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Hi all,
I’m using VGGish as a feature extractor for a project involving the analysis of specific types of audio recordings. My aim is to extract distinctive features from these recordings for advanced classification purposes. Although the audio type is covered in VGGish’s training, my focus is on identifying subtle differences within these recordings and using these nuanced labels with the extracted embeddings for further model training.
I’m wondering if anyone is familiar with the VGGish feature extractor and how I can tell whether the model is running correctly and generates accurate embeddings for my use case explained above?
I’m using the following setup (as specified in the smoke test script):
NumPy version: 1.24.3
TensorFlow version: 2.13.0
Resampy version: 0.2.2
Python version: 3.10.0
When running the smoke test I get the following error:
vggish_smoke_test.py
NumPy version: 1.24.3
TensorFlow version: 2.13.0
Resampy version: 0.2.2
Python version: 3.10.0 | packaged by conda-forge | (default, Nov 20 2021, 02:27:15) [Clang 11.1.0 ]
Testing your install of VGGish
Resampling via resampy works!
Log Mel Spectrogram example: [[-4.48313252 -4.27083405 -4.17064267 ... -4.60069383 -4.60098887
-4.60116305]
[-4.48313252 -4.27083405 -4.17064267 ... -4.60069383 -4.60098887
-4.60116305]
[-4.48313252 -4.27083405 -4.17064267 ... -4.60069383 -4.60098887
-4.60116305]
...
[-4.48313252 -4.27083405 -4.17064267 ... -4.60069383 -4.60098887
-4.60116305]
[-4.48313252 -4.27083405 -4.17064267 ... -4.60069383 -4.60098887
-4.60116305]
[-4.48313252 -4.27083405 -4.17064267 ... -4.60069383 -4.60098887
-4.60116305]]
2024-01-22 13:39:58.532065: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1 Pro
2024-01-22 13:39:58.532117: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB
2024-01-22 13:39:58.532131: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB
2024-01-22 13:39:58.533086: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:303] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2024-01-22 13:39:58.533360: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:269] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
/Users/astrid/miniforge3/envs/mlp/lib/python3.10/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1697: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
warnings.warn('`layer.apply` is deprecated and '
/Users/astrid/miniforge3/envs/mlp/lib/python3.10/site-packages/tensorflow/python/keras/legacy_tf_layers/core.py:325: UserWarning: `tf.layers.flatten` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Flatten` instead.
warnings.warn('`tf.layers.flatten` is deprecated and '
2024-01-22 13:39:58.820285: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:375] MLIR V1 optimization pass is not enabled
2024-01-22 13:39:58.834282: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
2024-01-22 13:39:59.494764: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
Traceback (most recent call last):
File "/Users/astrid/PycharmProjects/tensorflow-fork/research/audioset/vggish/vggish_smoke_test.py", line 109, in <module>
np.testing.assert_allclose(
File "/Users/astrid/miniforge3/envs/mlp/lib/python3.10/site-packages/numpy/testing/_private/utils.py", line 1592, in assert_allclose
assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
File "/Users/astrid/miniforge3/envs/mlp/lib/python3.10/contextlib.py", line 79, in inner
return func(*args, **kwds)
File "/Users/astrid/miniforge3/envs/mlp/lib/python3.10/site-packages/numpy/testing/_private/utils.py", line 862, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=0.1, atol=0
Mismatched elements: 2 / 2 (100%)
Max absolute difference: 4677.1648125
Max relative difference: 3642446.46411244
x: array([-2393.0867, 4677.508 ], dtype=float32)
y: array([0.000657, 0.343 ])
VGGish embedding: [ 4145.806 -1775.6422 -4421.003 283.77875 280.54425
7062.1445 -2468.7595 -2667.1458 8583.2705 2895.051
1085.9359 112.46885 -674.6059 -628.6546 -639.0709
-5431.542 -8282.701 -7877.5376 -3698.3943 -5914.762
-4910.4116 4006.3538 -3452.2876 2604.2393 -11533.147
-9635.958 -5293.464 -9104.569 -2764.2964 1372.5797
-2867.9656 -3831.2227 -6376.5195 -7480.3164 -12168.685
-2922.416 2131.4229 1245.7706 4473.025 -991.0255
-8729.306 -968.88434 -1651.0012 1473.9858 -4756.5674
1671.9463 -12686.039 -7621.3125 2401.2542 -6626.9785
-3241.2998 -2296.9084 3245.5037 7920.159 -7880.0103
-3329.7896 -9680.734 1454.865 -1290.4586 -6949.8228
-1032.4684 240.48125 6042.2363 1059.341 -3920.9705
-1738.9835 -2570.2908 -2550.5198 -5161.6216 -3711.025
-8867.201 1133.5637 2433.5835 3250.356 -2027.6403
-4212.3267 2270.933 -8749.348 -5870.978 -10817.919
-3732.8555 -5644.2427 4470.46 3122.015 -1798.985
-7916.349 -3385.9292 -8242.644 -1519.1459 -10945.789
2603.645 -8356.439 2241.9624 -7348.066 -8720.624
-8423.815 -2160.2988 1883.056 582.0027 -3764.776
-1615.0001 -5850.113 -3389.6348 -6375.688 2802.1833
3134.701 -5422.21 3110.646 -5574.4707 -2304.9424
-401.61966 3439.3347 -6006.554 -1890.2675 -468.6196
2881.7344 -1482.3462 -9001.828 6980.145 -5279.7983
1888.4413 -9818.397 3517.1284 -670.00903 -4047.3115
4867.6484 -8735.253 -1674.2653 ]
embedding mean/stddev -2393.0867 4677.508
Postprocessed VGGish embedding: [ 0 0 0 0 0 255 255 255 0 0 0 0 255 0 0 255 255 255
255 255 0 255 0 255 0 0 255 0 255 255 255 0 255 0 0 255
0 0 255 0 0 255 0 0 0 0 255 0 0 255 255 0 255 0
255 0 255 0 255 255 0 255 0 255 255 255 255 0 0 0 255 0
255 255 0 0 0 0 255 0 0 255 255 255 255 0 255 255 0 0
0 0 255 0 0 0 0 0 255 0 0 255 255 255 0 0 255 0
255 0 255 0 255 255 0 0 255 255 255 255 255 255 255 255 255 255
0 0]
postproc embedding mean/stddev 123.515625 127.43772893401457
I’m thinking that despite following the core package requirements that this could still fail due to different overall environment setup, however I am wondering how to tell the model runs correctly?
This is vggish_inference_demo.py where I have added a processed .wav recording:
# Copyright 2017 The TensorFlow Authors All Rights Reserved.
#
# 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.
# ==============================================================================
r"""A simple demonstration of running VGGish in inference mode.
This is intended as a toy example that demonstrates how the various building
blocks (feature extraction, model definition and loading, postprocessing) work
together in an inference context.
A WAV file (assumed to contain signed 16-bit PCM samples) is read in, converted
into log mel spectrogram examples, fed into VGGish, the raw embedding output is
whitened and quantized, and the postprocessed embeddings are optionally written
in a SequenceExample to a TFRecord file (using the same format as the embedding
features released in AudioSet).
Usage:
# Run a WAV file through the model and print the embeddings. The model
# checkpoint is loaded from vggish_model.ckpt and the PCA parameters are
# loaded from vggish_pca_params.npz in the current directory.
$ python vggish_inference_demo.py --wav_file /path/to/a/wav/file
# Run a WAV file through the model and also write the embeddings to
# a TFRecord file. The model checkpoint and PCA parameters are explicitly
# passed in as well.
$ python vggish_inference_demo.py --wav_file /path/to/a/wav/file \
--tfrecord_file /path/to/tfrecord/file \
--checkpoint /path/to/model/checkpoint \
--pca_params /path/to/pca/params
# Run a built-in input (a sine wav) through the model and print the
# embeddings. Associated model files are read from the current directory.
$ python vggish_inference_demo.py
"""
from __future__ import print_function
import numpy as np
import pandas as pd
import six
import soundfile
import tensorflow.compat.v1 as tf
import vggish_input
import vggish_params
import vggish_postprocess
import vggish_slim
flags = tf.app.flags
flags.DEFINE_string(
'wav_file', 'file.wav',
'Path to a wav file. Should contain signed 16-bit PCM samples. '
'If none is provided, a synthetic sound is used.')
flags.DEFINE_string(
'checkpoint', 'vggish_model.ckpt',
'Path to the VGGish checkpoint file.')
flags.DEFINE_string(
'pca_params', 'vggish_pca_params.npz',
'Path to the VGGish PCA parameters file.')
flags.DEFINE_string(
'tfrecord_file', None,
'Path to a TFRecord file where embeddings will be written.')
FLAGS = flags.FLAGS
def main(_):
# In this simple example, we run the examples from a single audio file through
# the model. If none is provided, we generate a synthetic input.
if FLAGS.wav_file:
wav_file = FLAGS.wav_file
else:
# Write a WAV of a sine wav into an in-memory file object.
num_secs = 5
freq = 1000
sr = 44100
t = np.arange(0, num_secs, 1 / sr)
x = np.sin(2 * np.pi * freq * t)
# Convert to signed 16-bit samples.
samples = np.clip(x * 32768, -32768, 32767).astype(np.int16)
wav_file = six.BytesIO()
soundfile.write(wav_file, samples, sr, format='WAV', subtype='PCM_16')
wav_file.seek(0)
examples_batch = vggish_input.wavfile_to_examples(wav_file)
print(examples_batch)
# Prepare a postprocessor to munge the model embeddings.
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
# If needed, prepare a record writer to store the postprocessed embeddings.
writer = tf.python_io.TFRecordWriter(
FLAGS.tfrecord_file) if FLAGS.tfrecord_file else None
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
print(embedding_batch)
postprocessed_batch = pproc.postprocess(embedding_batch)
print(postprocessed_batch)
# For raw embeddings
embeddings_df = pd.DataFrame(embedding_batch)
embeddings_df.to_csv('raw_embeddings.csv', index=False)
# For post-processed embeddings
postprocessed_df = pd.DataFrame(postprocessed_batch)
postprocessed_df.to_csv('postprocessed_embeddings.csv', index=False)
# Write the postprocessed embeddings as a SequenceExample, in a similar
# format as the features released in AudioSet. Each row of the batch of
# embeddings corresponds to roughly a second of audio (96 10ms frames), and
# the rows are written as a sequence of bytes-valued features, where each
# feature value contains the 128 bytes of the whitened quantized embedding.
seq_example = tf.train.SequenceExample(
feature_lists=tf.train.FeatureLists(
feature_list={
vggish_params.AUDIO_EMBEDDING_FEATURE_NAME:
tf.train.FeatureList(
feature=[
tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[embedding.tobytes()]))
for embedding in postprocessed_batch
]
)
}
)
)
print(seq_example)
if writer:
writer.write(seq_example.SerializeToString())
if writer:
writer.close()
if __name__ == '__main__':
tf.app.run()
The raw embeddings:
[[ 2756.9326 -1199.8948 -2900.791 128.01233 182.32951 4674.142
-1631.9989 -1846.7615 5743.619 1904.8942 740.13043 102.50553
-457.76312 -324.87384 -355.0232 -3592.4082 -5487.47 -5329.719
-2412.3958 -3869.4138 -3269.5317 2790.9202 -2257.775 1789.078
-7727.908 -6410.806 -3456.2542 -6050.643 -1898.5787 953.5816
-1897.1648 -2591.8594 -4316.8213 -5088.4717 -8087.8438 -1908.6724
1354.7952 820.46063 2971.3604 -659.65045 -5878.903 -528.14923
-1065.45 1184.1643 -3248.1177 1140.3062 -8460.46 -4983.223
1698.3937 -4557.1777 -2265.003 -1537.0801 2131.691 5227.428
-5297.137 -2192.3735 -6419.795 931.5189 -963.0291 -4663.866
-712.4488 177.36548 3995.95 706.87085 -2463.083 -1164.5531
-1746.5039 -1806.945 -3413.1536 -2459.2554 -5883.6753 785.6369
1588.856 2182.188 -1261.9966 -2828.1328 1483.209 -5914.6494
-3952.074 -7239.8335 -2502.116 -3689.356 2927.5308 2035.9961
-1315.393 -5181.636 -2369.9778 -5498.729 -1013.7645 -7334.33
1748.266 -5488.7085 1524.1348 -4884.8755 -5866.6685 -5661.888
-1484.344 1223.3682 334.27832 -2474.7815 -1163.9679 -3900.7063
-2239.5137 -4280.021 1826.0135 2059.7024 -3626.6555 2093.119
-3686.918 -1527.8844 -279.44202 2218.9045 -3955.217 -1246.5123
-319.53705 1861.3494 -1048.6719 -5969.5107 4681.886 -3515.276
1330.0773 -6535.7925 2420.3372 -397.57562 -2741.638 3234.0208
-5772.872 -1068.8192 ]
[ 2457.0186 -1035.3966 -2673.0425 112.68889 173.80641 4238.5264
-1470.5931 -1662.3215 5182.865 1729.4012 716.36383 175.79173
-446.68665 -337.6289 -304.13583 -3293.7856 -4898.9624 -4797.105
-2275.0662 -3531.8345 -3005.336 2505.2292 -2007.4995 1530.7433
-7036.09 -5837.109 -3177.276 -5484.803 -1711.9209 900.7856
-1688.9238 -2372.5234 -3952.628 -4687.217 -7249.36 -1744.0284
1260.919 701.9071 2723.6436 -603.1626 -5366.029 -506.66946
-1007.3063 1026.9431 -2937.075 1003.5136 -7670.8125 -4532.009
1500.611 -4195.7603 -2028.9331 -1415.187 1922.1714 4728.35
-4812.224 -1949.5833 -5796.676 864.06433 -910.46344 -4247.7227
-711.0734 127.39277 3579.9036 613.7357 -2214.797 -1051.9226
-1602.5641 -1597.7485 -2999.3923 -2341.1975 -5286.994 730.96924
1435.4087 1934.6099 -1135.1221 -2594.132 1339.4258 -5310.2666
-3659.4177 -6533.5186 -2275.5522 -3372.8804 2682.113 1822.5903
-1211.9471 -4651.404 -2055.5298 -4964.5186 -897.50336 -6647.9585
1617.6327 -5030.092 1386.7338 -4390.19 -5343.241 -5209.279
-1409.8773 1116.4227 289.62033 -2203.8167 -1028.9636 -3541.5996
-2068.3115 -3924.0996 1642.9878 1865.5432 -3222.662 1896.2085
-3375.8242 -1408.7878 -285.27808 1964.8013 -3609.0054 -1150.9559
-260.9037 1730.4817 -895.07855 -5430.4873 4233.935 -3256.5818
1205.6384 -5877.106 2199.7102 -424.80838 -2538.0137 2910.4604
-5191.5586 -895.8431 ]
[ 2296.4055 -987.3507 -2485.729 124.33925 198.58687 4006.7634
-1373.579 -1537.2711 4897.963 1620.9253 712.42035 158.63565
-407.67886 -294.93695 -287.18225 -3083.267 -4687.002 -4513.9927
-2132.918 -3365.512 -2796.5828 2291.0002 -1888.6941 1448.4585
-6655.6436 -5462.2407 -2996.6199 -5169.9053 -1601.158 805.7517
-1610.6764 -2211.6794 -3670.8662 -4372.468 -6873.888 -1596.1859
1206.8888 686.93677 2577.5234 -580.5423 -5022.0396 -490.27615
-948.062 873.1976 -2772.5422 939.6318 -7173.763 -4222.4956
1416.5039 -3936.6462 -1894.5361 -1312.8009 1828.0969 4493.235
-4524.694 -1841.0607 -5414.3467 828.20776 -816.3447 -4004.6897
-586.1435 150.64182 3427.127 601.1416 -2082.328 -991.07983
-1493.0211 -1558.3323 -2803.5042 -2179.5542 -4942.416 707.5284
1367.0704 1839.8708 -1106.3308 -2454.5269 1290.5985 -4966.6074
-3436.9673 -6132.8975 -2129.554 -3166.4424 2552.6677 1724.22
-1057.1676 -4381.1265 -1899.9567 -4682.8975 -904.14594 -6266.201
1570.8909 -4750.665 1302.5356 -4114.8413 -5005.37 -4887.6357
-1294.8773 1068.483 300.72098 -2100.631 -993.5702 -3328.8289
-1900.2711 -3673.7434 1556.2346 1743.7675 -3048.4165 1794.3772
-3173.4653 -1323.4886 -250.27246 1872.7726 -3350.4636 -1078.0472
-189.5445 1632.3423 -835.99084 -5105.101 4015.5042 -3000.1921
1137.0171 -5556.279 2056.376 -355.69843 -2387.773 2715.4758
-4915.04 -873.0093 ]]
And the post-processed embeddings:
[[ 0 0 0 0 0 255 255 255 0 0 0 0 255 0 0 255 255 255
255 255 0 255 0 255 0 0 255 0 255 255 255 0 255 0 0 255
0 0 255 0 0 255 0 0 0 0 255 0 0 255 255 0 255 0
255 0 255 0 255 255 0 255 0 255 255 255 255 0 0 0 255 0
255 255 0 0 0 0 255 0 0 255 255 255 255 0 255 255 0 0
0 0 255 0 0 0 0 0 255 0 0 255 255 255 0 0 255 255
255 0 255 0 255 255 0 0 255 255 255 255 255 255 255 255 255 255
0 0]
[ 0 0 0 0 0 255 255 255 0 0 0 0 255 0 0 255 255 255
255 255 0 255 0 255 0 0 255 0 255 255 255 0 255 0 0 255
0 0 255 0 0 255 0 0 0 0 255 0 0 255 255 0 255 0
255 0 255 0 255 255 0 255 0 255 255 255 255 0 0 0 255 0
255 255 0 0 0 0 255 0 0 255 255 255 255 0 255 255 0 0
0 0 255 0 0 0 0 0 255 0 0 255 255 255 0 0 255 0
255 0 255 0 255 255 0 0 255 255 255 255 255 255 255 255 255 255
0 0]
[ 0 0 0 0 0 255 255 255 0 0 0 0 255 0 0 255 255 255
255 255 0 255 0 255 0 0 255 0 255 255 255 0 255 0 0 255
0 0 255 0 0 255 0 0 0 0 255 0 0 255 255 0 255 0
255 0 255 0 255 255 0 255 255 255 255 255 255 0 0 0 255 0
255 255 0 0 0 0 255 0 0 255 255 255 255 0 255 255 0 0
0 0 255 0 0 0 0 0 255 0 0 255 255 255 0 0 255 0
255 0 255 0 255 255 0 0 255 255 255 255 255 255 255 255 255 255
0 0]]
feature_lists {
feature_list {
key: "audio_embedding"
value {
feature {
bytes_list {
value: "\000\000\000\000\000\377\377\377\000\000\000\000\377\000\000\377\377\377\377\377\000\377\000\377\000\000\377\000\377\377\377\000\377\000\000\377\000\000\377\000\000\377\000\000\000\000\377\000\000\377\377\000\377\000\377\000\377\000\377\377\000\377\000\377\377\377\377\000\000\000\377\000\377\377\000\000\000\000\377\000\000\377\377\377\377\000\377\377\000\000\000\000\377\000\000\000\000\000\377\000\000\377\377\377\000\000\377\377\377\000\377\000\377\377\000\000\377\377\377\377\377\377\377\377\377\377\000\000"
}
}
feature {
bytes_list {
value: "\000\000\000\000\000\377\377\377\000\000\000\000\377\000\000\377\377\377\377\377\000\377\000\377\000\000\377\000\377\377\377\000\377\000\000\377\000\000\377\000\000\377\000\000\000\000\377\000\000\377\377\000\377\000\377\000\377\000\377\377\000\377\000\377\377\377\377\000\000\000\377\000\377\377\000\000\000\000\377\000\000\377\377\377\377\000\377\377\000\000\000\000\377\000\000\000\000\000\377\000\000\377\377\377\000\000\377\000\377\000\377\000\377\377\000\000\377\377\377\377\377\377\377\377\377\377\000\000"
}
}
feature {
bytes_list {
value: "\000\000\000\000\000\377\377\377\000\000\000\000\377\000\000\377\377\377\377\377\000\377\000\377\000\000\377\000\377\377\377\000\377\000\000\377\000\000\377\000\000\377\000\000\000\000\377\000\000\377\377\000\377\000\377\000\377\000\377\377\000\377\377\377\377\377\377\000\000\000\377\000\377\377\000\000\000\000\377\000\000\377\377\377\377\000\377\377\000\000\000\000\377\000\000\000\000\000\377\000\000\377\377\377\000\000\377\000\377\000\377\000\377\377\000\000\377\377\377\377\377\377\377\377\377\377\000\000"
}
}
}
}
}
This is the postprocess function:
def postprocess(self, embeddings_batch):
"""Applies postprocessing to a batch of embeddings.
Args:
embeddings_batch: An nparray of shape [batch_size, embedding_size]
containing output from the embedding layer of VGGish.
Returns:
An nparray of the same shape as the input but of type uint8,
containing the PCA-transformed and quantized version of the input.
"""
assert len(embeddings_batch.shape) == 2, (
'Expected 2-d batch, got %r' % (embeddings_batch.shape,))
assert embeddings_batch.shape[1] == vggish_params.EMBEDDING_SIZE, (
'Bad batch shape: %r' % (embeddings_batch.shape,))
# Apply PCA.
# - Embeddings come in as [batch_size, embedding_size].
# - Transpose to [embedding_size, batch_size].
# - Subtract pca_means column vector from each column.
# - Premultiply by PCA matrix of shape [output_dims, input_dims]
# where both are are equal to embedding_size in our case.
# - Transpose result back to [batch_size, embedding_size].
pca_applied = np.dot(self._pca_matrix,
(embeddings_batch.T - self._pca_means)).T
# Quantize by:
# - clipping to [min, max] range
clipped_embeddings = np.clip(
pca_applied, vggish_params.QUANTIZE_MIN_VAL,
vggish_params.QUANTIZE_MAX_VAL)
# - convert to 8-bit in range [0.0, 255.0]
quantized_embeddings = (
(clipped_embeddings - vggish_params.QUANTIZE_MIN_VAL) *
(255.0 /
(vggish_params.QUANTIZE_MAX_VAL - vggish_params.QUANTIZE_MIN_VAL)))
# - cast 8-bit float to uint8
quantized_embeddings = quantized_embeddings.astype(np.uint8)
return quantized_embeddings
So this particular file is split into three segments and the raw embeddings vary significantly, but the post-processed ones are nearly identical across different segments. Is this typical behaviour for VGGish, or could it be a sign of an issue in the way the embeddings are processed? Is this because they are all part of the same class that VGGish identifies as part of its training and thus would always have the same post processed embedding?
To clarify, I have a dataset of the same type of audio but with four different labels (characteristics) between and I am trying to retrieve embeddings so that I can build a model that can predict these four classes. Each file will correspond to one such class but might be recognised as all the same by VGGish since it’s the same type of data (I wonder ?).
Any insights, suggestions, or pointers towards resources would be greatly appreciated!
#tensorflow #vggish #feature-extraction #machine-learning #audio-processing
To ensure VGGish runs correctly and generates accurate embeddings for your use case:
- Check Environment: Verify your setup is compatible with VGGish requirements, especially TensorFlow versions.
- Address Warnings: Update deprecated TensorFlow functions to their latest equivalents.
- Analyze Embeddings: Investigate if the VGGish embeddings capture the nuances you’re interested in by visually inspecting or statistically analyzing the embeddings.
- Fine-tune: Consider fine-tuning VGGish with your specific dataset to better capture subtle differences.
- Consult Resources: Look into VGGish documentation and community forums for similar use cases and solutions.