Dateien nach "Notebooks" hochladen
This commit is contained in:
ringeisenlu77923 committed
1 parent
c51e5ab180
commit
9427c49222
3 files changed
+4761
No files matched your search
@@ -0,0 +1,815 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"c:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\ignite\\handlers\\checkpoint.py:16: DeprecationWarning: `TorchScript` support for functional optimizers is deprecated and will be removed in a future PyTorch release. Consider using the `torch.compile` optimizer instead.\n",
|
||||
" from torch.distributed.optim import ZeroRedundancyOptimizer\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Import notwendiger Bibliotheken\n",
|
||||
"import nussl\n",
|
||||
"from common import data, viz\n",
|
||||
"from IPython.display import Audio\n",
|
||||
"import IPython\n",
|
||||
"import tensorflow as tf\n",
|
||||
"from tensorflow.keras import layers, models\n",
|
||||
"import numpy as np\n",
|
||||
"import librosa\n",
|
||||
"import os\n",
|
||||
"from scipy import signal\n",
|
||||
"import matplotlib.pyplot as plt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# STFT Parameter setzen\n",
|
||||
"stft_params = nussl.STFTParams(window_length=512, hop_length=128, window_type='sqrt_hann')\n",
|
||||
"#stft_params = nussl.STFTParams(window_length=1024, hop_length=256, window_type='sqrt_hann')\n",
|
||||
"\n",
|
||||
"# Pfad zu den Trainingsdaten festlegen\n",
|
||||
"fg_path = \"C:\\\\Users\\\\Lukas\\\\.nussl\\\\2025-tutorial\\\\foreground\"\n",
|
||||
"\n",
|
||||
"data_num = 40000\n",
|
||||
"coherents = 0.5\n",
|
||||
"source = 'vocals'\n",
|
||||
"\n",
|
||||
"# Trainingsdaten mit `nussl` laden\n",
|
||||
"train_data = data.on_the_fly(stft_params, transform=None, fg_path=fg_path, num_mixtures=data_num, coherent_prob=coherents)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Funktionen zum Visualizieren der Waveform und Spectrogram\n",
|
||||
"\n",
|
||||
"def show_wav(sources):\n",
|
||||
" if isinstance(sources, list):\n",
|
||||
" sources = {f'Source {i}': s for i, s in enumerate(sources)}\n",
|
||||
" plt.figure(figsize=(10, 5))\n",
|
||||
" plt.plot()\n",
|
||||
" nussl.core.utils.visualize_sources_as_waveform(sources)\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
"def show_1wav(data):\n",
|
||||
" plt.figure(figsize=(10, 5))\n",
|
||||
" plt.plot()\n",
|
||||
" nussl.core.utils.visualize_waveform(data)\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
"def show_fre(sources):\n",
|
||||
" if isinstance(sources, list):\n",
|
||||
" sources = {f'Source {i}': s for i, s in enumerate(sources)}\n",
|
||||
" plt.figure(figsize=(10, 5))\n",
|
||||
" plt.plot()\n",
|
||||
" nussl.core.utils.visualize_sources_as_masks(sources, db_cutoff=-80)\n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
"def show_1fre(data):\n",
|
||||
" plt.figure(figsize=(10, 5))\n",
|
||||
" plt.plot()\n",
|
||||
" nussl.core.utils.visualize_spectrogram(data)\n",
|
||||
" plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"dict_keys(['mix', 'sources', 'metadata'])\n",
|
||||
"dict_keys(['vocals', 'drums', 'bass', 'other'])\n",
|
||||
"(1, 220500)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"item = train_data[5]\n",
|
||||
"print(item.keys())\n",
|
||||
"print(item['sources'].keys())\n",
|
||||
"print(item['mix'].audio_data.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"dict_keys(['mix', 'sources', 'metadata'])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
" <source src=\"data:audio/wav;base64,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 truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x500 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x500 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x500 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x500 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Beispielhafte Audioausgabe zur Überprüfung\n",
|
||||
"item = train_data[5]\n",
|
||||
"\n",
|
||||
"print(item.keys())\n",
|
||||
"display(Audio(data=item['mix'].audio_data, rate=item['mix'].sample_rate))\n",
|
||||
"show_wav(item['sources'])\n",
|
||||
"show_fre(item['sources'])\n",
|
||||
"show_1wav(item['sources']['vocals'])\n",
|
||||
"show_1fre(item['sources']['vocals'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def preprocess(array, target_size=(512, 128)):\n",
|
||||
" n_fft = 512\n",
|
||||
" hop_length = 128\n",
|
||||
" win_length = 512\n",
|
||||
"\n",
|
||||
" original_max_amplitude = np.max(np.abs(array))\n",
|
||||
" stft = librosa.stft(array, n_fft=n_fft, hop_length=hop_length, win_length=win_length)\n",
|
||||
" stft_magnitude = np.abs(stft)\n",
|
||||
" phase = np.angle(stft)\n",
|
||||
"\n",
|
||||
" stft_magnitude = np.nan_to_num(stft_magnitude, nan=0.0, posinf=0.0, neginf=0.0)\n",
|
||||
" # Prüfen, ob das Array leer oder lautlos ist\n",
|
||||
" if np.max(stft_magnitude) == 0: # Kein hörbares Signal\n",
|
||||
" mag_norm = np.zeros_like(stft_magnitude)\n",
|
||||
" else:\n",
|
||||
" mag_norm = stft_magnitude / np.max(stft_magnitude)\n",
|
||||
" \n",
|
||||
" # Sicherstellen, dass magnitude ein Tensor ist\n",
|
||||
" magnitude = tf.convert_to_tensor(mag_norm)\n",
|
||||
" \n",
|
||||
" # Füge eine Kanaldimension hinzu, falls nicht vorhanden\n",
|
||||
" if len(magnitude.shape) == 2: # Nur Height und Width vorhanden\n",
|
||||
" magnitude = tf.expand_dims(magnitude, axis=-1) # -> (Height, Width, 1)\n",
|
||||
" \n",
|
||||
" # Resize das Spektrogramm\n",
|
||||
" magnitude_resized = tf.image.resize(magnitude, target_size)\n",
|
||||
" return magnitude_resized, phase, original_max_amplitude\n",
|
||||
"\n",
|
||||
"def postprocess(after, original_max_amplitude, phase):\n",
|
||||
" n_fft = 512\n",
|
||||
" hop_length = 128\n",
|
||||
" win_length = 512\n",
|
||||
" after_resized = tf.image.resize(after, (257, 1723))\n",
|
||||
"\n",
|
||||
" after_resized = np.squeeze(after_resized, axis=-1)\n",
|
||||
"\n",
|
||||
" #after_resized = after_resized.numpy()\n",
|
||||
"\n",
|
||||
" after_resized = after_resized * original_max_amplitude\n",
|
||||
"\n",
|
||||
" complex_spectrogram = after_resized * np.exp(1j * phase)\n",
|
||||
" print(complex_spectrogram.shape)\n",
|
||||
" audio_reconstructed = librosa.istft(complex_spectrogram, hop_length=hop_length, win_length=win_length)\n",
|
||||
" #audio_reconstructed = librosa.griffinlim(complex_spectrogram, hop_length=hop_length, win_length=win_length)\n",
|
||||
" return audio_reconstructed"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def postprocess2(after, original_max_amplitude, phase):\n",
|
||||
" n_fft = 512\n",
|
||||
" hop_length = 39\n",
|
||||
" win_length = 512\n",
|
||||
" after_resized = tf.image.resize(after, (257, 1723))\n",
|
||||
"\n",
|
||||
" after_resized = np.squeeze(after_resized, axis=-1)\n",
|
||||
"\n",
|
||||
" #after_resized = after_resized.numpy()\n",
|
||||
"\n",
|
||||
" after_resized = after_resized * original_max_amplitude\n",
|
||||
"\n",
|
||||
" complex_spectrogram = after_resized * np.exp(1j * phase)\n",
|
||||
" print(complex_spectrogram.shape)\n",
|
||||
" audio_reconstructed = librosa.istft(complex_spectrogram, hop_length=hop_length, win_length=win_length)\n",
|
||||
" #audio_reconstructed = librosa.griffinlim(complex_spectrogram, hop_length=hop_length, win_length=win_length)\n",
|
||||
" return audio_reconstructed"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(512, 128, 1)\n",
|
||||
"(257, 1723)\n",
|
||||
"(67158,)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"item = train_data[5]\n",
|
||||
"\n",
|
||||
"test, ph, max = preprocess(item['mix'].audio_data[0])\n",
|
||||
"print(test.shape)\n",
|
||||
"wave = postprocess2(test, max, ph)\n",
|
||||
"print(wave.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0 : 6848.709534885453 \n",
|
||||
"\n",
|
||||
"1 : 6468.580738429507 \n",
|
||||
"\n",
|
||||
"2 : 13905.595422455506 \n",
|
||||
"\n",
|
||||
"3 : 10432.050489677735 \n",
|
||||
"\n",
|
||||
"4 : 1120.5283170313876 \n",
|
||||
"\n",
|
||||
"5 : 5332.468364854768 \n",
|
||||
"\n",
|
||||
"6 : 5940.126834581248 \n",
|
||||
"\n",
|
||||
"7 : 4123.659269121371 \n",
|
||||
"\n",
|
||||
"8 : 1772.244294191482 \n",
|
||||
"\n",
|
||||
"9 : 4258.558949016236 \n",
|
||||
"\n",
|
||||
"10 : 14699.863638133756 \n",
|
||||
"\n",
|
||||
"11 : 648.041383834664 \n",
|
||||
"\n",
|
||||
"12 : 526.1272396086764 \n",
|
||||
"\n",
|
||||
"13 : 10345.29681100698 \n",
|
||||
"\n",
|
||||
"14 : 6612.290402778461 \n",
|
||||
"\n",
|
||||
"15 : 3680.8267078030062 \n",
|
||||
"\n",
|
||||
"16 : 7039.188462802888 \n",
|
||||
"\n",
|
||||
"17 : 3762.1743564741546 \n",
|
||||
"\n",
|
||||
"18 : 10948.122786605356 \n",
|
||||
"\n",
|
||||
"19 : 0.0 \n",
|
||||
"\n",
|
||||
"20 : 1720.9700588512587 \n",
|
||||
"\n",
|
||||
"21 : 5440.087410303207 \n",
|
||||
"\n",
|
||||
"22 : 10468.225300001786 \n",
|
||||
"\n",
|
||||
"23 : 6456.743042588912 \n",
|
||||
"\n",
|
||||
"24 : 2073.1932550214615 \n",
|
||||
"\n",
|
||||
"25 : 12128.90078158991 \n",
|
||||
"\n",
|
||||
"26 : 6850.453102476413 \n",
|
||||
"\n",
|
||||
"27 : 11604.654475805952 \n",
|
||||
"\n",
|
||||
"28 : 8894.869297665397 \n",
|
||||
"\n",
|
||||
"29 : 6898.07557087773 \n",
|
||||
"\n",
|
||||
"30 : 4924.712461968794 \n",
|
||||
"\n",
|
||||
"31 : 20054.72045613474 \n",
|
||||
"\n",
|
||||
"32 : 10713.176503224926 \n",
|
||||
"\n",
|
||||
"33 : 4467.90180079444 \n",
|
||||
"\n",
|
||||
"34 : 4545.757711708051 \n",
|
||||
"\n",
|
||||
"35 : 11218.189294367377 \n",
|
||||
"\n",
|
||||
"36 : 10433.248441435917 \n",
|
||||
"\n",
|
||||
"37 : 1421.887525655315 \n",
|
||||
"\n",
|
||||
"38 : 3181.637612191356 \n",
|
||||
"\n",
|
||||
"39 : 4785.794909310695 \n",
|
||||
"\n",
|
||||
"40 : 8223.224561579449 \n",
|
||||
"\n",
|
||||
"41 : 8915.070463129938 \n",
|
||||
"\n",
|
||||
"42 : 10424.875259256787 \n",
|
||||
"\n",
|
||||
"43 : 7967.037647771511 \n",
|
||||
"\n",
|
||||
"44 : 4785.035202809481 \n",
|
||||
"\n",
|
||||
"45 : 7710.862742184484 \n",
|
||||
"\n",
|
||||
"46 : 0.0 \n",
|
||||
"\n",
|
||||
"47 : 1841.7623621525565 \n",
|
||||
"\n",
|
||||
"48 : 0.0 \n",
|
||||
"\n",
|
||||
"49 : 5880.082091322228 \n",
|
||||
"\n",
|
||||
"50 : 10320.690936264677 \n",
|
||||
"\n",
|
||||
"51 : 0.0 \n",
|
||||
"\n",
|
||||
"52 : 3085.0070848660957 \n",
|
||||
"\n",
|
||||
"53 : 16420.784025183162 \n",
|
||||
"\n",
|
||||
"54 : 5363.658823371644 \n",
|
||||
"\n",
|
||||
"55 : 5339.253712197819 \n",
|
||||
"\n",
|
||||
"56 : 11130.739466455585 \n",
|
||||
"\n",
|
||||
"57 : 7228.366555468903 \n",
|
||||
"\n",
|
||||
"58 : 7230.338826414808 \n",
|
||||
"\n",
|
||||
"59 : 8217.925667031588 \n",
|
||||
"\n",
|
||||
"60 : 13355.292676460482 \n",
|
||||
"\n",
|
||||
"61 : 11423.784228762132 \n",
|
||||
"\n",
|
||||
"62 : 4792.862810897156 \n",
|
||||
"\n",
|
||||
"63 : 7486.486962660074 \n",
|
||||
"\n",
|
||||
"64 : 4269.382053339133 \n",
|
||||
"\n",
|
||||
"65 : 6238.761286073407 \n",
|
||||
"\n",
|
||||
"66 : 2448.3334286368004 \n",
|
||||
"\n",
|
||||
"67 : 8668.104721424563 \n",
|
||||
"\n",
|
||||
"68 : 572.2854241712007 \n",
|
||||
"\n",
|
||||
"69 : 0.0 \n",
|
||||
"\n",
|
||||
"70 : 29335.393302397628 \n",
|
||||
"\n",
|
||||
"71 : 4471.980821341766 \n",
|
||||
"\n",
|
||||
"72 : 6721.274973926039 \n",
|
||||
"\n",
|
||||
"73 : 22386.030996900958 \n",
|
||||
"\n",
|
||||
"74 : 2926.328060691937 \n",
|
||||
"\n",
|
||||
"75 : 19882.076799220216 \n",
|
||||
"\n",
|
||||
"76 : 9380.051868712926 \n",
|
||||
"\n",
|
||||
"77 : 16807.314175380656 \n",
|
||||
"\n",
|
||||
"78 : 5935.6965868134475 \n",
|
||||
"\n",
|
||||
"79 : 16607.20097262637 \n",
|
||||
"\n",
|
||||
"80 : 6643.896668248015 \n",
|
||||
"\n",
|
||||
"81 : 10007.901636749804 \n",
|
||||
"\n",
|
||||
"82 : 6434.506083102107 \n",
|
||||
"\n",
|
||||
"83 : 5822.48687332519 \n",
|
||||
"\n",
|
||||
"84 : 0.0 \n",
|
||||
"\n",
|
||||
"85 : 5430.32316516226 \n",
|
||||
"\n",
|
||||
"86 : 10280.969932061053 \n",
|
||||
"\n",
|
||||
"87 : 16955.36048931313 \n",
|
||||
"\n",
|
||||
"88 : 8368.289605945814 \n",
|
||||
"\n",
|
||||
"89 : 6263.054419257352 \n",
|
||||
"\n",
|
||||
"90 : 4568.738485988595 \n",
|
||||
"\n",
|
||||
"91 : 3698.8469230282735 \n",
|
||||
"\n",
|
||||
"92 : 15355.233033386148 \n",
|
||||
"\n",
|
||||
"93 : 11498.838665794643 \n",
|
||||
"\n",
|
||||
"94 : 719.4307282773533 \n",
|
||||
"\n",
|
||||
"95 : 6917.473201172354 \n",
|
||||
"\n",
|
||||
"96 : 6837.700975997512 \n",
|
||||
"\n",
|
||||
"97 : 6525.43481268189 \n",
|
||||
"\n",
|
||||
"98 : 7513.791097227597 \n",
|
||||
"\n",
|
||||
"99 : 10524.588390222016 \n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for k in range(100):\n",
|
||||
" item = train_data[k]\n",
|
||||
" sum = 0\n",
|
||||
" for j in range(len(item['mix'].audio_data[0])): \n",
|
||||
" #print(y, end='\\r')\n",
|
||||
" sum = sum + abs(item['sources'][source].audio_data[0][j])\n",
|
||||
" print(k, \": \", sum, \"\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
" <source src=\"data:audio/wav;base64,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 truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"220500\n",
|
||||
"648.041383834664\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"item = train_data[11]\n",
|
||||
"\n",
|
||||
"display(Audio(data=item['mix'].audio_data, rate=item['mix'].sample_rate))\n",
|
||||
"print(len(item['mix'].audio_data[0]))\n",
|
||||
"#print(item['mix'].audio_data[0][5])\n",
|
||||
"\n",
|
||||
"sum = 0\n",
|
||||
"for j in range(len(item['mix'].audio_data[0])): \n",
|
||||
" #print(y, end='\\r')\n",
|
||||
" sum = sum + abs(item['sources'][source].audio_data[0][j])\n",
|
||||
"\n",
|
||||
"print(sum)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"25293 / 40000 | good frames: 25293\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"good_frames = 0\n",
|
||||
"threshold = 5000\n",
|
||||
"x_songs = []\n",
|
||||
"y_songs = []\n",
|
||||
"for i in range(data_num):\n",
|
||||
" item = train_data[i]\n",
|
||||
" sum = 0\n",
|
||||
" for j in range(len(item['mix'].audio_data[0])): \n",
|
||||
" #print(y, end='\\r')\n",
|
||||
" sum = sum + abs(item['sources'][source].audio_data[0][j])\n",
|
||||
"\n",
|
||||
" if(sum > threshold):\n",
|
||||
" good_frames = good_frames + 1\n",
|
||||
" x_datapoint, ph, max = preprocess(item['mix'].audio_data[0])\n",
|
||||
" x_songs.append(x_datapoint)\n",
|
||||
"\n",
|
||||
" y_datapoint, ph, max = preprocess(item['sources'][source].audio_data[0])\n",
|
||||
" y_songs.append(y_datapoint)\n",
|
||||
"\n",
|
||||
" print(i+1, \" / \", data_num, \"| good frames: \", good_frames, end='\\r')\n",
|
||||
" \n",
|
||||
"print(good_frames)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"193 / 16000\r"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[7], line 4\u001b[0m\n\u001b[0;32m 2\u001b[0m y_songs \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(data_num):\n\u001b[1;32m----> 4\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m 5\u001b[0m x_datapoint, ph, \u001b[38;5;28mmax\u001b[39m \u001b[38;5;241m=\u001b[39m preprocess(item[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmix\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39maudio_data[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m 6\u001b[0m x_songs\u001b[38;5;241m.\u001b[39mappend(x_datapoint)\n",
|
||||
"File \u001b[1;32mc:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nussl\\datasets\\base_dataset.py:223\u001b[0m, in \u001b[0;36mBaseDataset.__getitem__\u001b[1;34m(self, i)\u001b[0m\n\u001b[0;32m 221\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpost_cache_transforms(data)\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 223\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess_item\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 225\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m 226\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DataSetException(\n\u001b[0;32m 227\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe output of process_item must be a dictionary!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
|
||||
"File \u001b[1;32mc:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nussl\\datasets\\hooks.py:411\u001b[0m, in \u001b[0;36mOnTheFly.process_item\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprocess_item\u001b[39m(\u001b[38;5;28mself\u001b[39m, item):\n\u001b[1;32m--> 411\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmix_closure\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mitem\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 412\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m 413\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DataSetException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutput of mix_closure must be a dict!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
|
||||
"File \u001b[1;32mc:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\common\\data.py:421\u001b[0m, in \u001b[0;36mMUSDBMixer.__call__\u001b[1;34m(self, dataset, i)\u001b[0m\n\u001b[0;32m 419\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoherent(sc)\n\u001b[0;32m 420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 421\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mincoherent\u001b[49m\u001b[43m(\u001b[49m\u001b[43msc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 423\u001b[0m soundscape_audio, soundscape_jam, annotation_list, event_audio_list \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m 425\u001b[0m mix \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39m_load_audio_from_array(\n\u001b[0;32m 426\u001b[0m audio_data\u001b[38;5;241m=\u001b[39msoundscape_audio, sample_rate\u001b[38;5;241m=\u001b[39mdataset\u001b[38;5;241m.\u001b[39msample_rate\n\u001b[0;32m 427\u001b[0m )\n",
|
||||
"File \u001b[1;32mc:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\common\\data.py:389\u001b[0m, in \u001b[0;36mMUSDBMixer.incoherent\u001b[1;34m(self, sc)\u001b[0m\n\u001b[0;32m 387\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_events(sc, event_parameters)\n\u001b[0;32m 388\u001b[0m quick_pitch_time \u001b[38;5;241m=\u001b[39m sc\u001b[38;5;241m.\u001b[39mrandom_state\u001b[38;5;241m.\u001b[39mrand() \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquick_pitch_time_prob\n\u001b[1;32m--> 389\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfix_clipping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquick_pitch_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquick_pitch_time\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32mc:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\scaper\\core.py:2267\u001b[0m, in \u001b[0;36mScaper.generate\u001b[1;34m(self, audio_path, jams_path, allow_repeated_label, allow_repeated_source, reverb, fix_clipping, peak_normalization, quick_pitch_time, save_isolated_events, isolated_events_path, disable_sox_warnings, no_audio, txt_path, txt_sep, disable_instantiation_warnings)\u001b[0m\n\u001b[0;32m 2264\u001b[0m ref_db_change \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 2265\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_audio:\n\u001b[0;32m 2266\u001b[0m soundscape_audio, event_audio_list, scale_factor, ref_db_change \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m-> 2267\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate_audio\u001b[49m\u001b[43m(\u001b[49m\u001b[43maudio_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mann\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2268\u001b[0m \u001b[43m \u001b[49m\u001b[43mreverb\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreverb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2269\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_isolated_events\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_isolated_events\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2270\u001b[0m \u001b[43m \u001b[49m\u001b[43misolated_events_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43misolated_events_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2271\u001b[0m \u001b[43m \u001b[49m\u001b[43mdisable_sox_warnings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisable_sox_warnings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2272\u001b[0m \u001b[43m \u001b[49m\u001b[43mfix_clipping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfix_clipping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2273\u001b[0m \u001b[43m \u001b[49m\u001b[43mpeak_normalization\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpeak_normalization\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2274\u001b[0m \u001b[43m \u001b[49m\u001b[43mquick_pitch_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquick_pitch_time\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2276\u001b[0m \u001b[38;5;66;03m# TODO: Stick to heavy handed overwriting for now, in the future we\u001b[39;00m\n\u001b[0;32m 2277\u001b[0m \u001b[38;5;66;03m# should consolidate this with what happens inside _instantiate().\u001b[39;00m\n\u001b[0;32m 2278\u001b[0m ann\u001b[38;5;241m.\u001b[39msandbox\u001b[38;5;241m.\u001b[39mscaper\u001b[38;5;241m.\u001b[39maudio_path \u001b[38;5;241m=\u001b[39m audio_path\n",
|
||||
"File \u001b[1;32mc:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\scaper\\core.py:1960\u001b[0m, in \u001b[0;36mScaper._generate_audio\u001b[1;34m(self, audio_path, ann, reverb, fix_clipping, peak_normalization, quick_pitch_time, save_isolated_events, isolated_events_path, disable_sox_warnings)\u001b[0m\n\u001b[0;32m 1956\u001b[0m stop \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m((e\u001b[38;5;241m.\u001b[39mvalue[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msource_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m e\u001b[38;5;241m.\u001b[39mvalue[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mevent_duration\u001b[39m\u001b[38;5;124m'\u001b[39m]) \u001b[38;5;241m*\u001b[39m event_sr)\n\u001b[0;32m 1957\u001b[0m event_audio, event_sr \u001b[38;5;241m=\u001b[39m soundfile\u001b[38;5;241m.\u001b[39mread(\n\u001b[0;32m 1958\u001b[0m e\u001b[38;5;241m.\u001b[39mvalue[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msource_file\u001b[39m\u001b[38;5;124m'\u001b[39m], always_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 1959\u001b[0m start\u001b[38;5;241m=\u001b[39mstart, stop\u001b[38;5;241m=\u001b[39mstop)\n\u001b[1;32m-> 1960\u001b[0m event_audio \u001b[38;5;241m=\u001b[39m \u001b[43mtfm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1961\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_array\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevent_audio\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1962\u001b[0m \u001b[43m \u001b[49m\u001b[43msample_rate_in\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevent_sr\u001b[49m\n\u001b[0;32m 1963\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1964\u001b[0m event_audio \u001b[38;5;241m=\u001b[39m event_audio\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_channels)\n\u001b[0;32m 1966\u001b[0m \u001b[38;5;66;03m# NOW compute LUFS\u001b[39;00m\n",
|
||||
"File \u001b[1;32mc:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sox\\transform.py:844\u001b[0m, in \u001b[0;36mTransformer.build_array\u001b[1;34m(self, input_filepath, input_array, sample_rate_in, extra_args)\u001b[0m\n\u001b[0;32m 841\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mextra_args must be a list.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 842\u001b[0m args\u001b[38;5;241m.\u001b[39mextend(extra_args)\n\u001b[1;32m--> 844\u001b[0m status, out, err \u001b[38;5;241m=\u001b[39m \u001b[43msox\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_array\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 845\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 846\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m SoxError(\n\u001b[0;32m 847\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStdout: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mStderr: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(out, err)\n\u001b[0;32m 848\u001b[0m )\n",
|
||||
"File \u001b[1;32mc:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sox\\core.py:74\u001b[0m, in \u001b[0;36msox\u001b[1;34m(args, src_array, decode_out_with_utf)\u001b[0m\n\u001b[0;32m 65\u001b[0m process_handle \u001b[38;5;241m=\u001b[39m subprocess\u001b[38;5;241m.\u001b[39mPopen(\n\u001b[0;32m 66\u001b[0m args,\n\u001b[0;32m 67\u001b[0m stdin\u001b[38;5;241m=\u001b[39msubprocess\u001b[38;5;241m.\u001b[39mPIPE,\n\u001b[0;32m 68\u001b[0m stdout\u001b[38;5;241m=\u001b[39msubprocess\u001b[38;5;241m.\u001b[39mPIPE,\n\u001b[0;32m 69\u001b[0m stderr\u001b[38;5;241m=\u001b[39msubprocess\u001b[38;5;241m.\u001b[39mPIPE\n\u001b[0;32m 70\u001b[0m )\n\u001b[0;32m 71\u001b[0m \u001b[38;5;66;03m# We do order \"F\" for Fortran formatting of the numpy array, which is\u001b[39;00m\n\u001b[0;32m 72\u001b[0m \u001b[38;5;66;03m# sox expects. When we reshape stdout later, we need to use the same\u001b[39;00m\n\u001b[0;32m 73\u001b[0m \u001b[38;5;66;03m# order, otherwise tests fail.\u001b[39;00m\n\u001b[1;32m---> 74\u001b[0m out, err \u001b[38;5;241m=\u001b[39m \u001b[43mprocess_handle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcommunicate\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc_array\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mT\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtobytes\u001b[49m\u001b[43m(\u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mF\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 75\u001b[0m err \u001b[38;5;241m=\u001b[39m err\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 76\u001b[0m status \u001b[38;5;241m=\u001b[39m process_handle\u001b[38;5;241m.\u001b[39mreturncode\n",
|
||||
"File \u001b[1;32mc:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\subprocess.py:1028\u001b[0m, in \u001b[0;36mPopen.communicate\u001b[1;34m(self, input, timeout)\u001b[0m\n\u001b[0;32m 1025\u001b[0m endtime \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1027\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1028\u001b[0m stdout, stderr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_communicate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mendtime\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1029\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m 1030\u001b[0m \u001b[38;5;66;03m# https://bugs.python.org/issue25942\u001b[39;00m\n\u001b[0;32m 1031\u001b[0m \u001b[38;5;66;03m# See the detailed comment in .wait().\u001b[39;00m\n\u001b[0;32m 1032\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
||||
"File \u001b[1;32mc:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\subprocess.py:1393\u001b[0m, in \u001b[0;36mPopen._communicate\u001b[1;34m(self, input, endtime, orig_timeout)\u001b[0m\n\u001b[0;32m 1390\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr_thread\u001b[38;5;241m.\u001b[39mstart()\n\u001b[0;32m 1392\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstdin:\n\u001b[1;32m-> 1393\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stdin_write\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1395\u001b[0m \u001b[38;5;66;03m# Wait for the reader threads, or time out. If we time out, the\u001b[39;00m\n\u001b[0;32m 1396\u001b[0m \u001b[38;5;66;03m# threads remain reading and the fds left open in case the user\u001b[39;00m\n\u001b[0;32m 1397\u001b[0m \u001b[38;5;66;03m# calls communicate again.\u001b[39;00m\n\u001b[0;32m 1398\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstdout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
||||
"File \u001b[1;32mc:\\Users\\Lukas\\AppData\\Local\\Programs\\Python\\Python38\\lib\\subprocess.py:962\u001b[0m, in \u001b[0;36mPopen._stdin_write\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28minput\u001b[39m:\n\u001b[0;32m 961\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 962\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstdin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 963\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBrokenPipeError\u001b[39;00m:\n\u001b[0;32m 964\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m \u001b[38;5;66;03m# communicate() must ignore broken pipe errors.\u001b[39;00m\n",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x_songs = []\n",
|
||||
"y_songs = []\n",
|
||||
"for i in range(data_num):\n",
|
||||
" item = train_data[i]\n",
|
||||
" x_datapoint, ph, max = preprocess(item['mix'].audio_data[0])\n",
|
||||
" x_songs.append(x_datapoint)\n",
|
||||
"\n",
|
||||
" y_datapoint, ph, max = preprocess(item['sources'][source].audio_data[0])\n",
|
||||
" y_songs.append(y_datapoint)\n",
|
||||
"\n",
|
||||
" print(i+1, \" / \", data_num, end='\\r')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"25293\n",
|
||||
"25293\n",
|
||||
"(512, 128, 1)\n",
|
||||
"(257, 1723)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
" <source src=\"data:audio/wav;base64,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 truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
" <source src=\"data:audio/wav;base64,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 truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(257, 1723)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
" <source src=\"data:audio/wav;base64,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 truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(len(x_songs))\n",
|
||||
"print(len(y_songs))\n",
|
||||
"print(x_songs[good_frames-1].shape)\n",
|
||||
"\n",
|
||||
"audi = postprocess(x_songs[good_frames-1], max, ph)\n",
|
||||
"\n",
|
||||
"audio_vocal = nussl.AudioSignal()\n",
|
||||
"audio_vocal.audio_data = audi\n",
|
||||
"\n",
|
||||
"item = train_data[good_frames-1]\n",
|
||||
"\n",
|
||||
"display(Audio(data=item['mix'].audio_data, rate=item['mix'].sample_rate))\n",
|
||||
"\n",
|
||||
"display(Audio(data=audio_vocal.audio_data, rate=item['mix'].sample_rate))\n",
|
||||
"\n",
|
||||
"audi = postprocess(y_songs[good_frames-1], max, ph)\n",
|
||||
"\n",
|
||||
"audio_vocal2 = nussl.AudioSignal()\n",
|
||||
"audio_vocal2.audio_data = audi\n",
|
||||
"\n",
|
||||
"display(Audio(data=audio_vocal2.audio_data, rate=item['mix'].sample_rate))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(25293, 512, 128, 1)\n",
|
||||
"(25293, 512, 128, 1)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x_songs = np.array(x_songs)\n",
|
||||
"y_songs = np.array(y_songs)\n",
|
||||
"\n",
|
||||
"print(x_songs.shape)\n",
|
||||
"print(y_songs.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import zipfile\n",
|
||||
"# Speichere die Arrays in einer .zip-Datei\n",
|
||||
"with zipfile.ZipFile(f'E:\\\\Projektarbeit\\\\Daten\\\\Training_{source}_{good_frames}_co={coherents}_th={threshold}.zip', 'w') as zipf:\n",
|
||||
" # Speichere jedes Array als separate .npy-Datei\n",
|
||||
" np.save('X_train.npy', x_songs)\n",
|
||||
" np.save('y_train.npy', y_songs)\n",
|
||||
" \n",
|
||||
" # Füge die .npy-Dateien zur .zip hinzu\n",
|
||||
" zipf.write('X_train.npy')\n",
|
||||
" zipf.write('y_train.npy')\n",
|
||||
"\n",
|
||||
"os.remove('X_train.npy')\n",
|
||||
"os.remove('y_train.npy')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
Reference in new issue
Block a user