Module note_seq.notebook_utils
Python functions which run only within a Jupyter or Colab notebook.
Expand source code
# Copyright 2021 The Magenta Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Python functions which run only within a Jupyter or Colab notebook."""
import base64
import collections
import functools
import io
import os
import urllib
import bokeh
import bokeh.plotting
from IPython import display
from note_seq import midi_synth
import numpy as np
import pandas as pd
from scipy.io import wavfile
makedirs = functools.partial(os.makedirs, exist_ok=True)
_DEFAULT_SAMPLE_RATE = 44100
_play_id = 0 # Used for ephemeral colab_play.
def colab_play(array_of_floats, sample_rate, ephemeral=True, autoplay=False):
"""Creates an HTML5 audio widget to play a sound in Colab.
This function should only be called from a Colab notebook.
Args:
array_of_floats: A 1D or 2D array-like container of float sound
samples. Values outside of the range [-1, 1] will be clipped.
sample_rate: Sample rate in samples per second.
ephemeral: If set to True, the widget will be ephemeral, and disappear
on reload (and it won't be counted against realtime document size).
autoplay: If True, automatically start playing the sound when the
widget is rendered.
"""
from google.colab.output import _js_builder as js # pylint:disable=import-outside-toplevel,g-import-not-at-top,protected-access
normalizer = float(np.iinfo(np.int16).max)
array_of_ints = np.array(
np.asarray(array_of_floats) * normalizer, dtype=np.int16)
memfile = io.BytesIO()
wavfile.write(memfile, sample_rate, array_of_ints)
html = """<audio controls {autoplay}>
<source controls src="data:audio/wav;base64,{base64_wavfile}"
type="audio/wav" />
Your browser does not support the audio element.
</audio>"""
html = html.format(
autoplay='autoplay' if autoplay else '',
base64_wavfile=base64.b64encode(memfile.getvalue()).decode('ascii'))
memfile.close()
global _play_id
_play_id += 1
if ephemeral:
element = 'id_%s' % _play_id
display.display(display.HTML('<div id="%s"> </div>' % element))
js.Js('document', mode=js.EVAL).getElementById(element).innerHTML = html
else:
display.display(display.HTML(html))
def play_sequence(sequence,
synth=midi_synth.synthesize,
sample_rate=_DEFAULT_SAMPLE_RATE,
colab_ephemeral=True,
**synth_args):
"""Creates an interactive player for a synthesized note sequence.
This function should only be called from a Jupyter or Colab notebook.
Args:
sequence: A music_pb2.NoteSequence to synthesize and play.
synth: A synthesis function that takes a sequence and sample rate as input.
sample_rate: The sample rate at which to synthesize.
colab_ephemeral: If set to True, the widget will be ephemeral in Colab, and
disappear on reload (and it won't be counted against realtime document
size).
**synth_args: Additional keyword arguments to pass to the synth function.
"""
array_of_floats = synth(sequence, sample_rate=sample_rate, **synth_args)
try:
import google.colab # pylint:disable=import-outside-toplevel,g-import-not-at-top,unused-import
colab_play(array_of_floats, sample_rate, colab_ephemeral)
except ImportError:
display.display(display.Audio(array_of_floats, rate=sample_rate))
def plot_sequence(sequence, show_figure=True):
"""Creates an interactive pianoroll for a NoteSequence.
Example usage: plot a random melody.
sequence = mm.Melody(np.random.randint(36, 72, 30)).to_sequence()
bokeh_pianoroll(sequence)
Args:
sequence: A NoteSequence.
show_figure: A boolean indicating whether or not to show the figure.
Returns:
If show_figure is False, a Bokeh figure; otherwise None.
"""
def _sequence_to_pandas_dataframe(sequence):
"""Generates a pandas dataframe from a sequence."""
pd_dict = collections.defaultdict(list)
for note in sequence.notes:
pd_dict['start_time'].append(note.start_time)
pd_dict['end_time'].append(note.end_time)
pd_dict['duration'].append(note.end_time - note.start_time)
pd_dict['pitch'].append(note.pitch)
pd_dict['bottom'].append(note.pitch - 0.4)
pd_dict['top'].append(note.pitch + 0.4)
pd_dict['velocity'].append(note.velocity)
pd_dict['fill_alpha'].append(note.velocity / 128.0)
pd_dict['instrument'].append(note.instrument)
pd_dict['program'].append(note.program)
# If no velocity differences are found, set alpha to 1.0.
if np.max(pd_dict['velocity']) == np.min(pd_dict['velocity']):
pd_dict['fill_alpha'] = [1.0] * len(pd_dict['fill_alpha'])
return pd.DataFrame(pd_dict)
# These are hard-coded reasonable values, but the user can override them
# by updating the figure if need be.
fig = bokeh.plotting.figure(
tools='hover,pan,box_zoom,reset,save')
fig.plot_width = 500
fig.plot_height = 200
fig.xaxis.axis_label = 'time (sec)'
fig.yaxis.axis_label = 'pitch (MIDI)'
fig.yaxis.ticker = bokeh.models.SingleIntervalTicker(interval=12)
fig.ygrid.ticker = bokeh.models.SingleIntervalTicker(interval=12)
# Pick indexes that are maximally different in Spectral8 colormap.
spectral_color_indexes = [7, 0, 6, 1, 5, 2, 3]
# Create a Pandas dataframe and group it by instrument.
dataframe = _sequence_to_pandas_dataframe(sequence)
instruments = sorted(set(dataframe['instrument']))
grouped_dataframe = dataframe.groupby('instrument')
for counter, instrument in enumerate(instruments):
instrument_df = grouped_dataframe.get_group(instrument)
color_idx = spectral_color_indexes[counter % len(spectral_color_indexes)]
color = bokeh.palettes.Spectral8[color_idx]
source = bokeh.plotting.ColumnDataSource(instrument_df)
fig.quad(top='top', bottom='bottom', left='start_time', right='end_time',
line_color='black', fill_color=color,
fill_alpha='fill_alpha', source=source)
fig.select(dict(type=bokeh.models.HoverTool)).tooltips = (
{'pitch': '@pitch',
'program': '@program',
'velo': '@velocity',
'duration': '@duration',
'start_time': '@start_time',
'end_time': '@end_time',
'velocity': '@velocity',
'fill_alpha': '@fill_alpha'})
if show_figure:
bokeh.plotting.output_notebook()
bokeh.plotting.show(fig)
return None
return fig
def download_bundle(bundle_name, target_dir, force_reload=False):
"""Downloads a Magenta bundle to target directory.
Target directory target_dir will be created if it does not already exist.
Args:
bundle_name: A string Magenta bundle name to download.
target_dir: A string local directory in which to write the bundle.
force_reload: A boolean that when True, reloads the bundle even if present.
"""
makedirs(target_dir)
bundle_target = os.path.join(target_dir, bundle_name)
if not os.path.exists(bundle_target) or force_reload:
response = urllib.request.urlopen(
'http://download.magenta.tensorflow.org/models/%s' % bundle_name)
data = response.read()
local_file = open(bundle_target, 'wb')
local_file.write(data)
local_file.close()Functions
def colab_play(array_of_floats, sample_rate, ephemeral=True, autoplay=False)-
Creates an HTML5 audio widget to play a sound in Colab.
This function should only be called from a Colab notebook.
Args
array_of_floats- A 1D or 2D array-like container of float sound samples. Values outside of the range [-1, 1] will be clipped.
sample_rate- Sample rate in samples per second.
ephemeral- If set to True, the widget will be ephemeral, and disappear on reload (and it won't be counted against realtime document size).
autoplay- If True, automatically start playing the sound when the widget is rendered.
Expand source code
def colab_play(array_of_floats, sample_rate, ephemeral=True, autoplay=False): """Creates an HTML5 audio widget to play a sound in Colab. This function should only be called from a Colab notebook. Args: array_of_floats: A 1D or 2D array-like container of float sound samples. Values outside of the range [-1, 1] will be clipped. sample_rate: Sample rate in samples per second. ephemeral: If set to True, the widget will be ephemeral, and disappear on reload (and it won't be counted against realtime document size). autoplay: If True, automatically start playing the sound when the widget is rendered. """ from google.colab.output import _js_builder as js # pylint:disable=import-outside-toplevel,g-import-not-at-top,protected-access normalizer = float(np.iinfo(np.int16).max) array_of_ints = np.array( np.asarray(array_of_floats) * normalizer, dtype=np.int16) memfile = io.BytesIO() wavfile.write(memfile, sample_rate, array_of_ints) html = """<audio controls {autoplay}> <source controls src="data:audio/wav;base64,{base64_wavfile}" type="audio/wav" /> Your browser does not support the audio element. </audio>""" html = html.format( autoplay='autoplay' if autoplay else '', base64_wavfile=base64.b64encode(memfile.getvalue()).decode('ascii')) memfile.close() global _play_id _play_id += 1 if ephemeral: element = 'id_%s' % _play_id display.display(display.HTML('<div id="%s"> </div>' % element)) js.Js('document', mode=js.EVAL).getElementById(element).innerHTML = html else: display.display(display.HTML(html)) def download_bundle(bundle_name, target_dir, force_reload=False)-
Downloads a Magenta bundle to target directory.
Target directory target_dir will be created if it does not already exist.
Args
bundle_name- A string Magenta bundle name to download.
target_dir- A string local directory in which to write the bundle.
force_reload- A boolean that when True, reloads the bundle even if present.
Expand source code
def download_bundle(bundle_name, target_dir, force_reload=False): """Downloads a Magenta bundle to target directory. Target directory target_dir will be created if it does not already exist. Args: bundle_name: A string Magenta bundle name to download. target_dir: A string local directory in which to write the bundle. force_reload: A boolean that when True, reloads the bundle even if present. """ makedirs(target_dir) bundle_target = os.path.join(target_dir, bundle_name) if not os.path.exists(bundle_target) or force_reload: response = urllib.request.urlopen( 'http://download.magenta.tensorflow.org/models/%s' % bundle_name) data = response.read() local_file = open(bundle_target, 'wb') local_file.write(data) local_file.close() def play_sequence(sequence, synth=<function synthesize>, sample_rate=44100, colab_ephemeral=True, **synth_args)-
Creates an interactive player for a synthesized note sequence.
This function should only be called from a Jupyter or Colab notebook.
Args
sequence- A music_pb2.NoteSequence to synthesize and play.
synth- A synthesis function that takes a sequence and sample rate as input.
sample_rate- The sample rate at which to synthesize.
colab_ephemeral- If set to True, the widget will be ephemeral in Colab, and disappear on reload (and it won't be counted against realtime document size).
**synth_args- Additional keyword arguments to pass to the synth function.
Expand source code
def play_sequence(sequence, synth=midi_synth.synthesize, sample_rate=_DEFAULT_SAMPLE_RATE, colab_ephemeral=True, **synth_args): """Creates an interactive player for a synthesized note sequence. This function should only be called from a Jupyter or Colab notebook. Args: sequence: A music_pb2.NoteSequence to synthesize and play. synth: A synthesis function that takes a sequence and sample rate as input. sample_rate: The sample rate at which to synthesize. colab_ephemeral: If set to True, the widget will be ephemeral in Colab, and disappear on reload (and it won't be counted against realtime document size). **synth_args: Additional keyword arguments to pass to the synth function. """ array_of_floats = synth(sequence, sample_rate=sample_rate, **synth_args) try: import google.colab # pylint:disable=import-outside-toplevel,g-import-not-at-top,unused-import colab_play(array_of_floats, sample_rate, colab_ephemeral) except ImportError: display.display(display.Audio(array_of_floats, rate=sample_rate)) def plot_sequence(sequence, show_figure=True)-
Creates an interactive pianoroll for a NoteSequence.
Example usage: plot a random melody. sequence = mm.Melody(np.random.randint(36, 72, 30)).to_sequence() bokeh_pianoroll(sequence)
Args
sequence- A NoteSequence.
show_figure- A boolean indicating whether or not to show the figure.
Returns
If show_figure is False, a Bokeh figure; otherwise None.
Expand source code
def plot_sequence(sequence, show_figure=True): """Creates an interactive pianoroll for a NoteSequence. Example usage: plot a random melody. sequence = mm.Melody(np.random.randint(36, 72, 30)).to_sequence() bokeh_pianoroll(sequence) Args: sequence: A NoteSequence. show_figure: A boolean indicating whether or not to show the figure. Returns: If show_figure is False, a Bokeh figure; otherwise None. """ def _sequence_to_pandas_dataframe(sequence): """Generates a pandas dataframe from a sequence.""" pd_dict = collections.defaultdict(list) for note in sequence.notes: pd_dict['start_time'].append(note.start_time) pd_dict['end_time'].append(note.end_time) pd_dict['duration'].append(note.end_time - note.start_time) pd_dict['pitch'].append(note.pitch) pd_dict['bottom'].append(note.pitch - 0.4) pd_dict['top'].append(note.pitch + 0.4) pd_dict['velocity'].append(note.velocity) pd_dict['fill_alpha'].append(note.velocity / 128.0) pd_dict['instrument'].append(note.instrument) pd_dict['program'].append(note.program) # If no velocity differences are found, set alpha to 1.0. if np.max(pd_dict['velocity']) == np.min(pd_dict['velocity']): pd_dict['fill_alpha'] = [1.0] * len(pd_dict['fill_alpha']) return pd.DataFrame(pd_dict) # These are hard-coded reasonable values, but the user can override them # by updating the figure if need be. fig = bokeh.plotting.figure( tools='hover,pan,box_zoom,reset,save') fig.plot_width = 500 fig.plot_height = 200 fig.xaxis.axis_label = 'time (sec)' fig.yaxis.axis_label = 'pitch (MIDI)' fig.yaxis.ticker = bokeh.models.SingleIntervalTicker(interval=12) fig.ygrid.ticker = bokeh.models.SingleIntervalTicker(interval=12) # Pick indexes that are maximally different in Spectral8 colormap. spectral_color_indexes = [7, 0, 6, 1, 5, 2, 3] # Create a Pandas dataframe and group it by instrument. dataframe = _sequence_to_pandas_dataframe(sequence) instruments = sorted(set(dataframe['instrument'])) grouped_dataframe = dataframe.groupby('instrument') for counter, instrument in enumerate(instruments): instrument_df = grouped_dataframe.get_group(instrument) color_idx = spectral_color_indexes[counter % len(spectral_color_indexes)] color = bokeh.palettes.Spectral8[color_idx] source = bokeh.plotting.ColumnDataSource(instrument_df) fig.quad(top='top', bottom='bottom', left='start_time', right='end_time', line_color='black', fill_color=color, fill_alpha='fill_alpha', source=source) fig.select(dict(type=bokeh.models.HoverTool)).tooltips = ( {'pitch': '@pitch', 'program': '@program', 'velo': '@velocity', 'duration': '@duration', 'start_time': '@start_time', 'end_time': '@end_time', 'velocity': '@velocity', 'fill_alpha': '@fill_alpha'}) if show_figure: bokeh.plotting.output_notebook() bokeh.plotting.show(fig) return None return fig