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225 lines
7.3 KiB
225 lines
7.3 KiB
3 years ago
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "matched-camera",
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"metadata": {},
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"outputs": [],
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"source": [
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"from nnAudio import Spectrogram\n",
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"from scipy.io import wavfile\n",
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"import torch\n",
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"import soundfile as sf\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "middle-salem",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"16000\n",
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"[43 75 69 ... 7 6 3]\n",
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"(83792,)\n",
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"int16\n",
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"sampling rate = 16000. Please make sure the sampling rate is correct in order toget a valid freq range\n",
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"STFT kernels created, time used = 0.2142 seconds\n",
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"tensor([[[[-4.0940e+03, 1.2600e+04],\n",
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" [ 8.5108e+03, -5.4930e+03],\n",
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" [-3.3631e+03, -1.7904e+03],\n",
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" ...,\n",
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" [ 8.2279e+03, -9.3340e+03],\n",
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" [-3.1990e+03, 2.0969e+03],\n",
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" [-1.2669e+03, 4.4488e+03]],\n",
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"\n",
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" [[ 3.4886e+03, -9.9620e+03],\n",
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" [-4.5364e+03, 4.1907e+02],\n",
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" [ 2.5074e+03, 7.1339e+03],\n",
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" ...,\n",
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" [-5.4819e+03, 3.9258e+01],\n",
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" [ 4.7221e+03, 6.5887e+01],\n",
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" [ 9.6492e+02, -3.4386e+03]],\n",
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"\n",
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" [[-3.4947e+03, 9.2981e+03],\n",
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" [-7.5164e+03, 8.1856e+02],\n",
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" [-5.3766e+03, -9.0889e+03],\n",
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" ...,\n",
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" [ 1.4317e+03, 5.7447e+03],\n",
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" [-3.1178e+03, 3.0740e+03],\n",
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" [-3.4351e+03, 5.6900e+02]],\n",
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"\n",
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" ...,\n",
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"\n",
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" [[ 6.7112e+01, -4.5737e+00],\n",
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" [-9.6295e+00, 3.5554e+01],\n",
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" [ 1.8527e+00, -1.0491e+01],\n",
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" ...,\n",
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" [-1.1157e+01, 3.4423e+00],\n",
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" [ 3.1193e+00, -4.4388e+00],\n",
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" [-8.8242e+00, 8.0324e+00]],\n",
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"\n",
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" [[-6.5080e+01, 2.9543e+00],\n",
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" [ 3.9992e+01, -1.3836e+01],\n",
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" [-9.2803e+00, 1.0318e+01],\n",
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" ...,\n",
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" [ 4.2928e+00, 9.2397e+00],\n",
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" [ 3.6642e+00, 9.4680e+00],\n",
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" [ 4.8932e+00, -2.5199e+01]],\n",
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"\n",
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" [[ 4.7264e+01, -1.0721e+00],\n",
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" [-6.0516e+00, -1.4589e+01],\n",
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" [ 1.3127e+01, 1.4995e+00],\n",
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" ...,\n",
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" [ 1.7333e+01, -1.4380e+01],\n",
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" [-3.6046e+00, -6.1019e+00],\n",
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" [ 1.3321e+01, 2.3184e+01]]]])\n"
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]
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}
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],
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"source": [
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"sr, song = wavfile.read('./BAC009S0764W0124.wav') # Loading your audio\n",
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"print(sr)\n",
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"print(song)\n",
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"print(song.shape)\n",
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"print(song.dtype)\n",
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"x = song\n",
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"x = torch.tensor(x).float() # casting the array into a PyTorch Tensor\n",
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"\n",
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"spec_layer = Spectrogram.STFT(n_fft=2048, freq_bins=None, hop_length=512,\n",
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" window='hann', freq_scale='linear', center=True, pad_mode='reflect',\n",
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" fmin=50,fmax=8000, sr=sr) # Initializing the model\n",
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"\n",
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"spec = spec_layer(x) # Feed-forward your waveform to get the spectrogram\n",
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"print(spec)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "finished-sterling",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"16000\n",
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"[43 75 69 ... 7 6 3]\n",
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"(83792,)\n",
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"int16\n",
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"True\n",
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"sampling rate = 16000. Please make sure the sampling rate is correct in order toget a valid freq range\n",
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"STFT kernels created, time used = 0.2495 seconds\n",
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"torch.Size([1, 1025, 164, 2])\n",
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"tensor([[[[-4.0940e+03, 1.2600e+04],\n",
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" [ 8.5108e+03, -5.4930e+03],\n",
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" [-3.3631e+03, -1.7904e+03],\n",
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" ...,\n",
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" [ 8.2279e+03, -9.3340e+03],\n",
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" [-3.1990e+03, 2.0969e+03],\n",
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" [-1.2669e+03, 4.4488e+03]],\n",
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"\n",
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" [[ 3.4886e+03, -9.9620e+03],\n",
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" [-4.5364e+03, 4.1907e+02],\n",
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" [ 2.5074e+03, 7.1339e+03],\n",
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" ...,\n",
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" [-5.4819e+03, 3.9258e+01],\n",
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" [ 4.7221e+03, 6.5887e+01],\n",
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" [ 9.6492e+02, -3.4386e+03]],\n",
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"\n",
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" [[-3.4947e+03, 9.2981e+03],\n",
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" [-7.5164e+03, 8.1856e+02],\n",
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" [-5.3766e+03, -9.0889e+03],\n",
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" ...,\n",
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" [ 1.4317e+03, 5.7447e+03],\n",
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" [-3.1178e+03, 3.0740e+03],\n",
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" [-3.4351e+03, 5.6900e+02]],\n",
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"\n",
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" ...,\n",
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"\n",
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" [[ 6.7112e+01, -4.5737e+00],\n",
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" [-9.6295e+00, 3.5554e+01],\n",
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" [ 1.8527e+00, -1.0491e+01],\n",
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" ...,\n",
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" [-1.1157e+01, 3.4423e+00],\n",
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" [ 3.1193e+00, -4.4388e+00],\n",
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" [-8.8242e+00, 8.0324e+00]],\n",
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"\n",
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" [[-6.5080e+01, 2.9543e+00],\n",
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" [ 3.9992e+01, -1.3836e+01],\n",
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" [-9.2803e+00, 1.0318e+01],\n",
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" ...,\n",
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" [ 4.2928e+00, 9.2397e+00],\n",
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" [ 3.6642e+00, 9.4680e+00],\n",
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" [ 4.8932e+00, -2.5199e+01]],\n",
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"\n",
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" [[ 4.7264e+01, -1.0721e+00],\n",
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" [-6.0516e+00, -1.4589e+01],\n",
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" [ 1.3127e+01, 1.4995e+00],\n",
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" ...,\n",
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" [ 1.7333e+01, -1.4380e+01],\n",
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" [-3.6046e+00, -6.1019e+00],\n",
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" [ 1.3321e+01, 2.3184e+01]]]])\n",
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"True\n"
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]
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}
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],
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"source": [
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"wav, sr = sf.read('./BAC009S0764W0124.wav', dtype='int16')\n",
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"print(sr)\n",
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"print(wav)\n",
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"print(wav.shape)\n",
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"print(wav.dtype)\n",
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"print(np.allclose(wav, song))\n",
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"\n",
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"x = wav\n",
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"x = torch.tensor(x).float() # casting the array into a PyTorch Tensor\n",
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"\n",
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"spec_layer = Spectrogram.STFT(n_fft=2048, freq_bins=None, hop_length=512,\n",
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" window='hann', freq_scale='linear', center=True, pad_mode='reflect',\n",
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" fmin=50,fmax=8000, sr=sr) # Initializing the model\n",
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"\n",
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"wav_spec = spec_layer(x) # Feed-forward your waveform to get the spectrogram\n",
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"print(wav_spec.shape)\n",
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"print(wav_spec)\n",
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"print(np.allclose(wav_spec, spec))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "running-technology",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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