 Xem trên TensorFlow.org |  Chạy trong Google Colab |  Xem nguồn trên GitHub |  Tải xuống sổ ghi chép |
Hướng dẫn này chỉ cho bạn cách tạo các nốt nhạc bằng RNN đơn giản. Bạn sẽ đào tạo một người mẫu bằng cách sử dụng một bộ sưu tập các tệp MIDI piano từ bộ dữ liệu MAESTRO . Đưa ra một chuỗi các nốt, mô hình của bạn sẽ học cách dự đoán nốt tiếp theo trong chuỗi. Bạn có thể tạo chuỗi ghi chú dài hơn bằng cách gọi mô hình nhiều lần.
Hướng dẫn này chứa mã hoàn chỉnh để phân tích cú pháp và tạo tệp MIDI. Bạn có thể tìm hiểu thêm về cách RNN hoạt động bằng cách truy cập Tạo văn bản với RNN .
Thành lập
Hướng dẫn này sử dụng thư viện pretty_midi để tạo và phân tích cú pháp các tệp MIDI và pyfluidsynth để tạo phát lại âm thanh trong 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
Tải xuống tập dữ liệu 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
Tập dữ liệu chứa khoảng 1.200 tệp MIDI.
filenames = glob.glob(str(data_dir/'**/*.mid*'))
print('Number of files:', len(filenames))
Number of files: 1282
Xử lý tệp MIDI
Đầu tiên, sử dụng pretty_midi để phân tích cú pháp một tệp MIDI và kiểm tra định dạng của các ghi chú. Nếu bạn muốn tải xuống tệp MIDI dưới đây để phát trên máy tính của mình, bạn có thể thực hiện việc này trong colab bằng cách viết 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
Tạo một đối tượng PrettyMIDI cho tệp MIDI mẫu.
pm = pretty_midi.PrettyMIDI(sample_file)
Phát tệp mẫu. Có thể mất vài giây để tải tiện ích con phát lại.
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)
Thực hiện một số kiểm tra trên tệp MIDI. Những loại nhạc cụ nào được sử dụng?
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
Trích xuất ghi chú
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
Bạn sẽ sử dụng ba biến để đại diện cho một lưu ý khi đào tạo mô hình: pitch , step và duration . Cao độ là chất lượng cảm nhận của âm thanh dưới dạng số nốt MIDI. step là thời gian trôi qua từ nốt trước hoặc phần bắt đầu của bản nhạc. duration là khoảng thời gian mà nốt nhạc sẽ phát tính bằng giây và là sự khác biệt giữa thời gian kết thúc nốt và thời gian bắt đầu nốt.
Trích xuất các ghi chú từ tệp MIDI mẫu.
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()
Có thể dễ dàng hiểu tên nốt hơn là cao độ, vì vậy bạn có thể sử dụng chức năng bên dưới để chuyển đổi từ giá trị cao độ số sang tên nốt. Tên nốt cho biết loại nốt, tình cờ và số quãng tám (ví dụ: 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')
Để hình dung bản nhạc, hãy vẽ cao độ nốt, bắt đầu và kết thúc theo chiều dài của bản nhạc (tức là cuộn piano). Bắt đầu với 100 nốt đầu tiên
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)
Vẽ các ghi chú cho toàn bộ bản nhạc.
plot_piano_roll(raw_notes)
Kiểm tra sự phân bố của từng biến nốt.
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)
Tạo tệp MIDI
Bạn có thể tạo tệp MIDI của riêng mình từ danh sách các ghi chú bằng cách sử dụng chức năng bên dưới.
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)
Phát tệp MIDI đã tạo và xem có sự khác biệt nào không.
display_audio(example_pm)
Như trước đây, bạn có thể ghi files.download(example_file) để tải xuống và phát tệp này.
Tạo tập dữ liệu đào tạo
Tạo tập dữ liệu đào tạo bằng cách trích xuất ghi chú từ tệp MIDI. Bạn có thể bắt đầu bằng cách sử dụng một số lượng nhỏ tệp và thử nghiệm sau với nhiều tệp hơn. Quá trình này có thể mất vài phút.
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
Tiếp theo, tạo tf.data.Dataset từ các ghi chú đã được phân tích cú pháp.
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)
Bạn sẽ đào tạo mô hình về hàng loạt các chuỗi ghi chú. Mỗi ví dụ sẽ bao gồm một chuỗi các ghi chú làm tính năng đầu vào và ghi chú tiếp theo là nhãn. Bằng cách này, người mẫu sẽ được đào tạo để dự đoán nốt tiếp theo trong một chuỗi. Bạn có thể tìm thấy sơ đồ giải thích quá trình này (và thêm chi tiết) trong Phân loại văn bản với RNN .
Bạn có thể sử dụng chức năng cửa sổ tiện dụng với kích thước seq_length để tạo các tính năng và nhãn ở định dạng này.
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)
Đặt độ dài trình tự cho mỗi ví dụ. Thử nghiệm với các độ dài khác nhau (ví dụ: 50, 100, 150) để xem độ dài nào phù hợp nhất với dữ liệu hoặc sử dụng điều chỉnh siêu tham số. Kích thước của từ vựng ( vocab_size ) được đặt thành 128 đại diện cho tất cả các cao độ được hỗ trợ bởi 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)})
Hình dạng của tập dữ liệu là (100,1) , có nghĩa là mô hình sẽ lấy 100 ghi chú làm đầu vào và học cách dự đoán ghi chú sau dưới dạng đầu ra.
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>}
Hàng loạt các ví dụ và định cấu hình tập dữ liệu để đạt được hiệu suất.
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)})
Tạo và đào tạo mô hình
Mô hình sẽ có ba đầu ra, một đầu ra cho mỗi biến nốt. Đối với pitch và duration , bạn sẽ sử dụng hàm mất mát tùy chỉnh dựa trên lỗi bình phương trung bình khuyến khích mô hình xuất ra các giá trị không âm.
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
__________________________________________________________________________________________________
Kiểm tra chức năng model.evaluate , bạn có thể thấy rằng tổn thất cao pitch lớn hơn đáng kể so với tổn thất về step và duration . Lưu ý rằng loss là tổng tổn thất được tính bằng cách cộng tất cả các tổn thất khác và hiện đang bị chi phối bởi thua 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}
Một cách để cân bằng điều này là sử dụng đối số loss_weights để biên dịch:
model.compile(
loss=loss,
loss_weights={
'pitch': 0.05,
'step': 1.0,
'duration':1.0,
},
optimizer=optimizer,
)
Khi đó loss trở thành tổng trọng số của các khoản lỗ riêng lẻ.
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}
Huấn luyện mô hình.
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()
Tạo ghi chú
Để sử dụng mô hình này để tạo ghi chú, trước tiên bạn cần cung cấp chuỗi ghi chú bắt đầu. Hàm bên dưới tạo một ghi chú từ một chuỗi các ghi chú.
Đối với cao độ nốt, nó lấy một mẫu từ phân phối softmax của các nốt do mô hình tạo ra và không chỉ chọn nốt có xác suất cao nhất. Luôn chọn ghi chú với xác suất cao nhất sẽ dẫn đến việc tạo ra các chuỗi ghi chú lặp đi lặp lại.
Thông số temperature có thể được sử dụng để kiểm soát tính ngẫu nhiên của các ghi chú được tạo ra. Bạn có thể tìm thêm thông tin chi tiết về nhiệt độ trong Tạo văn bản bằng 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)
Bây giờ tạo một số ghi chú. Bạn có thể thử với nhiệt độ và trình tự bắt đầu trong next_notes và xem điều gì sẽ xảy ra.
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)
Bạn cũng có thể tải xuống tệp âm thanh bằng cách thêm hai dòng bên dưới:
from google.colab import files
files.download(out_file)
Hình dung các ghi chú đã tạo.
plot_piano_roll(generated_notes)
Kiểm tra sự phân bố pitch , step và duration .
plot_distributions(generated_notes)
Trong các biểu đồ trên, bạn sẽ nhận thấy sự thay đổi trong phân phối của các biến ghi chú. Vì có một vòng phản hồi giữa các đầu ra và đầu vào của mô hình, nên mô hình có xu hướng tạo ra các chuỗi đầu ra tương tự nhau để giảm tổn thất. Điều này đặc biệt liên quan đến step và duration , có sử dụng MSE mất. Đối với pitch , bạn có thể tăng tính ngẫu nhiên bằng cách tăng temperature trong predict_next_note .
Bước tiếp theo
Hướng dẫn này đã trình bày cơ chế sử dụng RNN để tạo chuỗi ghi chú từ tập dữ liệu các tệp MIDI. Để tìm hiểu thêm, bạn có thể truy cập phần Tạo văn bản có liên quan chặt chẽ với hướng dẫn RNN , bao gồm các sơ đồ và giải thích bổ sung.
Một giải pháp thay thế cho việc sử dụng RNN để tạo nhạc là sử dụng GAN. Thay vì tạo âm thanh, phương pháp dựa trên GAN có thể tạo song song toàn bộ chuỗi. Nhóm Magenta đã thực hiện công việc ấn tượng về cách tiếp cận này với GANSynth . Bạn cũng có thể tìm thấy nhiều dự án âm nhạc và nghệ thuật tuyệt vời và mã nguồn mở trên trang web của dự án Magenta .