19 [00:02<00:00, 6.87it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.3983064211210967\n"
+ "Training loss: 0.3983064100292073\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.43it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 8.37it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.35374657173996316\n",
+ "Validation loss: 0.3537466584559757\n",
"\n",
"Epoch 47/100\n",
"\n"
@@ -2385,28 +2455,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 19/19 [00:08<00:00, 2.15it/s]\n"
+ "100%|██████████| 19/19 [00:02<00:00, 6.97it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.4008916879602819\n"
+ "Training loss: 0.4008916739528101\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.20it/s]\n"
+ "100%|██████████| 4/4 [00:00<00:00, 7.83it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.35690008571400866\n",
+ "Validation loss: 0.35690013865854076\n",
"\n",
"Epoch 48/100\n",
"\n"
@@ -2416,28 +2486,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 19/19 [00:09<00:00, 2.09it/s]\n"
+ "100%|██████████| 19/19 [00:02<00:00, 6.87it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training loss: 0.4001141216135289\n"
+ "Training loss: 0.4001141152879828\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 4/4 [00:01<00:00, 2.33it/s]"
+ "100%|██████████| 4/4 [00:00<00:00, 8.33it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Validation loss: 0.35569876096049496\n",
+ "Validation loss: 0.35569881854692376\n",
"\n",
"Training done!\n"
]
@@ -2529,11 +2599,12 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 26,
"status": "ok",
@@ -2545,6 +2616,9 @@
"user_tz": -60
},
"id": "A2FDUAgvFSjT",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "a9deb310-f8ca-4667-8b1e-bcebefe7b1c2"
},
"outputs": [
@@ -2554,7 +2628,7 @@
"<All keys matched successfully>"
]
},
- "execution_count": 52,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -2566,12 +2640,13 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 430
},
+ "collapsed": true,
"executionInfo": {
"elapsed": 396,
"status": "ok",
@@ -2583,12 +2658,15 @@
"user_tz": -60
},
"id": "Y6Gh4UOUdAJm",
+ "jupyter": {
+ "outputs_hidden": true
+ },
"outputId": "2128dfc7-b049-4d0b-f99b-02b5de248776"
},
"outputs": [
{
"data": {
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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -2614,8 +2692,13 @@
},
{
"cell_type": "code",
- "execution_count": 54,
- "metadata": {},
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -2623,18 +2706,18 @@
"text": [
"VALIDATION\n",
"\n",
- "Micro AUPRC: 0.6144345431514495\n",
+ "Micro AUPRC: 0.6145232383795827\n",
"Micro F1-score (@0.5): 0.5529308836395451\n",
- "Macro AUPRC: 0.3666736994104178\n",
+ "Macro AUPRC: 0.3681745957814433\n",
"Coarse Tag AUPRC:\n",
- " - 1: 0.7749544886438909\n",
- " - 2: 0.3311363877198092\n",
- " - 3: 0.18355672310440047\n",
- " - 4: 0.27904221303689974\n",
- " - 5: 0.5309844246832589\n",
- " - 6: 0.07569277752197645\n",
- " - 7: 0.7415846746837877\n",
- " - 8: 0.01643790588931903\n"
+ " - 1: 0.7751769158855797\n",
+ " - 2: 0.3383970886963229\n",
+ " - 3: 0.18414510752931912\n",
+ " - 4: 0.2824330753716959\n",
+ " - 5: 0.5318597959329995\n",
+ " - 6: 0.07506947342549426\n",
+ " - 7: 0.7417077293771523\n",
+ " - 8: 0.01660758003298232\n"
]
}
],
@@ -2689,8 +2772,13 @@
},
{
"cell_type": "code",
- "execution_count": 55,
- "metadata": {},
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
"outputs": [],
"source": [
"y_pred = predict(log_mel_spec_list, test_file_idxs, model)\n",
@@ -2725,9 +2813,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "version": "3.12.3"
}
},
"nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
}
diff --git a/baseline/paths.py b/baseline/paths.py
index 72b6bb8..490dcf7 100755
--- a/baseline/paths.py
+++ b/baseline/paths.py
@@ -1,6 +1,6 @@
import os
-root_path = "" # YOU SHOULD PUT HERE THE PATH TO THE FOLDER THAT CONTAINS THE README
+root_path = "/home/silly_thompson/workdir/urban-sound-tagging-project" # YOU SHOULD PUT HERE THE PATH TO THE FOLDER THAT CONTAINS THE README
ust_data_dir = os.path.join(root_path, "data/ust-data")
diff --git a/data/output/baseline_2024-09-03-12h38m17s/final_results_validation.txt b/data/output/baseline_2024-09-03-12h38m17s/final_results_validation.txt
deleted file mode 100755
index b090a82..0000000
--- a/data/output/baseline_2024-09-03-12h38m17s/final_results_validation.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-Micro AUPRC: 0.6144345431514495
-Micro F1-score (@0.5): 0.5529308836395451
-Macro AUPRC: 0.3666736994104178
-Coarse Tag AUPRC:
- - 1: 0.7749544886438909
- - 2: 0.3311363877198092
- - 3: 0.18355672310440047
- - 4: 0.27904221303689974
- - 5: 0.5309844246832589
- - 6: 0.07569277752197645
- - 7: 0.7415846746837877
- - 8: 0.01643790588931903
diff --git a/data/output/baseline_2024-09-03-12h38m17s/hyper_params.json b/data/output/baseline_2024-09-03-12h38m17s/hyper_params.json
deleted file mode 100755
index 525fb37..0000000
--- a/data/output/baseline_2024-09-03-12h38m17s/hyper_params.json
+++ /dev/null
@@ -1,10 +0,0 @@
-{
- "annotation_file": "/data/enseignement/2024-2025/3A-IA-DL/UST-project-complete/data/ust-data/sonyc-ust/annotations.csv",
- "taxonomy_file": "/data/enseignement/2024-2025/3A-IA-DL/UST-project-complete/data/ust-data/sonyc-ust/dcase-ust-taxonomy.yaml",
- "exp_id": "baseline_2024-09-03-12h38m17s",
- "log_mel_spec_dir": "/data/enseignement/2024-2025/3A-IA-DL/UST-project-complete/data/ust-data/log-mel-spectrograms",
- "learning_rate": 0.01,
- "batch_size": 128,
- "num_epochs": 100,
- "patience": 20
-}
\ No newline at end of file
diff --git a/data/output/baseline_2024-09-03-12h38m17s/log.txt b/data/output/baseline_2024-09-03-12h38m17s/log.txt
deleted file mode 100755
index 5d42483..0000000
--- a/data/output/baseline_2024-09-03-12h38m17s/log.txt
+++ /dev/null
@@ -1,192 +0,0 @@
-Epoch: 1/100
- Training loss: 0.5116179110850744
- Validation loss: 0.43115173095235976
- Best epoch / loss: 0 / inf
-Epoch: 2/100
- Training loss: 0.4549653892160121
- Validation loss: 0.3879189138756918
- Best epoch / loss: 1 / 0.43115173095235976
-Epoch: 3/100
- Training loss: 0.44149337766627866
- Validation loss: 0.3982476512008004
- Best epoch / loss: 2 / 0.3879189138756918
-Epoch: 4/100
- Training loss: 0.4375808220602309
- Validation loss: 0.3893466655745194
- Best epoch / loss: 2 / 0.3879189138756918
-Epoch: 5/100
- Training loss: 0.4336092874426783
- Validation loss: 0.38681010340191174
- Best epoch / loss: 2 / 0.3879189138756918
-Epoch: 6/100
- Training loss: 0.42993238319847643
- Validation loss: 0.3777852108866044
- Best epoch / loss: 5 / 0.38681010340191174
-Epoch: 7/100
- Training loss: 0.42765030432183504
- Validation loss: 0.38583240626089726
- Best epoch / loss: 6 / 0.3777852108866044
-Epoch: 8/100
- Training loss: 0.4253459416111192
- Validation loss: 0.3898058629870146
- Best epoch / loss: 6 / 0.3777852108866044
-Epoch: 9/100
- Training loss: 0.4240761897833689
- Validation loss: 0.3810898496792494
- Best epoch / loss: 6 / 0.3777852108866044
-Epoch: 10/100
- Training loss: 0.4219647049523576
- Validation loss: 0.36943493921385395
- Best epoch / loss: 6 / 0.3777852108866044
-Epoch: 11/100
- Training loss: 0.4196228338068772
- Validation loss: 0.3632496683511454
- Best epoch / loss: 10 / 0.36943493921385395
-Epoch: 12/100
- Training loss: 0.41839490296586124
- Validation loss: 0.36096879624620787
- Best epoch / loss: 11 / 0.3632496683511454
-Epoch: 13/100
- Training loss: 0.417352295439074
- Validation loss: 0.3615072171790336
- Best epoch / loss: 12 / 0.36096879624620787
-Epoch: 14/100
- Training loss: 0.4171233050674137
- Validation loss: 0.36007951499377094
- Best epoch / loss: 12 / 0.36096879624620787
-Epoch: 15/100
- Training loss: 0.41568095691150325
- Validation loss: 0.3597040529579395
- Best epoch / loss: 14 / 0.36007951499377094
-Epoch: 16/100
- Training loss: 0.41340483365693736
- Validation loss: 0.36264628757203404
- Best epoch / loss: 15 / 0.3597040529579395
-Epoch: 17/100
- Training loss: 0.41357709552977046
- Validation loss: 0.3659792911118363
- Best epoch / loss: 15 / 0.3597040529579395
-Epoch: 18/100
- Training loss: 0.4133159603300829
- Validation loss: 0.3544525173797564
- Best epoch / loss: 15 / 0.3597040529579395
-Epoch: 19/100
- Training loss: 0.4116452308042868
- Validation loss: 0.3651303077793552
- Best epoch / loss: 18 / 0.3544525173797564
-Epoch: 20/100
- Training loss: 0.4106754020319143
- Validation loss: 0.3604016483637334
- Best epoch / loss: 18 / 0.3544525173797564
-Epoch: 21/100
- Training loss: 0.40971104145962756
- Validation loss: 0.36152440417162723
- Best epoch / loss: 18 / 0.3544525173797564
-Epoch: 22/100
- Training loss: 0.40820785634368595
- Validation loss: 0.35410889317165917
- Best epoch / loss: 18 / 0.3544525173797564
-Epoch: 23/100
- Training loss: 0.40931316435666654
- Validation loss: 0.3689355082624922
- Best epoch / loss: 22 / 0.35410889317165917
-Epoch: 24/100
- Training loss: 0.4093705119350422
- Validation loss: 0.3653377590545413
- Best epoch / loss: 22 / 0.35410889317165917
-Epoch: 25/100
- Training loss: 0.4073535799473106
- Validation loss: 0.3659973564992909
- Best epoch / loss: 22 / 0.35410889317165917
-Epoch: 26/100
- Training loss: 0.40783119361383363
- Validation loss: 0.3543065335626796
- Best epoch / loss: 22 / 0.35410889317165917
-Epoch: 27/100
- Training loss: 0.4070389539396342
- Validation loss: 0.35335856655919523
- Best epoch / loss: 22 / 0.35410889317165917
-Epoch: 28/100
- Training loss: 0.4051198484871247
- Validation loss: 0.3471278150652117
- Best epoch / loss: 27 / 0.35335856655919523
-Epoch: 29/100
- Training loss: 0.4047151429001091
- Validation loss: 0.3541979596657893
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 30/100
- Training loss: 0.4040214652738283
- Validation loss: 0.3597200516922479
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 31/100
- Training loss: 0.4040892327886498
- Validation loss: 0.3627333724471839
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 32/100
- Training loss: 0.40386267995793784
- Validation loss: 0.35807823408268913
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 33/100
- Training loss: 0.4034483810654603
- Validation loss: 0.3481229167625005
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 34/100
- Training loss: 0.4037856414996528
- Validation loss: 0.356013410516571
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 35/100
- Training loss: 0.40282806446074626
- Validation loss: 0.35196270388351336
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 36/100
- Training loss: 0.40271469889829026
- Validation loss: 0.3501295138562506
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 37/100
- Training loss: 0.40106454533447566
- Validation loss: 0.3617598793172944
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 38/100
- Training loss: 0.4010404799781622
- Validation loss: 0.34903007342099607
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 39/100
- Training loss: 0.4019123530296608
- Validation loss: 0.3502418740608353
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 40/100
- Training loss: 0.4018245420269335
- Validation loss: 0.35784488075473775
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 41/100
- Training loss: 0.4005504657947373
- Validation loss: 0.3680538980471092
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 42/100
- Training loss: 0.4016602750485727
- Validation loss: 0.3618762901485908
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 43/100
- Training loss: 0.4004241489111642
- Validation loss: 0.3617446670144729
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 44/100
- Training loss: 0.4005723379485813
- Validation loss: 0.3552824944997749
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 45/100
- Training loss: 0.3997899769900049
- Validation loss: 0.34941496240904313
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 46/100
- Training loss: 0.3983064211210967
- Validation loss: 0.35374657173996316
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 47/100
- Training loss: 0.4008916879602819
- Validation loss: 0.35690008571400866
- Best epoch / loss: 28 / 0.3471278150652117
-Epoch: 48/100
- Training loss: 0.4001141216135289
- Validation loss: 0.35569876096049496
- Best epoch / loss: 28 / 0.3471278150652117
diff --git a/data/output/baseline_2024-09-03-12h38m17s/output_predictions_test.csv b/data/output/baseline_2024-09-03-12h38m17s/output_predictions_test.csv
deleted file mode 100755
index 1457371..0000000
--- a/data/output/baseline_2024-09-03-12h38m17s/output_predictions_test.csv
+++ /dev/null
@@ -1,275 +0,0 @@
-audio_filename,1-1_small-sounding-engine,1-2_medium-sounding-engine,1-3_large-sounding-engine,1-X_engine-of-uncertain-size,2-1_rock-drill,2-2_jackhammer,2-3_hoe-ram,2-4_pile-driver,2-X_other-unknown-impact-machinery,3-1_non-machinery-impact,4-1_chainsaw,4-2_small-medium-rotating-saw,4-3_large-rotating-saw,4-X_other-unknown-powered-saw,5-1_car-horn,5-2_car-alarm,5-3_siren,5-4_reverse-beeper,5-X_other-unknown-alert-signal,6-1_stationary-music,6-2_mobile-music,6-3_ice-cream-truck,6-X_music-from-uncertain-source,7-1_person-or-small-group-talking,7-2_person-or-small-group-shouting,7-3_large-crowd,7-4_amplified-speech,7-X_other-unknown-human-voice,8-1_dog-barking-whining,1_engine,2_machinery-impact,3_non-machinery-impact,4_powered-saw,5_alert-signal,6_music,7_human-voice,8_dog
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diff --git a/data/output/baseline_2024-09-03-12h38m17s/output_predictions_validation.csv b/data/output/baseline_2024-09-03-12h38m17s/output_predictions_validation.csv
deleted file mode 100755
index c09e151..0000000
--- a/data/output/baseline_2024-09-03-12h38m17s/output_predictions_validation.csv
+++ /dev/null
@@ -1,444 +0,0 @@
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diff --git a/data/output/baseline_2024-11-27-12h35m18s/.ipynb_checkpoints/final_results_test-checkpoint.txt b/data/output/baseline_2024-11-27-12h35m18s/.ipynb_checkpoints/final_results_test-checkpoint.txt
new file mode 100644
index 0000000..ef730b4
--- /dev/null
+++ b/data/output/baseline_2024-11-27-12h35m18s/.ipynb_checkpoints/final_results_test-checkpoint.txt
@@ -0,0 +1,12 @@
+Micro AUPRC: 0.5391948087340379
+Micro F1-score (@0.5): 0.5160349854227405
+Macro AUPRC: 0.3478030740214353
+Coarse Tag AUPRC:
+ - 1: 0.6188475800196518
+ - 2: 0.44550907526075045
+ - 3: 0.0747045988927203
+ - 4: 0.2600340818349339
+ - 5: 0.5073419186501394
+ - 6: 0.08765162849295172
+ - 7: 0.5822789755551128
+ - 8: 0.2060567334652221
diff --git a/data/output/baseline_2024-11-27-12h35m18s/.ipynb_checkpoints/final_results_validation-checkpoint.txt b/data/output/baseline_2024-11-27-12h35m18s/.ipynb_checkpoints/final_results_validation-checkpoint.txt
new file mode 100644
index 0000000..b525c01
--- /dev/null
+++ b/data/output/baseline_2024-11-27-12h35m18s/.ipynb_checkpoints/final_results_validation-checkpoint.txt
@@ -0,0 +1,12 @@
+Micro AUPRC: 0.6145232383795827
+Micro F1-score (@0.5): 0.5529308836395451
+Macro AUPRC: 0.3681745957814433
+Coarse Tag AUPRC:
+ - 1: 0.7751769158855797
+ - 2: 0.3383970886963229
+ - 3: 0.18414510752931912
+ - 4: 0.2824330753716959
+ - 5: 0.5318597959329995
+ - 6: 0.07506947342549426
+ - 7: 0.7417077293771523
+ - 8: 0.01660758003298232
diff --git a/data/output/baseline_2024-09-03-12h38m17s/best_model.pth b/data/output/baseline_2024-11-27-12h35m18s/best_model.pth
old mode 100755
new mode 100644
similarity index 99%
rename from data/output/baseline_2024-09-03-12h38m17s/best_model.pth
rename to data/output/baseline_2024-11-27-12h35m18s/best_model.pth
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diff --git a/data/output/baseline_2024-11-27-12h35m18s/final_results_validation.txt b/data/output/baseline_2024-11-27-12h35m18s/final_results_validation.txt
new file mode 100644
index 0000000..b525c01
--- /dev/null
+++ b/data/output/baseline_2024-11-27-12h35m18s/final_results_validation.txt
@@ -0,0 +1,12 @@
+Micro AUPRC: 0.6145232383795827
+Micro F1-score (@0.5): 0.5529308836395451
+Macro AUPRC: 0.3681745957814433
+Coarse Tag AUPRC:
+ - 1: 0.7751769158855797
+ - 2: 0.3383970886963229
+ - 3: 0.18414510752931912
+ - 4: 0.2824330753716959
+ - 5: 0.5318597959329995
+ - 6: 0.07506947342549426
+ - 7: 0.7417077293771523
+ - 8: 0.01660758003298232
diff --git a/data/output/baseline_2024-11-27-12h35m18s/hyper_params.json b/data/output/baseline_2024-11-27-12h35m18s/hyper_params.json
new file mode 100644
index 0000000..f92d170
--- /dev/null
+++ b/data/output/baseline_2024-11-27-12h35m18s/hyper_params.json
@@ -0,0 +1,10 @@
+{
+ "annotation_file": "/home/silly_thompson/workdir/urban-sound-tagging-project/data/ust-data/sonyc-ust/annotations.csv",
+ "taxonomy_file": "/home/silly_thompson/workdir/urban-sound-tagging-project/data/ust-data/sonyc-ust/dcase-ust-taxonomy.yaml",
+ "exp_id": "baseline_2024-11-27-12h35m18s",
+ "log_mel_spec_dir": "/home/silly_thompson/workdir/urban-sound-tagging-project/data/ust-data/log-mel-spectrograms",
+ "learning_rate": 0.01,
+ "batch_size": 128,
+ "num_epochs": 100,
+ "patience": 20
+}
\ No newline at end of file
diff --git a/data/output/baseline_2024-11-27-12h35m18s/log.txt b/data/output/baseline_2024-11-27-12h35m18s/log.txt
new file mode 100644
index 0000000..a8dca2b
--- /dev/null
+++ b/data/output/baseline_2024-11-27-12h35m18s/log.txt
@@ -0,0 +1,192 @@
+Epoch: 1/100
+ Training loss: 0.5116179013495646
+ Validation loss: 0.4311517137302502
+ Best epoch / loss: 0 / inf
+Epoch: 2/100
+ Training loss: 0.4549653902428042
+ Validation loss: 0.38791888804252744
+ Best epoch / loss: 1 / 0.4311517137302502
+Epoch: 3/100
+ Training loss: 0.4414933697181476
+ Validation loss: 0.39824765516995847
+ Best epoch / loss: 2 / 0.38791888804252744
+Epoch: 4/100
+ Training loss: 0.43758081881506095
+ Validation loss: 0.3893466821238903
+ Best epoch / loss: 2 / 0.38791888804252744
+Epoch: 5/100
+ Training loss: 0.43360928582009334
+ Validation loss: 0.3868101153093859
+ Best epoch / loss: 2 / 0.38791888804252744
+Epoch: 6/100
+ Training loss: 0.4299323833632702
+ Validation loss: 0.37778522279407856
+ Best epoch / loss: 5 / 0.3868101153093859
+Epoch: 7/100
+ Training loss: 0.42765029399053234
+ Validation loss: 0.38583244003237777
+ Best epoch / loss: 6 / 0.37778522279407856
+Epoch: 8/100
+ Training loss: 0.4253459379349501
+ Validation loss: 0.3898058835055435
+ Best epoch / loss: 6 / 0.37778522279407856
+Epoch: 9/100
+ Training loss: 0.4240761812394449
+ Validation loss: 0.3810898741669364
+ Best epoch / loss: 6 / 0.37778522279407856
+Epoch: 10/100
+ Training loss: 0.42196470776652845
+ Validation loss: 0.36943496834343764
+ Best epoch / loss: 6 / 0.37778522279407856
+Epoch: 11/100
+ Training loss: 0.41962283175329307
+ Validation loss: 0.36324972264115485
+ Best epoch / loss: 10 / 0.36943496834343764
+Epoch: 12/100
+ Training loss: 0.41839488938938857
+ Validation loss: 0.36096882934494967
+ Best epoch / loss: 11 / 0.36324972264115485
+Epoch: 13/100
+ Training loss: 0.41735228202739505
+ Validation loss: 0.36150727940735916
+ Best epoch / loss: 12 / 0.36096882934494967
+Epoch: 14/100
+ Training loss: 0.4171233040406216
+ Validation loss: 0.36007953948145793
+ Best epoch / loss: 12 / 0.36096882934494967
+Epoch: 15/100
+ Training loss: 0.4156809535015395
+ Validation loss: 0.35970411518626505
+ Best epoch / loss: 14 / 0.36007953948145793
+Epoch: 16/100
+ Training loss: 0.41340482170304965
+ Validation loss: 0.36264635841141435
+ Best epoch / loss: 15 / 0.35970411518626505
+Epoch: 17/100
+ Training loss: 0.4135770821180916
+ Validation loss: 0.3659793619512166
+ Best epoch / loss: 15 / 0.35970411518626505
+Epoch: 18/100
+ Training loss: 0.413315953839743
+ Validation loss: 0.3544525504784982
+ Best epoch / loss: 15 / 0.35970411518626505
+Epoch: 19/100
+ Training loss: 0.4116452052739264
+ Validation loss: 0.3651303534583098
+ Best epoch / loss: 18 / 0.3544525504784982
+Epoch: 20/100
+ Training loss: 0.41067539170061157
+ Validation loss: 0.36040170198100413
+ Best epoch / loss: 18 / 0.3544525504784982
+Epoch: 21/100
+ Training loss: 0.4097110382144576
+ Validation loss: 0.3615244405667884
+ Best epoch / loss: 18 / 0.3544525504784982
+Epoch: 22/100
+ Training loss: 0.40820785975364965
+ Validation loss: 0.35410892162850427
+ Best epoch / loss: 18 / 0.3544525504784982
+Epoch: 23/100
+ Training loss: 0.4093131568395346
+ Validation loss: 0.36893557445997577
+ Best epoch / loss: 22 / 0.35410892162850427
+Epoch: 24/100
+ Training loss: 0.4093705113392493
+ Validation loss: 0.3653378040607573
+ Best epoch / loss: 22 / 0.35410892162850427
+Epoch: 25/100
+ Training loss: 0.40735356756242375
+ Validation loss: 0.3659974180548778
+ Best epoch / loss: 22 / 0.35410892162850427
+Epoch: 26/100
+ Training loss: 0.40783118864466716
+ Validation loss: 0.3543065832107923
+ Best epoch / loss: 22 / 0.35410892162850427
+Epoch: 27/100
+ Training loss: 0.40703894506612265
+ Validation loss: 0.3533586241456241
+ Best epoch / loss: 22 / 0.35410892162850427
+Epoch: 28/100
+ Training loss: 0.4051198415657856
+ Validation loss: 0.34712789848480485
+ Best epoch / loss: 27 / 0.3533586241456241
+Epoch: 29/100
+ Training loss: 0.4047151269404648
+ Validation loss: 0.3541980344743277
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 30/100
+ Training loss: 0.40402145570311226
+ Validation loss: 0.35972010066762194
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 31/100
+ Training loss: 0.40408922748989573
+ Validation loss: 0.36273344725572226
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 32/100
+ Training loss: 0.4038626666476705
+ Validation loss: 0.35807823603363126
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 33/100
+ Training loss: 0.4034483798738745
+ Validation loss: 0.3481229697070326
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 34/100
+ Training loss: 0.40378563527551825
+ Validation loss: 0.3560134336587807
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 35/100
+ Training loss: 0.40282805250685855
+ Validation loss: 0.35196274888972934
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 36/100
+ Training loss: 0.40271468489081846
+ Validation loss: 0.35012958866478894
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 37/100
+ Training loss: 0.40106453192279673
+ Validation loss: 0.3617599620641489
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 38/100
+ Training loss: 0.40104045931555676
+ Validation loss: 0.34903011842721204
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 39/100
+ Training loss: 0.40191234140536064
+ Validation loss: 0.35024190978325787
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 40/100
+ Training loss: 0.40182452563629006
+ Validation loss: 0.3578449250882151
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 41/100
+ Training loss: 0.4005504395037902
+ Validation loss: 0.36805393841142847
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 42/100
+ Training loss: 0.40166026683423633
+ Validation loss: 0.3618763219018553
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 43/100
+ Training loss: 0.4004241353346915
+ Validation loss: 0.3617447285700598
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 44/100
+ Training loss: 0.4005723364907901
+ Validation loss: 0.3552825348640941
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 45/100
+ Training loss: 0.39978997245183756
+ Validation loss: 0.349415019995472
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 46/100
+ Training loss: 0.3983064100292073
+ Validation loss: 0.3537466584559757
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 47/100
+ Training loss: 0.4008916739528101
+ Validation loss: 0.35690013865854076
+ Best epoch / loss: 28 / 0.34712789848480485
+Epoch: 48/100
+ Training loss: 0.4001141152879828
+ Validation loss: 0.35569881854692376
+ Best epoch / loss: 28 / 0.34712789848480485
diff --git a/data/output/baseline_2024-09-03-12h38m17s/loss.pdf b/data/output/baseline_2024-11-27-12h35m18s/loss.pdf
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similarity index 72%
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diff --git a/data/output/baseline_2024-11-27-12h35m18s/output_predictions_test.csv b/data/output/baseline_2024-11-27-12h35m18s/output_predictions_test.csv
new file mode 100644
index 0000000..6e587f1
--- /dev/null
+++ b/data/output/baseline_2024-11-27-12h35m18s/output_predictions_test.csv
@@ -0,0 +1,275 @@
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diff --git a/data/output/baseline_2024-11-27-12h35m18s/output_predictions_validation.csv b/data/output/baseline_2024-11-27-12h35m18s/output_predictions_validation.csv
new file mode 100644
index 0000000..3c4709f
--- /dev/null
+++ b/data/output/baseline_2024-11-27-12h35m18s/output_predictions_validation.csv
@@ -0,0 +1,444 @@
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diff --git a/readme.md b/readme.md
index f9e6021..36f89af 100755
--- a/readme.md
+++ b/readme.md
@@ -45,32 +45,51 @@ Source: [SONYC project](https://wp.nyu.edu/sonyc/) and [DCASE 2019 Task 5](https
## Project overview
-You will work in teams and compete with others to develop the best-performing urban sound tagging system. At the end of the project, we will rank all proposed systems (a team can submit up to 3 entries).
+You will work in teams. Each team will compete with others to develop the best-performing urban sound tagging system. At the end of the project, all proposed systems will be ranked based on their performance.
-### Local installation (skip this section if you work in a remote environment)
+### MyDocker
-Run the following lines in a terminal to set up your Python environment.
-```bash
-conda create -n ust-project python=3.10
-conda activate ust-project
-pip install -r requirements.txt
-```
+In order to have access to a GPU, you will work in a remote computing environment with [MyDocker](https://mydocker.centralesupelec.fr/) (see the [documentation](https://centralesupelec.github.io/mydocker)). You will find on the [Edunao](https://centralesupelec.edunao.com/) webpage of the course the link to access the docker image we prepared for this project.
+
+- **Classwork**
+
+ For the in-class lab sessions, each group is allowed to start one and one single MyDocker environment. This is because we booked only 1 GPU per group.
+
+ Once this environment is started, just copy the URL to share it with your team mates along with the token value that you will find by entering the command `jupyter server list` in a terminal.
+
+ You will all be connected to the same machine, so you will share the home directory, the CPU, GPU and RAM resources, etc. This requires some organization for team work! For instance, you can execute `nvidia-smi` in a terminal to monitor the use of the GPU and see if you can run multiple trainings of deep learning models in parallel.
+
+ And be careful, by default Jupyter Lab does not support collaborative editing of the files.
+
+- **Homework**
+
+ Be reassured, you will be able to work on the project also at home 😉. You can always ask for an access to the MyDocker environment, at any time, but if it's not during the class you will have to wait for the resources to be available.
-You should now be able to run the Jupyter Notebooks described in the next section.
+## Set up your environment
+
+When connected to the MyDocker environment,
+- In a terminal
+ - execute `pwd` to see your current directory, this is your home directory, which you can always access by executing `cd ~`.
+ - execute `ls` to see the files and folders contained in your current directory, there should be a `workdir` folder.
+ - execute ```cd workdir``` to change your current directory.
+- Clone this Gitlab repository:
+
+ ```git clone https://gitlab-research.centralesupelec.fr/sleglaive/urban-sound-tagging-project.git```
+- Refresh the file browser on the left panel. You should now be able to see the cloned repository.
+- Edit the variable ```root_path``` in ```baseline/paths.py``` so that it corresponds to the path of the folder `urban-sound-tagging-project` in your environment.
+- Run the notebook `setup.ipynb` This notebook will install the Python libraries listed in `requirements.txt` (most of them should already be installed) and it will download the [SONYC-UST dataset](https://zenodo.org/record/2590742#.XIkTPBNKjuM).
### Baseline
To help you start with this project, you have in the `baseline` folder the following Jupyter Notebooks:
-* `0-setup.ipynb`: This notebook will install the Python libraries listed in `requirements.txt` and it will download the [SONYC-UST dataset](https://zenodo.org/record/2590742#.XIkTPBNKjuM).
-
* `1-preliminaries.ipynb`: This notebook introduces the [SONYC-UST dataset](https://zenodo.org/record/2590742#.XIkTPBNKjuM). You will also get familiar with:
* how to manipulate and analyze the dataset;
* how to read, write, play, and visualize audio files;
* how to compute a log-Mel spectrogram from a raw audio waveform.
-* `2-feature-extraction.ipynb`: In this notebook, you will extract the log-Mel spectrograms for all the audio files in the SONYC-UST dataset. It may take a significant amount of time, so anticipate!
+* `2-feature-extraction.ipynb`: In this notebook, you will extract the log-Mel spectrograms for all the audio files in the SONYC-UST dataset. It will take a several minutes.
* `3-baseline-dev.ipynb`: This notebook implements and trains a simple baseline to perform urban sound tagging with PyTorch.
@@ -81,12 +100,12 @@ To help you start with this project, you have in the `baseline` folder the follo
After training, the performance of the baseline model is evaluated using several metrics described in the [baseline of the DCASE 2019 Challenge - Task 5](https://github.com/sonyc-project/urban-sound-tagging-baseline): micro-averaged area under the precision-recall curve (AUPRC), macro-averaged AUPRC, and micro-averaged F1-score.
- Note that the provided implementation of the baseline training is very inefficient, because it requires a pass of the complete dataset through the frozen VGGish model at each epoch. A much more efficient solution would be to extract and store the VGGish embeddings for the whole SONYC UST dataset and then use these embeddings as input data to the multi-label logistic regression model. However, we chose the above inefficient implementation to make it easier for you to modify and build upon the baseline model. Remember that if you use Google Colab inefficiently and insensibly, your acess to computational resources will be restricted quite quickly.
+ Note that the provided implementation of the baseline training is very inefficient, because it requires a pass of the complete dataset through the frozen VGGish model at each epoch. A much more efficient solution would be to extract and store the VGGish embeddings for the whole SONYC UST dataset and then use these embeddings as input data to the multi-label logistic regression model. However, we chose the above inefficient implementation to make it easier for you to modify and build upon the baseline model.
## Results
-The `data/output/baseline_...` folder contains several output files produced by running the notebook `3-baseline-dev.ipynb`.
+The `data/output/baseline_...` folder contains several output files produced by running the notebook `3-baseline-dev.ipynb`. Check them out.
## Rules
@@ -95,11 +114,11 @@ During the development stage of your model, you **must** only use the training a
## Next steps
-Your task is now to develop the best-performing UST system on the SONYC-UST dataset. To do so, you should probably exploit/combine multiple sources of information, for instance:
+Your task is now to develop the best-performing urban sound tagging system on the SONYC-UST dataset. To do so, you should probably exploit/combine multiple sources of information, for instance:
1. the knowledge you acquired by watching the video courses on deep learning;
2. the strengths and weaknesses of the baseline, which you should try to identify;
-3. the other methods found by exploring the literature (e.g., papers published along with the DCASE challenge, papers that cite the SONYS-UST dataset, etc.).
+3. the other methods found by exploring the literature (e.g., papers published along with the DCASE challenge, papers that cite the SONYC-UST dataset, etc.).
For instance, when looking at the structure of the baseline model, you will realize that the temporal pooling of VGGish embeddings obtained for different time frames is very naive, and probably limits the overall performance of the system. Would it be possible to use recurrent neural networks (RNN) to aggregate the information over time? Similarly as the encoder network in [Sequence-to-sequence models for machine translation](https://d2l.ai/chapter_recurrent-modern/seq2seq.html#sec-seq2seq)? But aren't those RNN-based models now outperformed by [attention-based models](https://d2l.ai/chapter_attention-mechanisms-and-transformers/bahdanau-attention.html)? Can we build a temporal pooling operation based on attention mechanisms?
diff --git a/requirements.txt b/requirements.txt
index 8111eaf..c5e42ff 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,13 +1,14 @@
-librosa==0.10.2.post1
-matplotlib==3.7.1
-notebook==6.5.5
-numpy==1.26.4
-oyaml==1.0
-pandas==2.1.4
-resampy==0.4.3
-scikit-learn==1.3.2
-soundfile==0.12.1
torch==2.4.0
torchaudio==2.4.0
torchvision==0.19.0
-tqdm==4.66.5
+librosa
+matplotlib
+jupyter
+numpy
+oyaml
+pandas
+resampy
+scikit-learn
+soundfile
+tqdm
+
diff --git a/setup.ipynb b/setup.ipynb
new file mode 100755
index 0000000..13db62a
--- /dev/null
+++ b/setup.ipynb
@@ -0,0 +1,417 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hAyWzMHRd_bW"
+ },
+ "source": [
+ "# Environment setup\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Install Python libraries"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We first pip-install the Python libraries listed in ```requirements.txt```."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "vscode": {
+ "languageId": "plaintext"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Defaulting to user installation because normal site-packages is not writeable\n",
+ "\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/dill-0.3.9-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
+ "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/looseversion-1.3.0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
+ "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/lightning_utilities-0.11.8-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
+ "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/igraph-0.11.8-py3.12-linux-x86_64.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
+ "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/nvfuser-0.2.13a0+0d33366-py3.12-linux-x86_64.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
+ "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/lightning_thunder-0.2.0.dev0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
+ "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/texttable-1.7.0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
+ "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/opt_einsum-3.4.0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
+ "\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+ "Requirement already satisfied: torch==2.4.0 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 1)) (2.4.0)\n",
+ "Requirement already satisfied: torchaudio==2.4.0 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 2)) (2.4.0)\n",
+ "Requirement already satisfied: torchvision==0.19.0 in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 3)) (0.19.0)\n",
+ "Requirement already satisfied: librosa in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 4)) (0.10.2.post1)\n",
+ "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 5)) (3.7.1)\n",
+ "Requirement already satisfied: jupyter in /home/silly_thompson/.local/lib/python3.12/site-packages (from -r requirements.txt (line 6)) (1.1.1)\n",
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 7)) (1.26.4)\n",
+ "Requirement already satisfied: oyaml in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 8)) (1.0)\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 9)) (2.1.4)\n",
+ "Requirement already satisfied: resampy in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 10)) (0.4.3)\n",
+ "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 11)) (1.3.2)\n",
+ "Requirement already satisfied: soundfile in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 12)) (0.12.1)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from -r requirements.txt (line 13)) (4.66.5)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (3.16.1)\n",
+ "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (4.12.2)\n",
+ "Requirement already satisfied: sympy in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (1.13.1)\n",
+ "Requirement already satisfied: networkx in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (3.4.2)\n",
+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (3.1.4)\n",
+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (2024.10.0)\n",
+ "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (70.3.0)\n",
+ "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (12.1.105)\n",
+ "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (9.1.0.70)\n",
+ "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (12.1.3.1)\n",
+ "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (11.0.2.54)\n",
+ "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (10.3.2.106)\n",
+ "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (11.4.5.107)\n",
+ "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (12.1.0.106)\n",
+ "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (2.20.5)\n",
+ "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (12.1.105)\n",
+ "Requirement already satisfied: triton==3.0.0 in /usr/local/lib/python3.12/dist-packages (from torch==2.4.0->-r requirements.txt (line 1)) (3.0.0)\n",
+ "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.12/dist-packages (from torchvision==0.19.0->-r requirements.txt (line 3)) (11.0.0)\n",
+ "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.12/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.4.0->-r requirements.txt (line 1)) (12.6.85)\n",
+ "Requirement already satisfied: audioread>=2.1.9 in /usr/local/lib/python3.12/dist-packages (from librosa->-r requirements.txt (line 4)) (3.0.1)\n",
+ "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from librosa->-r requirements.txt (line 4)) (1.14.1)\n",
+ "Requirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.12/dist-packages (from librosa->-r requirements.txt (line 4)) (1.4.2)\n",
+ "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.12/dist-packages (from librosa->-r requirements.txt (line 4)) (5.1.1)\n",
+ "Requirement already satisfied: numba>=0.51.0 in /usr/local/lib/python3.12/dist-packages (from librosa->-r requirements.txt (line 4)) (0.59.1)\n",
+ "Requirement already satisfied: pooch>=1.1 in /usr/local/lib/python3.12/dist-packages (from librosa->-r requirements.txt (line 4)) (1.8.2)\n",
+ "Requirement already satisfied: soxr>=0.3.2 in /usr/local/lib/python3.12/dist-packages (from librosa->-r requirements.txt (line 4)) (0.5.0.post1)\n",
+ "Requirement already satisfied: lazy-loader>=0.1 in /usr/local/lib/python3.12/dist-packages (from librosa->-r requirements.txt (line 4)) (0.4)\n",
+ "Requirement already satisfied: msgpack>=1.0 in /usr/local/lib/python3.12/dist-packages (from librosa->-r requirements.txt (line 4)) (1.1.0)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->-r requirements.txt (line 5)) (1.3.1)\n",
+ "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib->-r requirements.txt (line 5)) (0.12.1)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->-r requirements.txt (line 5)) (4.55.0)\n",
+ "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->-r requirements.txt (line 5)) (1.4.7)\n",
+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->-r requirements.txt (line 5)) (23.2)\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->-r requirements.txt (line 5)) (3.2.0)\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib->-r requirements.txt (line 5)) (2.9.0.post0)\n",
+ "Requirement already satisfied: notebook in /usr/local/lib/python3.12/dist-packages (from jupyter->-r requirements.txt (line 6)) (7.2.2)\n",
+ "Requirement already satisfied: jupyter-console in /home/silly_thompson/.local/lib/python3.12/site-packages (from jupyter->-r requirements.txt (line 6)) (6.6.3)\n",
+ "Requirement already satisfied: nbconvert in /usr/local/lib/python3.12/dist-packages (from jupyter->-r requirements.txt (line 6)) (7.16.4)\n",
+ "Requirement already satisfied: ipykernel in /usr/local/lib/python3.12/dist-packages (from jupyter->-r requirements.txt (line 6)) (6.29.5)\n",
+ "Requirement already satisfied: ipywidgets in /home/silly_thompson/.local/lib/python3.12/site-packages (from jupyter->-r requirements.txt (line 6)) (8.1.5)\n",
+ "Requirement already satisfied: jupyterlab in /usr/local/lib/python3.12/dist-packages (from jupyter->-r requirements.txt (line 6)) (4.2.5)\n",
+ "Requirement already satisfied: pyyaml in /usr/local/lib/python3.12/dist-packages (from oyaml->-r requirements.txt (line 8)) (6.0.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas->-r requirements.txt (line 9)) (2023.4)\n",
+ "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.12/dist-packages (from pandas->-r requirements.txt (line 9)) (2024.2)\n",
+ "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn->-r requirements.txt (line 11)) (3.5.0)\n",
+ "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.12/dist-packages (from soundfile->-r requirements.txt (line 12)) (1.17.1)\n",
+ "Requirement already satisfied: pycparser in /usr/local/lib/python3.12/dist-packages (from cffi>=1.0->soundfile->-r requirements.txt (line 12)) (2.22)\n",
+ "Requirement already satisfied: llvmlite<0.43,>=0.42.0dev0 in /usr/local/lib/python3.12/dist-packages (from numba>=0.51.0->librosa->-r requirements.txt (line 4)) (0.42.0)\n",
+ "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from pooch>=1.1->librosa->-r requirements.txt (line 4)) (4.3.6)\n",
+ "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.12/dist-packages (from pooch>=1.1->librosa->-r requirements.txt (line 4)) (2.32.3)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib->-r requirements.txt (line 5)) (1.16.0)\n",
+ "Requirement already satisfied: comm>=0.1.1 in /usr/local/lib/python3.12/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 6)) (0.2.2)\n",
+ "Requirement already satisfied: debugpy>=1.6.5 in /usr/local/lib/python3.12/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 6)) (1.8.8)\n",
+ "Requirement already satisfied: ipython>=7.23.1 in /usr/local/lib/python3.12/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 6)) (8.29.0)\n",
+ "Requirement already satisfied: jupyter-client>=6.1.12 in /usr/local/lib/python3.12/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 6)) (8.6.3)\n",
+ "Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /usr/local/lib/python3.12/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 6)) (5.7.2)\n",
+ "Requirement already satisfied: matplotlib-inline>=0.1 in /usr/local/lib/python3.12/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 6)) (0.1.7)\n",
+ "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.12/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 6)) (1.6.0)\n",
+ "Requirement already satisfied: psutil in /usr/local/lib/python3.12/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 6)) (6.1.0)\n",
+ "Requirement already satisfied: pyzmq>=24 in /usr/local/lib/python3.12/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 6)) (26.2.0)\n",
+ "Requirement already satisfied: tornado>=6.1 in /usr/local/lib/python3.12/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 6)) (6.2)\n",
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+ "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.12/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 6)) (0.10.0)\n",
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+ "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx>=0.25.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (2024.8.30)\n",
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+ "Requirement already satisfied: jupyter-server-terminals>=0.4.4 in /usr/local/lib/python3.12/dist-packages (from jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (0.5.3)\n",
+ "Requirement already satisfied: overrides>=5.0 in /usr/local/lib/python3.12/dist-packages (from jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (7.7.0)\n",
+ "Requirement already satisfied: prometheus-client>=0.9 in /usr/local/lib/python3.12/dist-packages (from jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (0.21.0)\n",
+ "Requirement already satisfied: send2trash>=1.8.2 in /usr/local/lib/python3.12/dist-packages (from jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (1.8.3)\n",
+ "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.12/dist-packages (from jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (0.18.1)\n",
+ "Requirement already satisfied: websocket-client>=1.7 in /usr/local/lib/python3.12/dist-packages (from jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (1.8.0)\n",
+ "Requirement already satisfied: babel>=2.10 in /usr/local/lib/python3.12/dist-packages (from jupyterlab-server<3,>=2.27.1->jupyterlab->jupyter->-r requirements.txt (line 6)) (2.16.0)\n",
+ "Requirement already satisfied: json5>=0.9.0 in /usr/local/lib/python3.12/dist-packages (from jupyterlab-server<3,>=2.27.1->jupyterlab->jupyter->-r requirements.txt (line 6)) (0.9.28)\n",
+ "Requirement already satisfied: jsonschema>=4.18.0 in /usr/local/lib/python3.12/dist-packages (from jupyterlab-server<3,>=2.27.1->jupyterlab->jupyter->-r requirements.txt (line 6)) (4.23.0)\n",
+ "Requirement already satisfied: fastjsonschema>=2.15 in /usr/local/lib/python3.12/dist-packages (from nbformat>=5.7->nbconvert->jupyter->-r requirements.txt (line 6)) (2.20.0)\n",
+ "Requirement already satisfied: wcwidth in /usr/local/lib/python3.12/dist-packages (from prompt-toolkit>=3.0.30->jupyter-console->jupyter->-r requirements.txt (line 6)) (0.2.13)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.19.0->pooch>=1.1->librosa->-r requirements.txt (line 4)) (3.4.0)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.19.0->pooch>=1.1->librosa->-r requirements.txt (line 4)) (2.0.7)\n",
+ "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4->nbconvert->jupyter->-r requirements.txt (line 6)) (2.6)\n",
+ "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.12/dist-packages (from argon2-cffi>=21.1->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (21.2.0)\n",
+ "Requirement already satisfied: parso<0.9.0,>=0.8.4 in /usr/local/lib/python3.12/dist-packages (from jedi>=0.16->ipython>=7.23.1->ipykernel->jupyter->-r requirements.txt (line 6)) (0.8.4)\n",
+ "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.12/dist-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->jupyterlab->jupyter->-r requirements.txt (line 6)) (24.2.0)\n",
+ "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.12/dist-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->jupyterlab->jupyter->-r requirements.txt (line 6)) (2024.10.1)\n",
+ "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.12/dist-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->jupyterlab->jupyter->-r requirements.txt (line 6)) (0.35.1)\n",
+ "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.12/dist-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->jupyterlab->jupyter->-r requirements.txt (line 6)) (0.21.0)\n",
+ "Requirement already satisfied: python-json-logger>=2.0.4 in /usr/local/lib/python3.12/dist-packages (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (2.0.7)\n",
+ "Requirement already satisfied: rfc3339-validator in /usr/local/lib/python3.12/dist-packages (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (0.1.4)\n",
+ "Requirement already satisfied: rfc3986-validator>=0.1.1 in /usr/local/lib/python3.12/dist-packages (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (0.1.1)\n",
+ "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.12/dist-packages (from pexpect>4.3->ipython>=7.23.1->ipykernel->jupyter->-r requirements.txt (line 6)) (0.7.0)\n",
+ "Requirement already satisfied: executing>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from stack-data->ipython>=7.23.1->ipykernel->jupyter->-r requirements.txt (line 6)) (2.1.0)\n",
+ "Requirement already satisfied: asttokens>=2.1.0 in /usr/local/lib/python3.12/dist-packages (from stack-data->ipython>=7.23.1->ipykernel->jupyter->-r requirements.txt (line 6)) (2.4.1)\n",
+ "Requirement already satisfied: pure-eval in /usr/local/lib/python3.12/dist-packages (from stack-data->ipython>=7.23.1->ipykernel->jupyter->-r requirements.txt (line 6)) (0.2.3)\n",
+ "Requirement already satisfied: fqdn in /usr/local/lib/python3.12/dist-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (1.5.1)\n",
+ "Requirement already satisfied: isoduration in /usr/local/lib/python3.12/dist-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (20.11.0)\n",
+ "Requirement already satisfied: jsonpointer>1.13 in /usr/local/lib/python3.12/dist-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (3.0.0)\n",
+ "Requirement already satisfied: uri-template in /usr/local/lib/python3.12/dist-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (1.3.0)\n",
+ "Requirement already satisfied: webcolors>=24.6.0 in /usr/local/lib/python3.12/dist-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (24.11.1)\n",
+ "Requirement already satisfied: arrow>=0.15.0 in /usr/local/lib/python3.12/dist-packages (from isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (1.3.0)\n",
+ "Requirement already satisfied: types-python-dateutil>=2.8.10 in /usr/local/lib/python3.12/dist-packages (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 6)) (2.9.0.20241003)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -r requirements.txt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Download data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will download the [SONYC-UST dataset](https://zenodo.org/record/2590742#.XIkTPBNKjuM). The dataset was released along with the following paper (it's probably a good idea to have a look at it):\n",
+ "\n",
+ "> Cartwright, M., Mendez, A.E.M., Cramer, J., Lostanlen, V., Dove, G., Wu, H., Salamon, J., Nov, O., Bello, J.P. SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network. In Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE) , 2019. [PDF](https://dcase.community/documents/workshop2019/proceedings/DCASE2019Workshop_Cartwright_4.pdf)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "root_path = \"./\"\n",
+ "ust_data_path = os.path.join(root_path, \"data/ust-data/sonyc-ust\")\n",
+ "os.makedirs(ust_data_path, exist_ok=True) # create a folder to store the data\n",
+ "os.makedirs(os.path.join(ust_data_path, 'audio-dev'), exist_ok=True) # create a folder to store the development data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2024-11-27 10:31:32-- https://zenodo.org/record/3338310/files/annotations.csv\n",
+ "Resolving zenodo.org (zenodo.org)... 188.185.48.194, 188.185.43.25, 188.185.45.92, ...\n",
+ "Connecting to zenodo.org (zenodo.org)|188.185.48.194|:443... connected.\n",
+ "HTTP request sent, awaiting response... 301 MOVED PERMANENTLY\n",
+ "Location: /records/3338310/files/annotations.csv [following]\n",
+ "--2024-11-27 10:31:32-- https://zenodo.org/records/3338310/files/annotations.csv\n",
+ "Reusing existing connection to zenodo.org:443.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 3163972 (3.0M) [text/plain]\n",
+ "Saving to: ‘annotations.csv’\n",
+ "\n",
+ "annotations.csv 100%[===================>] 3.02M 5.13MB/s in 0.6s \n",
+ "\n",
+ "2024-11-27 10:31:33 (5.13 MB/s) - ‘annotations.csv’ saved [3163972/3163972]\n",
+ "\n",
+ "--2024-11-27 10:31:33-- https://zenodo.org/record/3338310/files/audio-dev.tar.gz\n",
+ "Resolving zenodo.org (zenodo.org)... 188.185.45.92, 188.185.48.194, 188.185.43.25, ...\n",
+ "Connecting to zenodo.org (zenodo.org)|188.185.45.92|:443... connected.\n",
+ "HTTP request sent, awaiting response... 301 MOVED PERMANENTLY\n",
+ "Location: /records/3338310/files/audio-dev.tar.gz [following]\n",
+ "--2024-11-27 10:31:33-- https://zenodo.org/records/3338310/files/audio-dev.tar.gz\n",
+ "Reusing existing connection to zenodo.org:443.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 1892542516 (1.8G) [application/octet-stream]\n",
+ "Saving to: ‘audio-dev.tar.gz’\n",
+ "\n",
+ "audio-dev.tar.gz 100%[===================>] 1.76G 127MB/s in 85s \n",
+ "\n",
+ "2024-11-27 10:32:58 (21.2 MB/s) - ‘audio-dev.tar.gz’ saved [1892542516/1892542516]\n",
+ "\n",
+ "--2024-11-27 10:32:58-- https://zenodo.org/record/3338310/files/audio-eval.tar.gz\n",
+ "Resolving zenodo.org (zenodo.org)... 188.185.43.25, 188.185.48.194, 188.185.45.92, ...\n",
+ "Connecting to zenodo.org (zenodo.org)|188.185.43.25|:443... connected.\n",
+ "HTTP request sent, awaiting response... 301 MOVED PERMANENTLY\n",
+ "Location: /records/3338310/files/audio-eval.tar.gz [following]\n",
+ "--2024-11-27 10:32:59-- https://zenodo.org/records/3338310/files/audio-eval.tar.gz\n",
+ "Reusing existing connection to zenodo.org:443.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 180759660 (172M) [application/octet-stream]\n",
+ "Saving to: ‘audio-eval.tar.gz’\n",
+ "\n",
+ "audio-eval.tar.gz 100%[===================>] 172.39M 127MB/s in 1.4s \n",
+ "\n",
+ "2024-11-27 10:33:00 (127 MB/s) - ‘audio-eval.tar.gz’ saved [180759660/180759660]\n",
+ "\n",
+ "--2024-11-27 10:33:00-- https://zenodo.org/record/3338310/files/dcase-ust-taxonomy.yaml\n",
+ "Resolving zenodo.org (zenodo.org)... 188.185.43.25, 188.185.45.92, 188.185.48.194, ...\n",
+ "Connecting to zenodo.org (zenodo.org)|188.185.43.25|:443... connected.\n",
+ "HTTP request sent, awaiting response... 301 MOVED PERMANENTLY\n",
+ "Location: /records/3338310/files/dcase-ust-taxonomy.yaml [following]\n",
+ "--2024-11-27 10:33:00-- https://zenodo.org/records/3338310/files/dcase-ust-taxonomy.yaml\n",
+ "Reusing existing connection to zenodo.org:443.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 1081 (1.1K) [application/octet-stream]\n",
+ "Saving to: ‘dcase-ust-taxonomy.yaml’\n",
+ "\n",
+ "dcase-ust-taxonomy. 100%[===================>] 1.06K --.-KB/s in 0s \n",
+ "\n",
+ "2024-11-27 10:33:00 (472 MB/s) - ‘dcase-ust-taxonomy.yaml’ saved [1081/1081]\n",
+ "\n",
+ "--2024-11-27 10:33:01-- https://zenodo.org/record/3338310/files/README.md\n",
+ "Resolving zenodo.org (zenodo.org)... 188.185.48.194, 188.185.43.25, 188.185.45.92, ...\n",
+ "Connecting to zenodo.org (zenodo.org)|188.185.48.194|:443... connected.\n",
+ "HTTP request sent, awaiting response... 301 MOVED PERMANENTLY\n",
+ "Location: /records/3338310/files/README.md [following]\n",
+ "--2024-11-27 10:33:01-- https://zenodo.org/records/3338310/files/README.md\n",
+ "Reusing existing connection to zenodo.org:443.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 8716 (8.5K) [application/octet-stream]\n",
+ "Saving to: ‘README.md’\n",
+ "\n",
+ "README.md 100%[===================>] 8.51K --.-KB/s in 0.02s \n",
+ "\n",
+ "2024-11-27 10:33:01 (351 KB/s) - ‘README.md’ saved [8716/8716]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "os.chdir(ust_data_path)\n",
+ "!wget https://zenodo.org/record/3338310/files/annotations.csv\n",
+ "!wget https://zenodo.org/record/3338310/files/audio-dev.tar.gz\n",
+ "!wget https://zenodo.org/record/3338310/files/audio-eval.tar.gz\n",
+ "!wget https://zenodo.org/record/3338310/files/dcase-ust-taxonomy.yaml\n",
+ "!wget https://zenodo.org/record/3338310/files/README.md\n",
+ "os.chdir(\"audio-dev\")\n",
+ "!tar xf ../audio-dev.tar.gz\n",
+ "os.chdir(\"..\")\n",
+ "!rm audio-dev.tar.gz\n",
+ "!tar xf audio-eval.tar.gz\n",
+ "!rm audio-eval.tar.gz\n",
+ "os.chdir(root_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You should end-up with the following file structure:"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "```\n",
+ "data\n",
+ "+-- ust-data\n",
+ "| +-- sonyc-ust\n",
+ "| | +-- audio-dev\n",
+ "| | | +-- train\n",
+ "| | | | +-- 01_000006.wav\n",
+ "| | | | +-- ...\n",
+ "| | | +-- validate\n",
+ "| | | | +-- 00_000066.wav\n",
+ "| | | | +-- ...\n",
+ "| | +-- audio-eval\n",
+ "| | | +-- 00_010346.wav\n",
+ "| | | +-- ...\n",
+ "| | +-- annotations.csv\n",
+ "| | +-- dcase-ust-taxonomy.yaml\n",
+ "| | +-- README.md\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "vscode": {
+ "languageId": "plaintext"
+ }
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "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": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--
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