{
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"id": "27deb847",
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"id": "2536ab2b",
"metadata": {},
"source": [
"# Import Deps"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9bbb376c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: librosa in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (0.10.0)\n",
"Requirement already satisfied: soxr>=0.3.2 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (0.3.5)\n",
"Requirement already satisfied: lazy-loader>=0.1 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (0.1)\n",
"Requirement already satisfied: scikit-learn>=0.20.0 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (1.2.2)\n",
"Requirement already satisfied: joblib>=0.14 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (1.1.1)\n",
"Requirement already satisfied: audioread>=2.1.9 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (3.0.0)\n",
"Requirement already satisfied: decorator>=4.3.0 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (5.1.1)\n",
"Requirement already satisfied: soundfile>=0.12.1 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (0.12.1)\n",
"Requirement already satisfied: numba>=0.51.0 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (0.56.4)\n",
"Requirement already satisfied: pooch>=1.0 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (1.4.0)\n",
"Requirement already satisfied: msgpack>=1.0 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (1.0.3)\n",
"Requirement already satisfied: numpy>=1.20.3 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (1.23.5)\n",
"Requirement already satisfied: scipy>=1.2.0 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (1.10.0)\n",
"Requirement already satisfied: typing-extensions>=4.1.1 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from librosa) (4.5.0)\n",
"Requirement already satisfied: setuptools in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from numba>=0.51.0->librosa) (66.0.0)\n",
"Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from numba>=0.51.0->librosa) (0.39.1)\n",
"Requirement already satisfied: appdirs in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from pooch>=1.0->librosa) (1.4.4)\n",
"Requirement already satisfied: packaging in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from pooch>=1.0->librosa) (23.0)\n",
"Requirement already satisfied: requests in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from pooch>=1.0->librosa) (2.29.0)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from scikit-learn>=0.20.0->librosa) (2.2.0)\n",
"Requirement already satisfied: cffi>=1.0 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from soundfile>=0.12.1->librosa) (1.15.1)\n",
"Requirement already satisfied: pycparser in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from cffi>=1.0->soundfile>=0.12.1->librosa) (2.21)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from requests->pooch>=1.0->librosa) (1.26.15)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from requests->pooch>=1.0->librosa) (2.0.4)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from requests->pooch>=1.0->librosa) (2023.5.7)\n",
"Requirement already satisfied: idna<4,>=2.5 in /Users/amanmibra/anaconda3/envs/void/lib/python3.9/site-packages (from requests->pooch>=1.0->librosa) (3.4)\n"
]
}
],
"source": [
"!pip install librosa"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2019a9c0",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('..')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "24b482dc",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import Audio\n",
"import numpy as np\n",
"import librosa\n",
"\n",
"# torch\n",
"import torch\n",
"import torchaudio\n",
"from torch.utils.data import DataLoader\n",
"\n",
"# model training\n",
"from cnn import CNNetwork\n",
"from dataset import VoiceDataset"
]
},
{
"cell_type": "markdown",
"id": "2bf224a0",
"metadata": {},
"source": [
"# Test Dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "77a0394b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cpu device.\n"
]
}
],
"source": [
"if torch.cuda.is_available():\n",
" device = \"cuda\"\n",
"else:\n",
" device = \"cpu\"\n",
"print(f\"Using {device} device.\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a52cde0b",
"metadata": {},
"outputs": [],
"source": [
"DATA_PATH = os.path.join('..', 'data')\n",
"TEST_PATH = os.path.join(DATA_PATH, 'test')\n",
"SAMPLE_RATE = 48000\n",
"MEL_SPEC = torchaudio.transforms.MelSpectrogram(\n",
" sample_rate=SAMPLE_RATE,\n",
" n_fft=2048,\n",
" hop_length=512,\n",
" n_mels=128\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5c1d4624",
"metadata": {},
"outputs": [],
"source": [
"test_dataset = VoiceDataset(TEST_PATH, MEL_SPEC, device, SAMPLE_RATE)"
]
},
{
"cell_type": "markdown",
"id": "52aaf0b6",
"metadata": {},
"source": [
"## Choose Example"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "de395f46",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"19"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(test_dataset)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "58bb8a16",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i = 0\n",
"test_dataset[i][1]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5a7e8e7b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'aman': 0, 'imran': 1, 'labib': 2}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_dataset.label_mapping"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "6f759da6",
"metadata": {},
"outputs": [],
"source": [
"wav, actual_output = test_dataset[i]"
]
},
{
"cell_type": "markdown",
"id": "d79b6e47",
"metadata": {},
"source": [
"## Show Example Spec"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1cb1b126",
"metadata": {},
"outputs": [],
"source": [
"def plot_waveform(waveform, sample_rate, title = \"Waveform\"):\n",
" waveform = waveform.numpy()\n",
" num_channels, num_frames = waveform.shape\n",
" time = np.arange(0, num_frames) / sample_rate\n",
"\n",
" fig, axes = plt.subplots(num_channels, 1)\n",
"\n",
" if num_channels == 1:\n",
" axes = [axes]\n",
" for ch in range(num_channels):\n",
" axes[ch].plot(time, waveform[ch])\n",
" axes[ch].grid(True)\n",
"\n",
" if num_channels > 1:\n",
" axes[ch].set_ylabel(f\"Channel: {ch+1}\")\n",
" plt.suptitle(title)\n",
" plt.show(block = False)\n",
"\n",
"def plot_spectrogram(specgram, title=None, ylabel=\"freq_bin\"):\n",
" fig, axs = plt.subplots(1, 1)\n",
" axs.set_title(title or \"Spectrogram (db)\")\n",
" axs.set_ylabel(ylabel)\n",
" axs.set_xlabel(\"frame\")\n",
" im = axs.imshow(librosa.power_to_db(specgram), origin=\"lower\", aspect=\"auto\")\n",
" fig.colorbar(im, ax=axs)\n",
" plt.show(block=False)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "74ae8c37",
"metadata": {},
"outputs": [],
"source": [
"file, label = test_dataset.audio_files_labels[i]\n",
"path = os.path.join(TEST_PATH, label, file)\n",
"audio_wavform, sr = torchaudio.load(path)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "2c468e16",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Audio(audio_wavform, rate=sr)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c6ae3a30",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
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