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rnett/audio-denoiser-using-lip-reading.ipynb

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Audio Denoiser using Lip Reading.ipynb
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audio-denoiser-using-lip-reading.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Audio Denoiser using Lip Reading.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"language": "python",
"display_name": "Python 3"
},
"accelerator": "TPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/rnett/fb8b2646ae3dcd5e37933262e3c0c813/audio-denoiser-using-lip-reading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "4_jxmQj2MIOS",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"# Downloading and preprocessing data#\n"
]
},
{
"metadata": {
"pycharm": {
"metadata": false
},
"id": "pDq23_xo7CUU",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Files##"
]
},
{
"metadata": {
"id": "r83v0t7msyEp",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Raw video files get stores in `files`.\n",
"\n",
"Video is then loaded, turned into frames, then the numpy arrays are saved in `data`.\n"
]
},
{
"metadata": {
"id": "MUVnBSuVY0D-",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"from typing import List, Dict, Iterable\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"mpl.rc('image', cmap='gray')\n",
"from matplotlib import pyplot as plt\n",
"\n",
"#!pip install git+https://github.com/avivga/face-detection.git\n",
"# !pip3 install git+https://github.com/avivga/mediaio.git\n",
"# !pip3 install imageio\n",
"# !pip3 install imageio-ffmpeg\n",
" \n",
"# import sys\n",
" \n",
"# print(\"Version:\", sys.version)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "yFbgnqff_Jgj",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"File constants"
]
},
{
"metadata": {
"pycharm": {
"metadata": false,
"name": "#%%\n"
},
"id": "Ym3XZ_39Wq67",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"\n",
"raw_dir = 'files'\n",
"video_dir = 'videos'\n",
"audio_dir = 'audio'\n",
"test_dir = 'test'\n",
"train_dir = 'train'"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "h9iwenmr_ML7",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Download videos"
]
},
{
"metadata": {
"id": "ZR7wsmkMyDlA",
"colab_type": "code",
"outputId": "7f47c1d0-e755-427c-d2db-db9af9a81753",
"pycharm": {},
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"import requests, zipfile, io\n",
"\n",
"import shutil\n",
"import os\n",
"\n",
"\n",
"#@title Force file refresh\n",
"force = False #@param {type:\"boolean\"}\n",
"\n",
"if not os.path.isdir(raw_dir) or force: \n",
"\n",
" try:\n",
" shutil.rmtree(raw_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
"\n",
" os.mkdir(raw_dir)\n",
"\n",
" for i in range(3):\n",
" url = \"http://spandh.dcs.shef.ac.uk/gridcorpus/s{}/video/s{}.mpg_vcd.zip\".format(i+1, i+1)\n",
" r = requests.get(url)\n",
" z = zipfile.ZipFile(io.BytesIO(r.content))\n",
" z.extractall(\"tmp\")\n",
"\n",
" files = os.listdir(\"tmp\")\n",
"\n",
" for f in files:\n",
" files1 = os.listdir(\"tmp/\"+f)\n",
" for f1 in files1:\n",
" if f1 != 'Thumbs.db':\n",
" shutil.move(\"tmp/\"+f + \"/\" + f1, raw_dir + '/' + f1.replace('.mpg', f\"_s{i+1}.mpg\"))\n",
"\n",
" shutil.rmtree('tmp')\n",
" \n",
"print(\"Have {0} data files\".format(len(os.listdir(raw_dir))))\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Have 3000 data files\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "nhx0aGVd-TR6",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"test_s3_limit = 25\n",
"test_others_limit = 25\n",
"train_limit = 220\n",
"\n",
"test_s3_count = 0\n",
"test_other_count = 0\n",
"train1_count = 0\n",
"train2_count = 0\n",
"\n",
"if not (os.path.isdir(test_dir) and os.path.isdir(train_dir)) or force: \n",
"\n",
" try:\n",
" shutil.rmtree(test_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
" try:\n",
" shutil.rmtree(train_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
" \n",
" os.mkdir(test_dir)\n",
" os.mkdir(train_dir)\n",
"\n",
" for file in os.listdir(raw_dir):\n",
" if '_s3' in file:\n",
" if test_s3_count < test_s3_limit:\n",
" shutil.copy(raw_dir + '/' + file, test_dir)\n",
" test_s3_count += 1\n",
" elif '_s1' in file:\n",
" if train1_count < train_limit / 2:\n",
" shutil.copy(raw_dir + '/' + file, train_dir)\n",
" train1_count += 1\n",
" if test_other_count < test_others_limit:\n",
" shutil.copy(raw_dir + '/' + file, test_dir)\n",
" test_other_count += 1\n",
" elif '_s2' in file:\n",
" if train2_count < train_limit / 2:\n",
" shutil.copy(raw_dir + '/' + file, train_dir)\n",
" train2_count += 1\n",
" if test_other_count < test_others_limit:\n",
" shutil.copy(raw_dir + '/' + file, test_dir)\n",
" test_other_count += 1\n",
" "
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "uIiTuv51nwBX",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"import cv2, os, gc\n",
"import h5py, imageio\n",
"from PIL import Image\n",
"from mediaio.video_io import VideoFileReader\n",
"from imutils import face_utils\n",
"import numpy as np\n",
"import argparse\n",
"import imutils\n",
"import dlib\n",
"import cv2\n",
"\n",
"def rgb2gray(rgb):\n",
" return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
"\n",
"lip_size = (30, 75)\n",
" \n",
"class VideoLoader:\n",
" def __init__(self, file):\n",
" self.file = file\n",
" self.training = train_dir in file\n",
" self.name = self.file.split(\"/\",1)[1].replace(\".mpg\", \"\")\n",
" self.loaded = False\n",
" \n",
" def load_and_save(self):\n",
" \n",
" try:\n",
" with imageio.get_reader(self.file) as reader:\n",
"\n",
" size = reader.get_meta_data()[\"size\"]\n",
" video_shape = (75, size[1], size[0])\n",
" gray_frames = np.ndarray(shape=video_shape, dtype=np.uint8)\n",
"\n",
" data = np.zeros(shape=(len(gray_frames),lip_size[0],lip_size[1]), dtype=np.float32)\n",
"\n",
" # initialize dlib's face detector (HOG-based) and then create\n",
" # the facial landmark predictor\n",
" detector = dlib.get_frontal_face_detector()\n",
" predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')\n",
"\n",
" for i in range(75):\n",
" gray = cv2.cvtColor(reader.get_next_data(), cv2.COLOR_BGR2GRAY)\n",
" gray_frames[i, ] = gray\n",
"\n",
"\n",
" # detect faces in the grayscale image\n",
" rects = detector(gray, 1)\n",
"\n",
" isset = False\n",
"\n",
" # loop over the face detections\n",
" for (k, rect) in enumerate(rects):\n",
" # determine the facial landmarks for the face region, then\n",
" # convert the landmark (x, y)-coordinates to a NumPy array\n",
" shape = predictor(gray_frames[i, ], rect)\n",
" shape = face_utils.shape_to_np(shape)\n",
"\n",
" # loop over the face parts individually\n",
" for (name, (l, m)) in face_utils.FACIAL_LANDMARKS_IDXS.items():\n",
" # clone the original image so we can draw on it, then\n",
" # display the name of the face part on the image\n",
" if name == 'mouth':\n",
" # clone = gray_frames[i, ].copy()\n",
" # cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,\n",
" # 0.7, (0, 0, 255), 2)\n",
"\n",
" # # loop over the subset of facial landmarks, drawing the\n",
" # # specific face part\n",
" # for (x, y) in shape[l:m]:\n",
" # cv2.circle(clone, (x, y), 1, (0, 0, 255), -1)\n",
"\n",
" # extract the ROI of the face region as a separate image\n",
"\n",
" (x, y, w, h) = cv2.boundingRect(np.array([shape[l:m]]))\n",
" roi = gray_frames[i, ][y:y + h, x:x + w]\n",
" roi = imutils.resize(roi, width=250, inter=cv2.INTER_CUBIC)\n",
" #roi = np.resize(roi,(100,250))\n",
"\n",
" roi = np.array(Image.fromarray(roi).resize((lip_size[1], lip_size[0]), Image.ANTIALIAS))\n",
" isset = True\n",
"\n",
" if not isset:\n",
" print(\"\\nCould not find mouth for video\", self.file)\n",
"\n",
" del data\n",
" del gray_frames\n",
" gc.collect()\n",
" return False\n",
"\n",
" data[i] = roi\n",
"\n",
" if self.training:\n",
" h5f = h5py.File(train_dir + '/' + video_dir + '/' + self.name + '.hdf5', 'w')\n",
" else:\n",
" h5f = h5py.File(test_dir + '/' + video_dir + '/' + self.name + '.hdf5', 'w')\n",
" h5f.create_dataset('video', data=data, compression=\"gzip\")\n",
" h5f.close()\n",
"\n",
" del data\n",
" del gray_frames\n",
" gc.collect()\n",
" return True\n",
" except:\n",
" return False"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "9vCf-apD_VW4",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"## Video##\n",
"\n",
"Here we extract video frames from the file, and save them."
]
},
{
"metadata": {
"id": "zy1RgbHyRGPT",
"colab_type": "code",
"pycharm": {},
"outputId": "f6cc5582-c9b8-4653-d68d-b35207154838",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"cell_type": "code",
"source": [
"import sys\n",
"\n",
"#@title Force video refresh\n",
"video_force = False #@param {type:\"boolean\"}\n",
"limit = 200 #@param {type:\"slider\", min:10, max:1000, step:10}\n",
"\n",
"print(\"Training videos\")\n",
"\n",
"videos = (VideoLoader(train_dir + '/' + f) for f in os.listdir(train_dir))\n",
"\n",
"if not os.path.isdir(train_dir + '/' + video_dir) or video_force: \n",
"\n",
" try:\n",
" shutil.rmtree(train_dir + '/' + video_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
"\n",
" os.mkdir(train_dir + '/' + video_dir)\n",
" \n",
" done = 0\n",
" \n",
" while done <= limit:\n",
" try:\n",
" video = next(videos)\n",
" except StopIteration:\n",
" print(f\"\\nFinished with {done} training videos\")\n",
" break\n",
" #print(\"Video:\", video.file)\n",
" \n",
" if video.load_and_save():\n",
" done += 1\n",
" \n",
" sys.stdout.write('\\r{}/{} ({} %)'.format(done, limit, int(100 * done / limit)))\n",
" sys.stdout.flush()\n",
" \n",
" \n",
"print(\"\\nTest videos:\")\n",
" \n",
"test_limit = 40 #@param {type:\"slider\", min:10, max:1000, step:10}\n",
"\n",
"videos = (VideoLoader(test_dir + '/' + f) for f in os.listdir(test_dir))\n",
"\n",
"if not os.path.isdir(test_dir + '/' + video_dir) or video_force: \n",
"\n",
" try:\n",
" shutil.rmtree(test_dir + '/' + video_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
"\n",
" os.mkdir(test_dir + '/' + video_dir)\n",
" \n",
" done = 0\n",
" \n",
" while done <= test_limit:\n",
" try:\n",
" video = next(videos)\n",
" except StopIteration:\n",
" print(f\"\\nFinished with {done} test videos\")\n",
" break\n",
" #print(\"Video:\", video.file)\n",
" \n",
"# if os.isfile(video.file):\n",
"# done += 1\n",
"# continue\n",
" \n",
" if video.load_and_save():\n",
" done += 1\n",
" \n",
" sys.stdout.write('\\r{}/{} ({} %)'.format(done, test_limit, int(100 * done / test_limit)))\n",
" sys.stdout.flush()\n",
" "
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Training videos\n",
"\n",
"Test videos:\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "MVlBYV8L_dys",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"This provides methods for loading videos."
]
},
{
"metadata": {
"id": "Cy6-_EHrpJG4",
"colab_type": "code",
"pycharm": {},
"outputId": "0d054b54-0184-406e-8baa-b73207ad3d78",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"cell_type": "code",
"source": [
"import os\n",
"\n",
"class Video:\n",
" def __init__(self, name, training):\n",
" self.name = name.replace('.hdf5', '')\n",
" \n",
" if training:\n",
" self.file = train_dir + '/' + video_dir + '/' + name\n",
" else:\n",
" self.file = test_dir + '/' + video_dir + '/' + name\n",
" \n",
" #h5f = h5py.File(self.file,'r')\n",
" #self.data = h5f['video'][:]\n",
" #h5f.close()\n",
" \n",
" #self.data = np.load(self.file)\n",
" \n",
" def data(self):\n",
" h5f = h5py.File(self.file,'r')\n",
" data = h5f['video'][:]\n",
" h5f.close()\n",
" return data\n",
"\n",
"def get_videos(limit=10, training=True):\n",
" if training:\n",
" files = [f for f in os.listdir(train_dir + '/' + video_dir)][:limit]\n",
" else:\n",
" files = [f for f in os.listdir(test_dir + '/' + video_dir)][:limit]\n",
" \n",
" return [Video(f, training) for f in files]\n",
"\n",
"print(len(os.listdir(train_dir + '/' + video_dir)), \" Training Videos\")\n",
"print(len(os.listdir(test_dir + '/' + video_dir)), \"Test Videos\")\n",
"\n",
"videos = get_videos(5)\n",
"\n",
"video_shape = np.shape(videos[0].data())\n",
"\n",
"print(\"Shape: \", video_shape)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"201 Training Videos\n",
"41 Test Videos\n",
"Shape: (75, 30, 75)\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "bIyKCgla_hZ3",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"An example video.\n",
"\n",
"I'm not sure whats up with the colors, but it shouldn't matter."
]
},
{
"metadata": {
"id": "gxVNq9DjulY3",
"colab_type": "code",
"pycharm": {},
"outputId": "4ea02bff-909e-409e-b529-0f8aad7dcad6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 874
}
},
"cell_type": "code",
"source": [
"\n",
"video_shape = np.shape(videos[0].data())\n",
"print('Video shape:', video_shape, videos[0].data().dtype)\n",
"for i in range(0, 75, 15):\n",
" plt.imshow(videos[0].data()[i])\n",
" plt.show()\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Video shape: (75, 30, 75) float32\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "AADkvcGUZQoi",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Sucesfully loads visual data, now for audio"
]
},
{
"metadata": {
"id": "-fEODXR936sk",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"## Audio##\n",
"\n",
"\n",
"Here we extract audio from the video files and apply **mfcc** to it.\n",
"\n",
"We then save it for later use."
]
},
{
"metadata": {
"id": "9K6M6YZu3OmX",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"video_frame_rate = 25\n",
"framerate = 22050\n",
"n_fft = int(float(framerate) / video_frame_rate)\n",
"frame_step = int(n_fft / 4)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "5e57VlcA3c6t",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"def reconstruct_audio(magnitude, phase):\n",
" \n",
" magnitude = librosa.db_to_amplitude(magnitude)\n",
"\n",
" mel_filterbank = librosa.filters.mel(\n",
" sr=framerate,\n",
" n_fft=n_fft,\n",
" n_mels=80,\n",
" fmin=0,\n",
" fmax=8000\n",
" )\n",
" \n",
" magnitude = np.dot(np.linalg.pinv(mel_filterbank), magnitude)\n",
" \n",
" mag_phase = magnitude * phase\n",
" \n",
" used_mp2 = magnitude * phase\n",
" wave = librosa.istft(magnitude * phase, hop_length=frame_step)\n",
" \n",
" pad = 65664 - len(wave)\n",
" \n",
" return np.pad(wave, (0, pad), 'constant')"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "IMRtSoUuduPY",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"def signal_to_spectrogram(signal):\n",
" D = librosa.core.stft(signal.astype(np.float64), n_fft=n_fft, hop_length=frame_step)\n",
" magnitude, phase = librosa.core.magphase(D)\n",
"\n",
" mel_filterbank = librosa.filters.mel(\n",
" sr=framerate,\n",
" n_fft=n_fft,\n",
" n_mels=80,\n",
" fmin=0,\n",
" fmax=8000\n",
" )\n",
"\n",
" magnitude = np.dot(mel_filterbank, magnitude)\n",
"\n",
" magnitude = librosa.amplitude_to_db(magnitude)\n",
"\n",
" return magnitude, phase"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "7rq-5o2UtpJH",
"colab_type": "code",
"pycharm": {},
"outputId": "3354fc5f-1f3f-4861-e160-8d2e8082ec3f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"cell_type": "code",
"source": [
"audio_force = False #@param {type:\"boolean\"}\n",
"#limit = 100 #@param {type:\"slider\", min:10, max:1000, step:10}\n",
"\n",
"import librosa, scipy\n",
"import shutil\n",
"import tempfile\n",
"import urllib.request\n",
"import cv2, os, gc\n",
"import h5py\n",
"import sys\n",
"\n",
"print(\"Training audio\")\n",
"\n",
"#only load files we have video for\n",
"files = [train_dir + '/' + '/' + f.replace('.hdf5', '.mpg') \n",
" for f in os.listdir(train_dir + '/' + video_dir)]\n",
"\n",
"if not os.path.isdir(train_dir + '/' + audio_dir) or audio_force: \n",
"\n",
" try:\n",
" shutil.rmtree(train_dir + '/' + audio_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
"\n",
" os.mkdir(train_dir + '/' + audio_dir)\n",
" \n",
" total = len(files)\n",
" done = 0\n",
"\n",
" for f in files:\n",
" #audio = mp.VideoFileClip(f).audio\n",
"\n",
" #arr = audio.to_soundarray()\n",
"\n",
" wave, _ = librosa.load(f, mono=True, sr=framerate)\n",
" \n",
" noise = np.random.normal(0,0.05,len(wave))\n",
" \n",
" mel_spectrogram, phase = signal_to_spectrogram(wave)\n",
" \n",
" noisy = wave + noise\n",
" \n",
" noisy_spectrogram, _ = signal_to_spectrogram(noisy)\n",
" \n",
" #mag_phase = get_mag_phase(mel_spectrogram, phase)\n",
" \n",
" # this gets something, not entirely sure what\n",
" \n",
" # add any preprocessing here!\n",
"\n",
" name = f.split(\"/\", 1)[1].replace(\".mpg\", \"\")\n",
"\n",
" h5f = h5py.File(train_dir + '/' + audio_dir + '/' + name + '.hdf5', 'w')\n",
" h5f.create_dataset('spectrogram', data=mel_spectrogram.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('audio', data=wave.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('noisy_spectrogram', data=noisy_spectrogram.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('noisy_audio', data=noisy.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('phase', data=phase, compression=\"gzip\")\n",
" h5f.close()\n",
" \n",
" done += 1\n",
" \n",
" sys.stdout.write('\\r{}/{} ({} %)'.format(done, total, int(100 * done / total)))\n",
" sys.stdout.flush()\n",
"\n",
" \n",
"print(\"\\nTesting audio\")\n",
" \n",
" \n",
"#only load files we have video for\n",
"files = [test_dir + '/' + '/' + f.replace('.hdf5', '.mpg') \n",
" for f in os.listdir(test_dir + '/' + video_dir)]\n",
"\n",
"if not os.path.isdir(test_dir + '/' + audio_dir) or audio_force: \n",
"\n",
" try:\n",
" shutil.rmtree(test_dir + '/' + audio_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
"\n",
" os.mkdir(test_dir + '/' + audio_dir)\n",
" \n",
" total = len(files)\n",
" done = 0\n",
"\n",
" for f in files:\n",
" #audio = mp.VideoFileClip(f).audio\n",
"\n",
" #arr = audio.to_soundarray()\n",
"\n",
" wave, _ = librosa.load(f, mono=True, sr=framerate)\n",
" \n",
" noise = np.random.normal(0,0.05,len(wave))\n",
" \n",
" mel_spectrogram, phase = signal_to_spectrogram(wave)\n",
" \n",
" noisy = wave + noise\n",
" \n",
" noisy_spectrogram, _ = signal_to_spectrogram(noisy)\n",
" \n",
" #mag_phase = get_mag_phase(mel_spectrogram, phase)\n",
" \n",
" # this gets something, not entirely sure what\n",
" \n",
" # add any preprocessing here!\n",
"\n",
" name = f.split(\"/\", 1)[1].replace(\".mpg\", \"\")\n",
"\n",
" h5f = h5py.File(test_dir + '/' + audio_dir + '/' + name + '.hdf5', 'w')\n",
" h5f.create_dataset('spectrogram', data=mel_spectrogram.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('audio', data=wave.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('noisy_spectrogram', data=noisy_spectrogram.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('noisy_audio', data=noisy.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('phase', data=phase, compression=\"gzip\")\n",
" h5f.close()\n",
" \n",
" done += 1\n",
" \n",
" sys.stdout.write('\\r{}/{} ({} %)'.format(done, total, int(100 * done / total)))\n",
" sys.stdout.flush()\n",
" \n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Training audio\n",
"\n",
"Testing audio\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "AEIxgWb2ByMC",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"This is code for loading audio files.\n",
"\n",
"We show a sample audio file, that has been transformed."
]
},
{
"metadata": {
"id": "ltj7OJqGpJRC",
"colab_type": "code",
"pycharm": {},
"outputId": "bcf71d90-85d0-451d-b948-7fbcad72a876",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 492
}
},
"cell_type": "code",
"source": [
"#@title Default title text { run: \"auto\" }\n",
"noise_level = 0.5 #@param {type:\"slider\", min:0, max:1, step:0.05}\n",
"import librosa.display\n",
"np.random.seed(0)\n",
"\n",
"n_fft = int(float(framerate) / video_frame_rate)\n",
"hop_length = int(n_fft / 4)\n",
"\n",
"class Audio:\n",
" def __init__(self, name, training):\n",
" self.name = name.replace('.hdf5', '')\n",
" \n",
" if training:\n",
" self.file = train_dir + '/' + audio_dir + '/' + name\n",
" else:\n",
" self.file = test_dir + '/' + audio_dir + '/' + name\n",
" \n",
"# h5f = h5py.File(self.file,'r')\n",
"# self.data = h5f['mfcc'][:]\n",
" \n",
"# self.audio = h5f['audio'][:]\n",
" \n",
"# self.mfcc_formatted = self.data.reshape(self.data.shape[0], self.data.shape[1])\n",
" \n",
"# h5f.close()\n",
" \n",
" def get_data(self, keys):\n",
" \n",
" if isinstance(keys, str):\n",
" keys = [keys]\n",
" \n",
" h5f = h5py.File(self.file,'r')\n",
" data = {}\n",
" \n",
" for k in keys:\n",
" if k in h5f:\n",
" if k =='noisy_audio':\n",
" data_audio = h5f[k][:]\n",
" audio_shape= np.shape(data_audio)\n",
" data_noise = np.random.normal(0,noise_level,audio_shape)\n",
" data[k] = data_noise + data_audio\n",
" else:\n",
" data[k] = h5f[k][:]\n",
" \n",
" \n",
" if len(data) == 1:\n",
" return data[next(iter(data))]\n",
" \n",
" return data\n",
" \n",
" def __getitem__(self, item):\n",
" return self.get_data(item)\n",
" \n",
" def audio(self):\n",
" return self['audio']\n",
" \n",
" def spectrogram(self):\n",
" return self['spectrogram']\n",
" \n",
" def phase(self):\n",
" return self['phase']\n",
" \n",
" def noisy_audio(self):\n",
" return self['noisy_audio']\n",
" \n",
" def noisy_spectrogram(self):\n",
" return self['noisy_spectrogram']\n",
" \n",
" def noise_spectrogram(self):\n",
" return self['noise_spectrogram']\n",
" \n",
" def reconstruct_audio(self, spectrogram):\n",
" return reconstruct_audio(spectrogram, self['phase'])\n",
" \n",
" \n",
"def get_audios(limit=10, training=True) -> List[Audio]:\n",
" \n",
" if training:\n",
" files = [f for f in os.listdir(train_dir + '/' + audio_dir)][:limit]\n",
" else:\n",
" files = [f for f in os.listdir(test_dir + '/' + audio_dir)][:limit]\n",
" \n",
" return [Audio(f, training) for f in files]\n",
"\n",
"audios = get_audios(5)\n",
"\n",
"spectrogram_shape = np.shape(audios[0].spectrogram())\n",
"\n",
"phase_shape = audios[0].phase().shape\n",
"\n",
"audio_shape = np.shape(audios[0].audio())\n",
"\n",
"print(\"Sound Shape: \", audio_shape, audios[0].audio().dtype)\n",
"print(\"Spectrogram Shape: \", spectrogram_shape, audios[0].spectrogram().dtype)\n",
"print(\"Phase Shape: \", audios[0].phase().shape, audios[0].phase().dtype)\n",
"\n",
"print(\"Waveform\")\n",
"\n",
"librosa.display.waveplot(audios[0].audio())\n",
"plt.show()\n",
"\n",
"print(\"Spectrogram\")\n",
"plt.imshow(audios[0].spectrogram())\n",
"plt.show()\n",
"\n",
"# plt.imshow(audios[0].mfcc_formatted, aspect='auto')\n",
"# plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Sound Shape: (65664,) float32\n",
"Spectrogram Shape: (80, 299) float32\n",
"Phase Shape: (442, 299) complex64\n",
"Waveform\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Spectrogram\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "KlLsduShHZo6",
"colab_type": "code",
"pycharm": {},
"outputId": "f72ded93-228f-40bd-8569-4344f3b7ca25",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 392
}
},
"cell_type": "code",
"source": [
"import time\n",
"\n",
"print(\"Reconstructed Waveform\")\n",
"\n",
"start = time.time()\n",
"recon = audios[0].reconstruct_audio(audios[0].spectrogram())\n",
"end = time.time()\n",
"\n",
"print(\"Time: \", end - start)\n",
"\n",
"print(\"Recon Shape:\", recon.shape)\n",
"\n",
"librosa.display.waveplot(recon)\n",
"plt.show()\n",
"\n",
"import IPython.display as ipy_display\n",
"\n",
"ipy_display.Audio(recon, rate=framerate)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Reconstructed Waveform\n",
"Time: 0.05281829833984375\n",
"Recon Shape: (65664,)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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B4+V4+eudmPrs15z7mwLGwBDn7N4Lnv9iu/+dAmCnKCWaqsDLq6yLZ77d4Wo662tIjkA8tTQPK7cX432vcZasTnui3HxojiW5hfj1vO/x4VrXebr/fhFUFcfKKlF0zFUk9tbq3XXzRxDVYuVznIuH9vPRcgWlFVVo1SQNh0+aD6tdOwxTsC/5I//ZhN9OOAsfrXVNKORdRJRs0eHwSKn1MN/e82psP3QSs99bj9U7j+Csdk2RV3gCrZqkY1y/dsElmuIScwxxLloCw5vf7o50Emzrd/8Sy6AAnJ6H4cCx+hXUgeR6XvxqR716BXcVFrmVygZkx8b86Qus3unK2eQVup5rV/FJbGvgqLMU3xgY4ly0FCv7uuHFi+oaxdbC46bDdG87eBLHyvz3zPY1dEZFdbWt9Fl5dOFmXPrX/4bk3BSbGBjiXLTkGGqFu3dyOO0vKcPJCvOb9+7DpXh4gf/mtFbvV9P0FNNKZqfmzKioqglZqzGKPQwMcU6j7D78pcUcD/FgS+FxPLUkz3K7VVGQO6vbvEKx7+ipeuudHJxv5ms5OGlRkU2JhYEhzj2wIDfSSfCQFAPDbNjxlY+pQwO5hasq+px5Rr31pyqq8ejCzTh43HM4jiqHywp91aFQ4mBggKtnbyhH5oyUquoafPh9dE0tefd76xu0/7g/fxGilITfgnX7/b4f1TXmuYDaVeVVnkVVWwudrbvx7sVNicmRwCAiE0UkT0TyRWS2yfZ0EXnL2P6NiGS5bbvHWJ8nIhOcSE9D3fjyarz41Y5IPHVIRWOFYpHxi3fltuKAysf9jUsUa+54a63P7dU16rMeZnOBZ8X2Fc985Ui6ar357R785IVVdf0cKDHZDgwikgzgWQCXAugHYLqI9PPa7SYAR1S1J4CnATxhHNsPrjmi+wOYCODvxvlCYv7afZZDA/jqFVpaUYVV2wO7kUVK1uyF9cqgo/WmerysEtP/uQp7j5xOr6pG9evrJF/1AlU1NT6Lh25+LQevfL0DR0JY5PN1fjGG/eHTkJ0/HlRV18R1fYwTOYZhAPJVdbuqVgCYB2CK1z5TALxqPH4XwBhxjek8BcA8VS1X1R0A8o3z+bW54BgKvX7VbC08joqqGuQXncDhkxVYvKEARcfKsGDdfjzz2Vb8at5aPP/FNgBAftFxXPX3rzH6qc8BuHqr3vn2WvzmrbWorK7BB9/vxfS5qwAAzy7fhmlzV1mOx+N9U/t4YwGGPrIMOw6dxJpdR/DTF75xrJJww94SPDg/FwUl9Ssidx46iTW7Dkf9DXbAg67JbZbnFSF3fwm2Fh5Ht3sWods9i5B34Dh637sYqor56/ZHOKWh0eP3i3DHvO+xeEMBdhWfBHC6ddGq7cWo8tMv4cEFmzDkkWUhH2J7yjNfYf66/R45mFMV1Xg7Zw8On6yo1+P6u91H8O6avQBc4zPd9c46vP/d3nrnPVBSho37Tg8jXnKqEn9fno9dxScx/ukvsGn/MQCu12Tf0VNYv/cobv3XGry6Yme9cy3aUIDdxdbzaefuL8Eb3+y2/P7tOGTejLisshqFx8rwt8+2elz/df9chdvf/B53vbse/R9YgqeW5OGqf6yo+2FZVe1q3VX7fqpqvdxXdY1ix6GTmPb8Sr/BpbK6Bu9/t7de3ZK70ooqFB0//RzVNYoV2w7VpXtX8UmcLK/C26v3BDyWvti9iYjI1QAmqupMY/lnAM5T1dvc9tlo7LPXWN4G4DwADwJYpar/Mta/CGCxqr7r6zl79Buo1ZMfBwA8NLk/Hpifi/H92vkdhbNWVuvG2Onjw3TN0E54Z039D3TrJmm4dVQPiAgGdGyO73cfQc6uI1hmPO9T1wxCt8wmeGhBLtYb4+e3bpKG4pMVmHF+V/xnfQGapKfgiasGonXTNJRWVGPV9mK88c1uZDZNw/HyKlw/vCtaNknD0txC9G1/BtJTkrFm9xHcPron/r1qN15ftQsA0Lf9GUhNTsJNF3TD7uLSsE5pSc7698zzcKCkDP/7zrpIJ8XSTRd0wzs5e3CszPNG9tiPzsZ9H2zE3Zf2wRy3EWBTkwVV1a657Hq3a4qpgzuib/tm2Fp0HAvXF2Dd3hIM6Ngct4/uiaeW5mGLW11J7XcGAJLEsy/O/Zf3w/GySozp2w7PLM/HxxsPAABuvrAbRvdph9z9JRjSpQVW5Bdj0cYCpKckYe2eElwztBPundQXx8uqUHS8HC/8dztaNE7Fm9/uQdsz0nH76J7o3qYp/rN+Pw4cK8fyH4rqnjOzaVrdgIS+3DepL3J2HsHHuQdw2yU90axRCv6w6Ie612PWhd3x4dr99XL2E/q3Q2W14kBJGZIE2Lj/GO4c1xvJSYLcfSVYuqkQVTWKLq0ao22zdOTsPIIRPVrjjIwUXD6wA/6waDMKSspw36S+KDlViV3FpXU/qi7unYn/bj1U9xrufvqaH2rKS/v6u5aYCQwiMgvALABIbtZmaKdbX7aVbicJomfYBiIiKwWv3oHygq1+cw1OFCXtA9DZbbmTsc50HxFJAdAcQHGAxwIAVHWuqmarana7tm3r1l/Uu03QCU+2eHk6tMiwPGZkj9Y4p0sL3DmuN87NaomMlKS6oDB5UHvMuqg7UkzGtOnSqnHd45tGZuH+y/vhrvG9Mbbv6WsRAKP7tMVlA85EVuvGyM5qiX7tmwEAHryiH3q1bVq3b+1TnNetFUadFfxrQJE3fVgXXBLl76HVnWRol5YAUPc5dZckpz+nw7u3wozzu2JM37Zw/3pMO7ezz/O7S04SXHr2mRjRozVuubg7mqSfro4c0aM1br6wG8b0aYvfTjgLw7q1AgCckeEaDq5Lq8aY+7OhePa6c3DPpX1wRkYKerc7/X0a2aM1fnJeF7Rvbv3d96dV47S6x0M6t0BW68Ye2/u2r98M2cqFvTIxsFNznNks3XKMrGYZKZg6uAMyUl238TF922LUWW087jXNG6Ui1e1GV3WkYGsgz+9EjiEFwBYAY+C6qa8GcJ2q5rrt80sAA1T1f0RkGoArVfVaEekP4A246hU6APgUQC9V9dn3Pzs7Wx99ZQGaZaRiRM/MuvXL84rQq21TLN5wAGd3bI4vtxzEJX3a4pPNhdhaeBzL8w5iRI/WeOPm4fhkU6HHMNDTh3XBd7uOICkJePuW8/GnpVvwyoqd2PaHy3Dfhxvw5rd78OAV/XDDyG710nOivAoFR0+hZ9umEBG88vUOPLhgEz74xQgUn6jA7PfX46u7RyMtOQmVNTWmM5nV1CiqVZGa7DtWL9t0AE98nIe//Hgwzu54ekavrNkL8dxPh2LjvhL8emwvJIug++8X+TxXpI3v1w5XDOqARqnJde/F27ecj2ufX4kfHpmIv326Fc9+vi3CqQyN5o1SMXlQB1x5TkcM6dISqopu9yzCpIHtsSL/EI6URkfz6Z8N74rbR/dE22YZKKusxsHj5Xh95S5MGdIBjdNS0C2zSd2+H63dh5XbivHQlP44VVGN6/65Chf1boPZl3qWXPxw4Bh2HjqJiWe3B+AaZvzJJT/gR0M64cZXVuONmefh7E7NUVJaiSOlFdiwtwTPLM/HRb3b4ImrBtadR1Xxp6V5uKh327pA4K6mRvHl1oP4ZHMh7pvUDxmp9b93X+QVoWe7M+rNqb3ncCnyDhzHC19tx8s3DEOjNNex2Y8uQ40CnVo2wvq9JRjRozVWbCtG3qMTkZ6SjNKKKry2chduvrA7kpMEZZXVyN1/DEO7ugJoVXUNjpRWInd/CW54eTWW3zXK4zWsqVGcrKjCGRmpAIAjJyvw3Bfb8KNzOqLPmfWDLwDsP3oKh09W1N0PTlVU4501ezCiRya6ZTbB53lF6NGmKV76egce+dGgtVpTPcTi7a5jOzAAgIhcBuAvAJIBvKSqj4nIwwByVHW+iGQAeB3AEACHAUxT1e3GsfcC+DmAKgB3qOpif8+XnZ2tOTkNH9v/jx//gCGdW2Bc/zPr1tV+IW+7pCfuGNsLIlIvQhcdK8PSTYW4Nrsz0lICy2SdLK9Ck/TwDV6bNXshPrnzYvR0y1VE6/y/K2aPxog5n+GL345C19auL0VpRRWqahTNMlJRXaN170G0XoMd3/x+DNo0TUeS1+csa/ZCvH7TMNzz/gaPFlveBnRsjj9fOwhdWjfGWffZm0DJl51zJoXs3FaqqmuQYvLjyP0zEUllldVIS07CiYoqHDlZUff5jRUiskZVs/3t58idS1UXAVjkte5+t8dlAK6xOPYxAI85kQ5/fjexT711tZX0SUli+oEEgLbNMvDT4V0b9FzhDAoAsOHB8XW/Mmo1Sk3GqcrQDLxmR4cWjXDfpL4ev9Iap51+vaLhBhBK7ZpZF1ekJCX5vP6Hp/TH9ednhSBVp6UlJ+H1mwJqHOg4q+9gtHwmanMdzTJS0czr+xZP2PMZwC9G9cCVQzpGOhm2eAcFAFh5z+gIpCQwMy/sbnkTiGf3X+7dxcdTcpL4LE685Ky2Hst/dCtacUKHFhnY+NAEnNe9taPnpdiSeN9ME7+b2AdZmbGVJQxEi8Zp9cpOI+3lG85t0P6RKM4IleHdW2HGiCyf+yQJTBsv1PIOGlcM6uBE0urMu/n8gItLKX7xExDnFv3qwkgnwUO0DQMeTu2aZfgtEhER/GAyn0OTtGRcf35XtGuW7rE+xappXZCaN47f4hEKHANDnJMoe4ezs+q3HokXWa0b48NfjrTcHshcFFaBQwQY3r11vY6rvnIXDXXnuN5o3oiBgRgY4l60DXMdzzeerNZNLG/UqcmC+yb5rl8ArNvznyh3tYapt7+D7++vxvRy7FwU28LbdIbCLkoacySEpCRBv/bN0DgtGaVeM7ld0DMTHQKo7/EVyENV9j/zgm64OMo72FF4MccQ56Ilx3DfJL/Ds0S95XeN8tkztlmjFCQlCc4NsrisR5smHr1WvZl10AIaVpz0+k3D0KmlZ4Aa3r01LuzFwECnMccQ56IkLjha5BEp3TKboKDEep6CRsaN2+w+HUiV+6f/OwoAMLhzC9Ph4a06ozZvlFo36Jy3zi0bofB4ed20oiN7ZOKtW85HSWkl+nVohtve+A4DOzc3PZYSF3MMcS7Z5g25c6voau4aab5ezU4tXb/2zXJpDWmMNeeqAQBOjyNUq8xizmirepum6Sl45efDcOvFPQC4elwnJQk6tmiEfh1cwys8c905aHtG8OMDUXxijiHO2SlK+uu0wZgyuKMjw1KM69sOh05YjykfKzLSknGqon5v8qeuGYQrBrnG/rGbOepzZjPsnDMJK7YdwrzVe+rWX+A2Lpg7qw5xS39zETq0aIQZI7LQpVVjnz2uidwxxxDn7NykaotGfjGqBzY8ON5WOjLPSMPdJkOSOKVpmIYgMcuBDejYDFcP7eQ2OKJJjiGI5xrRwzMQWDVlTU+t/zXO7toSrZu6Rvts1SQNVw3tFEQKKFExMMQ5O2X7tcf+bmIfj7GMgmEn5+JvuOIfZ3fGxocmhKWCO92kZdCC2z07EZrdv+22Drv+fOuxurxzDLkPTcC7t44wHcWXKBAMDGTJ/V5mdxAzqxY1gXj/VutOY5MHdcAtF3cHEJ4WWB/8YiSW/eYin/uYJcNu2i4b0N5y24wRWbh8YHv86ZpBAMI/gCPFH36CyFK0DF9ROxa+mRtGZqF7G9dQ4+Hos9GltXVz0lpmQSDYwPD2LefjSGkFhvsY1G7yoA6YbIyZNKZvW8v9iALFHANZ8p4//T+3XxB1HeYq3FrqhHu01pYW4wqZ5xiCe45h3Vphgtv8If60cJtFjChYDAzkg2dkOLtjc2x/PHpGO33qmkE4x5haEjg9jWMorHugfuX77aN7IfehCfXWi0nlc7R0NCQKBAMDWaryzjJEmauHdvIYJuKSPm1DNneyWV+BJLEozzfLMfCbRjHE1sdVRFqJyDIR2Wr8b2myz2ARWSkiuSKyXkR+7LbtFRHZISJrjb/BdtJDzqqqju7A4K1ZRir+8dOh+N3Es3zu996tI0KaDrO8wc9N5gonilZ2f8fMBvCpqvYC8Kmx7K0UwPWq2h/ARAB/EZEWbtt/q6qDjb+1NtNDJswmSg+EWdPMSGp7RjqmD3P1Bl53v3m/iozUZPzMxzTWS+qGAAAOt0lEQVSsj0w9u25idruswqZ3E+HXfj4srocbp/hj95s/BcCrxuNXAUz13kFVt6jqVuPxfgBFADhiVxj9vwCGezYztl870/WRmlVNFXW9jn1NKJPio9wmmJL+F643nzvdqt7AvaK5aXoKLurNjzvFFruBoZ2qFhiPDwAwv5MYRGQYgDQA29xWP2YUMT0tIukWh5INwdZ7+pp7OBJqoBjUuQUym/pueZOWkoTOLRthx+OX1dvWooEzlA3LamUaIOfNGo4fe41lVMv95T43y5ncCVE4+W3GISKfADBrL3ev+4KqqohYFkqLSHsArwOYoaq1bQzvgSugpAGYC+BuAA9bHD8LwCwA6NKli79kk5t4ahBz48huuNFPeX1ykuC/d4+ut/7xKwdgko+OYt4u6JmJv//0HNNtvvoV1BYlJYtgrkVugyia+Q0MqjrWapuIFIpIe1UtMG78RRb7NQOwEMC9qrrK7dy1uY1yEXkZwF0+0jEXruCB7Ozs2KoVjbC4aSrpwLvekCFC2jfPQLOMhs841yTd1SEvPTUp6nJdRIGw+6mdD2CG8XgGgI+8dxCRNAAfAHhNVd/12tbe+C9w1U9stJkeMhFMYOhzpu/xiSLBblxoaE/ucothrv25fTSnyKTYZjcwzAEwTkS2AhhrLENEskXkBWOfawFcBOAGk2ap/xaRDQA2AMgE8KjN9JCJYHrdnhWFgcHOEB13jO2Fa7PN6wTMJAkwbVjg+7vj8NYU62x1FVXVYgBjTNbnAJhpPP4XgH9ZHF+/IJgcF4rZ0wZ1boF1JrOMhVJDprCstXPOJGTNXohumU0aVKxz57je9Ya9JkoULABNAHZHRjXz0S+tRzwNFV+D6flzoryqQfuPOouD0VHi4uiqCSCYuBBt1dXTh3XGWe2CK976f5f3w/h+gQ9E93/Th+DsjpwHmRIXA0MCiIdWSY9fOTDoY2+6oGHDUXTPbBL0cxHFAwaGBBBMXEjE9sDDurXCyzecy4luKOGxjiEBxEOOIRwentLf0aBQHeWj0xJZYWBIAKGofI5HNcF1W7BUWe3wCYnChIEhATDD4N99k/o62qkvSerPgEcUKxgYEgCLkvwbdVYbJDmYszKb2IcoVrCWLQEwMNTXsUUj7Dt6qm7ZRqdqUwtuv4B1DBSzmGNIAMkhCgxdWzUOyXnD4evZoz3S7/Q9vFPLxujams1eKTYxMCSA1JTQBIYvfndJSM4bLu7pb2yjVzVRvGFgSAC1M5plN2BKSydDyYcRGD6jId6YeR46x3Duh8hpDAwJIDXZdZufeHbgw0I4KdihLMKlX4dmkU4CUVRh5XMCqJtRLEL9GaK57jtS81cTRTPmGBJIo9TAy9F7RfmvfCIKHQaGBJH36ERceU4nZKQG9pb/8pKeIU4REUUrBoYEkZ6SjLSUJIzp2y7SSSGiKGcrMIhIKxFZJiJbjf+mzV5EpNptWs/5buu7icg3IpIvIm8Z80NTDLvuvC6RTgIR2WQ3xzAbwKeq2gvAp8aymVOqOtj4m+y2/gkAT6tqTwBHANxkMz3kRyTqgaO58pmI6rMbGKYAeNV4/CqAqYEeKK6mMqMBvBvM8RSdGAOIYp/dwNBOVQuMxwcAWBVgZ4hIjoisEpHam39rAEdVtXYy3r0AOtpMD4XZr0Z7VlL371B/SkxhuCCKKX4Dg4h8IiIbTf6muO+nqgrrib+6qmo2gOsA/EVEejQ0oSIyywguOQcPHmzo4RQid44/CwAwoX877Hj8MtYxEMUBvx3cVHWs1TYRKRSR9qpaICLtARRZnGOf8X+7iHwOYAiA9wC0EJEUI9fQCcA+H+mYC2AuAGRnZ3PYyiiTlpJc15HOG+sYiGKL3aKk+QBmGI9nAPjIewcRaSki6cbjTAAjAWwychjLAVzt63hyltXNm4iolt3AMAfAOBHZCmCssQwRyRaRF4x9+gLIEZF1cAWCOaq6ydh2N4A7RSQfrjqHF22mh6LAX6cNjnQSiMgGW2MlqWoxgDEm63MAzDQerwAwwOL47QCG2UkDNUxFVXXIn2PK4I749by1dcvMoxDFFvZ8TjClFaEPDEQU2xgYEsy12Z3x4+zOjp/XV66A9RpEsYWBIcFcMagDnrh6oOPnTU+x/igxLBDFFgYGckS6xaitb948HEkRmgeCiILDwJCg1j0w3tHzdc9sarq+c6tGjj4PEYUeZ3BLUM0bpTp2rtyHJlhOAsT6BaLYw8BAtjVJ58eIKJ6wKIlCivkFotjDwEAhlcyKZ6KYw8BAIZVhUfdARNGLgYHqmXF+V8fOZVUpTUTRi4GB6nloytmOnGfSwPZI89HxjYiiE7+1FDKzLuwe6SQQURAYGChkWjZOi3QSiCgIbIBOIbH1sUuRmszfHUSxiN9cCgkGBaLYxW8vERF5sBUYRKSViCwTka3G/5Ym+1wiImvd/spEZKqx7RUR2eG2jXNCEhFFmN0cw2wAn6pqLwCfGsseVHW5qg5W1cEARgMoBbDUbZff1m5X1bXexxMRUXjZDQxTALxqPH4VwFQ/+18NYLGqltp8XiIiChG7gaGdqhYYjw8AaOdn/2kA3vRa95iIrBeRp0Uk3WZ6qAGW3HFRpJNARFHIb3NVEfkEwJkmm+51X1BVFRH1cZ72AAYAWOK2+h64AkoagLkA7gbwsMXxswDMAoAuXbr4SzYFgMNVEJEZv4FBVcdabRORQhFpr6oFxo2/yMeprgXwgapWup27NrdRLiIvA7jLRzrmwhU8kJ2dbRmAiIjIHrtFSfMBzDAezwDwkY99p8OrGMkIJhDXNF9TAWy0mR5qAE6uRkRm7AaGOQDGichWAGONZYhItoi8ULuTiGQB6AzgC6/j/y0iGwBsAJAJ4FGb6SGbnv7xoEgngYgizNaQGKpaDGCMyfocADPdlncC6Giy32g7z0/Om9i/faSTQEQRxp7PRETkgYGB6vTv0AyN0thSiSjRMTBQnaFd641oQkQJiIEhgSUleTZLSuOIqEQEBoaEluzVXjU9lR8HImJgSGhJXu9+s4zUyCSEiKIKA0MCS/LKMdwwMisyCSGiqMLAkMC8A0N6ClskEREDQ0LzrmMgIgIYGBKadx0DERHAwJDQvIuSiIgABoaElpzEwEBE9TEwJDBmGIjIDANDAnPv6dyYYyQRkYGBIYGJCH4ztjcAYPGvL4xwaogoWjAwJLjUFFd5UtfWTSKcEiKKFgwMCW5YViu0bMyhMIjoNFuBQUSuEZFcEakRkWwf+00UkTwRyReR2W7ru4nIN8b6t0QkzU56qOGys1rh+/vHRzoZRBRF7OYYNgK4EsCXVjuISDKAZwFcCqAfgOki0s/Y/ASAp1W1J4AjAG6ymR4iIrLJVmBQ1c2qmudnt2EA8lV1u6pWAJgHYIqICIDRAN419nsVwFQ76SEiIvvCUcfQEcAet+W9xrrWAI6qapXXelMiMktEckQk5+DBgyFLLBFRokvxt4OIfALgTJNN96rqR84nyZyqzgUwFwCys7M1XM9LRJRo/AYGVR1r8zn2AejsttzJWFcMoIWIpBi5htr1REQUQeEoSloNoJfRAikNwDQA81VVASwHcLWx3wwAYcuBEBGRObvNVX8kInsBnA9goYgsMdZ3EJFFAGDkBm4DsATAZgBvq2qucYq7AdwpIvlw1Tm8aCc9RERkn7h+uMeW7OxszcnJiXQyiIhiioisUVXLPmd1+8ViYBCR4wD8NZONZZkADkU6ESEU79cHxP818vpiU1dVbeNvJ7+Vz1EqL5CoF6tEJIfXF9vi/Rp5ffGNYyUREZEHBgYiIvIQq4FhbqQTEGK8vtgX79fI64tjMVn5TEREoROrOQYiIgqRmAoMVvM6xCp/1yMiN4jIQRFZa/zNjEQ6nSIiL4lIkYhsjHRanODvekRklIiUuL1/94c7jU4Skc4islxENhnzsPw60mmyI5Dribf3MFAxU5RkzOuwBcA4uEZiXQ1guqpuimjCghTI9YjIDQCyVfW2iCTSYSJyEYATAF5T1bMjnR67/F2PiIwCcJeqXh7utIWCiLQH0F5VvxORMwCsATA1hr+Dfq8n3t7DQMVSjsF0XocIp8mOeLsev1T1SwCHI50Op8Tb9fijqgWq+p3x+DhcQ9xYDpUf7eLtepwUS4HBal6HWBXo9VwlIutF5F0R6WyynaLb+SKyTkQWi0j/SCfGKSKSBWAIgG8imxJn+LmeuHwPfYmlwJCIFgDIUtWBAJbBNcsdxY7v4BqCYBCAvwH4MMLpcYSINAXwHoA7VPVYpNNjl5/ricv30J9YCgxW8zrEKr/Xo6rFqlpuLL4AYGiY0kYOUNVjqnrCeLwIQKqIZEY4WbaISCpcN9F/q+r7kU6PXf6uJx7fw0DEUmAwndchwmmyw+/1GJVjtSbDVQZKMUJEzjTmNoeIDIPr+1Yc2VQFz7iWFwFsVtU/Rzo9dgVyPfH2HgYqZgbRU9UqEamd1yEZwEtu8zrEHKvrEZGHAeSo6nwAvxKRyQCq4KrkvCFiCXaAiLwJYBSATGMejwdUNWbn4DC7HgCpAKCqz8E1CdWtIlIF4BSAaRorzQDNjQTwMwAbRGStse73xi/pWGR6PQC6AHH7HgYkZpqrEhFReMRSURIREYUBAwMREXlgYCAiIg8MDERE5IGBgYiIPMRMc1WiSBCR1gA+NRbPBFAN4KCxXKqqIyKSMKIQYnNVogCJyIMATqjqU5FOC1EosSiJKEgicsL4P0pEvhCRj0Rku4jMEZGfiMi3IrJBRHoY+7URkfdEZLXxNzKyV0BkjoGByBmDAPwPgL5w9abtrarD4Brj6nZjn78CeFpVzwVwlbGNKOqwjoHIGatVtQAARGQbgKXG+g0ALjEejwXQzxh6BwCaiUjT2kHaiKIFAwORM8rdHte4Ldfg9PcsCcBwVS0LZ8KIGopFSUThsxSni5UgIoMjmBYiSwwMROHzKwDZxox8m+CqkyCKOmyuSkREHphjICIiDwwMRETkgYGBiIg8MDAQEZEHBgYiIvLAwEBERB4YGIiIyAMDAxERefj/JcWSO9MKw6MAAAAASUVORK5CYII=\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "execute_result",
"data": {
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\" type=\"audio/wav\" />\n",
" Your browser does not support the audio element.\n",
" </audio>\n",
" "
]
},
"metadata": {
"tags": []
},
"execution_count": 96
}
]
},
{
"metadata": {
"id": "gSPUXfj6XQ3k",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Remove old files"
]
},
{
"metadata": {
"id": "dTnSG4lOXXHN",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"#shutil.rmtree('files')"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "-KLR9J3phXNL",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Combined"
]
},
{
"metadata": {
"id": "_p2x5TXTMCmE",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"class AudioVideo:\n",
" def __init__(self, name, training=True):\n",
" self.video = Video(name, training)\n",
" self.audio = Audio(name, training)\n",
" \n",
" def get_data(self, keys):\n",
" \n",
" if isinstance(keys, str):\n",
" keys = [keys]\n",
" \n",
" if 'video' in keys:\n",
" data = {'video': self.video.data()}\n",
" keys = {k for k in keys if k != 'video'}\n",
" else:\n",
" data = {}\n",
" \n",
" d = self.audio[keys]\n",
" \n",
" if isinstance(d, dict):\n",
" for k, v in d.items():\n",
" data[k] = v\n",
" else:\n",
" data[keys[0]] = d\n",
" \n",
" \n",
" if len(data) == 1:\n",
" return data[next(iter(data))]\n",
" \n",
" return data\n",
" \n",
" def __getitem__(self, item: Iterable[str]):\n",
" return self.get_data(item)\n",
" \n",
" def audio_data(self):\n",
" return self['audio']\n",
" \n",
" def video_data(self):\n",
" return self['video']\n",
" \n",
" def spectrogram(self):\n",
" return self['spectrogram']\n",
" \n",
" def phase(self):\n",
" return self['phase']\n",
" \n",
" def video_data(self):\n",
" return self['video']\n",
" \n",
" def noisy_audio(self):\n",
" return self['noisy_audio']\n",
" \n",
" def noisy_spectrogram(self):\n",
" return self['noisy_spectrogram']\n",
" \n",
" \n",
" \n",
"def get_audio_and_video(limit=100, training=True):\n",
" \n",
" if training:\n",
" files = [f for f in os.listdir(train_dir + '/' + video_dir)][:limit]\n",
" else:\n",
" files = [f for f in os.listdir(test_dir + '/' + video_dir)][:limit]\n",
" \n",
" while True:\n",
" for f in files:\n",
" yield AudioVideo(f, training)\n",
"\n",
" \n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "DkFTUmqMhCoR",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Training Utilities"
]
},
{
"metadata": {
"id": "vldFdtwVhPAb",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Data generators\n",
"\n",
"Our data is quite large, and Keras supprots using generators for training and prediction, so we take advantage of this."
]
},
{
"metadata": {
"id": "_sG4xKle4ZSr",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"def get_training_data(limit=100, batch=10, use_video=False):\n",
" i = 0\n",
" \n",
" av_getter = get_audio_and_video(limit)\n",
" \n",
" while True:\n",
" \n",
" if use_video:\n",
" video_input = np.empty((batch,) + video_shape, dtype='uint8')\n",
" \n",
" spectrogram_input = np.empty((batch,) + spectrogram_shape, dtype='float32')\n",
" \n",
" spectrogram_output = np.empty((batch,) + spectrogram_shape, dtype='float32')\n",
" \n",
" for i in range(batch):\n",
" av = next(av_getter)\n",
" \n",
" keys = ['spectrogram', 'noisy_spectrogram']\n",
" \n",
" if use_video:\n",
" keys.append('video')\n",
" \n",
" data = av[keys]\n",
" \n",
" spectrogram_input[i] = data['noisy_spectrogram']\n",
" spectrogram_output[i] = data['spectrogram']\n",
" \n",
" if use_video:\n",
" video_input[i] = data['video']\n",
" \n",
" data = {}\n",
" \n",
" if use_video:\n",
" data['video'] = video_input\n",
" \n",
" data['noisy_spectrogram'] = spectrogram_input\n",
" \n",
" data['spectrogram'] = spectrogram_output\n",
" \n",
" yield data\n",
" \n",
"def get_testing_data(limit=100, use_video=False):\n",
" av_getter = get_audio_and_video(limit, training=False)\n",
" \n",
" noisy_spectrogram = np.empty((limit,) + spectrogram_shape, dtype='float32')\n",
" audio = np.empty((limit,) + audio_shape, dtype='float32')\n",
" noisy_audio = np.empty((limit,) + audio_shape, dtype='float32')\n",
" \n",
" if use_video:\n",
" video = np.empty((limit,) + video_shape, dtype='uint8')\n",
" \n",
" phase = np.empty((limit,) + phase_shape, dtype='complex64')\n",
" \n",
" for i in range(limit):\n",
" \n",
" av = next(av_getter)\n",
"\n",
" keys = ['noisy_spectrogram', 'audio', 'noisy_audio', 'video', 'phase']\n",
"\n",
" if use_video:\n",
" keys.append('video')\n",
" \n",
" data = av[keys]\n",
" noisy_spectrogram[i] = data['noisy_spectrogram']\n",
" audio[i] = data['audio']\n",
" noisy_audio[i] = data['noisy_audio']\n",
" \n",
" if use_video:\n",
" video[i] = data['video']\n",
" \n",
" phase[i] = data['phase']\n",
" \n",
" result = {\n",
" 'spectrogram_input': noisy_spectrogram,\n",
" 'audio': audio,\n",
" 'noisy_audio': noisy_audio,\n",
" 'phase_input': phase\n",
" }\n",
" \n",
" if use_video:\n",
" result['video_input'] = video\n",
" \n",
" return result"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "0bDj-2J57Q9j",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"## Tensorflow Inverse STFT\n",
"\n",
"Tensorflow's istft doesn't play nice with librosa's stft, so we had to implement our own. We use this for turning the spectrogram back into audio in the model, which isn't really nessecary now, but we implemented this so we could try a residual network (it didn't work)."
]
},
{
"metadata": {
"id": "RAIBdh6pH9mP",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"import tensorflow as tf\n",
"import tensorflow.signal\n",
"\n",
"from tensorflow import convert_to_tensor, map_fn\n",
"import tensorflow.keras.backend as K\n",
"from tensorflow import float32 as tf_float32\n",
"import tensorflow_probability as tfp\n",
"\n",
"def log10(x):\n",
" num = tf.log(x)\n",
" den = tf.log(tf.constant(10, dtype=num.dtype))\n",
" return num / den\n",
"\n",
"def tf_db_to_amp(signal):\n",
" return 10.0**(0.05 * signal)\n",
"\n",
"def tf_amp_to_db(signal):\n",
" amin = 1e-10\n",
" topdb = 80.0\n",
" signal = signal ** 2\n",
" \n",
" log_spec = 10.0 * log10(tf.math.maximum(amin, magnitude))\n",
" log_spec -= 10.0 * log10(tf.math.maximum(amin, 1.0))\n",
" log_spec = tf.math.maximum(log_spec, log_spec.max() - top_db)\n",
" \n",
" return log_spec\n",
"\n",
"tf_mel = tf.constant(\n",
" librosa.filters.mel(\n",
" sr=framerate,\n",
" n_fft=n_fft,\n",
" n_mels=80,\n",
" fmin=0,\n",
" fmax=8000\n",
" )\n",
")\n",
" \n",
"ifft_window = librosa.filters.get_window('hann', n_fft, fftbins=True)\n",
"ifft_window = librosa.util.pad_center(ifft_window, n_fft)\n",
"ifft_window = tf.constant(ifft_window, dtype='float32')\n",
"\n",
"ifft_window_sum = tf.constant(librosa.filters.window_sumsquare('hann',\n",
" 299,\n",
" win_length=n_fft,\n",
" n_fft=n_fft,\n",
" hop_length=frame_step))\n",
" \n",
"approx_nonzero_indices = ifft_window_sum > librosa.util.tiny(ifft_window_sum)\n",
" \n",
"divisor = tf.where(approx_nonzero_indices, ifft_window_sum, tf.ones(ifft_window_sum.shape[0]))\n",
" \n",
"def tf_istft(stft_matrix, hop_length, window='hann', center=True):\n",
" win_length = n_fft\n",
" \n",
" n_frames = int(stft_matrix.shape[1])\n",
" \n",
" expected_signal_len = n_fft + hop_length * (n_frames - 1)\n",
" \n",
" y = tf.zeros((expected_signal_len,), 'float32')\n",
" \n",
" i = tf.constant(0, dtype='int32')\n",
" \n",
" _, y = tf.while_loop(\n",
" lambda i, y: i < tf.constant(n_frames), \n",
" lambda i, y: tf_loop_body(y, i, hop_length, stft_matrix, ifft_window, expected_signal_len, n_fft), \n",
" loop_vars = [i,y])\n",
" \n",
" #y = tf.math.add_n(y)\n",
" \n",
" y /= divisor\n",
" \n",
" y = y[int(n_fft // 2):-int(n_fft // 2)]\n",
" \n",
" return y\n",
"\n",
"def tf_loop_body(y, i, hop_length, stft_matrix, ifft_window, expected_signal_len, n_fft):\n",
" \n",
" sample = i * hop_length\n",
" spec = tf.squeeze(stft_matrix[:, i])\n",
" spec = tf.concat((spec, tf.math.conj(spec[-2:0:-1])), 0)\n",
" ytmp = ifft_window * tf.math.real(tf.signal.ifft(spec))\n",
"\n",
" #tf.assign_add(y[sample:(sample + n_fft)], ytmp)\n",
"\n",
" ytmp = tf.pad(ytmp, [[sample, expected_signal_len - (sample + n_fft)]], mode='CONSTANT')\n",
" \n",
" return [tf.add(i, 1), tf.add(y, ytmp)]\n",
"\n",
"import functools\n",
"\n",
"def tf_reconstruct_audio(mp):\n",
" \n",
" magnitude = mp[0]\n",
" phase = mp[1]\n",
" \n",
" print(phase.dtype)\n",
" \n",
" magnitude = tf_db_to_amp(magnitude)\n",
"\n",
" mel_filterbank = tf_mel\n",
"\n",
" magnitude = tfp.math.pinv(mel_filterbank) @ tf.cast(magnitude, 'float64')\n",
" \n",
" \n",
" mag_phase = tf.cast(magnitude, 'complex64') * phase\n",
" \n",
" wave = tf_istft(mag_phase, hop_length=frame_step)\n",
" \n",
" pad = 65664 - int(wave.shape[0])\n",
" \n",
" if pad > 0:\n",
" wave = tf.pad(wave, [[0, pad]], 'constant')\n",
" \n",
" return wave\n",
"\n",
"def tensor_reconstruct_audio(ip):\n",
" tensor = tf.map_fn(\n",
" tf_reconstruct_audio, \n",
" ip, \n",
" dtype=tf_float32, infer_shape=False)\n",
" tensor.set_shape((None, 65664,))\n",
" return tensor\n",
" \n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "F5vpLYMCctGg",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"# with tf.Session() as sess:\n",
"# m = tf.constant(audios[0].spectrogram())\n",
"# p = tf.constant(audios[0].phase())\n",
"# recon = tf_reconstruct_audio([m, p])\n",
"# start = time.time()\n",
"# recon = sess.run(recon)\n",
"# end = time.time()\n",
"\n",
"# print(\"Time: \", end - start)\n",
"\n",
"# print(\"Recon Shape:\", recon.shape)\n",
"\n",
"# librosa.display.waveplot(recon)\n",
"# plt.show()\n",
"\n",
"# import IPython.display as ipy_display\n",
"\n",
"# ipy_display.Audio(recon, rate=framerate)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "uJJURBxOq94A",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"# Denoising Autoencoder#\n",
"\n",
"Audio encoder and decoder from [here](https://github.com/avivga/audio-visual-speech-enhancement).\n",
"\n",
"Video encoder from [here](https://github.com/rizkiarm/LipNet)."
]
},
{
"metadata": {
"id": "7JD6KOuZuXC-",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"from tensorflow.keras.layers import Input, Flatten, Dense, Reshape, Concatenate, Dropout\n",
"from tensorflow.keras.models import Model\n",
"from tensorflow.keras.optimizers import SGD, Adadelta, Adam\n",
"from tensorflow.keras.layers import Conv3D, ZeroPadding3D, UpSampling1D, ZeroPadding1D\n",
"from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Cropping2D, Cropping1D\n",
"from tensorflow.keras.layers import MaxPooling3D, MaxPooling2D, UpSampling2D\n",
"from tensorflow.keras.layers import Dense, Activation, SpatialDropout3D, Flatten\n",
"from tensorflow.keras.layers import Bidirectional, TimeDistributed, Subtract\n",
"from tensorflow.keras.layers import GRU, LSTM, Lambda, LeakyReLU, ZeroPadding2D\n",
"from tensorflow.keras.layers import BatchNormalization, MaxPooling1D, Conv1D\n",
"from tensorflow.keras.layers import SpatialDropout2D, Conv1D, Permute\n",
"\n",
"import tensorflow.keras.backend as K\n",
"\n",
"from progressbar import progressbar as tqdm\n",
"\n",
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \n",
"tf.logging.set_verbosity(tf.logging.ERROR)\n",
"\n",
"class Autoencoder:\n",
" def __init__(self, use_video=False):\n",
" \n",
" self.use_video = use_video\n",
" \n",
" self.reconstruct_layer = Lambda(tensor_reconstruct_audio, name='reconstruct_audio', output_shape=(65664,))\n",
" \n",
" self.video = [\n",
" ZeroPadding3D(padding=(1, 3, 3), name='zero1'),\n",
" Conv3D(32, (3, 5, 5), strides=(1, 2, 2), kernel_initializer='he_normal', name='conv1'),\n",
" BatchNormalization(name='batc1'),\n",
" Activation('relu', name='actv1'),\n",
" SpatialDropout3D(0.3),\n",
" MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max1'),\n",
" \n",
" ZeroPadding3D(padding=(1, 2, 2), name='zero2'),\n",
" Conv3D(64, (3, 5, 5), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv2'),\n",
" BatchNormalization(name='batc2'),\n",
" Activation('relu', name='actv2'),\n",
" SpatialDropout3D(0.3),\n",
" MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max2'),\n",
" \n",
" ZeroPadding3D(padding=(1, 1, 1), name='zero3'),\n",
" Conv3D(92, (3, 3, 3), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv3'),\n",
" BatchNormalization(name='batc3'),\n",
" Activation('relu', name='actv3'),\n",
" SpatialDropout3D(0.3),\n",
" MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max3'),\n",
" \n",
" TimeDistributed(Flatten()),\n",
" \n",
" Bidirectional(GRU(256, return_sequences=True, kernel_initializer='Orthogonal', name='gru1'), merge_mode='concat'),\n",
" \n",
" Dropout(0.3)\n",
" ]\n",
" \n",
" self.spectrogram_encoder = [\n",
" Reshape((spectrogram_shape) + (1,)),\n",
" \n",
" Conv2D(64, kernel_size=(5, 5), strides=(2, 2), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2D(64, kernel_size=(4, 4), strides=(1, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2D(128, kernel_size=(4, 4), strides=(2, 2), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2D(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2D(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2D(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Permute((2, 1, 3)),\n",
" TimeDistributed(Flatten())\n",
" ]\n",
" \n",
" self.spectrogram_latent = [\n",
" \n",
"# GRU(384, return_sequences=True, kernel_initializer='Orthogonal', name='latent_rnn1'),\n",
"# TimeDistributed(BatchNormalization()),\n",
"# TimeDistributed(LeakyReLU()),\n",
" \n",
"# LSTM(384, return_sequences=True, kernel_initializer='Orthogonal', name='latent_rnn2'),\n",
" \n",
" TimeDistributed(Dense(384)),\n",
" TimeDistributed(BatchNormalization()),\n",
" TimeDistributed(LeakyReLU()),\n",
" \n",
" \n",
" TimeDistributed(Reshape((3, 128))),\n",
" Permute((2, 1, 3)),\n",
" ]\n",
" \n",
" self.spectrogram_decoder = [\n",
" \n",
" Conv2DTranspose(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2DTranspose(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
"\n",
" Conv2DTranspose(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
"\n",
" Conv2DTranspose(128, kernel_size=(4, 4), strides=(2, 2), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
"\n",
" Conv2DTranspose(64, kernel_size=(4, 4), strides=(1, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
"\n",
" Conv2DTranspose(64, kernel_size=(5, 5), strides=(2, 2), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2DTranspose(1, kernel_size=(1, 1), strides=(1, 1), padding='same'),\n",
" \n",
" Cropping2D(((8, 8), (0, 1))),\n",
" \n",
" Reshape((80, 299), name='spectrogram_output'),\n",
" ]\n",
" \n",
" self.discriminator = [\n",
" Reshape((80, 299, 1)),\n",
" \n",
" Conv2D(32,4,strides=2,activation=None,padding='same'),\n",
" LeakyReLU(alpha=0.1),\n",
"\n",
" Conv2D(64,4,strides=2,activation=None,padding='same'),\n",
" LeakyReLU(alpha=0.1),\n",
"\n",
" Flatten(),\n",
" \n",
" Dense(1,activation='sigmoid')\n",
" ]\n",
" \n",
" def get_autoencoder(self):\n",
" \"\"\" Builds the full autoencoder model with encoder and decoder. \"\"\"\n",
" \n",
" \n",
" # Spectrogram Encoder\n",
" \n",
" \n",
" spectrogram_input = Input(shape=spectrogram_shape,name='spectrogram_input')\n",
"\n",
" s = spectrogram_input\n",
" \n",
" for l in self.spectrogram_encoder:\n",
" s = l(s)\n",
" encoding_layer = l\n",
"\n",
"\n",
" spectrogram_encoder_model = Model(inputs=spectrogram_input, outputs=s)\n",
"\n",
" print(\"Spectrogram encoder output:\", spectrogram_encoder_model.output_shape[1:])\n",
"\n",
" print(\"Spectrogram encoder:\")\n",
" print(spectrogram_encoder_model.summary())\n",
"\n",
" print(\"\\n\\n\\n\")\n",
" \n",
" inputs = [spectrogram_input]\n",
" \n",
" # Video Encoder\n",
" \n",
" if self.use_video:\n",
" \n",
" video_input = Input(shape=video_shape,name='video_input')\n",
"\n",
" v = video_input\n",
"\n",
" v = Reshape(video_shape + (1,))(v)\n",
"\n",
" for l in self.video:\n",
" v = l(v)\n",
"\n",
"\n",
" self.video_encoder_model = Model(inputs=video_input, outputs=v)\n",
"\n",
" print(\"Video encoder output:\", self.video_encoder_model.output_shape[1:])\n",
"\n",
" print(\"Video encoder:\")\n",
" print(self.video_encoder_model.summary())\n",
"\n",
" print(\"\\n\\n\\n\")\n",
" \n",
" inputs.append(video_input)\n",
" \n",
" encoding_layer = Concatenate(axis=2)\n",
" encoding = encoding_layer([s, v])\n",
" else:\n",
" encoding = s\n",
" \n",
" embedding_shape = encoding_layer.output_shape\n",
" \n",
" # Spectrogram Latent\n",
" \n",
" print(\"Embedding Shape:\", embedding_shape)\n",
" \n",
" se_in = Input(embedding_shape[1:])\n",
" \n",
" se = se_in\n",
" \n",
" s = encoding\n",
" \n",
" for l in self.spectrogram_latent:\n",
" s = l(s)\n",
" se = l(se)\n",
" \n",
" print(\"\\n\\n\\n\")\n",
"\n",
" print(\"Spectrogram Latent:\")\n",
" print(Model(inputs=se_in, outputs=se).summary())\n",
" print(\"\\n\\n\\n\")\n",
" \n",
" # Spectrogram Decoder\n",
" \n",
" sd_in = Input((3, 75, 128))\n",
" \n",
" sd = sd_in\n",
" \n",
" for l in self.spectrogram_decoder:\n",
" sd = l(sd)\n",
" s = l(s)\n",
" \n",
" spectrogram_decoder_model = Model(inputs=sd_in, outputs=sd)\n",
" \n",
" print(\"Spectrogram decoder output:\", spectrogram_decoder_model.output_shape[1:])\n",
" \n",
" print(\"Spectrogram decoder:\")\n",
" print(spectrogram_decoder_model.summary())\n",
" \n",
" print(\"\\n\\n\\n\")\n",
" \n",
" \n",
" self.spectrogram_model = Model(inputs=inputs, outputs=s)\n",
" \n",
" \n",
" discrim_in = Input((80, 299), name='discriminator_input')\n",
" \n",
" discrim = discrim_in\n",
" \n",
" discrim_on_decoded = s\n",
" \n",
" for l in self.discriminator:\n",
" discrim = l(discrim)\n",
" discrim_on_decoded = l(discrim_on_decoded)\n",
" \n",
" self.discriminator_model = Model(inputs=discrim_in, outputs=discrim)\n",
" self.discriminator_on_model = Model(inputs=inputs, outputs=discrim_on_decoded)\n",
" \n",
" print(\"Discriminator:\")\n",
" print(self.discriminator_model.summary())\n",
" print(\"\\n\\n\\n\")\n",
" \n",
" print(\"Discriminator on model:\")\n",
" print(self.discriminator_on_model.summary())\n",
" print(\"\\n\\n\\n\")\n",
" \n",
" phase_input = Input((442, 299), name='phase_input', dtype='complex64')\n",
" \n",
" reconstructed = self.reconstruct_layer([s, phase_input])\n",
" \n",
" reconstructed_model = Model(inputs=inputs + [phase_input,], outputs=reconstructed)\n",
" \n",
" #print(\"Reconstructed decoder output:\", reconstructed_model.output_shape[1:])\n",
" \n",
" self.model = Model(inputs=inputs + [phase_input,], outputs=[reconstructed])\n",
" \n",
" \n",
" print(\"Full Model:\")\n",
" print(self.model.summary())\n",
" \n",
" print(\"\\n\\n\\n\")\n",
" \n",
" \n",
" \n",
" return self.model, self.spectrogram_model, self.discriminator_model, self.discriminator_on_model\n",
" \n",
" def compile(self, ops, losses):\n",
" return self.spectrogram_model.compile(ops[0],loss=losses[0]), \\\n",
" self.discriminator_model.compile(ops[1], loss=losses[1]), \\\n",
" self.discriminator_on_model.compile(ops[2], loss=losses[2])\n",
" \n",
" def train(self, data_limit=100, data_batch=10, epochs=10, steps_per_epoch=10, autoencoder_reps=3, discriminator_reps=2, d_on_ae_reps=1):\n",
" data_gen = self.get_data(data_limit, data_batch)\n",
" \n",
" self.discriminator_model.trainable = False\n",
" \n",
" ae_loss_history = []\n",
" discrim_loss_history = []\n",
" d_on_ae_loss_history = []\n",
" \n",
" for i in tqdm(range(epochs)):\n",
" for j in tqdm(range(steps_per_epoch)):\n",
" data = next(data_gen)\n",
" \n",
" x_real = data['spectrogram']\n",
" y_real = [1]*data_batch\n",
" \n",
" if self.use_video:\n",
" x_gen = [data['noisy_spectrogram'], data['video']]\n",
" else:\n",
" x_gen = [data['noisy_spectrogram']]\n",
" \n",
" y_gen = [1]*data_batch\n",
" \n",
" # train autoencoder\n",
" \n",
" ae_loss = 0\n",
" for k in range(autoencoder_reps):\n",
" ae_loss += self.spectrogram_model.train_on_batch(x_gen, x_real)\n",
" \n",
" if autoencoder_reps > 0:\n",
" ae_loss /= autoencoder_reps\n",
" \n",
" \n",
" x_fake = self.spectrogram_model.predict(x_gen)\n",
" y_fake = [0]*data_batch\n",
" \n",
" # train discriminator\n",
" \n",
" self.discriminator_model.trainable = True\n",
" \n",
" real_loss = 0\n",
" fake_loss = 0\n",
" for k in range(discriminator_reps):\n",
" \n",
" real_loss += self.discriminator_model.train_on_batch(x_real, y_real)\n",
" fake_loss += self.discriminator_model.train_on_batch(x_fake, y_fake)\n",
" \n",
" self.discriminator_model.trainable = False\n",
" \n",
" discrim_loss = 0.5*(real_loss + fake_loss)\n",
" \n",
" if discriminator_reps > 0:\n",
" discrim_loss /= discriminator_reps\n",
" \n",
" # train discriminator on autoencoder\n",
" \n",
" d_on_ae_loss = 0\n",
" for k in range(d_on_ae_reps):\n",
" d_on_ae_loss += self.discriminator_on_model.train_on_batch(x_gen, y_gen)\n",
" \n",
" if d_on_ae_reps > 0:\n",
" d_on_ae_loss /= d_on_ae_reps\n",
" \n",
" ae_loss_history.append(ae_loss)\n",
" discrim_loss_history.append(discrim_loss)\n",
" d_on_ae_loss_history.append(d_on_ae_loss)\n",
" \n",
" return ae_loss_history, discrim_loss_history, d_on_ae_loss_history\n",
" \n",
" def get_data(self, limit=100, batch=10):\n",
" return get_training_data(limit, batch, self.use_video)\n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "ZL2XP9L9h8zN",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Compile the model.\n",
"\n",
"Using SGD with MAE for the audoencoder, and Adam with Binary Crossentropy for the discriminator and combined."
]
},
{
"metadata": {
"id": "wcO7ZQdFCjUB",
"colab_type": "code",
"pycharm": {},
"outputId": "61845869-f919-4a32-9d00-fb392a33f060",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 10557
}
},
"cell_type": "code",
"source": [
"from tensorflow.keras.optimizers import Adam\n",
"\n",
"#@title Autoencoder Type\n",
"use_video = True #@param {type:\"boolean\"}\n",
"#use_audio = True #@param {type:\"boolean\"}\n",
"#use_spectrogram = False #@param {type:\"boolean\"}\n",
"\n",
"autoencoder = Autoencoder(\n",
" use_video=use_video)\n",
"\n",
"model, spec, discrim, d_on_g = autoencoder.get_autoencoder()\n",
"\n",
"#TODO try other optimizers (Adam, Adagrad)\n",
"#SGD(0.02,momentum=0.9)\n",
"autoencoder.compile(ops = [\n",
" Adam(lr=5e-3),\n",
" Adam(lr=0.0002,beta_1=0.5,beta_2=0.999,epsilon=1e-3),\n",
" Adam(lr=0.0002,beta_1=0.5,beta_2=0.999,epsilon=1e-3)\n",
"], losses = [\n",
" 'mean_squared_error', #mean_squared_error\n",
" 'binary_crossentropy', \n",
" 'binary_crossentropy'\n",
"])"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Spectrogram encoder output: (75, 384)\n",
"Spectrogram encoder:\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"spectrogram_input (InputLaye (None, 80, 299) 0 \n",
"_________________________________________________________________\n",
"reshape_8 (Reshape) (None, 80, 299, 1) 0 \n",
"_________________________________________________________________\n",
"conv2d_16 (Conv2D) (None, 40, 150, 64) 1664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_26 (B (None, 40, 150, 64) 256 \n",
"_________________________________________________________________\n",
"leaky_re_lu_30 (LeakyReLU) (None, 40, 150, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_17 (Conv2D) (None, 40, 150, 64) 65600 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_27 (B (None, 40, 150, 64) 256 \n",
"_________________________________________________________________\n",
"leaky_re_lu_31 (LeakyReLU) (None, 40, 150, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, 20, 75, 128) 131200 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_28 (B (None, 20, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_32 (LeakyReLU) (None, 20, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_19 (Conv2D) (None, 10, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_29 (B (None, 10, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_33 (LeakyReLU) (None, 10, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_20 (Conv2D) (None, 5, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_30 (B (None, 5, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_34 (LeakyReLU) (None, 5, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_21 (Conv2D) (None, 3, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_31 (B (None, 3, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_35 (LeakyReLU) (None, 3, 75, 128) 0 \n",
"_________________________________________________________________\n",
"permute_4 (Permute) (None, 75, 3, 128) 0 \n",
"_________________________________________________________________\n",
"time_distributed_13 (TimeDis (None, 75, 384) 0 \n",
"=================================================================\n",
"Total params: 398,016\n",
"Trainable params: 396,736\n",
"Non-trainable params: 1,280\n",
"_________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n",
"Video encoder output: (75, 512)\n",
"Video encoder:\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"video_input (InputLayer) (None, 75, 30, 75) 0 \n",
"_________________________________________________________________\n",
"reshape_11 (Reshape) (None, 75, 30, 75, 1) 0 \n",
"_________________________________________________________________\n",
"zero1 (ZeroPadding3D) (None, 77, 36, 81, 1) 0 \n",
"_________________________________________________________________\n",
"conv1 (Conv3D) (None, 75, 16, 39, 32) 2432 \n",
"_________________________________________________________________\n",
"batc1 (BatchNormalizationV1) (None, 75, 16, 39, 32) 128 \n",
"_________________________________________________________________\n",
"actv1 (Activation) (None, 75, 16, 39, 32) 0 \n",
"_________________________________________________________________\n",
"spatial_dropout3d_6 (Spatial (None, 75, 16, 39, 32) 0 \n",
"_________________________________________________________________\n",
"max1 (MaxPooling3D) (None, 75, 8, 19, 32) 0 \n",
"_________________________________________________________________\n",
"zero2 (ZeroPadding3D) (None, 77, 12, 23, 32) 0 \n",
"_________________________________________________________________\n",
"conv2 (Conv3D) (None, 75, 8, 19, 64) 153664 \n",
"_________________________________________________________________\n",
"batc2 (BatchNormalizationV1) (None, 75, 8, 19, 64) 256 \n",
"_________________________________________________________________\n",
"actv2 (Activation) (None, 75, 8, 19, 64) 0 \n",
"_________________________________________________________________\n",
"spatial_dropout3d_7 (Spatial (None, 75, 8, 19, 64) 0 \n",
"_________________________________________________________________\n",
"max2 (MaxPooling3D) (None, 75, 4, 9, 64) 0 \n",
"_________________________________________________________________\n",
"zero3 (ZeroPadding3D) (None, 77, 6, 11, 64) 0 \n",
"_________________________________________________________________\n",
"conv3 (Conv3D) (None, 75, 4, 9, 92) 159068 \n",
"_________________________________________________________________\n",
"batc3 (BatchNormalizationV1) (None, 75, 4, 9, 92) 368 \n",
"_________________________________________________________________\n",
"actv3 (Activation) (None, 75, 4, 9, 92) 0 \n",
"_________________________________________________________________\n",
"spatial_dropout3d_8 (Spatial (None, 75, 4, 9, 92) 0 \n",
"_________________________________________________________________\n",
"max3 (MaxPooling3D) (None, 75, 2, 4, 92) 0 \n",
"_________________________________________________________________\n",
"time_distributed_12 (TimeDis (None, 75, 736) 0 \n",
"_________________________________________________________________\n",
"bidirectional_2 (Bidirection (None, 75, 512) 1525248 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 75, 512) 0 \n",
"=================================================================\n",
"Total params: 1,841,164\n",
"Trainable params: 1,840,788\n",
"Non-trainable params: 376\n",
"_________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n",
"Embedding Shape: (None, 75, 896)\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
"\n",
"Spectrogram Latent:\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_5 (InputLayer) (None, 75, 896) 0 \n",
"_________________________________________________________________\n",
"time_distributed_14 (TimeDis (None, 75, 384) 344448 \n",
"_________________________________________________________________\n",
"time_distributed_15 (TimeDis (None, 75, 384) 1536 \n",
"_________________________________________________________________\n",
"time_distributed_16 (TimeDis (None, 75, 384) 0 \n",
"_________________________________________________________________\n",
"time_distributed_17 (TimeDis (None, 75, 3, 128) 0 \n",
"_________________________________________________________________\n",
"permute_5 (Permute) (None, 3, 75, 128) 0 \n",
"=================================================================\n",
"Total params: 345,984\n",
"Trainable params: 345,216\n",
"Non-trainable params: 768\n",
"_________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Spectrogram decoder output: (80, 299)\n",
"Spectrogram decoder:\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_6 (InputLayer) (None, 3, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_14 (Conv2DT (None, 6, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_33 (B (None, 6, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_37 (LeakyReLU) (None, 6, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_15 (Conv2DT (None, 12, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_34 (B (None, 12, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_38 (LeakyReLU) (None, 12, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_16 (Conv2DT (None, 24, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_35 (B (None, 24, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_39 (LeakyReLU) (None, 24, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_17 (Conv2DT (None, 48, 150, 128) 262272 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_36 (B (None, 48, 150, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_40 (LeakyReLU) (None, 48, 150, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_18 (Conv2DT (None, 48, 150, 64) 131136 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_37 (B (None, 48, 150, 64) 256 \n",
"_________________________________________________________________\n",
"leaky_re_lu_41 (LeakyReLU) (None, 48, 150, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_19 (Conv2DT (None, 96, 300, 64) 102464 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_38 (B (None, 96, 300, 64) 256 \n",
"_________________________________________________________________\n",
"leaky_re_lu_42 (LeakyReLU) (None, 96, 300, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_20 (Conv2DT (None, 96, 300, 1) 65 \n",
"_________________________________________________________________\n",
"cropping2d_2 (Cropping2D) (None, 80, 299, 1) 0 \n",
"_________________________________________________________________\n",
"spectrogram_output (Reshape) (None, 80, 299) 0 \n",
"=================================================================\n",
"Total params: 695,489\n",
"Trainable params: 694,209\n",
"Non-trainable params: 1,280\n",
"_________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n",
"Discriminator:\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"discriminator_input (InputLa (None, 80, 299) 0 \n",
"_________________________________________________________________\n",
"reshape_10 (Reshape) (None, 80, 299, 1) 0 \n",
"_________________________________________________________________\n",
"conv2d_22 (Conv2D) (None, 40, 150, 32) 544 \n",
"_________________________________________________________________\n",
"leaky_re_lu_43 (LeakyReLU) (None, 40, 150, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_23 (Conv2D) (None, 20, 75, 64) 32832 \n",
"_________________________________________________________________\n",
"leaky_re_lu_44 (LeakyReLU) (None, 20, 75, 64) 0 \n",
"_________________________________________________________________\n",
"flatten_8 (Flatten) (None, 96000) 0 \n",
"_________________________________________________________________\n",
"dense_5 (Dense) (None, 1) 96001 \n",
"=================================================================\n",
"Total params: 129,377\n",
"Trainable params: 129,377\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n",
"Discriminator on model:\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"video_input (InputLayer) (None, 75, 30, 75) 0 \n",
"__________________________________________________________________________________________________\n",
"spectrogram_input (InputLayer) (None, 80, 299) 0 \n",
"__________________________________________________________________________________________________\n",
"reshape_11 (Reshape) (None, 75, 30, 75, 1 0 video_input[0][0] \n",
"__________________________________________________________________________________________________\n",
"reshape_8 (Reshape) (None, 80, 299, 1) 0 spectrogram_input[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero1 (ZeroPadding3D) (None, 77, 36, 81, 1 0 reshape_11[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_16 (Conv2D) (None, 40, 150, 64) 1664 reshape_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv1 (Conv3D) (None, 75, 16, 39, 3 2432 zero1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_26 (Batc (None, 40, 150, 64) 256 conv2d_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc1 (BatchNormalizationV1) (None, 75, 16, 39, 3 128 conv1[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_30 (LeakyReLU) (None, 40, 150, 64) 0 batch_normalization_v1_26[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv1 (Activation) (None, 75, 16, 39, 3 0 batc1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_17 (Conv2D) (None, 40, 150, 64) 65600 leaky_re_lu_30[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_6 (SpatialDro (None, 75, 16, 39, 3 0 actv1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_27 (Batc (None, 40, 150, 64) 256 conv2d_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"max1 (MaxPooling3D) (None, 75, 8, 19, 32 0 spatial_dropout3d_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_31 (LeakyReLU) (None, 40, 150, 64) 0 batch_normalization_v1_27[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero2 (ZeroPadding3D) (None, 77, 12, 23, 3 0 max1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, 20, 75, 128) 131200 leaky_re_lu_31[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2 (Conv3D) (None, 75, 8, 19, 64 153664 zero2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_28 (Batc (None, 20, 75, 128) 512 conv2d_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc2 (BatchNormalizationV1) (None, 75, 8, 19, 64 256 conv2[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_32 (LeakyReLU) (None, 20, 75, 128) 0 batch_normalization_v1_28[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv2 (Activation) (None, 75, 8, 19, 64 0 batc2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_19 (Conv2D) (None, 10, 75, 128) 65664 leaky_re_lu_32[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_7 (SpatialDro (None, 75, 8, 19, 64 0 actv2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_29 (Batc (None, 10, 75, 128) 512 conv2d_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"max2 (MaxPooling3D) (None, 75, 4, 9, 64) 0 spatial_dropout3d_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_33 (LeakyReLU) (None, 10, 75, 128) 0 batch_normalization_v1_29[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero3 (ZeroPadding3D) (None, 77, 6, 11, 64 0 max2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_20 (Conv2D) (None, 5, 75, 128) 65664 leaky_re_lu_33[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv3 (Conv3D) (None, 75, 4, 9, 92) 159068 zero3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_30 (Batc (None, 5, 75, 128) 512 conv2d_20[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc3 (BatchNormalizationV1) (None, 75, 4, 9, 92) 368 conv3[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_34 (LeakyReLU) (None, 5, 75, 128) 0 batch_normalization_v1_30[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv3 (Activation) (None, 75, 4, 9, 92) 0 batc3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_21 (Conv2D) (None, 3, 75, 128) 65664 leaky_re_lu_34[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_8 (SpatialDro (None, 75, 4, 9, 92) 0 actv3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_31 (Batc (None, 3, 75, 128) 512 conv2d_21[0][0] \n",
"__________________________________________________________________________________________________\n",
"max3 (MaxPooling3D) (None, 75, 2, 4, 92) 0 spatial_dropout3d_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_35 (LeakyReLU) (None, 3, 75, 128) 0 batch_normalization_v1_31[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_12 (TimeDistri (None, 75, 736) 0 max3[0][0] \n",
"__________________________________________________________________________________________________\n",
"permute_4 (Permute) (None, 75, 3, 128) 0 leaky_re_lu_35[0][0] \n",
"__________________________________________________________________________________________________\n",
"bidirectional_2 (Bidirectional) (None, 75, 512) 1525248 time_distributed_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_13 (TimeDistri (None, 75, 384) 0 permute_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_2 (Dropout) (None, 75, 512) 0 bidirectional_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_2 (Concatenate) (None, 75, 896) 0 time_distributed_13[0][0] \n",
" dropout_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_14 (TimeDistri (None, 75, 384) 344448 concatenate_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_15 (TimeDistri (None, 75, 384) 1536 time_distributed_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_16 (TimeDistri (None, 75, 384) 0 time_distributed_15[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_17 (TimeDistri (None, 75, 3, 128) 0 time_distributed_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"permute_5 (Permute) (None, 3, 75, 128) 0 time_distributed_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_14 (Conv2DTran (None, 6, 75, 128) 65664 permute_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_33 (Batc (None, 6, 75, 128) 512 conv2d_transpose_14[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_37 (LeakyReLU) (None, 6, 75, 128) 0 batch_normalization_v1_33[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_15 (Conv2DTran (None, 12, 75, 128) 65664 leaky_re_lu_37[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_34 (Batc (None, 12, 75, 128) 512 conv2d_transpose_15[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_38 (LeakyReLU) (None, 12, 75, 128) 0 batch_normalization_v1_34[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_16 (Conv2DTran (None, 24, 75, 128) 65664 leaky_re_lu_38[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_35 (Batc (None, 24, 75, 128) 512 conv2d_transpose_16[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_39 (LeakyReLU) (None, 24, 75, 128) 0 batch_normalization_v1_35[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_17 (Conv2DTran (None, 48, 150, 128) 262272 leaky_re_lu_39[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_36 (Batc (None, 48, 150, 128) 512 conv2d_transpose_17[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_40 (LeakyReLU) (None, 48, 150, 128) 0 batch_normalization_v1_36[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_18 (Conv2DTran (None, 48, 150, 64) 131136 leaky_re_lu_40[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_37 (Batc (None, 48, 150, 64) 256 conv2d_transpose_18[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_41 (LeakyReLU) (None, 48, 150, 64) 0 batch_normalization_v1_37[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_19 (Conv2DTran (None, 96, 300, 64) 102464 leaky_re_lu_41[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_38 (Batc (None, 96, 300, 64) 256 conv2d_transpose_19[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_42 (LeakyReLU) (None, 96, 300, 64) 0 batch_normalization_v1_38[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_20 (Conv2DTran (None, 96, 300, 1) 65 leaky_re_lu_42[1][0] \n",
"__________________________________________________________________________________________________\n",
"cropping2d_2 (Cropping2D) (None, 80, 299, 1) 0 conv2d_transpose_20[1][0] \n",
"__________________________________________________________________________________________________\n",
"spectrogram_output (Reshape) (None, 80, 299) 0 cropping2d_2[1][0] \n",
"__________________________________________________________________________________________________\n",
"reshape_10 (Reshape) (None, 80, 299, 1) 0 spectrogram_output[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_22 (Conv2D) (None, 40, 150, 32) 544 reshape_10[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_43 (LeakyReLU) (None, 40, 150, 32) 0 conv2d_22[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_23 (Conv2D) (None, 20, 75, 64) 32832 leaky_re_lu_43[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_44 (LeakyReLU) (None, 20, 75, 64) 0 conv2d_23[1][0] \n",
"__________________________________________________________________________________________________\n",
"flatten_8 (Flatten) (None, 96000) 0 leaky_re_lu_44[1][0] \n",
"__________________________________________________________________________________________________\n",
"dense_5 (Dense) (None, 1) 96001 flatten_8[1][0] \n",
"==================================================================================================\n",
"Total params: 3,410,030\n",
"Trainable params: 3,406,326\n",
"Non-trainable params: 3,704\n",
"__________________________________________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"<dtype: 'complex64'>\n",
"Full Model:\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"video_input (InputLayer) (None, 75, 30, 75) 0 \n",
"__________________________________________________________________________________________________\n",
"spectrogram_input (InputLayer) (None, 80, 299) 0 \n",
"__________________________________________________________________________________________________\n",
"reshape_11 (Reshape) (None, 75, 30, 75, 1 0 video_input[0][0] \n",
"__________________________________________________________________________________________________\n",
"reshape_8 (Reshape) (None, 80, 299, 1) 0 spectrogram_input[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero1 (ZeroPadding3D) (None, 77, 36, 81, 1 0 reshape_11[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_16 (Conv2D) (None, 40, 150, 64) 1664 reshape_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv1 (Conv3D) (None, 75, 16, 39, 3 2432 zero1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_26 (Batc (None, 40, 150, 64) 256 conv2d_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc1 (BatchNormalizationV1) (None, 75, 16, 39, 3 128 conv1[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_30 (LeakyReLU) (None, 40, 150, 64) 0 batch_normalization_v1_26[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv1 (Activation) (None, 75, 16, 39, 3 0 batc1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_17 (Conv2D) (None, 40, 150, 64) 65600 leaky_re_lu_30[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_6 (SpatialDro (None, 75, 16, 39, 3 0 actv1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_27 (Batc (None, 40, 150, 64) 256 conv2d_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"max1 (MaxPooling3D) (None, 75, 8, 19, 32 0 spatial_dropout3d_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_31 (LeakyReLU) (None, 40, 150, 64) 0 batch_normalization_v1_27[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero2 (ZeroPadding3D) (None, 77, 12, 23, 3 0 max1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, 20, 75, 128) 131200 leaky_re_lu_31[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2 (Conv3D) (None, 75, 8, 19, 64 153664 zero2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_28 (Batc (None, 20, 75, 128) 512 conv2d_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc2 (BatchNormalizationV1) (None, 75, 8, 19, 64 256 conv2[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_32 (LeakyReLU) (None, 20, 75, 128) 0 batch_normalization_v1_28[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv2 (Activation) (None, 75, 8, 19, 64 0 batc2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_19 (Conv2D) (None, 10, 75, 128) 65664 leaky_re_lu_32[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_7 (SpatialDro (None, 75, 8, 19, 64 0 actv2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_29 (Batc (None, 10, 75, 128) 512 conv2d_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"max2 (MaxPooling3D) (None, 75, 4, 9, 64) 0 spatial_dropout3d_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_33 (LeakyReLU) (None, 10, 75, 128) 0 batch_normalization_v1_29[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero3 (ZeroPadding3D) (None, 77, 6, 11, 64 0 max2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_20 (Conv2D) (None, 5, 75, 128) 65664 leaky_re_lu_33[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv3 (Conv3D) (None, 75, 4, 9, 92) 159068 zero3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_30 (Batc (None, 5, 75, 128) 512 conv2d_20[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc3 (BatchNormalizationV1) (None, 75, 4, 9, 92) 368 conv3[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_34 (LeakyReLU) (None, 5, 75, 128) 0 batch_normalization_v1_30[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv3 (Activation) (None, 75, 4, 9, 92) 0 batc3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_21 (Conv2D) (None, 3, 75, 128) 65664 leaky_re_lu_34[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_8 (SpatialDro (None, 75, 4, 9, 92) 0 actv3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_31 (Batc (None, 3, 75, 128) 512 conv2d_21[0][0] \n",
"__________________________________________________________________________________________________\n",
"max3 (MaxPooling3D) (None, 75, 2, 4, 92) 0 spatial_dropout3d_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_35 (LeakyReLU) (None, 3, 75, 128) 0 batch_normalization_v1_31[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_12 (TimeDistri (None, 75, 736) 0 max3[0][0] \n",
"__________________________________________________________________________________________________\n",
"permute_4 (Permute) (None, 75, 3, 128) 0 leaky_re_lu_35[0][0] \n",
"__________________________________________________________________________________________________\n",
"bidirectional_2 (Bidirectional) (None, 75, 512) 1525248 time_distributed_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_13 (TimeDistri (None, 75, 384) 0 permute_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_2 (Dropout) (None, 75, 512) 0 bidirectional_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_2 (Concatenate) (None, 75, 896) 0 time_distributed_13[0][0] \n",
" dropout_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_14 (TimeDistri (None, 75, 384) 344448 concatenate_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_15 (TimeDistri (None, 75, 384) 1536 time_distributed_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_16 (TimeDistri (None, 75, 384) 0 time_distributed_15[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_17 (TimeDistri (None, 75, 3, 128) 0 time_distributed_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"permute_5 (Permute) (None, 3, 75, 128) 0 time_distributed_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_14 (Conv2DTran (None, 6, 75, 128) 65664 permute_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_33 (Batc (None, 6, 75, 128) 512 conv2d_transpose_14[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_37 (LeakyReLU) (None, 6, 75, 128) 0 batch_normalization_v1_33[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_15 (Conv2DTran (None, 12, 75, 128) 65664 leaky_re_lu_37[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_34 (Batc (None, 12, 75, 128) 512 conv2d_transpose_15[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_38 (LeakyReLU) (None, 12, 75, 128) 0 batch_normalization_v1_34[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_16 (Conv2DTran (None, 24, 75, 128) 65664 leaky_re_lu_38[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_35 (Batc (None, 24, 75, 128) 512 conv2d_transpose_16[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_39 (LeakyReLU) (None, 24, 75, 128) 0 batch_normalization_v1_35[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_17 (Conv2DTran (None, 48, 150, 128) 262272 leaky_re_lu_39[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_36 (Batc (None, 48, 150, 128) 512 conv2d_transpose_17[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_40 (LeakyReLU) (None, 48, 150, 128) 0 batch_normalization_v1_36[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_18 (Conv2DTran (None, 48, 150, 64) 131136 leaky_re_lu_40[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_37 (Batc (None, 48, 150, 64) 256 conv2d_transpose_18[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_41 (LeakyReLU) (None, 48, 150, 64) 0 batch_normalization_v1_37[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_19 (Conv2DTran (None, 96, 300, 64) 102464 leaky_re_lu_41[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_38 (Batc (None, 96, 300, 64) 256 conv2d_transpose_19[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_42 (LeakyReLU) (None, 96, 300, 64) 0 batch_normalization_v1_38[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_20 (Conv2DTran (None, 96, 300, 1) 65 leaky_re_lu_42[1][0] \n",
"__________________________________________________________________________________________________\n",
"cropping2d_2 (Cropping2D) (None, 80, 299, 1) 0 conv2d_transpose_20[1][0] \n",
"__________________________________________________________________________________________________\n",
"spectrogram_output (Reshape) (None, 80, 299) 0 cropping2d_2[1][0] \n",
"__________________________________________________________________________________________________\n",
"phase_input (InputLayer) (None, 442, 299) 0 \n",
"__________________________________________________________________________________________________\n",
"reconstruct_audio (Lambda) (None, 65664) 0 spectrogram_output[1][0] \n",
" phase_input[0][0] \n",
"==================================================================================================\n",
"Total params: 3,280,653\n",
"Trainable params: 3,276,949\n",
"Non-trainable params: 3,704\n",
"__________________________________________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(None, None, None)"
]
},
"metadata": {
"tags": []
},
"execution_count": 103
}
]
},
{
"metadata": {
"id": "hLL_ydwVICNN",
"colab_type": "code",
"pycharm": {},
"outputId": "73185117-05e2-4346-bb3a-634b8fc41fff",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"data = next(autoencoder.get_data())\n",
"\n",
"size = 0\n",
"\n",
"for v in data.values():\n",
" size += v.nbytes\n",
"\n",
"print(\"Size: \", size / 10**6, \"MB\")"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Size: 3.6011 MB\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "3cg5_fYA3B5Y",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Fit the model using a generator."
]
},
{
"metadata": {
"id": "cHSZQRC8QEup",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Should train for testing with `autoencoder.train(data_limit=200, data_batch=20, steps_per_epoch=10, epochs=10)`."
]
},
{
"metadata": {
"id": "rKwZkytZrlpK",
"colab_type": "code",
"pycharm": {},
"outputId": "2836df38-ff59-4404-e444-556958abb17b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 884
}
},
"cell_type": "code",
"source": [
"#history = ae_model.fit_generator(get_data(1000, 10), steps_per_epoch = 5, epochs=20, verbose=2)\n",
"\n",
"ae_hist, discrim_hist, d_on_ae_hist = autoencoder.train(data_limit=200, data_batch=20, steps_per_epoch=10, epochs=50, autoencoder_reps=3, discriminator_reps=5, d_on_ae_reps=1)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"100% (10 of 10) |########################| Elapsed Time: 0:01:31 Time: 0:01:31\n",
"100% (10 of 10) |########################| Elapsed Time: 0:00:47 Time: 0:00:47\n",
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"100% (10 of 10) |########################| Elapsed Time: 0:00:47 Time: 0:00:47\n",
"100% (10 of 10) |########################| Elapsed Time: 0:00:47 Time: 0:00:47\n",
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"100% (10 of 10) |########################| Elapsed Time: 0:00:47 Time: 0:00:47\n",
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"100% (50 of 50) |########################| Elapsed Time: 0:40:19 Time: 0:40:19\n"
],
"name": "stderr"
}
]
},
{
"metadata": {
"id": "bXwWO-eReua-",
"colab_type": "code",
"pycharm": {},
"outputId": "7c40c8d3-4c6e-41e0-86f4-2bd4b6724f64",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
}
},
"cell_type": "code",
"source": [
"plt.plot(ae_hist,label='Autoencoder')\n",
"plt.plot(discrim_hist, label='Discriminator')\n",
"plt.plot(d_on_ae_hist, label='Together')\n",
"#plt.plot(history.history['val_loss'], label='val_loss')\n",
"plt.legend()\n",
"plt.xlabel('Step')\n",
"plt.ylabel('Loss')\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "YC4yK7FK34qg",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Losses after the 100th step"
]
},
{
"metadata": {
"id": "Uiokrr8J36ks",
"colab_type": "code",
"outputId": "3b02e3eb-f52e-4e4d-f930-6eaa199bf586",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
}
},
"cell_type": "code",
"source": [
"mean = np.mean(ae_hist) + np.mean(discrim_hist) + np.mean(d_on_ae_hist)\n",
"mean /= 3\n",
"\n",
"limit = mean * 2\n",
"\n",
"start = 50\n",
"for i in range(len(ae_hist)):\n",
" if ae_hist[i] <= limit and discrim_hist[i] <= limit and d_on_ae_hist[i] <= limit:\n",
" start = i\n",
"\n",
"start = 40\n",
" \n",
"plt.plot(ae_hist[start:],label='Autoencoder')\n",
"plt.plot(discrim_hist[start:], label='Discriminator')\n",
"plt.plot(d_on_ae_hist[start:], label='Together')\n",
"#plt.plot(history.history['val_loss'], label='val_loss')\n",
"plt.legend()\n",
"plt.xlabel('Step')\n",
"plt.ylabel('Loss')\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "fWLje2N33GX7",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Here is a sample output:"
]
},
{
"metadata": {
"id": "OkMYsWtpmrPl",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"def clean(av, use_video):\n",
" if use_video:\n",
" return reconstruct_audio(spec.predict([[av.noisy_spectrogram()], [av.video_data()]])[0], av.phase())\n",
" else:\n",
" return model.predict([[av.noisy_spectrogram()], [av.phase()]])[0]"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "ytszGuuDezYw",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Testing"
]
},
{
"metadata": {
"id": "Ak2KW608fCBM",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Test on seen data"
]
},
{
"metadata": {
"id": "zNUkuYGKfu5q",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Graphs"
]
},
{
"metadata": {
"id": "JvwlI25B7AxD",
"colab_type": "code",
"pycharm": {},
"cellView": "form",
"outputId": "87b41ebc-6dd2-4d4f-e0d6-464812a32128",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1007
}
},
"cell_type": "code",
"source": [
"#@title\n",
"import librosa.display, librosa.output\n",
"\n",
"\n",
"\n",
"gen = get_audio_and_video()\n",
"#next(gen)\n",
"test = next(gen)\n",
"\n",
"print(\"Known\")\n",
"audio = test.audio_data()\n",
"librosa.display.waveplot(audio)\n",
"plt.show()\n",
"\n",
"print(\"Known Spectrogram\")\n",
"plt.imshow(test.spectrogram())\n",
"plt.show()\n",
"\n",
"print(\"Noisy\")\n",
"noisy = test.noisy_audio()\n",
"\n",
"librosa.display.waveplot(noisy)\n",
"plt.show()\n",
"\n",
"# if autoencoder.use_video:\n",
"# p = spec.predict([[test.noisy_spectrogram()], [test.video()]])[0]\n",
"# else:\n",
"# p = spec.predict([[test.noisy_spectrogram()]])[0]\n",
"\n",
"p = clean(test, autoencoder.use_video)\n",
"\n",
"# print(\"Spectrogram\")\n",
"# plt.imshow(p)\n",
"# plt.show()\n",
"\n",
"# p = reconstruct_audio(p, test.phase())\n",
"\n",
"print(\"Predicted\")\n",
"librosa.display.waveplot(p)\n",
"plt.show()\n",
"\n",
"\n",
"# Use facial point recognition instead of video\n",
"# Jane Zang doing similar research"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Known\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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3UYCqsOHc7O84cNRvzapAAxXNW7+HM34/k1mrdvGiUxieM3YKhwJUHvjFW1/zk1cWsXZn7BoqmvhmgaGBi6RLhspqnSeMm8Y/522MKB3Rzh4vKYvCoBMBlNVhgItwPoeNBUf8zg90jmP/s4wdB49WPS1UFnQfDtCFRuWT1YVPf0bO2Cksq1azyxgLDA2cG9nSJeUVfLQ89EZh/kSSzRNPtTTf+nIzt/3rq6geI1B7heIAgaHUCVT7Cj1PCMOf8tQcW7frMKrKgg0FnPWHWRwoKmXy0m01ntzy9xe5lXTTQNRrX0mm/kXaiVvl3fHCCGv8/G7SiuArBZCaIlSUx77gtbCkjIcmrwh4ga5NOLFt/nr/w4oGemIIFHRHv/wlv7m4N6t2HGLrviJOefhjv+uVVVRwtLTcp82ESW4WGBq4SMsYeo6bFnylKEtLSaG0PLY1jgDGT1tVa1AQAo+VHU4HeE8HaAwXKGuo8knBnyenrw56vNvf/JqTjmvBh3ecHVoCTYNnWUkNXEPpcjse7DpY+7jRkb7Tq3YcrLWQ/+W537HrkG82kFv9Wa1waUAh0zBYYGjgyhtAYIin+vtZzTKCr+RHKM8LI575nNVBagpVb+xX5tJ7Ez/vsIkHFhgaOK2/yjpRU1KHWkDRoChNMuqW+6qEFuBKyipo2Tjw+NRfbfLt0bXMxbKXyUu3ubYvk9gsMDRwpzziv8AxVsJtWLU8P36qUh4sKqtz1s2sVbvocd9UtgWpAVShkNO2ScDl1csfqmctReKOt76msMRGiTMWGADYuOdIVZU/f4pKymsdGMaE7ievLPI7v+Bwsd9qkze8tDDaSQrZ/A0FbNkXWdXOC5/6lOdnr2PL3kK/yytUa32yyO3a2mf63CfnRJSe6goOl7i6P5OYkjow7Dvi+RGc96c5vFpLA66X5m5gxDOf88nKnfWUMnfEqlO5cJRXKKt3HGLgY58w1OmMzlubpnXL049XR0rK+eP01Tw0eQUVfgKAqtZabrB1XxEbdnsasPnbPlJn/3G26/s0iceVwCAiI0RktYisE5GxfpZnisg7zvKFIpLjtexeZ/5qEbnYjfQE8soX3/H87HUszz/AyL/N5dRHZzDC6aDtYJH/Kn9Hist44dP1pAiMeT0vpAHZVZVNAVqvRpOq+mR1/ODF+fWehlDM/HYnX23eh6py33+X+XSSlzN2Cos2HgtoG/bU//tYH2au2kX3+6ayq1r/R2XltT8x7Dh4lPP//CmHjpZG7fM98cGP2LrP/xNNOD5fu9vns0wkJWUVFJeVs7GW719dWsAniogDg4ikAs8BlwD9gFEi0q/aajcD+1S1J/A08Adn2354hgI9ERgBPO/sL6hQukD4bs+xEb7W7TrEw/9byR+nr+byv86tGuBllVPLo6i0nNEvL+QHL8yjvELZd6SEByYt55/zNiIIFQoZqSk8+uHKGvnMZeUVPul5bvY6zn1yDp+u2c289Xu46vkvXPsSPTd7HTljpzCn2tjFhSVlnPboDLrdO5Xv4vxievOreVz1/DxWbDvIO16d01XW3Ln2hfls2VvIjij1rRRPbnh5IXsOH6sGW65aa7Zmpb/PWU/epn1RSdOR4nLO+sNsjpaW1/iuz19fwPhp3/ot+1mef4CpyzxjUmzYfZjRL3/JtS/Mr/E0XlbuuehWp6rc8943rPFTM2tzQSEH/Ny8zV27J2CfUJX7rO39fG3eRhZs8G1QeOhoKRc+9Sm97/+I8/40h80Fx4Lklr2FlJRV8OT0VfQcN81vGU8on9/2A0XkjJ0StL+qI8VlTPhsPXuPhJ7FV1GhEZfNufHEMAhYp6obVLUEeBsYWW2dkcCrzuv3gAvE0+JnJPC2qhar6nfAOmd/QZ3++Cdc9+J85q7dU/VofbS0nF2HjnLbG4v53l/nMuxPcxj3wXL2HC7mXws3k556rNJg5de9sruFGSt38vnaPeRt2seLn65n1qpdvDZ/E09OX13VsOhoWQVvL9rCDCdLSVV5esZqeo6bxgn3T2PmtzspKinnudmeMX5f/WIjr8/fxFeb9/PsrHX8+t0lTK/WyVpZeUWN+usHj5YyfcWOGoO7LN60t6rB0r3OAPGV6bjir19UNXQa9qc5Nb7s8ei6Cb53vJXvgAAvfb6BYbUM0ZnoKr+La3Ye5qkZa6oueoePloVUBfX5CMeRDkWf331Et3unsr+whPz9RZRXKKP+sYAXPt3Ac7PX+az74TfbuPyvc/nZv77i/a+38uDkFaSKkCLw4OQVPuNsP/rhSnrf/xFFTjfpS7fs5+ZXF/Hnj1fzTt4WXvx0PUUl5azbdYhPVu7k5bkbOP/Pc7jo6U+rgsDR0nJ+9sZibnh5IT+qpRzqzx+vode4aT43S6rK+t2H6X7vFB6YvILb3ljMXGdkv09W7uT0xz9hi9cT0zlPzua9xVv5bs8Rzv7jbH48cWHVb3zYn+aQM3YKL37qmV6x7QC9xk2rCiZLtuwnZ+wUvtnqqU1WXFbOd3uOcMdbXwOe/qpWbvP8/tftOsQbCzZWjfZXUaE8OHkFT0xdxY9eWkhpeQWqSmFJGV9v3lf1Xry5cBMPTDrWq++kpflc/te53PbGYpZu2c+try/m5bkbyBk7hbSW7buE8tlLpA1kROQaYISq3uJMjwYGq+rtXussd9bZ6kyvBwYDDwELVPUNZ/7LwDRVfa+2Y+bm5uqe4Q+TIsf6Arr/sr48NqX2Loe91/cnNQVCvbHvf3xLLujbjmc+WUt6qlBarjTLTOVwceAWuqkpQnmF8uLogXy2Zjf7C0uZ4txhdW7TmAOFpeTmtGGW8zRwfKvGXHXa8TTLTOOCvu34xVtL+Ha7pxA8My2F751yHAO7tmbjniNVvWo2JCLu9Q4bz4J9L+NJRmpKVfXhi09sT6P0VCYt8VRzrTyPPh2as373YUq9qtK2bZrB9F+dw8ptB/nxxC9JTRHO7pnF+KtP5poX5pG/ryhoW4q0FOGsXlmc1TOLt77czPrdR6rmPzLyJDLTUigsKaNbVjO+3ryPd/K2sL+wlMKSMi7r35HfX9WfD77O58XPNrB1X1GN9/3Wc3vwzqLN7C8sDZqWjNQUKqqVB53XO5s5q3dXTT/1g1O4692lVdMPX3EiD04OrWuYO4f3YvWOQ0xz+ihrkpHK5Sd3JH9/EV+sO3bT16tdM9Y6N5CXntSBS0/uyCP/W8muQ8V+W+Ln/+P/DpQWbG0V7PgJExhEZAwwBiC1RfbATre9ElG6o6H6Fy1ZLmzGJLp4Cs7RvG7kTxizr3Rvfptg67mRlZQPdPaa7uTM87uOiKQBLYGCELcFQFUnqGququb2696pxvLa6n5XCtb6NJxePFMEemQ3BTx3LHDsi1W5H+8Pt0lGatXQjJec1IFTOrWkV7tmNfbbNCPV53Wvds3o06E5j448kcZeyxqlp5CeKlzQtx19OzYPPeEJxN8oZg1RWgKdZ/W0ZqQeu4R4L6reNVRmWgrjr+rPDWd0qVq3VeN0Hr3ypIDHqj60rAgc16oRZ/Zo6zM/ReCKU47jhjO6cMlJHbh9WE9OPr6lZxtnu25ZTXn5xlxGDTqWk1K59/RUTxni4G5tfObXJj1Vgl4vBnbxrVp8QZ92IezZc93o06E5TTNSq34DTTJqL3pNEchulsmYc7pXpSuSr5UbnegtAnqJSDc8F/XrgR9WW2cycCMwH7gGmKWqKiKTgTdF5CngOKAX8GWwA1Z+GbObZ3Lrud3p2a45556QzcY9R9hzuJgbX/mSI06WzmldWvHHa07m91NXMXftnhqtaCvvFNq3yGTnwWLSUoRRg7rQtW0THpvyLWkpUiPP96dnd2PcZf2oqFB+PPHLqqEcn/h+f87v247Bv/eMVDawa2uaZKTy+do9DOzamn2FJXzv5OP4v3N7VO1r3a7DrNpxkPP7tEMVmmamsWrHQSYv2ca5J2QzuPuxH0H7Fo0Y8/piBGiUlsrXD1yIiFBSVsEJ9/t2dvfrC0/gzzP8d8YW71IFhvdtz/QEqx4cqsqLVeXX6pwTsvn1RSdw2bNzGX9Vf/46a13ArrBj8RQ689fnsr+whHbNG1VVZz27Vxav3zwY8OT3P/3JGl781JOd+cDl/fjv11tZnn+wKs33XNKH652L8t7DJUxdvoOP7zqHds0b0bFFJg9OXkmvds2Ys2Y3F/Zrz32X9mX1joPk7y9i5baD/OerfFJFePf/htCpdRMOFJZy9pOzOFhURqfWTXh21Kk+ab774t5UVCj3f7CcN7/czDPXDeCUzq24oG977r+sLyu3H+TaFzxlXKrwwOV9+cnQbrw2fyMPTl5Z4wni+tM7M6RHW3759hIAn2wy8ASmZ0edyvz1BYz6xwLeGXMGg7u3Zc7qXdz0yiIeGXkio8/oSsGREpbnH+DOd5aw3ykT/MfogZx9Qjafr93Dgg0F5O8v4oUbBnKkuIzrJyxgxbYDNMtM48txw0lPFXYcOMqSzfvp07EFvTs059EPV7Bww14m3X4WqSlChxaNeOTDlXTLasod5/fiLzPXclrX1ry3eCuEWLkn4qwkABG5FHgGSAUmqurjIvIIkKeqk0WkEfA6cCqwF7heVTc4244D/h9QBtypqkG788zNzdVJn3xOVrMMMtP8n2dhSRmfrdnDBX3bkZ6awtIt+xn53BcB9zn6jK7MWrWTo6UVfPbbYewrLOHGiV/Ss10z5q7dw5GSctJThUHd2vDPnwwi3etOaeu+Qg4dLaNPh+aICA9MWs5r8zfxzHUDSE0Rfv3vpSx94CKfO/66+vW7S/jPV/k8/v2T+NHgrlXzdx48WhWQXr95EGf3yo7ZGM2hevX/DeLGicfuA7zzRD+682wqKuDSZz+PSdrq05y7zyMnqyk5Y6fw5k8Hc+9/l7GpoPbqoqMGdeatL4MPNxqJL8ddQEZqCq2aHGtLMuGz9UxdtoM7L+jFedXugN9dtIV56/fw5LWnsCz/AFc9Pw+AG87oymNeTwYHj5ZypLiMji0b+2xfVl7BVX+fx7hL+/rcEAEs3FBAh5aN6Nq2qc/8f37xHcP6tKsxv1JhSRnbDxylR3bNp/OKCuWOt7/mzB5t+aHXb2nHgaOc/cdZVRf/ST8fyimdPdnyX363l97tm/Ord5cwa9Uupt95Dm2bZZDVLBPwFGxv3VdE5zbHcjCOFJfRNNP3Hvzb7Qe55C+f88INpzHipI5+0w6w+1Axv5/6Lbef39PvOfhztLScj5bvYOSA43x69T10tJQWjTMWq2pusH24EhjqW25urubl5YW1jary2/e+oeBICT/I7cRd7y71GTz+F+f35Pbze6KKT7/0BYeLOXP8LErKKlA8P5Z2zRvVeqyjpeXMX1/Aeb2zw+puOdTzKKtQn8BUqaSsgj2HizmulecHF6+B4dGRJ5KZnsrVp3XiRy8tYMEG37ruD32vHzcN7QbE7zm4ZcavzqFXe09WYM7YKbwz5gzu/2B5VYFiIPPvPZ8hT9RsEOiWT+46h57tIsuifGPBJhqnp3L1wJpZv/Fu35ESDh0tY93uQwzr3a7G77i8Qik4Uhz0WlCbHQeO0qFl3bevCxEJKTAkzXgMIsKT155SNT3ipI4s3rSPUzu3ovt9U0lNEb9PH22bZfKjwV2Y+MVGHr7ixJC+CI3SUxkWYn5iuETEp9qtt4y0lKqgAJ4LcCQD5ETL6CE5Va8n3nQ6//kqn9994Klu9+0jI3yerHq2a1aj2m5DkNO2CZN+fhYtm/h2mJeaIrWWrbRqks5zPzyNji0bs/73l9Ljvqmup+27Jy515YbmhjO6Bl8pTrVumkHrphl0CVB2mZoiEQUFoN6DQjiSJjD4M9Dpd+atn55B/04tA65363k9yM1pwyUndaivpLli9JCcuAwM3ppkpDH6jK4M7dGWw8VlNbLb9oXRsCdRDOnelnsu6VMjKIAn8NcWGLq0acLQnllAdArnP/j5UNefck3iSeq+kioN6dGWZpmBY2S75o24tH9H+8G44KHvVW8U79E9uxknd6pZvfqhK06MdpJC1i2rKce3ahx8xVp88POhvDXmDAZ09l+VPEVqr6W0vtrT079uGRxReqrrnu0/r94kFwsMDdznvx0W6yT4qCw7CNX3TjkuSil8anFvAAAThElEQVQJ3wntm9X5Ln1I97Z8/bsLAwaESikifrt+qHTFAN/349QuQdsqhWz0GV1p0SjwWBAmeVhgaOD8ZVckmniq51/XHk2bZKTSOoSeYlNE2FhLjaRL+/vWYElLce8nXFubApNcLDA0cCkNIPvLXw2sWNkaZKCdQEIJJy+OHki/41rUuk6XNr6FoYEqIhgTiaQufE4GcXSzXWfxEtuaZQZ++qreaK26UKqFX3xi7ZUbLj+5Y436+m6Ve7VvkenKfkzDED+3YiYqIn1iWP5wVIfICImb4xpH4rcjege8Q1dq72snnDMI1HVCtyz/BcMtGgW+v7t2YCeGVGssVt0TV/Xnf784K/QEmgbPAkMDF+kNZWVtre4BLkqhuveSPnXetqwiPgZEad+iETedmUPXEPrlisSYc7r7ne/d8DKU+eOv6s/j3+/P6CGe9gTTfnl2VV9F3lo3yYi4Tr5pWCwwNHBulTFc0j+yNhyRDNEZL71eAoy7rB9T7zg77O3c6GAgI0BZS2a67/yXb/Q0bD23dzYZaSlc2r8j3z1xKX07tuCxK/vToYVvEGjZOPErKBh3WWBo4CIJDM9cNwCAbx66iF8NP8GtJCW86v3ehCKcaq7tWvi/e89I8/9zvX1YTwD+cHV/AC7o2x7wdLRYybssopETSF656XQWjRvOGd2D9sJskowFhgYuksLnyq5+WzRKJy3CmkHRbDg1alBnlj98Mbed1yP4ynV0XW7n4CsR+O47nM+hW1ZTZt99Xo35mQECw3Wnd2Hj+MsY3K0t/Z3uplc/NiJg9dh7RvThtyN6M6xPO7KbZ1rDTVODBYYGLpIfvZs5OAO7tmHj+Mtc3OMxQ3pk0SwzrUYWiZv+cM3JQdfp06E5037pP5sp3M/B39rB+tbJyWpaVYgcqNdhgEv6d+Rn5/UMKz0muVhgMAFVr2K59vFLYpKOF0cPDLhs6h1nc4XTOrpRemy/zrk5rTmuVeOqAZy8hfvkVu6nUMLNxmzG1Ma+aSag6uNfx6qhWW31+70HXjrxuMAdIUbTBz8fCkBGqucu3V+10nDLehKxO3zTcFhgMAH5u2uNVnZQXZV6BYaTjm/Jh1Gojx9sSMYBnVvRsnG615CTNYNAKN1heKusieXduaO/z8OYaIgoMIhIGxGZISJrnf+t/awzQETmi8gKEflGRK7zWvZPEflORJY4fwMiSY9xV6DqkfHiX7cM5rRq4+qWB6nbmt0svBa+3bOa8vJNp/tdlp4qDMrx1OhZ+uBFDO/nqQ1UPduoeWYav7vMf6+ygfTMbsaE0QMpLCmrmlfXfpqMCVekv/yxwExV7QXMdKarKwR+rKonAiOAZ0TEu0vI36jqAOdvSYTpMS4a3jc6gw25ZWjPrBrVQPsf35I3bg7cFfWvLjyBX17QK+Rj1HYpHnFSR969dUiN+dVzjbJbZIY9rGtKinDRiR182nCoq9UBjAks0sAwEnjVef0qcGX1FVR1jaqudV5vA3YB2REe19SDSKuoRlOgAumUFCE3x/MUkeOnhXKTjFR+daE7bTI6tw5tbIbL+gce0zcUlb3LxkkDcJMEIv3lt1fV7c7rHUD72lYWkUFABrDea/bjThbT0yJiPXklgIk3BR0yNupqK5BulJ7KlDvOYs5vfMeieOh7/Rg5ILzxHUYEGLVv+cMXc1eAAONd0NyycTq/vqh3WMf0dtOZOdx8tmcMCytjMPUlaGAQkU9EZLmfv5He66mnGkXAb66IdAReB36iqpX3PvcCfYDTgTbAPbVsP0ZE8kQkb/fu3cHPzFSZfPtQV/d3fp9a439c8FdDqUlmWljtCa4+rRP3jPDfx1OzzLSAT1Teh4i0dtFDV5zIvZf0dWVfxoQqaNt+VR0eaJmI7BSRjqq63bnw7wqwXgtgCjBOVRd47bvyaaNYRF4B7q4lHROACQC5ubn2CwmD+G0ulXjaNc/kvN7ZvJu3tU7bF5eWh7X+0TDXr+T9frv5RbUWyqa+RJqVNBm40Xl9IzCp+goikgG8D7ymqu9VW9bR+S94yieWR5ge40dDuZ4oUFRat4z2n57djWtD7NYCYFjvbO6/vG+djuX9fj8ZQovpUPzntiEM7xv/T2qmYYg0MIwHLhSRtcBwZxoRyRWRl5x1fgCcA9zkp1rqv0RkGbAMyAIeizA9xo8GExhUGXV6Z246Myfkbc7qmQV4spYCdU/tT25OGzq2DK1wubrKO/umGamMOCmygudKA7u2qfN408aEK6IR3FS1ALjAz/w84Bbn9RvAGwG2Pz+S45vQNIThPcHzxHBmzyzOdC72oXjjlsHkjJ1CYUndsoXqomG82yaZxW99ROOaugSGYLV3YnHzWtey1ytOOY7BYXYtHUlBr93Ym0RngSEJRONCteGJ+OoaozbPjjqVHtnNwtqmZ7vw1vdWmZVkNSRMorLAkAQaSm2Winqqrvm3H54aUdlAsO6xjYl3FhiSQF0KLeMxlAQaqMZtkcYfG+3OJDoLDEmgoeR5Nw6jVlEkmjeKqE5GwCE4jUkUkf0CTEKoS+FzvOWPv3nLYNo0C6/r6rp4ZOSJnHuCO115WUNlk6gsMCSBhlDEEE4V1Uj0bt+8wZTJGFNX9sybBKxhVOjSUl18r+xtNwnKAkMSaCgN3KKte1ZTerVvHutkGBNzFhiSQF3iQjKGkhdGD6RFo/RYJ8OYmLPAkASi9cQQzkhoiSDYsKCx3p8x9cUCQxJIjVJgcGsktFiYffd5NeY1y3S3LkZZuQ25ZhKTBYYkkFKHwufTu4XXt1Ci6ZbV1Gd6yh1n0blNzaFA6ypFwB4YTKKywJAE0utQ0+ZHg7tGISXx5fZhPateZ7g8vnV6HI+XbUww9u1NApUXqTuHx6ZM4JnrBgRfKQbuvvjYWMxu39w/98PT+NsPT3V5r8bUD2vglgTSnKykWA3xefGJHWJy3HC43Up5eD8bbc0kroieGESkjYjMEJG1zv/WAdYr9xq9bbLX/G4islBE1onIO84woMZllS1566t30kQzsGtrOrep22htxjREkWYljQVmqmovYKYz7U+Rqg5w/q7wmv8H4GlV7QnsA26OMD2mFm7XuglVPLev++ahi/jPbWfSJMMeno2pFGlgGAm86rx+Fbgy1A3Fcxt7PvBeXbY34dk4/rJ6uSs+tUurqB/DTdagzZiaIg0M7VV1u/N6BxAoY7WRiOSJyAIRqbz4twX2q2qZM70VOD7C9JhaXNSvA13bulcl05++HVtEdf/GmOgL+vwsIp8A/koPx3lPqKqKSKBM7K6qmi8i3YFZIrIMOBBOQkVkDDAGoEuXLuFsahwpKcJJx7VkU0Fh1I7xg9zOvLlwc9T2b4yJvqBPDKo6XFVP8vM3CdgpIh0BnP+7Auwj3/m/AZgDnAoUAK1EpDI4dQLya0nHBFXNVdXc7Gx3+ss3kcu7fzgAg7q1Ye3jlzCgc2JlJRljaoo0K2kycKPz+kZgUvUVRKS1iGQ6r7OAocBKVVVgNnBNbdsbl7lcEJzVLBOA9i0aBWzUFc+Fz8aYmiINDOOBC0VkLTDcmUZEckXkJWedvkCeiCzFEwjGq+pKZ9k9wF0isg5PmcPLEabHGGNMhCKqo6eqBcAFfubnAbc4r+cB/QNsvwEYFEkaTHhicfMeq4Z1xpi6sS4xjOt+kNsp1kkwxkTAAkOSidZ4xt57/eM1p1Q7ZlQOaYyJEgsMxhWZafZVMqahsF+zcUWj9NSAy+yBwZjEYoEhyUTjIp3dPJOhPdtGYc/GmFiwnsNMxBaNGx7rJBhjXGRPDEmmPAZdb0erwNsYEx0WGJJM1zZNaJRevx97HYacNsbEkAWGJPObi3uz/KGL6+14X4w9354YjEkwVsaQZESEtNTaL9RPXOW3oboxJknYE0OSWnBvjZ5Mqowa5F635vasYEziscCQpDq0bBTrJBhj4pQFBhNVGdYi2piEY79aE1XWVYYxicd+tSaqMtMCd5VhjIlPFhhMVKUHqQFljIk/EQUGEWkjIjNEZK3zv7WfdYaJyBKvv6MicqWz7J8i8p3XsgGRpMfElw9+PtTaMBiTgCJ9YhgLzFTVXsBMZ9qHqs5W1QGqOgA4HygEPvZa5TeVy1V1SYTpMXHEWjwbk5giDQwjgVed168CVwZZ/xpgmqoWRnhcE+daNk6nU+smsU6GMaYOIg0M7VV1u/N6B9A+yPrXA29Vm/e4iHwjIk+LSGaE6TFxYumDF9GmaUask2GMqYOgXWKIyCdABz+LxnlPqKqKSMCuO0WkI9AfmO41+148ASUDmADcAzwSYPsxwBiALl3ca5lrjDHGV9DAoKoBO9sXkZ0i0lFVtzsX/l217OoHwPuqWuq178qnjWIReQW4u5Z0TMATPMjNza3/vqONMSZJRJqVNBm40Xl9IzCplnVHUS0byQkmiKfqypXA8gjTYyI0oHOrWCfBGBNjkQaG8cCFIrIWGO5MIyK5IvJS5UoikgN0Bj6ttv2/RGQZsAzIAh6LMD0mQm/+dHCsk2CMibGIut1W1QKgRjedqpoH3OI1vRE43s9650dyfGOMMe6zls/GGGN8WGBIYp/9ZpjPdGZaCo3TrW8jY5KdBQZT5frTO1sXFsYYCwzJzGKAMcYfCwymij0tGGPAAoPxYoPqGGPAAoPxkmZjJxhjsMBgvKSn2tfBGGOBwXjpf3zLWCfBGBMHLDAksdRqI+lc0DdYr+nGmGRggSGJVQ8MxhgDFhiSmtVONcb4Y4EhiaVaZDDG+GGBIYlZVpIxxh8LDEksxQKDMcYPCwxJzLKSjDH+RBQYRORaEVkhIhUiklvLeiNEZLWIrBORsV7zu4nIQmf+OyKSEUl6THi8s5JsSE9jTKVInxiWA1cBnwVaQURSgeeAS4B+wCgR6ecs/gPwtKr2BPYBN0eYHhMG78Dw/s/OjGFKjDHxJKLAoKrfqurqIKsNAtap6gZVLQHeBkaKpyvP84H3nPVeBa6MJD0mPOmpKVyX2xmwnlWNMcfURxnD8cAWr+mtzry2wH5VLas239Sja3M70apJeqyTYYyJI2nBVhCRT4AOfhaNU9VJ7icpYDrGAGMAunTpUl+HbfByc9qw5IGLYp0MY0wcCRoYVHV4hMfIBzp7TXdy5hUArUQkzXlqqJwfKB0TgAkAubm5GmGajDHGBFAfWUmLgF5ODaQM4HpgsqoqMBu4xlnvRqDenkCMMcb4F2l11e+LyFZgCDBFRKY7848TkakAztPA7cB04FvgXVVd4eziHuAuEVmHp8zh5UjSY4wxJnLiuXFPLLm5uZqXlxfrZBhjTEIRkcWqGrDNWSVr+WyMMcaHBQZjjDE+LDAYY4zxYYHBGGOMj4QsfBaRQ0CwrjgSWRawJ9aJiKKGfn7Q8M/Rzi8xdVXV7GArBW3gFqdWh1KynqhEJM/OL7E19HO082vYLCvJGGOMDwsMxhhjfCRqYJgQ6wREmZ1f4mvo52jn14AlZOGzMcaY6EnUJwZjjDFRklCBIdDY0Ykq2PmIyE0isltEljh/t8QinW4RkYkisktElsc6LW4Idj4icp6IHPD6/B6o7zS6SUQ6i8hsEVnpjPX+y1inKRKhnE9D+wxDlTBZSc7Y0WuAC/GM9rYIGKWqK2OasDoK5XxE5CYgV1Vvj0kiXSYi5wCHgddU9aRYpydSwc5HRM4D7lbVy+s7bdEgIh2Bjqr6lYg0BxYDVybwbzDo+TS0zzBUifTE4Hfs6BinKRIN7XyCUtXPgL2xTodbGtr5BKOq21X1K+f1ITzd6CfscLwN7XzclEiBIdDY0Ykq1PO5WkS+EZH3RKSzn+Umvg0RkaUiMk1ETox1YtwiIjnAqcDC2KbEHUHOp0F+hrVJpMCQjP4H5KjqycAM4NUYp8eE5ys8XRCcAvwV+CDG6XGFiDQD/gPcqaoHY52eSAU5nwb5GQaTSIEh0NjRiSro+ahqgaoWO5MvAQPrKW3GBap6UFUPO6+nAukikhXjZEVERNLxXET/par/jXV6IhXsfBriZxiKRAoMfseOjnGaIhH0fJzCsUpX4MkDNQlCRDqIiDivB+H5vRXENlV155zLy8C3qvpUrNMTqVDOp6F9hqFKmE70VLVMRCrHjk4FJnqNHZ1wAp2PiDwC5KnqZOAOEbkCKMNTyHlTzBLsAhF5CzgPyHLGCn9QVRN2nG9/5wOkA6jqC8A1wG0iUgYUAddrolQD9G8oMBpYJiJLnHn3OXfSicjv+QBdoMF+hiFJmOqqxhhj6kciZSUZY4ypBxYYjDHG+LDAYIwxxocFBmOMMT4sMBhjjPGRMNVVjYkFEWkLzHQmOwDlwG5nulBVz4xJwoyJIquuakyIROQh4LCq/inWaTEmmiwryZg6EpHDzv/zRORTEZkkIhtEZLyI/EhEvhSRZSLSw1kvW0T+IyKLnL+hsT0DY/yzwGCMO04BbgX64mlNe4KqDsLTx9UvnHX+AjytqqcDVzvLjIk7VsZgjDsWqep2ABFZD3zszF8GDHNeDwf6OV3vALQQkWaVnbQZEy8sMBjjjmKv1xVe0xUc+52lAGeo6tH6TJgx4bKsJGPqz8ccy1ZCRAbEMC3GBGSBwZj6cweQ64zItxJPmYQxcceqqxpjjPFhTwzGGGN8WGAwxhjjwwKDMcYYHxYYjDHG+LDAYIwxxocFBmOMMT4sMBhjjPFhgcEYY4yP/w+lwrSvfcDy3gAAAABJRU5ErkJggg==\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Known Spectrogram\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Noisy\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Predicted\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "SO9tgJ9wfq6p",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Audio"
]
},
{
"metadata": {
"id": "VfWjzdU4vD_A",
"colab_type": "code",
"pycharm": {},
"cellView": "form",
"outputId": "8b348aeb-c4f1-43ca-d613-27bcd124a8b2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
}
},
"cell_type": "code",
"source": [
"#@title\n",
"\n",
"all_validation_audio = np.concatenate((test.audio_data(), test.noisy_audio(), p))\n",
"ipy_display.display(ipy_display.Audio(all_validation_audio, rate=framerate))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.lib.display.Audio object>"
],
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
" <source 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