Merge pull request #170 from craffel/transcription

Transcription [wip]

.gitignore

@@ -37,3 +37,9 @@ Thumbs.db
 
 # Vim
 *.swp
+
+# pycharm
+.idea/*
+
+# docs
+docs/_build/*

docs/index.rst

@@ -193,6 +193,14 @@ The following subsections document each submodule.
    :show-inheritance:
    :member-order: bysource
 
+:mod:`mir_eval.transcription`
+-----------------------------
+.. automodule:: mir_eval.transcription
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :member-order: bysource
+
 
 Indices and tables
 ==================

evaluators/transcription_eval.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python
+'''
+CREATED: 2/9/16 2:59 PM by Justin Salamon <[email protected]>
+
+Compute note transcription evaluation metrics
+
+Usage:
+
+./transcription_eval.py REFERENCE.TXT ESTIMATED.TXT
+'''
+
+from __future__ import print_function
+import argparse
+import sys
+import os
+import eval_utilities
+
+import mir_eval
+
+
+def process_arguments():
+    '''Argparse function to get the program parameters'''
+
+    parser = argparse.ArgumentParser(description='mir_eval transcription '
+                                                 'evaluation')
+
+    parser.add_argument('-o',
+                        dest='output_file',
+                        default=None,
+                        type=str,
+                        action='store',
+                        help='Store results in json format')
+
+    parser.add_argument('reference_file',
+                        action='store',
+                        help='path to the reference annotation file')
+
+    parser.add_argument('estimated_file',
+                        action='store',
+                        help='path to the estimated annotation file')
+
+    return vars(parser.parse_args(sys.argv[1:]))
+
+if __name__ == '__main__':
+    # Get the parameters
+    parameters = process_arguments()
+
+    # Load in data
+    ref_intervals, ref_pitches = mir_eval.io.load_valued_intervals(
+        parameters['reference_file'])
+    est_intervals, est_pitches = mir_eval.io.load_valued_intervals(
+        parameters['estimated_file'])
+    # Compute all the scores
+    scores = mir_eval.transcription.evaluate(ref_intervals, ref_pitches,
+                                             est_intervals, est_pitches)
+    print("{} vs. {}".format(os.path.basename(parameters['reference_file']),
+                             os.path.basename(parameters['estimated_file'])))
+    eval_utilities.print_evaluation(scores)
+
+    if parameters['output_file']:
+        print('Saving results to: ', parameters['output_file'])
+        eval_utilities.save_results(scores, parameters['output_file'])

mir_eval/__init__.py

@@ -14,5 +14,6 @@
 from . import pattern
 from . import tempo
 from . import hierarchy
+from . import transcription
 
 __version__ = '0.2'

mir_eval/io.py

@@ -399,3 +399,44 @@ def load_wav(path, mono=True):
     if mono and audio_data.ndim != 1:
         audio_data = audio_data.mean(axis=1)
     return audio_data, fs
+
+
+def load_valued_intervals(filename, delimiter=r'\s+'):
+    r"""Import valued intervals from an annotation file. The file should
+    consist of three columns: Two consisting of numeric values corresponding to
+    start and end time of each interval and a third, also of numeric values,
+    corresponding to the value of each interval. This is primarily useful for
+    processing events which span a duration and have a numeric value, such as
+    piano-roll notes which have an onset, offset, and a pitch value.
+
+    Parameters
+    ----------
+    filename : str
+        Path to the annotation file
+    delimiter : str
+        Separator regular expression.
+        By default, lines will be split by any amount of whitespace.
+
+    Returns
+    -------
+    intervals : np.ndarray, shape=(n_events, 2)
+        Array of event start and end times
+    values : np.ndarray, shape=(n_events,)
+        Array of values
+
+    """
+    # Use our universal function to load in the events
+    starts, ends, values = load_delimited(filename, [float, float, float],
+                                          delimiter)
+    # Stack into an interval matrix
+    intervals = np.array([starts, ends]).T
+    # Validate them, but throw a warning in place of an error
+    try:
+        util.validate_intervals(intervals)
+    except ValueError as error:
+        warnings.warn(error.args[0])
+
+    # return values as np.ndarray
+    values = np.array(values)
+
+    return intervals, values