From 6de51a6e6097aa30fc7caff7020f6b35ac1ae023 Mon Sep 17 00:00:00 2001 From: AnanthVivekanand Date: Tue, 1 Dec 2020 19:28:51 -0600 Subject: [PATCH] Added ResNet classifier using keras-resnet library --- .DS_Store | Bin 6148 -> 6148 bytes .../extract_fft_data-checkpoint.ipynb | 40341 ++++++++++++++++ preprocessing/extract_fft_data.ipynb | 2 +- training/Attempt_7_22khz_ResNet.ipynb | 790 + 4 files changed, 41132 insertions(+), 1 deletion(-) create mode 100755 preprocessing/.ipynb_checkpoints/extract_fft_data-checkpoint.ipynb create mode 100644 training/Attempt_7_22khz_ResNet.ipynb diff --git a/.DS_Store b/.DS_Store index d1d55eb41161e8ffc21ad608e96b50995a070711..3fadf23b6dd89cf440a655d70cb7930e7289547d 100644 GIT binary patch delta 153 zcmZoMXffDe%EEYRvKdPQk7RYVskw=cf}xRt>EwefvW&AP-)E6$oIQCWtK4K+*4KoT z{bN&9mwDgEz`(%4P{L5ekjRk9kO#);x%nM8XF_ge zRdr2m-OR}kSmYUJZDQqQbo-iAkds+lVqkECk%^gwm5rT)lZ%^^n~$42HaH`{Jh&vW zq_o&6u_#_ZCO9)CH7T(uJTs*vBP2D?H7B(!HP64uC$S`TvK5?~7Msk+&c&iV^(@z98Fm#ILt=6aJ5a?12C(V|_Sntr9Dn%%WiL>d diff --git a/preprocessing/.ipynb_checkpoints/extract_fft_data-checkpoint.ipynb b/preprocessing/.ipynb_checkpoints/extract_fft_data-checkpoint.ipynb new file mode 100755 index 0000000..424d607 --- /dev/null +++ b/preprocessing/.ipynb_checkpoints/extract_fft_data-checkpoint.ipynb @@ -0,0 +1,40341 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import librosa\n", + "import math\n", + "import os\n", + "import librosa.display\n", + "import pickle\n", + "import scipy\n", + "import sys\n", + "import soundfile as sf\n", + "import matplotlib.pyplot as plt\n", + "#!{sys.executable} -m pip install nlpaug\n", + "import nlpaug.flow as naf\n", + "from nlpaug.util.audio.loader import AudioLoader\n", + "from nlpaug.util.audio.visualizer import AudioVisualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "encodings = {'engine_idling': [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],\n", + " 'car_horn': [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " 'children_playing': [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " 'air_conditioner': [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " 'street_music': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],\n", + " 'dog_bark': [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " 'drilling': [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " 'siren': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],\n", + " 'gun_shot': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],\n", + " 'jackhammer': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_features(file_name):\n", + " \n", + " try:\n", + " \n", + " audio_y, sample_rate = librosa.load(file_name, sr=22050, res_type='kaiser_best', duration=4.0)\n", + " \n", + " #if len(audio_y) != (22050 * 4):\n", + " # print(\"incomplete file\")\n", + " # num_of_times_to_repeat = math.ceil((22050*4) / len(audio_y))\n", + " # audio_y = np.repeat(audio_y, num_of_times_to_repeat)\n", + " # audio_y = audio_y[:88200]\n", + " \n", + " if len(audio_y) != (22050 * 4):\n", + " print(\"incomplete file\")\n", + " num_of_times_to_repeat = math.ceil((22050*4) / len(audio_y))\n", + " audio_y = np.repeat(audio_y, num_of_times_to_repeat)\n", + " audio_y = audio_y[:88200]\n", + " \n", + " D = librosa.stft(audio_y, center=False, n_fft=1024, hop_length=256)\n", + " \n", + " #D = (librosa.stft(audio_y, center=False, n_fft=2048, hop_length=561, win_length=1654, window=scipy.signal.windows.blackmanharris))\n", + " \n", + " #print(D.shape)\n", + " #print(D)\n", + " \n", + " real = np.abs(D)\n", + " imag = np.imag(D)\n", + " \n", + " D_true = np.stack([real, imag], -1)\n", + " #print(D_true)\n", + " #print(D_true.shape)\n", + "\n", + " \n", + "\n", + " \n", + " except Exception as e:\n", + " print(\"Error encountered while parsing file: \", file)\n", + " raise \"error\"\n", + " return None\n", + " \n", + " return D_true #return (audio_y) # return D_true" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1: Processing /media/s/audio/UrbanSound8K/audio/fold5/100032-3-0-0_augmented.wav\n", + "incomplete file\n", + "2: Processing /media/s/audio/UrbanSound8K/audio/fold5/100263-2-0-117_augmented.wav\n", + "3: Processing /media/s/audio/UrbanSound8K/audio/fold5/100263-2-0-121_augmented.wav\n", + "4: Processing /media/s/audio/UrbanSound8K/audio/fold5/100263-2-0-126_augmented.wav\n", + "5: Processing /media/s/audio/UrbanSound8K/audio/fold5/100263-2-0-137_augmented.wav\n", + "6: Processing 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"Finished feature extraction from 8732 files\n" + ] + } + ], + "source": [ + "metadata = pd.read_csv('./UrbanSound8K/metadata/UrbanSound8K.csv')\n", + "features = []\n", + "labels = []\n", + "i = 0\n", + "\n", + "# Iterate through each sound file and extract the features \n", + "for index, row in metadata.iterrows():\n", + " \n", + " i = i +1\n", + " original_file_name = os.path.join(os.path.abspath('UrbanSound8K/audio/'),'fold'+str(row[\"fold\"])+'/',str(row[\"slice_file_name\"]))\n", + " \n", + " file_name = original_file_name.replace(\".wav\", \"_augmented.wav\")\n", + " \n", + " #class_label = row[\"class\"]\n", + " print(f'{i}: Processing {file_name}')\n", + " data = extract_features(file_name)\n", + " \n", + " pickle.dump(data, open(original_file_name.replace(\".wav\", \"_x_augmented.pkl\"), \"wb\"))\n", + " #pickle.dump(encodings[class_label], open(file_name.replace(\".wav\", \"_y2.pkl\"), \"wb\"))\n", + "\n", + "# Convert into a Panda dataframe \n", + "#featuresdf = pd.DataFrame(features, columns=['feature','class_label'])\n", + "#np.save('features.npy', features)\n", + "\n", + "print('Finished feature extraction from ', i, ' files')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1: Processing /media/s/audio/UrbanSound8K/audio/fold5/100032-3-0-0.wav\n", + "2: Processing /media/s/audio/UrbanSound8K/audio/fold5/100263-2-0-117.wav\n", + "3: Processing /media/s/audio/UrbanSound8K/audio/fold5/100263-2-0-121.wav\n", + "4: Processing /media/s/audio/UrbanSound8K/audio/fold5/100263-2-0-126.wav\n", + "5: Processing /media/s/audio/UrbanSound8K/audio/fold5/100263-2-0-137.wav\n", + "6: Processing /media/s/audio/UrbanSound8K/audio/fold5/100263-2-0-143.wav\n", + "7: Processing 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/media/s/audio/UrbanSound8K/audio/fold7/99812-1-1-0.wav\n", + "8728: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-2-0.wav\n", + "8729: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-3-0.wav\n", + "8730: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-4-0.wav\n", + "8731: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-5-0.wav\n", + "8732: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-6-0.wav\n", + "Finished feature extraction from 8732 files\n" + ] + } + ], + "source": [ + "## DATA AUGMENTATION\n", + "\n", + "import nlpaug\n", + "import nlpaug.augmenter.audio as naa\n", + "from nlpaug.util.audio.visualizer import AudioVisualizer\n", + "import librosa.display as librosa_display\n", + "\n", + "def augment_data(file_name):\n", + " audio_y, sample_rate = librosa.load(file_name, sr=None, res_type=\"kaiser_best\")\n", + " \n", + " aug = naa.PitchAug(sampling_rate=sample_rate, zone=(0,1), coverage=1.0)\n", + " augmented_sample = aug.augment(audio_y)\n", + " \n", + " return augmented_sample, sample_rate\n", + "\n", + "metadata = pd.read_csv('./UrbanSound8K/metadata/UrbanSound8K.csv')\n", + "features = []\n", + "labels = []\n", + "i = 0\n", + "\n", + "# Iterate through each sound file and extract the features \n", + "for index, row in metadata.iterrows():\n", + " \n", + " i = i +1\n", + " file_name = os.path.join(os.path.abspath('UrbanSound8K/audio/'),'fold'+str(row[\"fold\"])+'/',str(row[\"slice_file_name\"]))\n", + " \n", + " print(f'{i}: Processing {file_name}')\n", + " data, sample_rate = augment_data(file_name)\n", + " \n", + " sf.write(file_name.replace(\".wav\", \"_augmented_pitch_1.wav\"), data, sample_rate)\n", + " \n", + "print('Finished feature extraction from ', i, ' files')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1: Processing 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"44100\n", + "incomplete file\n", + "8635: Processing /media/s/audio/UrbanSound8K/audio/fold1/98223-7-8-0_augmented_pitch_1.wav\n", + "44100\n", + "incomplete file\n", + "8636: Processing /media/s/audio/UrbanSound8K/audio/fold1/98223-7-9-0_augmented_pitch_1.wav\n", + "44100\n", + "incomplete file\n", + "8637: Processing /media/s/audio/UrbanSound8K/audio/fold6/98263-9-0-1_augmented_pitch_1.wav\n", + "44100\n", + "8638: Processing /media/s/audio/UrbanSound8K/audio/fold6/98263-9-0-10_augmented_pitch_1.wav\n", + "44100\n", + "8639: Processing /media/s/audio/UrbanSound8K/audio/fold6/98263-9-0-22_augmented_pitch_1.wav\n", + "44100\n", + "8640: Processing /media/s/audio/UrbanSound8K/audio/fold6/98263-9-0-24_augmented_pitch_1.wav\n", + "44100\n", + "8641: Processing /media/s/audio/UrbanSound8K/audio/fold6/98263-9-0-33_augmented_pitch_1.wav\n", + "44100\n", + "8642: Processing /media/s/audio/UrbanSound8K/audio/fold6/98263-9-0-35_augmented_pitch_1.wav\n", + "44100\n", + "8643: Processing /media/s/audio/UrbanSound8K/audio/fold7/98525-8-0-0_augmented_pitch_1.wav\n", + "44100\n", + "8644: Processing /media/s/audio/UrbanSound8K/audio/fold8/98536-8-0-0_augmented_pitch_1.wav\n", + "44100\n", + "incomplete file\n", + "8645: Processing /media/s/audio/UrbanSound8K/audio/fold6/98680-9-0-11_augmented_pitch_1.wav\n", + "44100\n", + "8646: Processing /media/s/audio/UrbanSound8K/audio/fold6/98680-9-0-14_augmented_pitch_1.wav\n", + "44100\n", + "8647: Processing /media/s/audio/UrbanSound8K/audio/fold6/98680-9-0-2_augmented_pitch_1.wav\n", + "44100\n", + "8648: Processing /media/s/audio/UrbanSound8K/audio/fold6/98680-9-0-3_augmented_pitch_1.wav\n", + "44100\n", + "8649: Processing /media/s/audio/UrbanSound8K/audio/fold6/98680-9-0-5_augmented_pitch_1.wav\n", + "44100\n", + "8650: Processing /media/s/audio/UrbanSound8K/audio/fold3/98681-9-0-0_augmented_pitch_1.wav\n", + "44100\n", + "8651: Processing /media/s/audio/UrbanSound8K/audio/fold3/98681-9-0-11_augmented_pitch_1.wav\n", + "44100\n", + "8652: Processing /media/s/audio/UrbanSound8K/audio/fold3/98681-9-0-12_augmented_pitch_1.wav\n", + "44100\n", + "8653: Processing /media/s/audio/UrbanSound8K/audio/fold3/98681-9-0-5_augmented_pitch_1.wav\n", + "44100\n", + "8654: Processing /media/s/audio/UrbanSound8K/audio/fold3/98681-9-0-6_augmented_pitch_1.wav\n", + "44100\n", + "8655: Processing /media/s/audio/UrbanSound8K/audio/fold3/98681-9-0-7_augmented_pitch_1.wav\n", + "44100\n", + "8656: Processing /media/s/audio/UrbanSound8K/audio/fold9/98859-7-0-0_augmented_pitch_1.wav\n", + "44100\n", + "incomplete file\n", + "8657: Processing /media/s/audio/UrbanSound8K/audio/fold9/98859-7-1-0_augmented_pitch_1.wav\n", + "44100\n", + "incomplete file\n", + "8658: Processing /media/s/audio/UrbanSound8K/audio/fold9/98859-7-2-0_augmented_pitch_1.wav\n", + "44100\n", + "8659: Processing /media/s/audio/UrbanSound8K/audio/fold9/98859-7-2-1_augmented_pitch_1.wav\n", + "44100\n", + "8660: Processing /media/s/audio/UrbanSound8K/audio/fold9/98859-7-2-2_augmented_pitch_1.wav\n", + "44100\n", + "8661: Processing /media/s/audio/UrbanSound8K/audio/fold9/98859-7-2-3_augmented_pitch_1.wav\n", + "44100\n", + "8662: Processing /media/s/audio/UrbanSound8K/audio/fold9/98859-7-2-4_augmented_pitch_1.wav\n", + "44100\n", + "8663: Processing /media/s/audio/UrbanSound8K/audio/fold9/98859-7-2-5_augmented_pitch_1.wav\n", + "44100\n", + "8664: Processing /media/s/audio/UrbanSound8K/audio/fold4/99157-9-0-10_augmented_pitch_1.wav\n", + "44100\n", + "8665: Processing /media/s/audio/UrbanSound8K/audio/fold4/99157-9-0-15_augmented_pitch_1.wav\n", + "44100\n", + "8666: Processing /media/s/audio/UrbanSound8K/audio/fold4/99157-9-0-16_augmented_pitch_1.wav\n", + "44100\n", + "8667: Processing /media/s/audio/UrbanSound8K/audio/fold4/99157-9-0-22_augmented_pitch_1.wav\n", + "44100\n", + "8668: Processing /media/s/audio/UrbanSound8K/audio/fold4/99157-9-0-24_augmented_pitch_1.wav\n", + "44100\n", + "8669: 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Processing /media/s/audio/UrbanSound8K/audio/fold1/99180-9-0-36_augmented_pitch_1.wav\n", + "8000\n", + "8679: Processing /media/s/audio/UrbanSound8K/audio/fold1/99180-9-0-48_augmented_pitch_1.wav\n", + "8000\n", + "8680: Processing /media/s/audio/UrbanSound8K/audio/fold1/99180-9-0-49_augmented_pitch_1.wav\n", + "8000\n", + "8681: Processing /media/s/audio/UrbanSound8K/audio/fold1/99180-9-0-7_augmented_pitch_1.wav\n", + "8000\n", + "8682: Processing /media/s/audio/UrbanSound8K/audio/fold5/99185-3-0-0_augmented_pitch_1.wav\n", + "44100\n", + "incomplete file\n", + "8683: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-0_augmented_pitch_1.wav\n", + "44100\n", + "8684: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-1_augmented_pitch_1.wav\n", + "44100\n", + "8685: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-12_augmented_pitch_1.wav\n", + "44100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8686: Processing 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"8695: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-30_augmented_pitch_1.wav\n", + "44100\n", + "8696: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-32_augmented_pitch_1.wav\n", + "44100\n", + "8697: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-33_augmented_pitch_1.wav\n", + "44100\n", + "8698: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-38_augmented_pitch_1.wav\n", + "44100\n", + "8699: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-40_augmented_pitch_1.wav\n", + "44100\n", + "8700: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-41_augmented_pitch_1.wav\n", + "44100\n", + "8701: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-43_augmented_pitch_1.wav\n", + "44100\n", + "8702: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-47_augmented_pitch_1.wav\n", + "44100\n", + "8703: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-48_augmented_pitch_1.wav\n", + "44100\n", + "8704: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-49_augmented_pitch_1.wav\n", + "44100\n", + "8705: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-52_augmented_pitch_1.wav\n", + "44100\n", + "8706: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-53_augmented_pitch_1.wav\n", + "44100\n", + "8707: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-54_augmented_pitch_1.wav\n", + "44100\n", + "8708: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-55_augmented_pitch_1.wav\n", + "44100\n", + "8709: Processing /media/s/audio/UrbanSound8K/audio/fold10/99192-4-0-7_augmented_pitch_1.wav\n", + "44100\n", + "8710: Processing /media/s/audio/UrbanSound8K/audio/fold9/99500-2-0-1_augmented_pitch_1.wav\n", + "44100\n", + "8711: Processing /media/s/audio/UrbanSound8K/audio/fold9/99500-2-0-11_augmented_pitch_1.wav\n", + "44100\n", + "8712: Processing /media/s/audio/UrbanSound8K/audio/fold9/99500-2-0-2_augmented_pitch_1.wav\n", + "44100\n", + "8713: Processing /media/s/audio/UrbanSound8K/audio/fold9/99500-2-0-23_augmented_pitch_1.wav\n", + "44100\n", + "8714: Processing /media/s/audio/UrbanSound8K/audio/fold9/99500-2-0-29_augmented_pitch_1.wav\n", + "44100\n", + "8715: Processing /media/s/audio/UrbanSound8K/audio/fold9/99500-2-0-39_augmented_pitch_1.wav\n", + "44100\n", + "8716: Processing /media/s/audio/UrbanSound8K/audio/fold9/99500-2-0-41_augmented_pitch_1.wav\n", + "44100\n", + "8717: Processing /media/s/audio/UrbanSound8K/audio/fold9/99500-2-0-50_augmented_pitch_1.wav\n", + "44100\n", + "8718: Processing /media/s/audio/UrbanSound8K/audio/fold6/99710-9-0-11_augmented_pitch_1.wav\n", + "44100\n", + "8719: Processing /media/s/audio/UrbanSound8K/audio/fold6/99710-9-0-12_augmented_pitch_1.wav\n", + "44100\n", + "8720: Processing /media/s/audio/UrbanSound8K/audio/fold6/99710-9-0-16_augmented_pitch_1.wav\n", + "44100\n", + "8721: Processing /media/s/audio/UrbanSound8K/audio/fold6/99710-9-0-2_augmented_pitch_1.wav\n", + "44100\n", + "8722: Processing /media/s/audio/UrbanSound8K/audio/fold6/99710-9-0-5_augmented_pitch_1.wav\n", + "44100\n", + "8723: Processing /media/s/audio/UrbanSound8K/audio/fold6/99710-9-0-6_augmented_pitch_1.wav\n", + "44100\n", + "8724: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-0-0_augmented_pitch_1.wav\n", + "44100\n", + "8725: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-0-1_augmented_pitch_1.wav\n", + "44100\n", + "8726: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-0-2_augmented_pitch_1.wav\n", + "44100\n", + "8727: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-1-0_augmented_pitch_1.wav\n", + "44100\n", + "8728: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-2-0_augmented_pitch_1.wav\n", + "44100\n", + "8729: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-3-0_augmented_pitch_1.wav\n", + "44100\n", + "incomplete file\n", + "8730: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-4-0_augmented_pitch_1.wav\n", + "44100\n", + "incomplete file\n", + "8731: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-5-0_augmented_pitch_1.wav\n", + "44100\n", + "incomplete file\n", + "8732: Processing /media/s/audio/UrbanSound8K/audio/fold7/99812-1-6-0_augmented_pitch_1.wav\n", + "44100\n", + "incomplete file\n", + "Finished feature extraction from 8732 files\n" + ] + } + ], + "source": [ + "## SPECTOGRAMS\n", + "\n", + "def extract_features(file_name):\n", + " try:\n", + " audio_y, sample_rate = librosa.load(file_name, sr=None, res_type='kaiser_best', duration=4.0)\n", + " print(sample_rate)\n", + " audio_y = librosa.resample(audio_y, sample_rate, 44100)\n", + " sample_rate = 44100\n", + " if len(audio_y) != (sample_rate * 4):\n", + " print(\"incomplete file\")\n", + " num_of_times_to_repeat = math.ceil((sample_rate*4) / len(audio_y))\n", + " audio_y = np.repeat(audio_y, num_of_times_to_repeat)\n", + " audio_y = audio_y[:(sample_rate*4)]\n", + " \n", + " norm_sample = (audio_y - audio_y.mean()) / audio_y.std()\n", + " D = librosa.feature.melspectrogram(y=norm_sample, sr=sample_rate, win_length=2205, n_fft=4096, hop_length=529, n_mels=224)\n", + " \n", + " except Exception as e:\n", + " print(\"Error encountered while parsing file: \", file)\n", + " raise \"error\"\n", + " return None\n", + " \n", + " return D\n", + "\n", + "metadata = pd.read_csv('./UrbanSound8K/metadata/UrbanSound8K.csv')\n", + "features = []\n", + "labels = []\n", + "i = 0\n", + "\n", + "# Iterate through each sound file and extract the features \n", + "for index, row in metadata.iterrows():\n", + " \n", + " i = i +1\n", + " original_file_name = os.path.join(os.path.abspath('UrbanSound8K/audio/'),'fold'+str(row[\"fold\"])+'/',str(row[\"slice_file_name\"]))\n", + " file_name = original_file_name.replace(\".wav\", \"_augmented_pitch_1.wav\")\n", + " \n", + " #class_label = row[\"class\"]\n", + " print(f'{i}: Processing {file_name}')\n", + " data = extract_features(file_name)\n", + " \n", + " pickle.dump(data, open(file_name.replace(\".wav\", \"_spec.pkl\"), \"wb\"))\n", + " #pickle.dump(encodings[class_label], open(file_name.replace(\".wav\", \"_y2.pkl\"), \"wb\"))\n", + "\n", + "# Convert into a Panda dataframe \n", + "#featuresdf = pd.DataFrame(features, columns=['feature','class_label'])\n", + "#np.save('features.npy', features)\n", + "\n", + "print('Finished feature extraction from ', i, ' files')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'librosa.feature' has no attribute 'spectogram'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0maudio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/media/s/audio/UrbanSound8K/audio/fold1/101415-3-0-2.wav\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"kaiser_best\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspectogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maudio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_fft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2048\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_mels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhop_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m512\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspecshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'librosa.feature' has no attribute 'spectogram'" + ] + } + ], + "source": [ + "#audio_y, sample_rate = librosa.load(file_name, sr=22050, res_type='kaiser_best') \n", + "#D = (librosa.stft(audio_y, center=False, n_fft=2048, hop_length=512))\n", + "\n", + "audio, sample_rate = librosa.load(\"/media/s/audio/UrbanSound8K/audio/fold1/101415-3-0-2.wav\", res_type=\"kaiser_best\")\n", + "features = librosa.feature.spectogram(y=audio, sr=sample_rate, n_fft=2048, n_mels=256, hop_length=512)\n", + "librosa.display.specshow(features)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1025, 154, 2)\n" + ] + } + ], + "source": [ + "audio_y, sample_rate = librosa.load(\"/media/s/audio/UrbanSound8K/audio/fold1/101415-3-0-2.wav\", res_type=\"kaiser_best\")\n", + "#features = librosa.feature.spectogram(y=audio, sr=sample_rate, n_fft=2048, n_mels=256, hop_length=512)\n", + "features = librosa.stft(audio_y, center=False, n_fft=2048, hop_length=561, win_length=1654, window=scipy.signal.windows.blackmanharris)\n", + "features = np.stack((np.real(features), np.imag(features)), axis=-1)\n", + "print(features.shape)\n" + ] + }, + { + "cell_type": "code", + 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4.86049433e-02-4.69116678e-02j, 1.82184569e-02-2.02613421e-02j])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.fft.fft(audio_y, n=512, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-9.3449494e-03+0.0000000e+00j -5.8168271e-03+0.0000000e+00j\n", + " -2.1297217e-03+0.0000000e+00j ... -1.3660905e-02+0.0000000e+00j\n", + " -5.9770374e-03+0.0000000e+00j -2.9811289e-03+0.0000000e+00j]\n", + " [ 1.5432460e-03-5.4553631e-03j 9.0501952e-04+5.7070479e-03j\n", + " 5.7756123e-03-3.2197859e-03j ... 1.0396372e-02-1.5995786e-02j\n", + " -1.3774113e-03+2.0152189e-02j -9.0979924e-03-1.9049913e-02j]\n", + " [ 6.3618901e-03+1.3491958e-02j 3.7222607e-03-2.9931288e-02j\n", + " -2.0999718e-02+1.2647369e-02j ... -7.4456960e-02+6.0672585e-02j\n", + " 3.7625149e-02+1.9657088e-02j 5.0327677e-02-2.4916351e-02j]\n", + " ...\n", + " [ 5.7690858e-07-2.0043456e-06j -1.5508929e-06+1.8804542e-06j\n", + " 1.5686029e-06-1.6318737e-06j ... 2.5001557e-06-2.2013041e-06j\n", + " -3.9043431e-07-4.2368580e-07j -6.2705311e-07+1.0240427e-06j]\n", + " [-1.4091314e-06+1.3662200e-06j 7.0630634e-08-1.1051810e-06j\n", + " -1.7423193e-06+1.2347663e-06j ... -2.9198147e-06+5.5046905e-07j\n", + " 8.5834648e-07+9.2347665e-07j -1.6133598e-06-1.6121213e-06j]\n", + " [ 1.4366241e-06+0.0000000e+00j 1.2991345e-06+0.0000000e+00j\n", + " 1.0617053e-06+0.0000000e+00j ... 2.5096522e-06+0.0000000e+00j\n", + " 1.1837584e-06+0.0000000e+00j 6.4718893e-07+0.0000000e+00j]]\n", + "[[[-9.3449494e-03 0.0000000e+00]\n", + " [-5.8168271e-03 0.0000000e+00]\n", + " [-2.1297217e-03 0.0000000e+00]\n", + " ...\n", + " [-1.3660905e-02 0.0000000e+00]\n", + " [-5.9770374e-03 0.0000000e+00]\n", + " [-2.9811289e-03 0.0000000e+00]]\n", + "\n", + " [[ 1.5432460e-03 -5.4553631e-03]\n", + " [ 9.0501952e-04 5.7070479e-03]\n", + " [ 5.7756123e-03 -3.2197859e-03]\n", + " ...\n", + " [ 1.0396372e-02 -1.5995786e-02]\n", + " [-1.3774113e-03 2.0152189e-02]\n", + " [-9.0979924e-03 -1.9049913e-02]]\n", + "\n", + " [[ 6.3618901e-03 1.3491958e-02]\n", + " [ 3.7222607e-03 -2.9931288e-02]\n", + " [-2.0999718e-02 1.2647369e-02]\n", + " ...\n", + " [-7.4456960e-02 6.0672585e-02]\n", + " [ 3.7625149e-02 1.9657088e-02]\n", + " [ 5.0327677e-02 -2.4916351e-02]]\n", + "\n", + " ...\n", + "\n", + " [[ 5.7690858e-07 -2.0043456e-06]\n", + " [-1.5508929e-06 1.8804542e-06]\n", + " [ 1.5686029e-06 -1.6318737e-06]\n", + " ...\n", + " [ 2.5001557e-06 -2.2013041e-06]\n", + " [-3.9043431e-07 -4.2368580e-07]\n", + " [-6.2705311e-07 1.0240427e-06]]\n", + "\n", + " [[-1.4091314e-06 1.3662200e-06]\n", + " [ 7.0630634e-08 -1.1051810e-06]\n", + " [-1.7423193e-06 1.2347663e-06]\n", + " ...\n", + " [-2.9198147e-06 5.5046905e-07]\n", + " [ 8.5834648e-07 9.2347665e-07]\n", + " [-1.6133598e-06 -1.6121213e-06]]\n", + "\n", + " [[ 1.4366241e-06 0.0000000e+00]\n", + " [ 1.2991345e-06 0.0000000e+00]\n", + " [ 1.0617053e-06 0.0000000e+00]\n", + " ...\n", + " [ 2.5096522e-06 0.0000000e+00]\n", + " [ 1.1837584e-06 0.0000000e+00]\n", + " [ 6.4718893e-07 0.0000000e+00]]]\n" + ] + } + ], + "source": [ + "audio_y, sample_rate = librosa.load(\"/media/s/audio/UrbanSound8K/audio/fold1/101415-3-0-2.wav\", sr=22050, res_type=\"kaiser_best\")\n", + "\n", + "D = librosa.stft(audio_y, center=False, n_fft=1024, hop_length=256)\n", + "\n", + "print(D)\n", + "\n", + "D = pickle.load(open(\"/media/s/audio/UrbanSound8K/audio/fold1/101415-3-0-2_x2.pkl\", \"rb\"))\n", + "\n", + "print(D)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.02124286, 0.00054349, -0.00075629, ..., 0.01596838,\n", + " -0.01257505, -0.00900543], dtype=float32)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#!{sys.executable} -m pip install audiomentations\n", + "\n", + "from audiomentations import Compose, AddGaussianNoise, TimeStretch, PitchShift, Shift\n", + "\n", + "augment = Compose([\n", + " AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.015, p=0.5),\n", + " TimeStretch(min_rate=0.8, max_rate=1.25, p=0.5),\n", + " PitchShift(min_semitones=-4, max_semitones=4, p=0.5),\n", + " Shift(min_fraction=-0.5, max_fraction=0.5, p=0.5),\n", + "])\n", + "\n", + "augmented_samples = augment(samples=audio_y, sample_rate=22050)\n", + "\n", + "augmented_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[-9.3449494e-03, 0.0000000e+00],\n", + " [-5.8168271e-03, 0.0000000e+00],\n", + " [-2.1297217e-03, 0.0000000e+00],\n", + " ...,\n", + " [-1.3660905e-02, 0.0000000e+00],\n", + " [-5.9770374e-03, 0.0000000e+00],\n", + " [-2.9811289e-03, 0.0000000e+00]],\n", + "\n", + " [[ 1.5432460e-03, -5.4553631e-03],\n", + " [ 9.0501952e-04, 5.7070479e-03],\n", + " [ 5.7756123e-03, -3.2197859e-03],\n", + " ...,\n", + " [ 1.0396372e-02, -1.5995786e-02],\n", + " [-1.3774113e-03, 2.0152189e-02],\n", + " [-9.0979924e-03, -1.9049913e-02]],\n", + "\n", + " [[ 6.3618901e-03, 1.3491958e-02],\n", + " [ 3.7222607e-03, -2.9931288e-02],\n", + " [-2.0999718e-02, 1.2647369e-02],\n", + " ...,\n", + " [-7.4456960e-02, 6.0672585e-02],\n", + " [ 3.7625149e-02, 1.9657088e-02],\n", + " [ 5.0327677e-02, -2.4916351e-02]],\n", + "\n", + " ...,\n", + "\n", + " [[ 5.7690858e-07, -2.0043456e-06],\n", + " [-1.5508929e-06, 1.8804542e-06],\n", + " [ 1.5686029e-06, -1.6318737e-06],\n", + " ...,\n", + " [ 2.5001557e-06, -2.2013041e-06],\n", + " [-3.9043431e-07, -4.2368580e-07],\n", + " [-6.2705311e-07, 1.0240427e-06]],\n", + "\n", + " [[-1.4091314e-06, 1.3662200e-06],\n", + " [ 7.0630634e-08, -1.1051810e-06],\n", + " [-1.7423193e-06, 1.2347663e-06],\n", + " ...,\n", + " [-2.9198147e-06, 5.5046905e-07],\n", + " [ 8.5834648e-07, 9.2347665e-07],\n", + " [-1.6133598e-06, -1.6121213e-06]],\n", + "\n", + " [[ 1.4366241e-06, 0.0000000e+00],\n", + " [ 1.2991345e-06, 0.0000000e+00],\n", + " [ 1.0617053e-06, 0.0000000e+00],\n", + " ...,\n", + " [ 2.5096522e-06, 0.0000000e+00],\n", + " [ 1.1837584e-06, 0.0000000e+00],\n", + " [ 6.4718893e-07, 0.0000000e+00]]], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(pickle.load(open(\"/media/s/audio/UrbanSound8K/audio/fold1/101415-3-0-2_x2.pkl\", \"rb\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "rate must be specified when data is a numpy array or list of audio samples.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mipd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mipd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAudio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maudio_y\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# load a local WAV file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/.anaconda/envs/py37/lib/python3.7/site-packages/IPython/lib/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, filename, url, embed, rate, autoplay, normalize, element_id)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rate must be specified when data is a numpy array or list of audio samples.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 115\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAudio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_wav\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: rate must be specified when data is a numpy array or list of audio samples." + ] + } + ], + "source": [ + "import IPython.display as ipd\n", + "ipd.Audio('/media/s/audio/UrbanSound8K/audio/fold1/101415-3-0-2.wav') # load a local WAV file" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(224, 334)" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(pickle.load(open('/media/s/audio/UrbanSound8K/audio/fold7/99812-1-2-0_spec.pkl', 'rb'))).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Audio\n", + "\n", + "Audio(np.sin(augmented_samples), rate=20000)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "incomplete file\n", + "0.9999695\n", + "0.9999695\n", + "176400\n", + "0.9999695\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "audio_y, sample_rate = librosa.load('/media/s/audio/UrbanSound8K/audio/fold4/38231-3-0-0.wav', sr=44100, res_type='kaiser_best', duration=4.0)\n", + "\n", + "if len(audio_y) != (sample_rate * 4):\n", + " print(\"incomplete file\")\n", + " num_of_times_to_repeat = math.ceil((sample_rate*4) / len(audio_y))\n", + "\n", + " audio_y = np.repeat(audio_y, num_of_times_to_repeat)\n", + "\n", + " audio_y = audio_y[:(sample_rate*4)]\n", + "\n", + "norm_sample = (audio_y - audio_y.mean()) / audio_y.std()\n", + "spec = librosa.feature.melspectrogram(y=norm_sample, sr=44100, win_length=2205, n_fft=4096, hop_length=529, n_mels=224)\n", + "AudioVisualizer.spectrogram('Combine Frequency Masking and Time Masking', spec)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1139885\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "audio_y, sample_rate = librosa.load('/media/s/audio/UrbanSound8K/audio/fold4/38231-3-0-0.wav', sr=44100, res_type='kaiser_best', duration=4.0)\n", + "spec = librosa.feature.melspectrogram(y=audio_y)\n", + "AudioVisualizer.spectrogram('Combine Frequency Masking and Time Masking', spec)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "audio_y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/preprocessing/extract_fft_data.ipynb b/preprocessing/extract_fft_data.ipynb index 77a81dd..424d607 100755 --- a/preprocessing/extract_fft_data.ipynb +++ b/preprocessing/extract_fft_data.ipynb @@ -40333,7 +40333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.8.6" } }, "nbformat": 4, diff --git a/training/Attempt_7_22khz_ResNet.ipynb b/training/Attempt_7_22khz_ResNet.ipynb new file mode 100644 index 0000000..da5683a --- /dev/null +++ b/training/Attempt_7_22khz_ResNet.ipynb @@ -0,0 +1,790 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Attempt 7 22khz ResNet.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "xKwIVHsxsx__", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c3fb7561-8e66-42b0-d9dd-99ef9ad66d06" + }, + "source": [ + "pip install keras_resnet" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting keras_resnet\n", + " Downloading https://files.pythonhosted.org/packages/76/d4/a35cbd07381139dda4db42c81b88c59254faac026109022727b45b31bcad/keras-resnet-0.2.0.tar.gz\n", + "Requirement already satisfied: keras>=2.2.4 in /usr/local/lib/python3.6/dist-packages (from keras_resnet) (2.4.3)\n", + "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras_resnet) (3.13)\n", + "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras_resnet) (1.18.5)\n", + "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras_resnet) (2.10.0)\n", + "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras_resnet) (1.4.1)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py->keras>=2.2.4->keras_resnet) (1.15.0)\n", + "Building wheels for collected packages: keras-resnet\n", + " Building wheel for keras-resnet (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for keras-resnet: filename=keras_resnet-0.2.0-py2.py3-none-any.whl size=20487 sha256=f8c8b72cd1cd2410e8dc93242bc3257fefd6ce782d2c565bc7cd6c324b06b629\n", + " Stored in directory: /root/.cache/pip/wheels/5f/09/a5/497a30fd9ad9964e98a1254d1e164bcd1b8a5eda36197ecb3c\n", + "Successfully built keras-resnet\n", + "Installing collected packages: keras-resnet\n", + "Successfully installed keras-resnet-0.2.0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sPN9q9THtAx5" + }, + "source": [ + "from tensorflow.keras.layers import Dense, Dropout\n", + "from tensorflow.keras import Input, Model\n", + "import tensorflow.keras as keras\n", + "import tensorflow as tf\n", + "from keras.models import Model, Sequential, load_model\n", + "from tensorflow.keras.callbacks import History, ModelCheckpoint, TensorBoard\n", + "import numpy as np\n", + "import keras_resnet.blocks\n", + "#import tcn\n", + "#from tcn import TCN, tcn_full_summary\n", + "import pickle\n", + "from urllib.request import urlopen\n", + "import pandas as pd\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from keras.utils import to_categorical\n", + "#from tcn import compiled_tcn\n", + "import os\n", + "import keras_resnet.models" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vGERASomu-I8", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fa0fcb45-2d16-42ae-f697-430a9f8f493c" + }, + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mounted at /content/drive\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "phcY5PRevHqi" + }, + "source": [ + "#with open('/content/drive/My Drive/wavenet-test/features.pickle', 'rb') as handle:\n", + "# features = pickle.load(handle)\n", + " \n", + "#with open('/content/drive/My Drive/wavenet-test/labels.pickle', 'rb') as handle:\n", + "# labels = pickle.load(handle)\n", + "\n", + "#!cp '/content/drive/My Drive/wavenet-test/US8K.tar' ." + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "gBguM79zyEFq", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "003bc8ac-e9fb-4ee9-effc-b8c105ce7484" + }, + "source": [ + "!nvidia-smi" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Wed Dec 2 20:32:14 2020 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 455.38 Driver Version: 418.67 CUDA Version: 10.1 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 34C P0 26W / 250W | 0MiB / 16280MiB | 0% Default |\n", + "| | | ERR! |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "| No running processes found |\n", + "+-----------------------------------------------------------------------------+\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CqI7BGbPYWIE" + }, + "source": [ + "## Utils\n", + "\n", + "def create_new_model():\n", + "\n", + " shape, classes = (341, 513, 2), 10\n", + "\n", + " x = keras.layers.Input(shape)\n", + " blocks = [2, 2, 2, 2]\n", + "\n", + " block = keras_resnet.blocks.basic_2d\n", + "\n", + " model = keras_resnet.models.ResNet2D(x, blocks=blocks, block=block, classes=classes)\n", + "\n", + " model.compile(\"adam\", \"categorical_crossentropy\", [\"accuracy\"])\n", + "\n", + " return model\n", + "\n", + "\n", + "\n", + "\n", + "def load_model(depth, filters, kernel_size, save_dir=\"./\"):\n", + " model = create_new_model(depth, filters, kernel_size)\n", + " saved_model = save_dir + \"saved_wavenet_clasifier.h5\"\n", + " # model.load here\n", + " if os.path.isfile(saved_model):\n", + " model.load_weights(saved_model)\n", + " else:\n", + " print(\"NO MODEL ON DISK, USING BRAND NEW ONE\")\n", + " return model\n", + "\n", + "def create_callbacks(save_dir=\"./\"):\n", + " saved_model = save_dir + \"saved_wavenet_clasifier.h5\"\n", + " saved_hist = save_dir + 'wavenet_classifier_training_history.csv'\n", + " checkpointer = ModelCheckpoint(filepath=saved_model, monitor='val_categorical_accuracy', verbose=1, save_best_only=True)\n", + " #tensorboard = TensorBoard(log_dir=save_dir)\n", + " history_cb = tf.keras.callbacks.CSVLogger(saved_hist, separator=\",\", append=True)\n", + "\n", + " return [checkpointer, history_cb] # add tensorboard if needed" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QhEhWKmnZQ7O", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "45255bf9-e918-4d35-fec2-1e505d62c2e0" + }, + "source": [ + "all_X = []\n", + "all_Y = []\n", + "fulldatasetpath = './'\n", + "metadata = pd.read_csv('http://76.213.149.126:8081/metadata/UrbanSound8K.csv')\n", + "print(metadata)\n", + "for index, row in metadata.iterrows():\n", + " \n", + " file_name = 'fold'+str(row[\"fold\"])+'/' + str(row[\"slice_file_name\"])\n", + " all_X.append(file_name.replace(\".wav\", \"_x2.pkl\"))\n", + " all_Y.append(file_name.replace(\".wav\", \"_y2.pkl\"))\n", + "\n", + "import requests\n", + "from multiprocessing.pool import ThreadPool\n", + " \n", + "def download_url(url):\n", + " #print(\"downloading: \",url)\n", + " # assumes that the last segment after the / represents the file name\n", + " # if url is abc/xyz/file.txt, the file name will be file.txt\n", + " path = url.split(\"/\")\n", + " path = path[-2] + \"/\" + path[-1]\n", + " \n", + " r = requests.get(url, stream=True)\n", + " if r.status_code == requests.codes.ok:\n", + " if not os.path.exists(os.path.dirname(path)):\n", + " try:\n", + " os.makedirs(os.path.dirname(path))\n", + " except OSError as exc: # Guard against race condition\n", + " if exc.errno != errno.EEXIST:\n", + " raise\n", + " with open(path, 'wb') as f:\n", + " for data in r:\n", + " f.write(data)\n", + " return url\n", + "\n", + "def set_shapes(x, y):\n", + " y.set_shape([4, 10])\n", + " x.set_shape([4, 341, 513, 2])\n", + " return x, y\n", + "\n", + "#from sklearn.model_selection import train_test_split \n", + "#x_train, x_test, y_train, y_test = train_test_split(all_X, all_Y, test_size=0.2, random_state = 42)\n", + "\n", + "x_train = []\n", + "x_test = []\n", + "y_train = []\n", + "y_test = []\n", + "\n", + "for x in all_X:\n", + " if x[0:5] != \"fold5\":\n", + " x_train.append(x)\n", + " x_train.append(x.replace(\"_x2\", \"_x_augmented\"))\n", + " else:\n", + " x_test.append(x)\n", + "\n", + "\n", + "for y in all_Y:\n", + " if y[0:5] != \"fold5\":\n", + " y_train.append(y)\n", + " y_train.append(y)\n", + " else:\n", + " y_test.append(y)\n", + " \n", + "\n", + "def file_to_arr(t: tf.Tensor):\n", + " x = []\n", + " y = []\n", + "\n", + " urls = []\n", + "\n", + " for a in t:\n", + " a = np.array(a)\n", + " # check whether we have them\n", + " # if not, schedule download\n", + " if not os.path.exists(a[0]):\n", + " urls.append(\"http://76.213.149.126:8081/audio/\" + a[0].decode('UTF-8'))\n", + " if not os.path.exists(a[1]):\n", + " urls.append(\"http://76.213.149.126:8081/audio/\" + a[1].decode('UTF-8'))\n", + " \n", + " if (len(urls) > 0):\n", + " ThreadPool(4).map(download_url, urls)\n", + "\n", + " for a in t:\n", + " a = np.array(a)\n", + " #print(a[0])\n", + " try:\n", + " x.append(pickle.load(open(a[0], 'rb')))\n", + " y.append(pickle.load(open(a[1], 'rb')))\n", + " except:\n", + " os.remove(a[0])\n", + " os.remove(a[1])\n", + " return file_to_arr(t)\n", + "\n", + " x = np.array(x)\n", + " x = np.swapaxes(x, 1, 2)\n", + " #x = tf.convert_to_tensor(x)\n", + " #x.set_shape([341, 513, 2])\n", + " y = np.array(y).astype(int)\n", + " res = (x,y)\n", + " #print (res)\n", + " return (res)\n", + "\n", + "print((list(zip(x_train, y_train))))\n", + "train_ds = tf.data.Dataset.from_tensor_slices(list(zip(x_train, y_train)))\n", + "train_ds = train_ds.shuffle(len(train_ds),seed=42)\n", + "train_ds = train_ds.batch(4)\n", + "train_ds = train_ds.map((lambda x: tf.py_function(func=file_to_arr,inp=[x], Tout=(tf.float32, tf.int64))))\n", + "train_ds = train_ds.map(lambda x, y: set_shapes(x,y) )\n", + "\n", + "print(train_ds.element_spec)\n", + "train_ds = train_ds.prefetch(32)\n", + "\n", + "test_ds = tf.data.Dataset.from_tensor_slices(list(zip(x_test, y_test)))\n", + "test_ds = test_ds.batch(4)\n", + "test_ds = test_ds.map((lambda x: tf.py_function(func=file_to_arr,inp=[x], Tout=(tf.float32, tf.int64))))\n", + "test_ds = test_ds.prefetch(32)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + " slice_file_name fsID start ... fold classID class\n", + "0 100032-3-0-0.wav 100032 0.000000 ... 5 3 dog_bark\n", + "1 100263-2-0-117.wav 100263 58.500000 ... 5 2 children_playing\n", + "2 100263-2-0-121.wav 100263 60.500000 ... 5 2 children_playing\n", + "3 100263-2-0-126.wav 100263 63.000000 ... 5 2 children_playing\n", + "4 100263-2-0-137.wav 100263 68.500000 ... 5 2 children_playing\n", + "... ... ... ... ... ... ... ...\n", + "8727 99812-1-2-0.wav 99812 159.522205 ... 7 1 car_horn\n", + "8728 99812-1-3-0.wav 99812 181.142431 ... 7 1 car_horn\n", + "8729 99812-1-4-0.wav 99812 242.691902 ... 7 1 car_horn\n", + "8730 99812-1-5-0.wav 99812 253.209850 ... 7 1 car_horn\n", + "8731 99812-1-6-0.wav 99812 332.289233 ... 7 1 car_horn\n", + "\n", + "[8732 rows x 8 columns]\n", + "[('fold10/100648-1-0-0_x2.pkl', 'fold10/100648-1-0-0_y2.pkl'), ('fold10/100648-1-0-0_x_augmented.pkl', 'fold10/100648-1-0-0_y2.pkl'), ('fold10/100648-1-1-0_x2.pkl', 'fold10/100648-1-1-0_y2.pkl'), ('fold10/100648-1-1-0_x_augmented.pkl', 'fold10/100648-1-1-0_y2.pkl'), ('fold10/100648-1-2-0_x2.pkl', 'fold10/100648-1-2-0_y2.pkl'), ('fold10/100648-1-2-0_x_augmented.pkl', 'fold10/100648-1-2-0_y2.pkl'), ('fold10/100648-1-3-0_x2.pkl', 'fold10/100648-1-3-0_y2.pkl'), ('fold10/100648-1-3-0_x_augmented.pkl', 'fold10/100648-1-3-0_y2.pkl'), ('fold10/100648-1-4-0_x2.pkl', 'fold10/100648-1-4-0_y2.pkl'), ('fold10/100648-1-4-0_x_augmented.pkl', 'fold10/100648-1-4-0_y2.pkl'), ('fold2/100652-3-0-0_x2.pkl', 'fold2/100652-3-0-0_y2.pkl'), ('fold2/100652-3-0-0_x_augmented.pkl', 'fold2/100652-3-0-0_y2.pkl'), ('fold2/100652-3-0-1_x2.pkl', 'fold2/100652-3-0-1_y2.pkl'), ('fold2/100652-3-0-1_x_augmented.pkl', 'fold2/100652-3-0-1_y2.pkl'), ('fold2/100652-3-0-2_x2.pkl', 'fold2/100652-3-0-2_y2.pkl'), ('fold2/100652-3-0-2_x_augmented.pkl', 'fold2/100652-3-0-2_y2.pkl'), ('fold2/100652-3-0-3_x2.pkl', 'fold2/100652-3-0-3_y2.pkl'), ('fold2/100652-3-0-3_x_augmented.pkl', 'fold2/100652-3-0-3_y2.pkl'), ('fold10/100795-3-0-0_x2.pkl', 'fold10/100795-3-0-0_y2.pkl'), ('fold10/100795-3-0-0_x_augmented.pkl', 'fold10/100795-3-0-0_y2.pkl'), ('fold10/100795-3-1-0_x2.pkl', 'fold10/100795-3-1-0_y2.pkl'), ('fold10/100795-3-1-0_x_augmented.pkl', 'fold10/100795-3-1-0_y2.pkl'), ('fold10/100795-3-1-1_x2.pkl', 'fold10/100795-3-1-1_y2.pkl'), ('fold10/100795-3-1-1_x_augmented.pkl', 'fold10/100795-3-1-1_y2.pkl'), ('fold10/100795-3-1-2_x2.pkl', 'fold10/100795-3-1-2_y2.pkl'), ('fold10/100795-3-1-2_x_augmented.pkl', 'fold10/100795-3-1-2_y2.pkl'), ('fold6/101281-3-0-0_x2.pkl', 'fold6/101281-3-0-0_y2.pkl'), ('fold6/101281-3-0-0_x_augmented.pkl', 'fold6/101281-3-0-0_y2.pkl'), 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'fold7/99812-1-3-0_y2.pkl'), ('fold7/99812-1-3-0_x_augmented.pkl', 'fold7/99812-1-3-0_y2.pkl'), ('fold7/99812-1-4-0_x2.pkl', 'fold7/99812-1-4-0_y2.pkl'), ('fold7/99812-1-4-0_x_augmented.pkl', 'fold7/99812-1-4-0_y2.pkl'), ('fold7/99812-1-5-0_x2.pkl', 'fold7/99812-1-5-0_y2.pkl'), ('fold7/99812-1-5-0_x_augmented.pkl', 'fold7/99812-1-5-0_y2.pkl'), ('fold7/99812-1-6-0_x2.pkl', 'fold7/99812-1-6-0_y2.pkl'), ('fold7/99812-1-6-0_x_augmented.pkl', 'fold7/99812-1-6-0_y2.pkl')]\n", + "(TensorSpec(shape=(4, 341, 513, 2), dtype=tf.float32, name=None), TensorSpec(shape=(4, 10), dtype=tf.int64, name=None))\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UUR4vnrGlV49" + }, + "source": [ + "model = create_new_model()\n", + "#model = load_model(9, 32, 2, \"./drive/My Drive/wavenet-test/new2/9_32_2_skipconnections_customdropout_run2/\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "qFmf6xmbtHjj", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9ab234a6-3aba-4db4-b588-2696c5912ce0" + }, + "source": [ + "callbacks = create_callbacks(\"./drive/My Drive/wavenet-test/resnet/2_2_2_2_default\")\n", + "\n", + "model.summary()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model: \"res_net2d\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) [(None, 341, 513, 2) 0 \n", + "__________________________________________________________________________________________________\n", + "conv1 (Conv2D) (None, 171, 257, 64) 6272 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn_conv1 (BatchNormalization) (None, 171, 257, 64) 256 conv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1_relu (Activation) (None, 171, 257, 64) 0 bn_conv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "pool1 (MaxPooling2D) (None, 86, 129, 64) 0 conv1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding2a_branch2a (ZeroPadding (None, 88, 131, 64) 0 pool1[0][0] \n", + "__________________________________________________________________________________________________\n", + "res2a_branch2a (Conv2D) (None, 86, 129, 64) 36864 padding2a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn2a_branch2a (BatchNormalizati (None, 86, 129, 64) 256 res2a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "res2a_branch2a_relu (Activation (None, 86, 129, 64) 0 bn2a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding2a_branch2b (ZeroPadding (None, 88, 131, 64) 0 res2a_branch2a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res2a_branch2b (Conv2D) (None, 86, 129, 64) 36864 padding2a_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "res2a_branch1 (Conv2D) (None, 86, 129, 64) 4096 pool1[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn2a_branch2b (BatchNormalizati (None, 86, 129, 64) 256 res2a_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn2a_branch1 (BatchNormalizatio (None, 86, 129, 64) 256 res2a_branch1[0][0] \n", + "__________________________________________________________________________________________________\n", + "res2a (Add) (None, 86, 129, 64) 0 bn2a_branch2b[0][0] \n", + " bn2a_branch1[0][0] \n", + "__________________________________________________________________________________________________\n", + "res2a_relu (Activation) (None, 86, 129, 64) 0 res2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding2b1_branch2a (ZeroPaddin (None, 88, 131, 64) 0 res2a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res2b1_branch2a (Conv2D) (None, 86, 129, 64) 36864 padding2b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn2b1_branch2a (BatchNormalizat (None, 86, 129, 64) 256 res2b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "res2b1_branch2a_relu (Activatio (None, 86, 129, 64) 0 bn2b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding2b1_branch2b (ZeroPaddin (None, 88, 131, 64) 0 res2b1_branch2a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res2b1_branch2b (Conv2D) (None, 86, 129, 64) 36864 padding2b1_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn2b1_branch2b (BatchNormalizat (None, 86, 129, 64) 256 res2b1_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "res2b1 (Add) (None, 86, 129, 64) 0 bn2b1_branch2b[0][0] \n", + " res2a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res2b1_relu (Activation) (None, 86, 129, 64) 0 res2b1[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding3a_branch2a (ZeroPadding (None, 88, 131, 64) 0 res2b1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res3a_branch2a (Conv2D) (None, 43, 65, 128) 73728 padding3a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn3a_branch2a (BatchNormalizati (None, 43, 65, 128) 512 res3a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "res3a_branch2a_relu (Activation (None, 43, 65, 128) 0 bn3a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding3a_branch2b (ZeroPadding (None, 45, 67, 128) 0 res3a_branch2a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res3a_branch2b (Conv2D) (None, 43, 65, 128) 147456 padding3a_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "res3a_branch1 (Conv2D) (None, 43, 65, 128) 8192 res2b1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn3a_branch2b (BatchNormalizati (None, 43, 65, 128) 512 res3a_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn3a_branch1 (BatchNormalizatio (None, 43, 65, 128) 512 res3a_branch1[0][0] \n", + "__________________________________________________________________________________________________\n", + "res3a (Add) (None, 43, 65, 128) 0 bn3a_branch2b[0][0] \n", + " bn3a_branch1[0][0] \n", + "__________________________________________________________________________________________________\n", + "res3a_relu (Activation) (None, 43, 65, 128) 0 res3a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding3b1_branch2a (ZeroPaddin (None, 45, 67, 128) 0 res3a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res3b1_branch2a (Conv2D) (None, 43, 65, 128) 147456 padding3b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn3b1_branch2a (BatchNormalizat (None, 43, 65, 128) 512 res3b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "res3b1_branch2a_relu (Activatio (None, 43, 65, 128) 0 bn3b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding3b1_branch2b (ZeroPaddin (None, 45, 67, 128) 0 res3b1_branch2a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res3b1_branch2b (Conv2D) (None, 43, 65, 128) 147456 padding3b1_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn3b1_branch2b (BatchNormalizat (None, 43, 65, 128) 512 res3b1_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "res3b1 (Add) (None, 43, 65, 128) 0 bn3b1_branch2b[0][0] \n", + " res3a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res3b1_relu (Activation) (None, 43, 65, 128) 0 res3b1[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding4a_branch2a (ZeroPadding (None, 45, 67, 128) 0 res3b1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res4a_branch2a (Conv2D) (None, 22, 33, 256) 294912 padding4a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn4a_branch2a (BatchNormalizati (None, 22, 33, 256) 1024 res4a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "res4a_branch2a_relu (Activation (None, 22, 33, 256) 0 bn4a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding4a_branch2b (ZeroPadding (None, 24, 35, 256) 0 res4a_branch2a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res4a_branch2b (Conv2D) (None, 22, 33, 256) 589824 padding4a_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "res4a_branch1 (Conv2D) (None, 22, 33, 256) 32768 res3b1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn4a_branch2b (BatchNormalizati (None, 22, 33, 256) 1024 res4a_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn4a_branch1 (BatchNormalizatio (None, 22, 33, 256) 1024 res4a_branch1[0][0] \n", + "__________________________________________________________________________________________________\n", + "res4a (Add) (None, 22, 33, 256) 0 bn4a_branch2b[0][0] \n", + " bn4a_branch1[0][0] \n", + "__________________________________________________________________________________________________\n", + "res4a_relu (Activation) (None, 22, 33, 256) 0 res4a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding4b1_branch2a (ZeroPaddin (None, 24, 35, 256) 0 res4a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res4b1_branch2a (Conv2D) (None, 22, 33, 256) 589824 padding4b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn4b1_branch2a (BatchNormalizat (None, 22, 33, 256) 1024 res4b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "res4b1_branch2a_relu (Activatio (None, 22, 33, 256) 0 bn4b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding4b1_branch2b (ZeroPaddin (None, 24, 35, 256) 0 res4b1_branch2a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res4b1_branch2b (Conv2D) (None, 22, 33, 256) 589824 padding4b1_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn4b1_branch2b (BatchNormalizat (None, 22, 33, 256) 1024 res4b1_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "res4b1 (Add) (None, 22, 33, 256) 0 bn4b1_branch2b[0][0] \n", + " res4a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res4b1_relu (Activation) (None, 22, 33, 256) 0 res4b1[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding5a_branch2a (ZeroPadding (None, 24, 35, 256) 0 res4b1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res5a_branch2a (Conv2D) (None, 11, 17, 512) 1179648 padding5a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn5a_branch2a (BatchNormalizati (None, 11, 17, 512) 2048 res5a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "res5a_branch2a_relu (Activation (None, 11, 17, 512) 0 bn5a_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding5a_branch2b (ZeroPadding (None, 13, 19, 512) 0 res5a_branch2a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res5a_branch2b (Conv2D) (None, 11, 17, 512) 2359296 padding5a_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "res5a_branch1 (Conv2D) (None, 11, 17, 512) 131072 res4b1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn5a_branch2b (BatchNormalizati (None, 11, 17, 512) 2048 res5a_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn5a_branch1 (BatchNormalizatio (None, 11, 17, 512) 2048 res5a_branch1[0][0] \n", + "__________________________________________________________________________________________________\n", + "res5a (Add) (None, 11, 17, 512) 0 bn5a_branch2b[0][0] \n", + " bn5a_branch1[0][0] \n", + "__________________________________________________________________________________________________\n", + "res5a_relu (Activation) (None, 11, 17, 512) 0 res5a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding5b1_branch2a (ZeroPaddin (None, 13, 19, 512) 0 res5a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res5b1_branch2a (Conv2D) (None, 11, 17, 512) 2359296 padding5b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn5b1_branch2a (BatchNormalizat (None, 11, 17, 512) 2048 res5b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "res5b1_branch2a_relu (Activatio (None, 11, 17, 512) 0 bn5b1_branch2a[0][0] \n", + "__________________________________________________________________________________________________\n", + "padding5b1_branch2b (ZeroPaddin (None, 13, 19, 512) 0 res5b1_branch2a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res5b1_branch2b (Conv2D) (None, 11, 17, 512) 2359296 padding5b1_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn5b1_branch2b (BatchNormalizat (None, 11, 17, 512) 2048 res5b1_branch2b[0][0] \n", + "__________________________________________________________________________________________________\n", + "res5b1 (Add) (None, 11, 17, 512) 0 bn5b1_branch2b[0][0] \n", + " res5a_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "res5b1_relu (Activation) (None, 11, 17, 512) 0 res5b1[0][0] \n", + "__________________________________________________________________________________________________\n", + "pool5 (GlobalAveragePooling2D) (None, 512) 0 res5b1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "fc1000 (Dense) (None, 10) 5130 pool5[0][0] \n", + "==================================================================================================\n", + "Total params: 11,192,458\n", + "Trainable params: 11,173,002\n", + "Non-trainable params: 19,456\n", + "__________________________________________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FSx_1_XYh8oU" + }, + "source": [ + "!mkdir \"./drive/My Drive/wavenet-test/resnet\"" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zxV56ElGt2rK", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3f251681-1eda-4e76-e5ab-01233734433f" + }, + "source": [ + "model.fit(train_ds, validation_data=test_ds, callbacks=callbacks, epochs=300)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Epoch 1/300\n", + " 1/3898 [..............................] - ETA: 0s - loss: 5.0015 - accuracy: 0.0000e+00WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0153s vs `on_train_batch_end` time: 0.0296s). Check your callbacks.\n", + "3898/3898 [==============================] - ETA: 0s - loss: 2.4161 - accuracy: 0.1845WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 712s 183ms/step - loss: 2.4161 - accuracy: 0.1845 - val_loss: 2.0877 - val_accuracy: 0.3130\n", + "Epoch 2/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 2.0215 - accuracy: 0.3032WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 631s 162ms/step - loss: 2.0215 - accuracy: 0.3032 - val_loss: 1.8558 - val_accuracy: 0.2906\n", + "Epoch 3/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 1.7542 - accuracy: 0.4071WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 632s 162ms/step - loss: 1.7542 - accuracy: 0.4071 - val_loss: 1.7891 - val_accuracy: 0.4338\n", + "Epoch 4/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 1.5708 - accuracy: 0.4785WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 632s 162ms/step - loss: 1.5708 - accuracy: 0.4785 - val_loss: 1.6842 - val_accuracy: 0.4754\n", + "Epoch 5/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 1.4495 - accuracy: 0.5266WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 631s 162ms/step - loss: 1.4495 - accuracy: 0.5266 - val_loss: 1.5661 - val_accuracy: 0.4915\n", + "Epoch 6/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 1.3295 - accuracy: 0.5730WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 630s 162ms/step - loss: 1.3295 - accuracy: 0.5730 - val_loss: 1.4735 - val_accuracy: 0.5609\n", + "Epoch 7/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 1.2439 - accuracy: 0.6058WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 632s 162ms/step - loss: 1.2439 - accuracy: 0.6058 - val_loss: 1.4651 - val_accuracy: 0.6175\n", + "Epoch 8/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 1.1643 - accuracy: 0.6360WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 632s 162ms/step - loss: 1.1643 - accuracy: 0.6360 - val_loss: 1.6585 - val_accuracy: 0.5566\n", + "Epoch 9/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 1.1465 - accuracy: 0.6428WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 631s 162ms/step - loss: 1.1465 - accuracy: 0.6428 - val_loss: 1.6099 - val_accuracy: 0.5801\n", + "Epoch 10/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 1.1290 - accuracy: 0.6457WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 631s 162ms/step - loss: 1.1290 - accuracy: 0.6457 - val_loss: 1.5186 - val_accuracy: 0.6197\n", + "Epoch 11/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 1.1186 - accuracy: 0.6590WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 632s 162ms/step - loss: 1.1186 - accuracy: 0.6590 - val_loss: 1.5855 - val_accuracy: 0.5919\n", + "Epoch 12/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 1.0222 - accuracy: 0.6848WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 632s 162ms/step - loss: 1.0222 - accuracy: 0.6848 - val_loss: 1.5989 - val_accuracy: 0.5620\n", + "Epoch 13/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 0.9567 - accuracy: 0.7043WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 631s 162ms/step - loss: 0.9567 - accuracy: 0.7043 - val_loss: 2.1016 - val_accuracy: 0.6303\n", + "Epoch 14/300\n", + "3898/3898 [==============================] - ETA: 0s - loss: 0.9543 - accuracy: 0.7057WARNING:tensorflow:Can save best model only with val_categorical_accuracy available, skipping.\n", + "3898/3898 [==============================] - 631s 162ms/step - loss: 0.9543 - accuracy: 0.7057 - val_loss: 1.5651 - val_accuracy: 0.6026\n", + "Epoch 15/300\n", + "3395/3898 [=========================>....] - ETA: 1:16 - loss: 0.9468 - accuracy: 0.7144Buffered data was truncated after reaching the output size limit." + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XdG-aHgBjOPb" + }, + "source": [ + "!cat \"./drive/My Drive/wavenet-test/new2/8_32_2_skipconnections_dropout0.1_convkernalsize2/wavenet_classifier_training_history.csv\"" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "sXj9Q2lnIV3E" + }, + "source": [ + "optimizer = keras.optimizers.Adam(lr=0.0003, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n", + "model.compile(optimizer, loss=keras.losses.categorical_crossentropy, metrics=[\"categorical_accuracy\"])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "FI8W5jDM1Azs" + }, + "source": [ + "[2 ** i for i in range(7)]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5BUW2V0VoxNG" + }, + "source": [ + "t = iter(train_ds)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5c-fwSkZE149" + }, + "source": [ + "g = next(t)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "_efzcidn2GlQ" + }, + "source": [ + "model_as_json = model.to_json()\n", + "with open('model.json', \"w\") as json_file:\n", + " json_file.write(model_as_json)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "MbI0_51MwA4s" + }, + "source": [ + "!rm -rf *.tgz sample_data" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "-7arGMmNIqAI" + }, + "source": [ + "#!wget http://76.213.149.126:8080/audio/fold1/9031-3-1-0_x2.pkl\n", + "a = pickle.load(open(\"9031-3-1-0_x2.pkl\", \"rb\"))\n", + "print(a.shape)\n", + "print(a[0])\n", + "a = a.reshape((349866,1))\n", + "print(a)\n", + "print(a.shape)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "KbVK1OwaeHGQ" + }, + "source": [ + "print(x_train[6473])\n", + "y_train[6473]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "h_YLqL4sCaNv" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file