 Посмотреть на TensorFlow.org |  Запустить в Google Colab |  Посмотреть исходный код на GitHub |  Скачать блокнот |
В этом руководстве показано, как создавать музыкальные ноты с помощью простого RNN. Вы будете обучать модель, используя коллекцию MIDI-файлов пианино из набора данных MAESTRO . Учитывая последовательность нот, ваша модель научится предсказывать следующую ноту в последовательности. Вы можете создавать более длинные последовательности заметок, многократно вызывая модель.
Этот учебник содержит полный код для анализа и создания MIDI-файлов. Вы можете узнать больше о том, как работают RNN, посетив раздел Генерация текста с помощью RNN .
Настраивать
В этом руководстве используется библиотека pretty_midi для создания и анализа MIDI-файлов, а pyfluidsynth для создания воспроизведения звука в Colab.
sudo apt install -y fluidsynth
The following packages were automatically installed and are no longer required: linux-gcp-5.4-headers-5.4.0-1040 linux-gcp-5.4-headers-5.4.0-1043 linux-gcp-5.4-headers-5.4.0-1044 linux-gcp-5.4-headers-5.4.0-1049 linux-headers-5.4.0-1049-gcp linux-image-5.4.0-1049-gcp linux-modules-5.4.0-1049-gcp linux-modules-extra-5.4.0-1049-gcp Use 'sudo apt autoremove' to remove them. The following additional packages will be installed: fluid-soundfont-gm libasyncns0 libdouble-conversion1 libevdev2 libflac8 libfluidsynth1 libgudev-1.0-0 libinput-bin libinput10 libjack-jackd2-0 libmtdev1 libogg0 libpulse0 libqt5core5a libqt5dbus5 libqt5gui5 libqt5network5 libqt5svg5 libqt5widgets5 libqt5x11extras5 libsamplerate0 libsndfile1 libvorbis0a libvorbisenc2 libwacom-bin libwacom-common libwacom2 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-shape0 libxcb-util1 libxcb-xinerama0 libxcb-xkb1 libxkbcommon-x11-0 qsynth qt5-gtk-platformtheme qttranslations5-l10n Suggested packages: fluid-soundfont-gs timidity jackd2 pulseaudio qt5-image-formats-plugins qtwayland5 jackd The following NEW packages will be installed: fluid-soundfont-gm fluidsynth libasyncns0 libdouble-conversion1 libevdev2 libflac8 libfluidsynth1 libgudev-1.0-0 libinput-bin libinput10 libjack-jackd2-0 libmtdev1 libogg0 libpulse0 libqt5core5a libqt5dbus5 libqt5gui5 libqt5network5 libqt5svg5 libqt5widgets5 libqt5x11extras5 libsamplerate0 libsndfile1 libvorbis0a libvorbisenc2 libwacom-bin libwacom-common libwacom2 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-shape0 libxcb-util1 libxcb-xinerama0 libxcb-xkb1 libxkbcommon-x11-0 qsynth qt5-gtk-platformtheme qttranslations5-l10n 0 upgraded, 41 newly installed, 0 to remove and 120 not upgraded. Need to get 132 MB of archives. After this operation, 198 MB of additional disk space will be used. Get:1 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libogg0 amd64 1.3.2-1 [17.2 kB] Get:2 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libdouble-conversion1 amd64 2.0.1-4ubuntu1 [33.0 kB] Get:3 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libqt5core5a amd64 5.9.5+dfsg-0ubuntu2.6 [2035 kB] Get:4 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libevdev2 amd64 1.5.8+dfsg-1ubuntu0.1 [28.9 kB] Get:5 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libmtdev1 amd64 1.1.5-1ubuntu3 [13.8 kB] Get:6 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libgudev-1.0-0 amd64 1:232-2 [13.6 kB] Get:7 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libwacom-common all 0.29-1 [36.9 kB] Get:8 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libwacom2 amd64 0.29-1 [17.7 kB] Get:9 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libinput-bin amd64 1.10.4-1ubuntu0.18.04.2 [11.2 kB] Get:10 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libinput10 amd64 1.10.4-1ubuntu0.18.04.2 [86.2 kB] Get:11 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libqt5dbus5 amd64 5.9.5+dfsg-0ubuntu2.6 [195 kB] Get:12 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libqt5network5 amd64 5.9.5+dfsg-0ubuntu2.6 [634 kB] Get:13 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-icccm4 amd64 0.4.1-1ubuntu1 [10.4 kB] Get:14 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-util1 amd64 0.4.0-0ubuntu3 [11.2 kB] Get:15 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-image0 amd64 0.4.0-1build1 [12.3 kB] Get:16 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-keysyms1 amd64 0.4.0-1 [8406 B] Get:17 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libxcb-randr0 amd64 1.13-2~ubuntu18.04 [16.4 kB] Get:18 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-render-util0 amd64 0.3.9-1 [9638 B] Get:19 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libxcb-shape0 amd64 1.13-2~ubuntu18.04 [5972 B] Get:20 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libxcb-xinerama0 amd64 1.13-2~ubuntu18.04 [5264 B] Get:21 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libxcb-xkb1 amd64 1.13-2~ubuntu18.04 [30.1 kB] Get:22 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libxkbcommon-x11-0 amd64 0.8.2-1~ubuntu18.04.1 [13.4 kB] Get:23 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libqt5gui5 amd64 5.9.5+dfsg-0ubuntu2.6 [2568 kB] Get:24 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libqt5widgets5 amd64 5.9.5+dfsg-0ubuntu2.6 [2203 kB] Get:25 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libqt5svg5 amd64 5.9.5-0ubuntu1.1 [129 kB] Get:26 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 fluid-soundfont-gm all 3.1-5.1 [119 MB] Get:27 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libsamplerate0 amd64 0.1.9-1 [938 kB] Get:28 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libjack-jackd2-0 amd64 1.9.12~dfsg-2 [263 kB] Get:29 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libasyncns0 amd64 0.8-6 [12.1 kB] Get:30 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libflac8 amd64 1.3.2-1 [213 kB] Get:31 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libvorbis0a amd64 1.3.5-4.2 [86.4 kB] Get:32 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libvorbisenc2 amd64 1.3.5-4.2 [70.7 kB] Get:33 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libsndfile1 amd64 1.0.28-4ubuntu0.18.04.2 [170 kB] Get:34 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpulse0 amd64 1:11.1-1ubuntu7.11 [266 kB] Get:35 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 libfluidsynth1 amd64 1.1.9-1 [137 kB] Get:36 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 fluidsynth amd64 1.1.9-1 [20.7 kB] Get:37 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 libqt5x11extras5 amd64 5.9.5-0ubuntu1 [8596 B] Get:38 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libwacom-bin amd64 0.29-1 [4712 B] Get:39 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 qsynth amd64 0.5.0-2 [191 kB] Get:40 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 qt5-gtk-platformtheme amd64 5.9.5+dfsg-0ubuntu2.6 [117 kB] Get:41 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 qttranslations5-l10n all 5.9.5-0ubuntu1 [1485 kB] Fetched 132 MB in 9s (14.0 MB/s) Extracting templates from packages: 100% 7[0;23r8[1ASelecting previously unselected package libogg0:amd64. (Reading database ... 285125 files and directories currently installed.) Preparing to unpack .../00-libogg0_1.3.2-1_amd64.deb ... 7[24;0fProgress: [ 0%] [..........................................................] 8Unpacking libogg0:amd64 (1.3.2-1) ... 7[24;0fProgress: [ 1%] [..........................................................] 8Selecting previously unselected package libdouble-conversion1:amd64. Preparing to unpack .../01-libdouble-conversion1_2.0.1-4ubuntu1_amd64.deb ... Unpacking libdouble-conversion1:amd64 (2.0.1-4ubuntu1) ... 7[24;0fProgress: [ 2%] [#.........................................................] 8Selecting previously unselected package libqt5core5a:amd64. Preparing to unpack .../02-libqt5core5a_5.9.5+dfsg-0ubuntu2.6_amd64.deb ... 7[24;0fProgress: [ 3%] [#.........................................................] 8Unpacking libqt5core5a:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 4%] [##........................................................] 8Selecting previously unselected package libevdev2:amd64. Preparing to unpack .../03-libevdev2_1.5.8+dfsg-1ubuntu0.1_amd64.deb ... Unpacking libevdev2:amd64 (1.5.8+dfsg-1ubuntu0.1) ... 7[24;0fProgress: [ 5%] [###.......................................................] 8Selecting previously unselected package libmtdev1:amd64. Preparing to unpack .../04-libmtdev1_1.1.5-1ubuntu3_amd64.deb ... 7[24;0fProgress: [ 6%] [###.......................................................] 8Unpacking libmtdev1:amd64 (1.1.5-1ubuntu3) ... 7[24;0fProgress: [ 7%] [####......................................................] 8Selecting previously unselected package libgudev-1.0-0:amd64. Preparing to unpack .../05-libgudev-1.0-0_1%3a232-2_amd64.deb ... Unpacking libgudev-1.0-0:amd64 (1:232-2) ... 7[24;0fProgress: [ 8%] [####......................................................] 8Selecting previously unselected package libwacom-common. Preparing to unpack .../06-libwacom-common_0.29-1_all.deb ... 7[24;0fProgress: [ 9%] [#####.....................................................] 8Unpacking libwacom-common (0.29-1) ... 7[24;0fProgress: [ 10%] [#####.....................................................] 8Selecting previously unselected package libwacom2:amd64. Preparing to unpack .../07-libwacom2_0.29-1_amd64.deb ... Unpacking libwacom2:amd64 (0.29-1) ... 7[24;0fProgress: [ 11%] [######....................................................] 8Selecting previously unselected package libinput-bin. Preparing to unpack .../08-libinput-bin_1.10.4-1ubuntu0.18.04.2_amd64.deb ... 7[24;0fProgress: [ 12%] [#######...................................................] 8Unpacking libinput-bin (1.10.4-1ubuntu0.18.04.2) ... 7[24;0fProgress: [ 13%] [#######...................................................] 8Selecting previously unselected package libinput10:amd64. Preparing to unpack .../09-libinput10_1.10.4-1ubuntu0.18.04.2_amd64.deb ... Unpacking libinput10:amd64 (1.10.4-1ubuntu0.18.04.2) ... 7[24;0fProgress: [ 14%] [########..................................................] 8Selecting previously unselected package libqt5dbus5:amd64. Preparing to unpack .../10-libqt5dbus5_5.9.5+dfsg-0ubuntu2.6_amd64.deb ... 7[24;0fProgress: [ 15%] [########..................................................] 8Unpacking libqt5dbus5:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 16%] [#########.................................................] 8Selecting previously unselected package libqt5network5:amd64. Preparing to unpack .../11-libqt5network5_5.9.5+dfsg-0ubuntu2.6_amd64.deb ... Unpacking libqt5network5:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 17%] [##########................................................] 8Selecting previously unselected package libxcb-icccm4:amd64. Preparing to unpack .../12-libxcb-icccm4_0.4.1-1ubuntu1_amd64.deb ... Unpacking libxcb-icccm4:amd64 (0.4.1-1ubuntu1) ... 7[24;0fProgress: [ 18%] [##########................................................] 8Selecting previously unselected package libxcb-util1:amd64. Preparing to unpack .../13-libxcb-util1_0.4.0-0ubuntu3_amd64.deb ... 7[24;0fProgress: [ 19%] [###########...............................................] 8Unpacking libxcb-util1:amd64 (0.4.0-0ubuntu3) ... 7[24;0fProgress: [ 20%] [###########...............................................] 8Selecting previously unselected package libxcb-image0:amd64. Preparing to unpack .../14-libxcb-image0_0.4.0-1build1_amd64.deb ... Unpacking libxcb-image0:amd64 (0.4.0-1build1) ... 7[24;0fProgress: [ 21%] [############..............................................] 8Selecting previously unselected package libxcb-keysyms1:amd64. Preparing to unpack .../15-libxcb-keysyms1_0.4.0-1_amd64.deb ... 7[24;0fProgress: [ 22%] [############..............................................] 8Unpacking libxcb-keysyms1:amd64 (0.4.0-1) ... 7[24;0fProgress: [ 23%] [#############.............................................] 8Selecting previously unselected package libxcb-randr0:amd64. Preparing to unpack .../16-libxcb-randr0_1.13-2~ubuntu18.04_amd64.deb ... Unpacking libxcb-randr0:amd64 (1.13-2~ubuntu18.04) ... 7[24;0fProgress: [ 24%] [##############............................................] 8Selecting previously unselected package libxcb-render-util0:amd64. Preparing to unpack .../17-libxcb-render-util0_0.3.9-1_amd64.deb ... 7[24;0fProgress: [ 25%] [##############............................................] 8Unpacking libxcb-render-util0:amd64 (0.3.9-1) ... 7[24;0fProgress: [ 26%] [###############...........................................] 8Selecting previously unselected package libxcb-shape0:amd64. Preparing to unpack .../18-libxcb-shape0_1.13-2~ubuntu18.04_amd64.deb ... Unpacking libxcb-shape0:amd64 (1.13-2~ubuntu18.04) ... 7[24;0fProgress: [ 27%] [###############...........................................] 8Selecting previously unselected package libxcb-xinerama0:amd64. Preparing to unpack .../19-libxcb-xinerama0_1.13-2~ubuntu18.04_amd64.deb ... 7[24;0fProgress: [ 28%] [################..........................................] 8Unpacking libxcb-xinerama0:amd64 (1.13-2~ubuntu18.04) ... 7[24;0fProgress: [ 29%] [################..........................................] 8Selecting previously unselected package libxcb-xkb1:amd64. Preparing to unpack .../20-libxcb-xkb1_1.13-2~ubuntu18.04_amd64.deb ... Unpacking libxcb-xkb1:amd64 (1.13-2~ubuntu18.04) ... 7[24;0fProgress: [ 30%] [#################.........................................] 8Selecting previously unselected package libxkbcommon-x11-0:amd64. Preparing to unpack .../21-libxkbcommon-x11-0_0.8.2-1~ubuntu18.04.1_amd64.deb ... 7[24;0fProgress: [ 31%] [##################........................................] 8Unpacking libxkbcommon-x11-0:amd64 (0.8.2-1~ubuntu18.04.1) ... 7[24;0fProgress: [ 32%] [##################........................................] 8Selecting previously unselected package libqt5gui5:amd64. Preparing to unpack .../22-libqt5gui5_5.9.5+dfsg-0ubuntu2.6_amd64.deb ... Unpacking libqt5gui5:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 33%] [###################.......................................] 8Selecting previously unselected package libqt5widgets5:amd64. Preparing to unpack .../23-libqt5widgets5_5.9.5+dfsg-0ubuntu2.6_amd64.deb ... Unpacking libqt5widgets5:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 34%] [###################.......................................] 8Selecting previously unselected package libqt5svg5:amd64. Preparing to unpack .../24-libqt5svg5_5.9.5-0ubuntu1.1_amd64.deb ... 7[24;0fProgress: [ 35%] [####################......................................] 8Unpacking libqt5svg5:amd64 (5.9.5-0ubuntu1.1) ... 7[24;0fProgress: [ 36%] [#####################.....................................] 8Selecting previously unselected package fluid-soundfont-gm. Preparing to unpack .../25-fluid-soundfont-gm_3.1-5.1_all.deb ... Unpacking fluid-soundfont-gm (3.1-5.1) ... 7[24;0fProgress: [ 37%] [#####################.....................................] 8Selecting previously unselected package libsamplerate0:amd64. Preparing to unpack .../26-libsamplerate0_0.1.9-1_amd64.deb ... 7[24;0fProgress: [ 38%] [######################....................................] 8Unpacking libsamplerate0:amd64 (0.1.9-1) ... 7[24;0fProgress: [ 39%] [######################....................................] 8Selecting previously unselected package libjack-jackd2-0:amd64. Preparing to unpack .../27-libjack-jackd2-0_1.9.12~dfsg-2_amd64.deb ... Unpacking libjack-jackd2-0:amd64 (1.9.12~dfsg-2) ... 7[24;0fProgress: [ 40%] [#######################...................................] 8Selecting previously unselected package libasyncns0:amd64. Preparing to unpack .../28-libasyncns0_0.8-6_amd64.deb ... 7[24;0fProgress: [ 41%] [#######################...................................] 8Unpacking libasyncns0:amd64 (0.8-6) ... 7[24;0fProgress: [ 42%] [########################..................................] 8Selecting previously unselected package libflac8:amd64. Preparing to unpack .../29-libflac8_1.3.2-1_amd64.deb ... Unpacking libflac8:amd64 (1.3.2-1) ... 7[24;0fProgress: [ 43%] [#########################.................................] 8Selecting previously unselected package libvorbis0a:amd64. Preparing to unpack .../30-libvorbis0a_1.3.5-4.2_amd64.deb ... 7[24;0fProgress: [ 44%] [#########################.................................] 8Unpacking libvorbis0a:amd64 (1.3.5-4.2) ... 7[24;0fProgress: [ 45%] [##########################................................] 8Selecting previously unselected package libvorbisenc2:amd64. Preparing to unpack .../31-libvorbisenc2_1.3.5-4.2_amd64.deb ... Unpacking libvorbisenc2:amd64 (1.3.5-4.2) ... 7[24;0fProgress: [ 46%] [##########################................................] 8Selecting previously unselected package libsndfile1:amd64. Preparing to unpack .../32-libsndfile1_1.0.28-4ubuntu0.18.04.2_amd64.deb ... 7[24;0fProgress: [ 47%] [###########################...............................] 8Unpacking libsndfile1:amd64 (1.0.28-4ubuntu0.18.04.2) ... 7[24;0fProgress: [ 48%] [###########################...............................] 8Selecting previously unselected package libpulse0:amd64. Preparing to unpack .../33-libpulse0_1%3a11.1-1ubuntu7.11_amd64.deb ... Unpacking libpulse0:amd64 (1:11.1-1ubuntu7.11) ... 7[24;0fProgress: [ 49%] [############################..............................] 8Selecting previously unselected package libfluidsynth1:amd64. Preparing to unpack .../34-libfluidsynth1_1.1.9-1_amd64.deb ... 7[24;0fProgress: [ 50%] [#############################.............................] 8Unpacking libfluidsynth1:amd64 (1.1.9-1) ... Selecting previously unselected package fluidsynth. Preparing to unpack .../35-fluidsynth_1.1.9-1_amd64.deb ... 7[24;0fProgress: [ 51%] [#############################.............................] 8Unpacking fluidsynth (1.1.9-1) ... 7[24;0fProgress: [ 52%] [##############################............................] 8Selecting previously unselected package libqt5x11extras5:amd64. Preparing to unpack .../36-libqt5x11extras5_5.9.5-0ubuntu1_amd64.deb ... Unpacking libqt5x11extras5:amd64 (5.9.5-0ubuntu1) ... 7[24;0fProgress: [ 53%] [##############################............................] 8Selecting previously unselected package libwacom-bin. Preparing to unpack .../37-libwacom-bin_0.29-1_amd64.deb ... 7[24;0fProgress: [ 54%] [###############################...........................] 8Unpacking libwacom-bin (0.29-1) ... 7[24;0fProgress: [ 55%] [################################..........................] 8Selecting previously unselected package qsynth. Preparing to unpack .../38-qsynth_0.5.0-2_amd64.deb ... Unpacking qsynth (0.5.0-2) ... 7[24;0fProgress: [ 56%] [################################..........................] 8Selecting previously unselected package qt5-gtk-platformtheme:amd64. Preparing to unpack .../39-qt5-gtk-platformtheme_5.9.5+dfsg-0ubuntu2.6_amd64.deb ... 7[24;0fProgress: [ 57%] [#################################.........................] 8Unpacking qt5-gtk-platformtheme:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 58%] [#################################.........................] 8Selecting previously unselected package qttranslations5-l10n. Preparing to unpack .../40-qttranslations5-l10n_5.9.5-0ubuntu1_all.deb ... Unpacking qttranslations5-l10n (5.9.5-0ubuntu1) ... 7[24;0fProgress: [ 59%] [##################################........................] 8Setting up libxcb-xinerama0:amd64 (1.13-2~ubuntu18.04) ... 7[24;0fProgress: [ 60%] [##################################........................] 8Setting up libxcb-render-util0:amd64 (0.3.9-1) ... 7[24;0fProgress: [ 61%] [###################################.......................] 8Setting up libxcb-randr0:amd64 (1.13-2~ubuntu18.04) ... 7[24;0fProgress: [ 62%] [####################################......................] 8Setting up libxcb-icccm4:amd64 (0.4.1-1ubuntu1) ... 7[24;0fProgress: [ 63%] [####################################......................] 8Setting up libasyncns0:amd64 (0.8-6) ... 7[24;0fProgress: [ 64%] [#####################################.....................] 8Setting up libwacom-common (0.29-1) ... 7[24;0fProgress: [ 65%] [#####################################.....................] 8Setting up libdouble-conversion1:amd64 (2.0.1-4ubuntu1) ... 7[24;0fProgress: [ 66%] [######################################....................] 8Setting up libevdev2:amd64 (1.5.8+dfsg-1ubuntu0.1) ... 7[24;0fProgress: [ 67%] [#######################################...................] 8Setting up fluid-soundfont-gm (3.1-5.1) ... 7[24;0fProgress: [ 68%] [#######################################...................] 8Setting up libxcb-util1:amd64 (0.4.0-0ubuntu3) ... 7[24;0fProgress: [ 69%] [########################################..................] 8Setting up libogg0:amd64 (1.3.2-1) ... 7[24;0fProgress: [ 70%] [########################################..................] 8Setting up qttranslations5-l10n (5.9.5-0ubuntu1) ... 7[24;0fProgress: [ 71%] [#########################################.................] 8Setting up libmtdev1:amd64 (1.1.5-1ubuntu3) ... 7[24;0fProgress: [ 72%] [#########################################.................] 8Setting up libxcb-shape0:amd64 (1.13-2~ubuntu18.04) ... 7[24;0fProgress: [ 73%] [##########################################................] 8Setting up libgudev-1.0-0:amd64 (1:232-2) ... 7[24;0fProgress: [ 74%] [###########################################...............] 8Setting up libxcb-keysyms1:amd64 (0.4.0-1) ... 7[24;0fProgress: [ 75%] [###########################################...............] 8Setting up libsamplerate0:amd64 (0.1.9-1) ... 7[24;0fProgress: [ 76%] [############################################..............] 8Setting up libvorbis0a:amd64 (1.3.5-4.2) ... 7[24;0fProgress: [ 77%] [############################################..............] 8Setting up libxcb-xkb1:amd64 (1.13-2~ubuntu18.04) ... 7[24;0fProgress: [ 78%] [#############################################.............] 8Setting up libqt5core5a:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 79%] [#############################################.............] 8Setting up libqt5dbus5:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 80%] [##############################################............] 8Setting up libqt5network5:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 81%] [###############################################...........] 8Setting up libwacom2:amd64 (0.29-1) ... 7[24;0fProgress: [ 82%] [###############################################...........] 8Setting up libxcb-image0:amd64 (0.4.0-1build1) ... 7[24;0fProgress: [ 83%] [################################################..........] 8Setting up libflac8:amd64 (1.3.2-1) ... Setting up libinput-bin (1.10.4-1ubuntu0.18.04.2) ... 7[24;0fProgress: [ 84%] [################################################..........] 8Setting up libxkbcommon-x11-0:amd64 (0.8.2-1~ubuntu18.04.1) ... 7[24;0fProgress: [ 85%] [#################################################.........] 8Setting up libwacom-bin (0.29-1) ... 7[24;0fProgress: [ 86%] [##################################################........] 8Setting up libjack-jackd2-0:amd64 (1.9.12~dfsg-2) ... 7[24;0fProgress: [ 87%] [##################################################........] 8Setting up libvorbisenc2:amd64 (1.3.5-4.2) ... 7[24;0fProgress: [ 88%] [###################################################.......] 8Setting up libinput10:amd64 (1.10.4-1ubuntu0.18.04.2) ... 7[24;0fProgress: [ 89%] [###################################################.......] 8Setting up libsndfile1:amd64 (1.0.28-4ubuntu0.18.04.2) ... 7[24;0fProgress: [ 90%] [####################################################......] 8Setting up libqt5gui5:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 91%] [####################################################......] 8Setting up qt5-gtk-platformtheme:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 92%] [#####################################################.....] 8Setting up libqt5x11extras5:amd64 (5.9.5-0ubuntu1) ... 7[24;0fProgress: [ 93%] [######################################################....] 8Setting up libqt5widgets5:amd64 (5.9.5+dfsg-0ubuntu2.6) ... 7[24;0fProgress: [ 94%] [######################################################....] 8Setting up libpulse0:amd64 (1:11.1-1ubuntu7.11) ... 7[24;0fProgress: [ 95%] [#######################################################...] 8Setting up libqt5svg5:amd64 (5.9.5-0ubuntu1.1) ... 7[24;0fProgress: [ 96%] [#######################################################...] 8Setting up libfluidsynth1:amd64 (1.1.9-1) ... 7[24;0fProgress: [ 97%] [########################################################..] 8Setting up fluidsynth (1.1.9-1) ... 7[24;0fProgress: [ 98%] [########################################################..] 8Setting up qsynth (0.5.0-2) ... 7[24;0fProgress: [ 99%] [#########################################################.] 8Processing triggers for hicolor-icon-theme (0.17-2) ... Processing triggers for mime-support (3.60ubuntu1) ... Processing triggers for libc-bin (2.27-3ubuntu1.2) ... Processing triggers for udev (237-3ubuntu10.50) ... Processing triggers for man-db (2.8.3-2ubuntu0.1) ... 7[0;24r8[1A[J
pip install --upgrade pyfluidsynth
pip install pretty_midi
import collections
import datetime
import fluidsynth
import glob
import numpy as np
import pathlib
import pandas as pd
import pretty_midi
import seaborn as sns
import tensorflow as tf
from IPython import display
from matplotlib import pyplot as plt
from typing import Dict, List, Optional, Sequence, Tuple
seed = 42
tf.random.set_seed(seed)
np.random.seed(seed)
# Sampling rate for audio playback
_SAMPLING_RATE = 16000
Загрузите набор данных Maestro
data_dir = pathlib.Path('data/maestro-v2.0.0')
if not data_dir.exists():
tf.keras.utils.get_file(
'maestro-v2.0.0-midi.zip',
origin='https://storage.googleapis.com/magentadata/datasets/maestro/v2.0.0/maestro-v2.0.0-midi.zip',
extract=True,
cache_dir='.', cache_subdir='data',
)
Downloading data from https://storage.googleapis.com/magentadata/datasets/maestro/v2.0.0/maestro-v2.0.0-midi.zip 59244544/59243107 [==============================] - 3s 0us/step 59252736/59243107 [==============================] - 3s 0us/step
Набор данных содержит около 1200 файлов MIDI.
filenames = glob.glob(str(data_dir/'**/*.mid*'))
print('Number of files:', len(filenames))
Number of files: 1282
Обработка MIDI-файла
Во-первых, используйте pretty_midi для разбора одного MIDI-файла и проверки формата нот. Если вы хотите загрузить приведенный ниже MIDI-файл для воспроизведения на своем компьютере, вы можете сделать это в colab, написав files.download(sample_file) .
sample_file = filenames[1]
print(sample_file)
data/maestro-v2.0.0/2013/ORIG-MIDI_02_7_6_13_Group__MID--AUDIO_08_R1_2013_wav--3.midi
Создайте объект PrettyMIDI для образца MIDI-файла.
pm = pretty_midi.PrettyMIDI(sample_file)
Воспроизведите образец файла. Загрузка виджета воспроизведения может занять несколько секунд.
def display_audio(pm: pretty_midi.PrettyMIDI, seconds=30):
waveform = pm.fluidsynth(fs=_SAMPLING_RATE)
# Take a sample of the generated waveform to mitigate kernel resets
waveform_short = waveform[:seconds*_SAMPLING_RATE]
return display.Audio(waveform_short, rate=_SAMPLING_RATE)
display_audio(pm)
Сделайте некоторую проверку файла MIDI. Какие инструменты используются?
print('Number of instruments:', len(pm.instruments))
instrument = pm.instruments[0]
instrument_name = pretty_midi.program_to_instrument_name(instrument.program)
print('Instrument name:', instrument_name)
Number of instruments: 1 Instrument name: Acoustic Grand Piano
Извлечь заметки
for i, note in enumerate(instrument.notes[:10]):
note_name = pretty_midi.note_number_to_name(note.pitch)
duration = note.end - note.start
print(f'{i}: pitch={note.pitch}, note_name={note_name},'
f' duration={duration:.4f}')
0: pitch=56, note_name=G#3, duration=0.0352 1: pitch=44, note_name=G#2, duration=0.0417 2: pitch=68, note_name=G#4, duration=0.0651 3: pitch=80, note_name=G#5, duration=0.1693 4: pitch=78, note_name=F#5, duration=0.1523 5: pitch=76, note_name=E5, duration=0.1120 6: pitch=75, note_name=D#5, duration=0.0612 7: pitch=49, note_name=C#3, duration=0.0378 8: pitch=85, note_name=C#6, duration=0.0352 9: pitch=37, note_name=C#2, duration=0.0417
Вы будете использовать три переменные для представления ноты при обучении модели: pitch , step и duration . Высота тона — это воспринимаемое качество звука как номер MIDI-ноты. step — это время, прошедшее с предыдущей ноты или начала дорожки. duration — это продолжительность воспроизведения ноты в секундах и разница между временем окончания и начала ноты.
Извлеките ноты из образца MIDI-файла.
def midi_to_notes(midi_file: str) -> pd.DataFrame:
pm = pretty_midi.PrettyMIDI(midi_file)
instrument = pm.instruments[0]
notes = collections.defaultdict(list)
# Sort the notes by start time
sorted_notes = sorted(instrument.notes, key=lambda note: note.start)
prev_start = sorted_notes[0].start
for note in sorted_notes:
start = note.start
end = note.end
notes['pitch'].append(note.pitch)
notes['start'].append(start)
notes['end'].append(end)
notes['step'].append(start - prev_start)
notes['duration'].append(end - start)
prev_start = start
return pd.DataFrame({name: np.array(value) for name, value in notes.items()})
raw_notes = midi_to_notes(sample_file)
raw_notes.head()
Возможно, будет проще интерпретировать названия нот, а не высоту тона, поэтому вы можете использовать приведенную ниже функцию для преобразования числовых значений высоты тона в названия нот. Название ноты показывает тип ноты, случайный номер и номер октавы (например, C#4).
get_note_names = np.vectorize(pretty_midi.note_number_to_name)
sample_note_names = get_note_names(raw_notes['pitch'])
sample_note_names[:10]
array(['G#3', 'G#5', 'G#4', 'G#2', 'F#5', 'E5', 'D#5', 'C#3', 'C#6',
'C#5'], dtype='<U3')
Чтобы визуализировать музыкальное произведение, отметьте высоту ноты, начало и конец по всей длине дорожки (например, пианино). Начните с первых 100 нот
def plot_piano_roll(notes: pd.DataFrame, count: Optional[int] = None):
if count:
title = f'First {count} notes'
else:
title = f'Whole track'
count = len(notes['pitch'])
plt.figure(figsize=(20, 4))
plot_pitch = np.stack([notes['pitch'], notes['pitch']], axis=0)
plot_start_stop = np.stack([notes['start'], notes['end']], axis=0)
plt.plot(
plot_start_stop[:, :count], plot_pitch[:, :count], color="b", marker=".")
plt.xlabel('Time [s]')
plt.ylabel('Pitch')
_ = plt.title(title)
plot_piano_roll(raw_notes, count=100)
Постройте ноты для всей дорожки.
plot_piano_roll(raw_notes)
Проверьте распределение каждой переменной ноты.
def plot_distributions(notes: pd.DataFrame, drop_percentile=2.5):
plt.figure(figsize=[15, 5])
plt.subplot(1, 3, 1)
sns.histplot(notes, x="pitch", bins=20)
plt.subplot(1, 3, 2)
max_step = np.percentile(notes['step'], 100 - drop_percentile)
sns.histplot(notes, x="step", bins=np.linspace(0, max_step, 21))
plt.subplot(1, 3, 3)
max_duration = np.percentile(notes['duration'], 100 - drop_percentile)
sns.histplot(notes, x="duration", bins=np.linspace(0, max_duration, 21))
plot_distributions(raw_notes)
Создать MIDI-файл
Вы можете создать свой собственный MIDI-файл из списка нот, используя функцию ниже.
def notes_to_midi(
notes: pd.DataFrame,
out_file: str,
instrument_name: str,
velocity: int = 100, # note loudness
) -> pretty_midi.PrettyMIDI:
pm = pretty_midi.PrettyMIDI()
instrument = pretty_midi.Instrument(
program=pretty_midi.instrument_name_to_program(
instrument_name))
prev_start = 0
for i, note in notes.iterrows():
start = float(prev_start + note['step'])
end = float(start + note['duration'])
note = pretty_midi.Note(
velocity=velocity,
pitch=int(note['pitch']),
start=start,
end=end,
)
instrument.notes.append(note)
prev_start = start
pm.instruments.append(instrument)
pm.write(out_file)
return pm
example_file = 'example.midi'
example_pm = notes_to_midi(
raw_notes, out_file=example_file, instrument_name=instrument_name)
Воспроизведите сгенерированный MIDI-файл и посмотрите, есть ли разница.
display_audio(example_pm)
Как и прежде, вы можете написать files.download(example_file) для загрузки и воспроизведения этого файла.
Создайте обучающий набор данных
Создайте набор обучающих данных, извлекая заметки из MIDI-файлов. Вы можете начать с небольшого количества файлов, а позже поэкспериментировать с большим количеством. Это может занять пару минут.
num_files = 5
all_notes = []
for f in filenames[:num_files]:
notes = midi_to_notes(f)
all_notes.append(notes)
all_notes = pd.concat(all_notes)
n_notes = len(all_notes)
print('Number of notes parsed:', n_notes)
Number of notes parsed: 23163
Затем создайте tf.data.Dataset из проанализированных заметок.
key_order = ['pitch', 'step', 'duration']
train_notes = np.stack([all_notes[key] for key in key_order], axis=1)
notes_ds = tf.data.Dataset.from_tensor_slices(train_notes)
notes_ds.element_spec
TensorSpec(shape=(3,), dtype=tf.float64, name=None)
Вы будете обучать модель на пакетах последовательностей заметок. Каждый пример будет состоять из последовательности заметок в качестве входных функций и следующей заметки в качестве метки. Таким образом, модель будет обучена предсказывать следующую ноту в последовательности. Вы можете найти схему, объясняющую этот процесс (и более подробную информацию) в разделе Классификация текста с RNN .
Вы можете использовать удобную оконную функцию с размером seq_length для создания объектов и меток в этом формате.
def create_sequences(
dataset: tf.data.Dataset,
seq_length: int,
vocab_size = 128,
) -> tf.data.Dataset:
"""Returns TF Dataset of sequence and label examples."""
seq_length = seq_length+1
# Take 1 extra for the labels
windows = dataset.window(seq_length, shift=1, stride=1,
drop_remainder=True)
# `flat_map` flattens the" dataset of datasets" into a dataset of tensors
flatten = lambda x: x.batch(seq_length, drop_remainder=True)
sequences = windows.flat_map(flatten)
# Normalize note pitch
def scale_pitch(x):
x = x/[vocab_size,1.0,1.0]
return x
# Split the labels
def split_labels(sequences):
inputs = sequences[:-1]
labels_dense = sequences[-1]
labels = {key:labels_dense[i] for i,key in enumerate(key_order)}
return scale_pitch(inputs), labels
return sequences.map(split_labels, num_parallel_calls=tf.data.AUTOTUNE)
Установите длину последовательности для каждого примера. Поэкспериментируйте с различными длинами (например, 50, 100, 150), чтобы увидеть, какая из них лучше всего подходит для данных, или используйте настройку гиперпараметров . Размер словаря ( vocab_size ) установлен равным 128, представляющим все высоты тона, поддерживаемые pretty_midi .
seq_length = 25
vocab_size = 128
seq_ds = create_sequences(notes_ds, seq_length, vocab_size)
seq_ds.element_spec
(TensorSpec(shape=(25, 3), dtype=tf.float64, name=None),
{'pitch': TensorSpec(shape=(), dtype=tf.float64, name=None),
'step': TensorSpec(shape=(), dtype=tf.float64, name=None),
'duration': TensorSpec(shape=(), dtype=tf.float64, name=None)})
Набор данных имеет форму (100,1) , что означает, что модель будет принимать 100 заметок в качестве входных данных и учиться предсказывать следующую заметку в качестве вывода.
for seq, target in seq_ds.take(1):
print('sequence shape:', seq.shape)
print('sequence elements (first 10):', seq[0: 10])
print()
print('target:', target)
sequence shape: (25, 3)
sequence elements (first 10): tf.Tensor(
[[0.578125 0. 0.1484375 ]
[0.390625 0.00130208 0.0390625 ]
[0.3828125 0.03255208 0.07421875]
[0.390625 0.08203125 0.14713542]
[0.5625 0.14973958 0.07421875]
[0.546875 0.09375 0.07421875]
[0.5390625 0.12239583 0.04947917]
[0.296875 0.01692708 0.31119792]
[0.5234375 0.09895833 0.04036458]
[0.5078125 0.12369792 0.06380208]], shape=(10, 3), dtype=float64)
target: {'pitch': <tf.Tensor: shape=(), dtype=float64, numpy=67.0>, 'step': <tf.Tensor: shape=(), dtype=float64, numpy=0.1171875>, 'duration': <tf.Tensor: shape=(), dtype=float64, numpy=0.04947916666666652>}
Объедините примеры и настройте набор данных для повышения производительности.
batch_size = 64
buffer_size = n_notes - seq_length # the number of items in the dataset
train_ds = (seq_ds
.shuffle(buffer_size)
.batch(batch_size, drop_remainder=True)
.cache()
.prefetch(tf.data.experimental.AUTOTUNE))
train_ds.element_spec
(TensorSpec(shape=(64, 25, 3), dtype=tf.float64, name=None),
{'pitch': TensorSpec(shape=(64,), dtype=tf.float64, name=None),
'step': TensorSpec(shape=(64,), dtype=tf.float64, name=None),
'duration': TensorSpec(shape=(64,), dtype=tf.float64, name=None)})
Создайте и обучите модель
Модель будет иметь три выхода, по одному для каждой переменной ноты. Для pitch и duration вы будете использовать пользовательскую функцию потерь, основанную на среднеквадратической ошибке, которая побуждает модель выводить неотрицательные значения.
def mse_with_positive_pressure(y_true: tf.Tensor, y_pred: tf.Tensor):
mse = (y_true - y_pred) ** 2
positive_pressure = 10 * tf.maximum(-y_pred, 0.0)
return tf.reduce_mean(mse + positive_pressure)
input_shape = (seq_length, 3)
learning_rate = 0.005
inputs = tf.keras.Input(input_shape)
x = tf.keras.layers.LSTM(128)(inputs)
outputs = {
'pitch': tf.keras.layers.Dense(128, name='pitch')(x),
'step': tf.keras.layers.Dense(1, name='step')(x),
'duration': tf.keras.layers.Dense(1, name='duration')(x),
}
model = tf.keras.Model(inputs, outputs)
loss = {
'pitch': tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True),
'step': mse_with_positive_pressure,
'duration': mse_with_positive_pressure,
}
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
model.compile(loss=loss, optimizer=optimizer)
model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 25, 3)] 0 []
lstm (LSTM) (None, 128) 67584 ['input_1[0][0]']
duration (Dense) (None, 1) 129 ['lstm[0][0]']
pitch (Dense) (None, 128) 16512 ['lstm[0][0]']
step (Dense) (None, 1) 129 ['lstm[0][0]']
==================================================================================================
Total params: 84,354
Trainable params: 84,354
Non-trainable params: 0
__________________________________________________________________________________________________
model.evaluate функцию model.evaluate, вы можете видеть, что потери pitch значительно больше, чем потери step и duration . Обратите внимание, что loss — это общая потеря, рассчитанная путем суммирования всех других потерь, и в настоящее время преобладает потеря pitch .
losses = model.evaluate(train_ds, return_dict=True)
losses
361/361 [==============================] - 6s 4ms/step - loss: 5.0011 - duration_loss: 0.1213 - pitch_loss: 4.8476 - step_loss: 0.0322
{'loss': 5.001128196716309,
'duration_loss': 0.12134315073490143,
'pitch_loss': 4.847629547119141,
'step_loss': 0.03215572610497475}
Один из способов сбалансировать это — использовать аргумент loss_weights для компиляции:
model.compile(
loss=loss,
loss_weights={
'pitch': 0.05,
'step': 1.0,
'duration':1.0,
},
optimizer=optimizer,
)
Затем loss становятся взвешенной суммой отдельных потерь.
model.evaluate(train_ds, return_dict=True)
361/361 [==============================] - 2s 4ms/step - loss: 0.3959 - duration_loss: 0.1213 - pitch_loss: 4.8476 - step_loss: 0.0322
{'loss': 0.39588069915771484,
'duration_loss': 0.12134315073490143,
'pitch_loss': 4.847629547119141,
'step_loss': 0.03215572610497475}
Обучите модель.
callbacks = [
tf.keras.callbacks.ModelCheckpoint(
filepath='./training_checkpoints/ckpt_{epoch}',
save_weights_only=True),
tf.keras.callbacks.EarlyStopping(
monitor='loss',
patience=5,
verbose=1,
restore_best_weights=True),
]
%%time
epochs = 50
history = model.fit(
train_ds,
epochs=epochs,
callbacks=callbacks,
)
Epoch 1/50 361/361 [==============================] - 4s 5ms/step - loss: 0.3075 - duration_loss: 0.0732 - pitch_loss: 4.0974 - step_loss: 0.0294 Epoch 2/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2950 - duration_loss: 0.0696 - pitch_loss: 3.9526 - step_loss: 0.0278 Epoch 3/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2927 - duration_loss: 0.0682 - pitch_loss: 3.9372 - step_loss: 0.0276 Epoch 4/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2918 - duration_loss: 0.0681 - pitch_loss: 3.9232 - step_loss: 0.0275 Epoch 5/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2874 - duration_loss: 0.0657 - pitch_loss: 3.9079 - step_loss: 0.0264 Epoch 6/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2842 - duration_loss: 0.0653 - pitch_loss: 3.8509 - step_loss: 0.0263 Epoch 7/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2820 - duration_loss: 0.0650 - pitch_loss: 3.8090 - step_loss: 0.0265 Epoch 8/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2806 - duration_loss: 0.0654 - pitch_loss: 3.7903 - step_loss: 0.0257 Epoch 9/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2806 - duration_loss: 0.0651 - pitch_loss: 3.7888 - step_loss: 0.0261 Epoch 10/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2778 - duration_loss: 0.0637 - pitch_loss: 3.7690 - step_loss: 0.0256 Epoch 11/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2762 - duration_loss: 0.0624 - pitch_loss: 3.7704 - step_loss: 0.0253 Epoch 12/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2746 - duration_loss: 0.0616 - pitch_loss: 3.7644 - step_loss: 0.0248 Epoch 13/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2728 - duration_loss: 0.0604 - pitch_loss: 3.7591 - step_loss: 0.0244 Epoch 14/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2710 - duration_loss: 0.0584 - pitch_loss: 3.7573 - step_loss: 0.0247 Epoch 15/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2694 - duration_loss: 0.0574 - pitch_loss: 3.7610 - step_loss: 0.0239 Epoch 16/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2686 - duration_loss: 0.0569 - pitch_loss: 3.7529 - step_loss: 0.0240 Epoch 17/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2695 - duration_loss: 0.0577 - pitch_loss: 3.7486 - step_loss: 0.0243 Epoch 18/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2663 - duration_loss: 0.0560 - pitch_loss: 3.7473 - step_loss: 0.0229 Epoch 19/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2642 - duration_loss: 0.0543 - pitch_loss: 3.7366 - step_loss: 0.0231 Epoch 20/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2691 - duration_loss: 0.0587 - pitch_loss: 3.7421 - step_loss: 0.0233 Epoch 21/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2636 - duration_loss: 0.0547 - pitch_loss: 3.7314 - step_loss: 0.0223 Epoch 22/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2613 - duration_loss: 0.0533 - pitch_loss: 3.7313 - step_loss: 0.0215 Epoch 23/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2595 - duration_loss: 0.0516 - pitch_loss: 3.7219 - step_loss: 0.0218 Epoch 24/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2548 - duration_loss: 0.0493 - pitch_loss: 3.7148 - step_loss: 0.0198 Epoch 25/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2539 - duration_loss: 0.0483 - pitch_loss: 3.7150 - step_loss: 0.0199 Epoch 26/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2526 - duration_loss: 0.0474 - pitch_loss: 3.7138 - step_loss: 0.0196 Epoch 27/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2502 - duration_loss: 0.0460 - pitch_loss: 3.7036 - step_loss: 0.0190 Epoch 28/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2467 - duration_loss: 0.0442 - pitch_loss: 3.6970 - step_loss: 0.0177 Epoch 29/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2458 - duration_loss: 0.0438 - pitch_loss: 3.6938 - step_loss: 0.0172 Epoch 30/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2434 - duration_loss: 0.0418 - pitch_loss: 3.6836 - step_loss: 0.0174 Epoch 31/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2404 - duration_loss: 0.0403 - pitch_loss: 3.6703 - step_loss: 0.0166 Epoch 32/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2421 - duration_loss: 0.0412 - pitch_loss: 3.6833 - step_loss: 0.0168 Epoch 33/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2391 - duration_loss: 0.0399 - pitch_loss: 3.6585 - step_loss: 0.0163 Epoch 34/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2376 - duration_loss: 0.0390 - pitch_loss: 3.6467 - step_loss: 0.0163 Epoch 35/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2403 - duration_loss: 0.0417 - pitch_loss: 3.6448 - step_loss: 0.0164 Epoch 36/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2394 - duration_loss: 0.0417 - pitch_loss: 3.6218 - step_loss: 0.0166 Epoch 37/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2337 - duration_loss: 0.0369 - pitch_loss: 3.6155 - step_loss: 0.0161 Epoch 38/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2320 - duration_loss: 0.0357 - pitch_loss: 3.6080 - step_loss: 0.0158 Epoch 39/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2291 - duration_loss: 0.0353 - pitch_loss: 3.5896 - step_loss: 0.0143 Epoch 40/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2285 - duration_loss: 0.0352 - pitch_loss: 3.5784 - step_loss: 0.0144 Epoch 41/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2276 - duration_loss: 0.0338 - pitch_loss: 3.5928 - step_loss: 0.0142 Epoch 42/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2233 - duration_loss: 0.0316 - pitch_loss: 3.5582 - step_loss: 0.0137 Epoch 43/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2211 - duration_loss: 0.0304 - pitch_loss: 3.5453 - step_loss: 0.0134 Epoch 44/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2206 - duration_loss: 0.0307 - pitch_loss: 3.5396 - step_loss: 0.0129 Epoch 45/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2223 - duration_loss: 0.0322 - pitch_loss: 3.5352 - step_loss: 0.0133 Epoch 46/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2213 - duration_loss: 0.0312 - pitch_loss: 3.5323 - step_loss: 0.0135 Epoch 47/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2240 - duration_loss: 0.0329 - pitch_loss: 3.5405 - step_loss: 0.0142 Epoch 48/50 361/361 [==============================] - 2s 6ms/step - loss: 0.2217 - duration_loss: 0.0322 - pitch_loss: 3.5160 - step_loss: 0.0137 Epoch 49/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2167 - duration_loss: 0.0296 - pitch_loss: 3.4894 - step_loss: 0.0126 Epoch 50/50 361/361 [==============================] - 2s 5ms/step - loss: 0.2142 - duration_loss: 0.0278 - pitch_loss: 3.4757 - step_loss: 0.0126 CPU times: user 2min 16s, sys: 23.9 s, total: 2min 40s Wall time: 1min 41s
plt.plot(history.epoch, history.history['loss'], label='total loss')
plt.show()
Создание заметок
Чтобы использовать модель для создания заметок, сначала необходимо указать начальную последовательность заметок. Приведенная ниже функция генерирует одну ноту из последовательности нот.
Для высоты ноты он берет образец из распределения нот softmax, созданного моделью, а не просто выбирает ноту с наибольшей вероятностью. Всегда выбор заметки с наибольшей вероятностью приведет к созданию повторяющихся последовательностей заметок.
Параметр temperature можно использовать для управления случайностью генерируемых нот. Вы можете найти более подробную информацию о температуре в разделе Генерация текста с помощью RNN .
def predict_next_note(
notes: np.ndarray,
keras_model: tf.keras.Model,
temperature: float = 1.0) -> int:
"""Generates a note IDs using a trained sequence model."""
assert temperature > 0
# Add batch dimension
inputs = tf.expand_dims(notes, 0)
predictions = model.predict(inputs)
pitch_logits = predictions['pitch']
step = predictions['step']
duration = predictions['duration']
pitch_logits /= temperature
pitch = tf.random.categorical(pitch_logits, num_samples=1)
pitch = tf.squeeze(pitch, axis=-1)
duration = tf.squeeze(duration, axis=-1)
step = tf.squeeze(step, axis=-1)
# `step` and `duration` values should be non-negative
step = tf.maximum(0, step)
duration = tf.maximum(0, duration)
return int(pitch), float(step), float(duration)
Теперь сгенерируйте несколько заметок. Вы можете поиграть с температурой и последовательностью запуска в next_notes и посмотреть, что произойдет.
temperature = 2.0
num_predictions = 120
sample_notes = np.stack([raw_notes[key] for key in key_order], axis=1)
# The initial sequence of notes; pitch is normalized similar to training
# sequences
input_notes = (
sample_notes[:seq_length] / np.array([vocab_size, 1, 1]))
generated_notes = []
prev_start = 0
for _ in range(num_predictions):
pitch, step, duration = predict_next_note(input_notes, model, temperature)
start = prev_start + step
end = start + duration
input_note = (pitch, step, duration)
generated_notes.append((*input_note, start, end))
input_notes = np.delete(input_notes, 0, axis=0)
input_notes = np.append(input_notes, np.expand_dims(input_note, 0), axis=0)
prev_start = start
generated_notes = pd.DataFrame(
generated_notes, columns=(*key_order, 'start', 'end'))
generated_notes.head(10)
out_file = 'output.mid'
out_pm = notes_to_midi(
generated_notes, out_file=out_file, instrument_name=instrument_name)
display_audio(out_pm)
Вы также можете скачать аудиофайл, добавив две строки ниже:
from google.colab import files
files.download(out_file)
Визуализируйте сгенерированные заметки.
plot_piano_roll(generated_notes)
Проверьте распределение pitch , step и duration .
plot_distributions(generated_notes)
На приведенных выше графиках вы заметите изменение распределения переменных ноты. Поскольку между выходами и входами модели существует петля обратной связи, модель имеет тенденцию генерировать аналогичные последовательности выходов, чтобы уменьшить потери. Это особенно актуально для step и duration , которые используют потери MSE. Для pitch вы можете увеличить случайность, увеличив temperature в predict_next_note .
Следующие шаги
В этом руководстве продемонстрирована механика использования RNN для создания последовательностей нот из набора данных MIDI-файлов. Чтобы узнать больше, вы можете посетить тесно связанную генерацию текста с учебным пособием по RNN , которое содержит дополнительные диаграммы и пояснения.
Альтернативой использованию RNN для генерации музыки является использование GAN. Вместо того, чтобы генерировать звук, подход на основе GAN может генерировать всю последовательность параллельно. Команда Magenta проделала впечатляющую работу над этим подходом с помощью GANSynth . Вы также можете найти много замечательных музыкальных и художественных проектов и открытый код на веб- сайте проекта Magenta .