File size: 5,907 Bytes
45ee559
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook computes the average SNR a given Voice Dataset. If the SNR is too low, that might reduce the performance or prevent model to learn. SNR paper can be seen here: https://www.cs.cmu.edu/~robust/Papers/KimSternIS08.pdf\n",
    "\n",
    "To use this notebook, you need:\n",
    "- WADA SNR estimation: http://www.cs.cmu.edu/~robust/archive/algorithms/WADA_SNR_IS_2008/\n",
    "    1. extract in the same folder as this notebook\n",
    "    2. under MacOS you'll have to rebuild the executable. In the build folder: 1) remove existing .o files and 2) run make\n",
    "\n",
    "\n",
    "- FFMPEG: ```sudo apt-get install ffmpeg ```     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import subprocess\n",
    "import IPython\n",
    "import soundfile as sf\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from multiprocessing import Pool\n",
    "from matplotlib import pylab as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the meta parameters\n",
    "DATA_PATH = \"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/eva_k/\"\n",
    "NUM_PROC = 1\n",
    "CURRENT_PATH = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def compute_file_snr(file_path):\n",
    "    \"\"\" Convert given file to required format with FFMPEG and process with WADA.\"\"\"\n",
    "    _, sr = sf.read(file_path)\n",
    "    new_file = file_path.replace(\".wav\", \"_tmp.wav\")\n",
    "    if sr != 16000:\n",
    "        command = f'ffmpeg -i \"{file_path}\" -ac 1 -acodec pcm_s16le -y -ar 16000 \"{new_file}\"'\n",
    "    else:\n",
    "        command = f'cp \"{file_path}\" \"{new_file}\"'\n",
    "    os.system(command)\n",
    "    command = [f'\"{CURRENT_PATH}/WadaSNR/Exe/WADASNR\"', f'-i \"{new_file}\"', f'-t \"{CURRENT_PATH}/WadaSNR/Exe/Alpha0.400000.txt\"', '-ifmt mswav']\n",
    "    output = subprocess.check_output(\" \".join(command), shell=True)\n",
    "    try:\n",
    "        output = float(output.split()[-3].decode(\"utf-8\"))\n",
    "    except:\n",
    "        raise RuntimeError(\" \".join(command))\n",
    "    os.system(f'rm \"{new_file}\"')\n",
    "    return output, file_path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wav_file = \"/home/erogol/Data/LJSpeech-1.1/wavs/LJ001-0001.wav\"\n",
    "output = compute_file_snr(wav_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wav_files = glob.glob(f\"{DATA_PATH}/**/*.wav\", recursive=True)\n",
    "print(f\" > Number of wav files {len(wav_files)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if NUM_PROC == 1:\n",
    "    file_snrs = [None] * len(wav_files) \n",
    "    for idx, wav_file in tqdm(enumerate(wav_files)):\n",
    "        tup = compute_file_snr(wav_file)\n",
    "        file_snrs[idx] = tup\n",
    "else:\n",
    "    with Pool(NUM_PROC) as pool:\n",
    "        file_snrs = list(tqdm(pool.imap(compute_file_snr, wav_files), total=len(wav_files)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "snrs = [tup[0] for tup in file_snrs]\n",
    "\n",
    "error_idxs = np.where(np.isnan(snrs) == True)[0]\n",
    "error_files = [wav_files[idx] for idx in error_idxs]\n",
    "\n",
    "file_snrs = [i for j, i in enumerate(file_snrs) if j not in error_idxs]\n",
    "file_names = [tup[1] for tup in file_snrs]\n",
    "snrs = [tup[0] for tup in file_snrs]\n",
    "file_idxs = np.argsort(snrs)\n",
    "\n",
    "\n",
    "print(f\" > Average SNR of the dataset:{np.mean(snrs)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def output_snr_with_audio(idx):\n",
    "    file_idx = file_idxs[idx]\n",
    "    file_name = file_names[file_idx]\n",
    "    wav, sr = sf.read(file_name)\n",
    "    # multi channel to single channel\n",
    "    if len(wav.shape) == 2:\n",
    "        wav = wav[:, 0]\n",
    "    print(f\" > {file_name} - snr:{snrs[file_idx]}\")\n",
    "    IPython.display.display(IPython.display.Audio(wav, rate=sr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find worse SNR files\n",
    "N = 10  # number of files to fetch\n",
    "for i in range(N):\n",
    "    output_snr_with_audio(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find best recordings\n",
    "N = 10  # number of files to fetch\n",
    "for i in range(N):\n",
    "    output_snr_with_audio(-i-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(snrs, bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}