Example usage¶
Storing annotations¶
This section demonstrates a complete use-case of JAMS for storing estimated annotations. The example uses librosa to estimate global tempo and beat timings.
example_beat.py¶
The following script loads the librosa example audio clip, estimates the track duration, tempo, and beat timings, and constructs a JAMS object to store the estimations.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | #!/usr/bin/env python
import librosa
import jams
def beat_track(infile, outfile):
# Load the audio file
y, sr = librosa.load(infile)
# Compute the track duration
track_duration = librosa.get_duration(y=y, sr=sr)
# Extract tempo and beat estimates
tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
# Convert beat frames to time
beat_times = librosa.frames_to_time(beat_frames, sr=sr)
# Construct a new JAMS object and annotation records
jam = jams.JAMS()
# Store the track duration
jam.file_metadata.duration = track_duration
beat_a = jams.Annotation(namespace='beat')
beat_a.annotation_metadata = jams.AnnotationMetadata(data_source='librosa beat tracker')
# Add beat timings to the annotation record.
# The beat namespace does not require value or confidence fields,
# so we can leave those blank.
for t in beat_times:
beat_a.append(time=t, duration=0.0)
# Store the new annotation in the jam
jam.annotations.append(beat_a)
# Add tempo estimation to the annotation.
tempo_a = jams.Annotation(namespace='tempo', time=0, duration=track_duration)
tempo_a.annotation_metadata = jams.AnnotationMetadata(data_source='librosa tempo estimator')
# The tempo estimate is global, so it should start at time=0 and cover the full
# track duration.
# If we had a likelihood score on the estimation, it could be stored in
# `confidence`. Since we have no competing estimates, we'll set it to 1.0.
tempo_a.append(time=0.0,
duration=track_duration,
value=tempo,
confidence=1.0)
# Store the new annotation in the jam
jam.annotations.append(tempo_a)
# Save to disk
jam.save(outfile)
if __name__ == '__main__':
infile = librosa.util.example_audio_file()
beat_track(infile, 'output.jams')
|
example_beat_output.jams¶
The above script generates the following JAMS object.
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"title": "",
"release": "",
"identifiers": {},
"artist": "",
"jams_version": "0.2.3"
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"namespace": "beat",
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"annotation_metadata": {
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"curator": {
"name": "",
"email": ""
},
"annotation_rules": "",
"annotator": {},
"data_source": "librosa beat tracker"
}
},
{
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{
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}
],
"namespace": "tempo",
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"annotation_metadata": {
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"annotation_tools": "",
"version": "",
"curator": {
"name": "",
"email": ""
},
"annotation_rules": "",
"annotator": {},
"data_source": "librosa tempo estimator"
}
}
]
}
|
Evaluating annotations¶
The following script illustrates how to evaluate one JAMS annotation object against another using the built-in eval submodule to wrap mir_eval.
Given two jams files, say, reference.jams and estimate.jams, the script first loads them as objects
(j_ref and j_est, respectively). It then uses the JAMS.search method to locate all
annotations of namespace "beat". If no matching annotations are found, an empty list is returned.
In this example, we are assuming that each JAMS file contains only a single annotation of interest, so the first result is taken by indexing the results at 0. (In general, you may want to use annotation_metadata to select a specific annotation from the JAMS object, if multiple are present.)
Finally, the two annotations are compared by calling jams.eval.beat, which returns an ordered
dictionary of evaluation metrics for the annotations in question.
example_eval.py¶
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | #!/usr/bin/env python
import sys
import jams
from pprint import pprint
def compare_beats(f_ref, f_est):
# f_ref contains the reference annotations
j_ref = jams.load(f_ref)
# f_est contains the estimated annotations
j_est = jams.load(f_est)
# Get the first reference beats
beat_ref = j_ref.search(namespace='beat')[0]
beat_est = j_est.search(namespace='beat')[0]
# Get the scores
return jams.eval.beat(beat_ref, beat_est)
if __name__ == '__main__':
f_ref, f_est = sys.argv[1:]
scores = compare_beats(f_ref, f_est)
# Print them out
pprint(dict(scores))
|
Data conversion¶
JAMS provides some basic functionality to help convert from flat file formats (e.g., CSV or LAB).
example_chord_import.py¶
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | #!/usr/bin/env python
import jams
import sys
def import_chord_jams(infile, outfile):
# import_lab returns a new jams object,
# and a handle to the newly created annotation
chords = jams.util.import_lab('chord', infile)
# Infer the track duration from the end of the last annotation
duration = max([obs.time + obs.duration for obs in chords])
chords.time = 0
chords.duration = duration
# Create a jams object
jam = jams.JAMS()
jam.file_metadata.duration = duration
jam.annotations.append(chords)
# save to disk
jam.save(outfile)
if __name__ == '__main__':
infile, outfile = sys.argv[1:]
import_chord_jams(infile, outfile)
|
chord_output.jams¶
Calling the above script on 01_-_I_Saw_Her_Standing_There.lab from IsoPhonics should produce the following JAMS object:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 | {
"annotations": [
{
"duration": 175.804082,
"data": [
{
"duration": 2.6122669999999997,
"value": "N",
"confidence": 1.0,
"time": 0.0
},
{
"duration": 8.846803000000001,
"value": "E",
"confidence": 1.0,
"time": 2.6122669999999997
},
{
"duration": 1.4628569999999996,
"value": "A",
"confidence": 1.0,
"time": 11.45907
},
{
"duration": 4.521546999999998,
"value": "E",
"confidence": 1.0,
"time": 12.921927
},
{
"duration": 2.966888000000001,
"value": "B",
"confidence": 1.0,
"time": 17.443474
},
{
"duration": 1.497686999999999,
"value": "E",
"confidence": 1.0,
"time": 20.410362
},
{
"duration": 1.4628580000000007,
"value": "E:7/3",
"confidence": 1.0,
"time": 21.908049
},
{
"duration": 1.4860770000000016,
"value": "A",
"confidence": 1.0,
"time": 23.370907
},
{
"duration": 1.486076999999998,
"value": "A:min/b3",
"confidence": 1.0,
"time": 24.856984
},
{
"duration": 1.497686999999999,
"value": "E",
"confidence": 1.0,
"time": 26.343061
},
{
"duration": 1.5092970000000037,
"value": "B",
"confidence": 1.0,
"time": 27.840747999999998
},
{
"duration": 5.955917999999997,
"value": "E",
"confidence": 1.0,
"time": 29.350045
},
{
"duration": 1.497686999999999,
"value": "A",
"confidence": 1.0,
"time": 35.305963
},
{
"duration": 4.459452000000006,
"value": "E",
"confidence": 1.0,
"time": 36.80365
},
{
"duration": 2.982543999999997,
"value": "B",
"confidence": 1.0,
"time": 41.263102
},
{
"duration": 1.474466999999997,
"value": "E",
"confidence": 1.0,
"time": 44.245646
},
{
"duration": 1.4860770000000016,
"value": "E:7/3",
"confidence": 1.0,
"time": 45.720113
},
{
"duration": 1.4860770000000016,
"value": "A",
"confidence": 1.0,
"time": 47.20619
},
{
"duration": 1.4628569999999996,
"value": "A:min/b3",
"confidence": 1.0,
"time": 48.692267
},
{
"duration": 1.497686999999999,
"value": "E",
"confidence": 1.0,
"time": 50.155124
},
{
"duration": 1.4860770000000016,
"value": "B",
"confidence": 1.0,
"time": 51.652811
},
{
"duration": 2.9721550000000008,
"value": "E",
"confidence": 1.0,
"time": 53.138888
},
{
"duration": 9.020951999999987,
"value": "A",
"confidence": 1.0,
"time": 56.111043
},
{
"duration": 3.0185940000000073,
"value": "B",
"confidence": 1.0,
"time": 65.13199499999999
},
{
"duration": 3.0418140000000022,
"value": "A",
"confidence": 1.0,
"time": 68.150589
},
{
"duration": 3.0069840000000028,
"value": "E",
"confidence": 1.0,
"time": 71.192403
},
{
"duration": 1.497686999999999,
"value": "A",
"confidence": 1.0,
"time": 74.199387
},
{
"duration": 4.539501000000001,
"value": "E",
"confidence": 1.0,
"time": 75.697074
},
{
"duration": 2.9721550000000008,
"value": "B",
"confidence": 1.0,
"time": 80.236575
},
{
"duration": 3.012962999999999,
"value": "E",
"confidence": 1.0,
"time": 83.20873
},
{
"duration": 1.5149279999999976,
"value": "A",
"confidence": 1.0,
"time": 86.221693
},
{
"duration": 1.5209070000000082,
"value": "A:min/b3",
"confidence": 1.0,
"time": 87.736621
},
{
"duration": 1.4628569999999854,
"value": "E",
"confidence": 1.0,
"time": 89.25752800000001
},
{
"duration": 1.4370680000000107,
"value": "B",
"confidence": 1.0,
"time": 90.720385
},
{
"duration": 11.949235999999999,
"value": "E",
"confidence": 1.0,
"time": 92.157453
},
{
"duration": 3.0185940000000073,
"value": "B",
"confidence": 1.0,
"time": 104.106689
},
{
"duration": 3.0534239999999926,
"value": "E",
"confidence": 1.0,
"time": 107.12528300000001
},
{
"duration": 2.9453800000000143,
"value": "A",
"confidence": 1.0,
"time": 110.178707
},
{
"duration": 1.4896309999999744,
"value": "E",
"confidence": 1.0,
"time": 113.12408700000002
},
{
"duration": 1.4860770000000088,
"value": "B",
"confidence": 1.0,
"time": 114.61371799999999
},
{
"duration": 2.845166000000006,
"value": "E",
"confidence": 1.0,
"time": 116.099795
},
{
"duration": 9.101501000000013,
"value": "A",
"confidence": 1.0,
"time": 118.944961
},
{
"duration": 3.0069839999999886,
"value": "B",
"confidence": 1.0,
"time": 128.04646200000002
},
{
"duration": 2.983764000000008,
"value": "A",
"confidence": 1.0,
"time": 131.053446
},
{
"duration": 3.006984999999986,
"value": "E",
"confidence": 1.0,
"time": 134.03721000000002
},
{
"duration": 1.4313290000000052,
"value": "A",
"confidence": 1.0,
"time": 137.044195
},
{
"duration": 4.582638999999972,
"value": "E",
"confidence": 1.0,
"time": 138.475524
},
{
"duration": 2.9837640000000363,
"value": "B",
"confidence": 1.0,
"time": 143.05816299999998
},
{
"duration": 1.5092969999999752,
"value": "E",
"confidence": 1.0,
"time": 146.04192700000002
},
{
"duration": 1.5092970000000037,
"value": "E:7/3",
"confidence": 1.0,
"time": 147.551224
},
{
"duration": 1.451246999999995,
"value": "A",
"confidence": 1.0,
"time": 149.060521
},
{
"duration": 1.5092970000000037,
"value": "A:min/b3",
"confidence": 1.0,
"time": 150.511768
},
{
"duration": 1.509297000000032,
"value": "E",
"confidence": 1.0,
"time": 152.021065
},
{
"duration": 1.5325169999999844,
"value": "B",
"confidence": 1.0,
"time": 153.53036200000003
},
{
"duration": 4.469842,
"value": "E",
"confidence": 1.0,
"time": 155.062879
},
{
"duration": 1.5325169999999844,
"value": "B",
"confidence": 1.0,
"time": 159.532721
},
{
"duration": 4.516280999999992,
"value": "E",
"confidence": 1.0,
"time": 161.065238
},
{
"duration": 1.5325170000000128,
"value": "B",
"confidence": 1.0,
"time": 165.581519
},
{
"duration": 1.5325170000000128,
"value": "A",
"confidence": 1.0,
"time": 167.114036
},
{
"duration": 1.0908560000000023,
"value": "E",
"confidence": 1.0,
"time": 168.646553
},
{
"duration": 1.9497639999999876,
"value": "E:9",
"confidence": 1.0,
"time": 169.737409
},
{
"duration": 4.116908999999993,
"value": "N",
"confidence": 1.0,
"time": 171.687173
}
],
"namespace": "chord",
"time": 0,
"annotation_metadata": {
"version": "",
"annotation_tools": "",
"annotator": {},
"curator": {
"email": "",
"name": ""
},
"data_source": "",
"corpus": "",
"annotation_rules": "",
"validation": ""
},
"sandbox": {}
}
],
"file_metadata": {
"duration": 175.804082,
"jams_version": "0.2.3",
"artist": "",
"identifiers": {},
"release": "",
"title": ""
},
"sandbox": {}
}
|
More examples¶
In general, converting a dataset to JAMS format will require a bit more work to ensure that value fields conform to the specified namespace schema, but the import script above should serve as a simple starting point.
For further reference, a separate repository jams-data has been created to house conversion scripts for publicly available datasets. Note that development of converters is a work in progress, so proceed with caution!