Adding research/audioset/yamnet, a pre-trained audio event classifier (#7850)

Add files for YamNet, a sound event classifier.

research/audioset/yamnet/README.md

+# YAMNet
+
+YAMNet is a pretrained deep net that predicts 521 audio event classes based on
+the [AudioSet-YouTube corpus](http://g.co/audioset), and employing the
+[Mobilenet_v1](https://arxiv.org/pdf/1704.04861.pdf) depthwise-separable
+convolution architecture.
+
+This directory contains the Keras code to construct the model, and example code
+for applying the model to input sound files.
+
+## Installation
+
+YAMNet depends on the following Python packages:
+
+* [`numpy`](http://www.numpy.org/)
+* [`scipy`](http://www.scipy.org/)
+* [`resampy`](http://resampy.readthedocs.io/en/latest/)
+* [`tensorflow`](http://www.tensorflow.org/)
+* [`pysoundfile`](https://pysoundfile.readthedocs.io/)
+
+These are all easily installable via, e.g., `pip install numpy` (as in the
+example command sequence below).
+
+Any reasonably recent version of these packages should work. TensorFlow should
+be at least version 1.8 to ensure Keras support is included.  We have tested
+that everything works on Ubuntu and MacOS with Python 3.7.2, Numpy v1.15.4,
+SciPy v1.1.0, resampy v0.2.1, TensorFlow v1.14.0, and PySoundFile 0.9.0.
+
+YAMNet also requires downloading the following data file:
+
+* [YAMNet model weights](https://storage.googleapis.com/audioset/yamnet.h5)
+  in Keras saved weights in HDF5 format.
+
+After downloading this file into the same directory as this README, the
+installation can be tested by running `python yamnet_test.py` which
+runs some synthetic signals through the model and checks the outputs.
+
+Here's a sample installation and test session:
+
+```shell
+# Upgrade pip first.
+python -m pip install --upgrade pip
+
+# Install dependences. Resampy needs to be installed after NumPy and SciPy
+# are already installed.
+pip install numpy scipy
+pip install resampy tensorflow pysoundfile
+
+# Clone TensorFlow models repo into a 'models' directory.
+git clone https://github.com/tensorflow/models.git
+cd models/research/audioset/yamnet
+# Download data file into same directory as code.
+curl -O https://storage.googleapis.com/audioset/yamnet.h5
+
+# Installation ready, let's test it.
+python yamnet_test.py
+# If we see "Ran 4 tests ... OK ...", then we're all set.
+```
+
+## Usage
+
+You can run the model over existing soundfiles using inference.py:
+
+```shell
+python inference.py input_sound.wav
+```
+The code will report the top-5 highest-scoring classes averaged over all the
+frames of the input.  You can access greater detail by modifying the example
+code in inference.py.
+
+See the jupyter notebook `yamnet_visualization.ipynb` for an example of
+displaying the per-frame model output scores.
+
+
+## About the Model
+
+The YAMNet code layout is as follows:
+
+* `yamnet.py`: Model definition in Keras.
+* `params.py`: Hyperparameters.  You can usefully modify PATCH_HOP_SECONDS.
+* `features.py`: Audio feature extraction helpers.
+* `inference.py`: Example code to classify input wav files.
+* `yamnet_test.py`: Simple test of YAMNet installation
+
+### Input: Audio Features
+
+See `features.py`.
+
+As with our previous release
+[VGGish](https://github.com/tensorflow/models/tree/master/research/audioset/vggish),
+YAMNet was trained with audio features computed as follows:
+
+* All audio is resampled to 16 kHz mono.
+* A spectrogram is computed using magnitudes of the Short-Time Fourier Transform
+  with a window size of 25 ms, a window hop of 10 ms, and a periodic Hann
+  window.
+* A mel spectrogram is computed by mapping the spectrogram to 64 mel bins
+  covering the range 125-7500 Hz.
+* A stabilized log mel spectrogram is computed by applying
+  log(mel-spectrum + 0.001) where the offset is used to avoid taking a logarithm
+  of zero.
+* These features are then framed into 50%-overlapping examples of 0.96 seconds,
+  where each example covers 64 mel bands and 96 frames of 10 ms each.
+
+These 96x64 patches are then fed into the Mobilenet_v1 model to yield a 3x2
+array of activations for 1024 kernels at the top of the convolution.  These are
+averaged to give a 1024-dimension embedding, then put through a single logistic
+layer to get the 521 per-class output scores corresponding to the 960 ms input
+waveform segment.  (Because of the window framing, you need at least 975 ms of
+input waveform to get the first frame of output scores.)
+
+### Class vocabulary
+
+The file `yamnet_class_map.csv` describes the audio event classes associated
+with each of the 521 outputs of the network.  Its format is:
+
+```text
+index,mid,display_name
+```
+
+where `index` is the model output index (0..520), `mid` is the machine
+identifier for that class (e.g. `/m/09x0r`), and display_name is a
+human-readable description of the class (e.g. `Speech`).
+
+The original Audioset data release had 527 classes.  This model drops six of
+them on the recommendation of our Fairness reviewers to avoid potentially
+offensive mislabelings.  We dropped the gendered versions (Male/Female) of
+Speech and Singing.  We also dropped Battle cry and Funny music.
+
+### Performance
+
+On the 20,366-segment AudioSet eval set, over the 521 included classes, the
+balanced average d-prime is 2.318, balanced mAP is 0.306, and the balanced
+average lwlrap is 0.393.
+
+According to our calculations, the classifier has 3.7M weights and performs
+69.2M multiplies for each 960ms input frame.
+
+### Contact information
+
+This model repository is maintained by [Manoj Plakal](https://github.com/plakal) and [Dan Ellis](https://github.com/dpwe).

research/audioset/yamnet/features.py

+# Copyright 2019 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.
+# ==============================================================================
+
+"""Feature computation for YAMNet."""
+
+import numpy as np
+import tensorflow as tf
+
+
+def waveform_to_log_mel_spectrogram(waveform, params):
+  """Compute log mel spectrogram of a 1-D waveform."""
+  with tf.name_scope('log_mel_features'):
+    # waveform has shape [<# samples>]
+
+    # Convert waveform into spectrogram using a Short-Time Fourier Transform.
+    # Note that tf.signal.stft() uses a periodic Hann window by default.
+    window_length_samples = int(
+      round(params.SAMPLE_RATE * params.STFT_WINDOW_SECONDS))
+    hop_length_samples = int(
+      round(params.SAMPLE_RATE * params.STFT_HOP_SECONDS))
+    fft_length = 2 ** int(np.ceil(np.log(window_length_samples) / np.log(2.0)))
+    num_spectrogram_bins = fft_length // 2 + 1
+    magnitude_spectrogram = tf.abs(tf.signal.stft(
+        signals=waveform,
+        frame_length=window_length_samples,
+        frame_step=hop_length_samples,
+        fft_length=fft_length))
+    # magnitude_spectrogram has shape [<# STFT frames>, num_spectrogram_bins]
+
+    # Convert spectrogram into log mel spectrogram.
+    linear_to_mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix(
+        num_mel_bins=params.MEL_BANDS,
+        num_spectrogram_bins=num_spectrogram_bins,
+        sample_rate=params.SAMPLE_RATE,
+        lower_edge_hertz=params.MEL_MIN_HZ,
+        upper_edge_hertz=params.MEL_MAX_HZ)
+    mel_spectrogram = tf.matmul(
+      magnitude_spectrogram, linear_to_mel_weight_matrix)
+    log_mel_spectrogram = tf.math.log(mel_spectrogram + params.LOG_OFFSET)
+    # log_mel_spectrogram has shape [<# STFT frames>, MEL_BANDS]
+
+    return log_mel_spectrogram
+
+
+def spectrogram_to_patches(spectrogram, params):
+  """Break up a spectrogram into a stack of fixed-size patches."""
+  with tf.name_scope('feature_patches'):
+    # Frame spectrogram (shape [<# STFT frames>, MEL_BANDS]) into patches 
+    # (the input examples).
+    # Only complete frames are emitted, so if there is less than 
+    # PATCH_WINDOW_SECONDS of waveform then nothing is emitted 
+    # (to avoid this, zero-pad before processing).
+    hop_length_samples = int(
+      round(params.SAMPLE_RATE * params.STFT_HOP_SECONDS))
+    spectrogram_sr = params.SAMPLE_RATE / hop_length_samples
+    patch_window_length_samples = int(
+      round(spectrogram_sr * params.PATCH_WINDOW_SECONDS))
+    patch_hop_length_samples = int(
+      round(spectrogram_sr * params.PATCH_HOP_SECONDS))
+    features = tf.signal.frame(
+        signal=spectrogram,
+        frame_length=patch_window_length_samples,
+        frame_step=patch_hop_length_samples,
+        axis=0)
+    # features has shape [<# patches>, <# STFT frames in an patch>, MEL_BANDS]
+
+    return features

research/audioset/yamnet/inference.py

+# Copyright 2019 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.
+# ==============================================================================
+
+"""Inference demo for YAMNet."""
+from __future__ import division, print_function
+
+import sys
+
+import numpy as np
+import resampy
+import soundfile as sf
+
+import params
+import yamnet as yamnet_model
+
+
+def main(argv):
+  assert argv
+
+  yamnet = yamnet_model.yamnet_frames_model(params)
+  yamnet.load_weights('yamnet.h5')
+  yamnet_classes = yamnet_model.class_names('yamnet_class_map.csv')
+
+  for file_name in argv:
+    # Decode the WAV file.
+    wav_data, sr = sf.read(file_name, dtype=np.int16)
+    assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
+    waveform = wav_data / 32768.0  # Convert to [-1.0, +1.0]
+
+    # Convert to mono and the sample rate expected by YAMNet.
+    if len(waveform.shape) > 1:
+      waveform = np.mean(waveform, axis=1)
+    if sr != params.SAMPLE_RATE:
+      waveform = resampy.resample(waveform, sr, params.SAMPLE_RATE)
+
+    # Predict YAMNet classes.
+    # Second output is log-mel-spectrogram array (used for visualizations).
+    # (steps=1 is a work around for Keras batching limitations.)
+    scores, _ = yamnet.predict(np.reshape(waveform, [1, -1]), steps=1)
+    # Scores is a matrix of (time_frames, num_classes) classifier scores.
+    # Average them along time to get an overall classifier output for the clip.
+    prediction = np.mean(scores, axis=0)
+    # Report the highest-scoring classes and their scores.
+    top5_i = np.argsort(prediction)[::-1][:5]
+    print(file_name, ':\n' + 
+          '\n'.join('  {:12s}: {:.3f}'.format(yamnet_classes[i], prediction[i])
+                    for i in top5_i))
+
+
+if __name__ == '__main__':
+  main(sys.argv[1:])

research/audioset/yamnet/params.py

+# Copyright 2019 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.
+# ==============================================================================
+
+"""Hyperparameters for YAMNet."""
+
+# The following hyperparameters (except PATCH_HOP_SECONDS) were used to train YAMNet,
+# so expect some variability in performance if you change these. The patch hop can
+# be changed arbitrarily: a smaller hop should give you more patches from the same
+# clip and possibly better performance at a larger computational cost.
+SAMPLE_RATE = 16000
+STFT_WINDOW_SECONDS = 0.025
+STFT_HOP_SECONDS = 0.010
+MEL_BANDS = 64
+MEL_MIN_HZ = 125
+MEL_MAX_HZ = 7500
+LOG_OFFSET = 0.001
+PATCH_WINDOW_SECONDS = 0.96
+PATCH_HOP_SECONDS = 0.48
+
+PATCH_FRAMES = int(round(PATCH_WINDOW_SECONDS / STFT_HOP_SECONDS))
+PATCH_BANDS = MEL_BANDS
+NUM_CLASSES = 521
+CONV_PADDING = 'same'
+BATCHNORM_CENTER = True
+BATCHNORM_SCALE = False
+BATCHNORM_EPSILON = 1e-4
+CLASSIFIER_ACTIVATION = 'sigmoid'
+
+FEATURES_LAYER_NAME = 'features'
+EXAMPLE_PREDICTIONS_LAYER_NAME = 'predictions'

research/audioset/yamnet/yamnet.py

+# Copyright 2019 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.
+# ==============================================================================
+
+"""Core model definition of YAMNet."""
+
+import csv
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import Model, layers
+
+import features as features_lib
+import params
+
+
+def _batch_norm(name):
+  def _bn_layer(layer_input):
+    return layers.BatchNormalization(
+      name=name,
+      center=params.BATCHNORM_CENTER,
+      scale=params.BATCHNORM_SCALE,
+      epsilon=params.BATCHNORM_EPSILON)(layer_input)
+  return _bn_layer
+
+
+def _conv(name, kernel, stride, filters):
+  def _conv_layer(layer_input):
+    output = layers.Conv2D(name='{}/conv'.format(name),
+                           filters=filters,
+                           kernel_size=kernel,
+                           strides=stride,
+                           padding=params.CONV_PADDING,
+                           use_bias=False,
+                           activation=None)(layer_input)
+    output = _batch_norm(name='{}/conv/bn'.format(name))(output)
+    output = layers.ReLU(name='{}/relu'.format(name))(output)
+    return output
+  return _conv_layer
+
+
+def _separable_conv(name, kernel, stride, filters):
+  def _separable_conv_layer(layer_input):
+    output = layers.DepthwiseConv2D(name='{}/depthwise_conv'.format(name),
+                                    kernel_size=kernel,
+                                    strides=stride,
+                                    depth_multiplier=1,
+                                    padding=params.CONV_PADDING,
+                                    use_bias=False,
+                                    activation=None)(layer_input)
+    output = _batch_norm(name='{}/depthwise_conv/bn'.format(name))(output)
+    output = layers.ReLU(name='{}/depthwise_conv/relu'.format(name))(output)
+    output = layers.Conv2D(name='{}/pointwise_conv'.format(name),
+                           filters=filters,
+                           kernel_size=(1, 1),
+                           strides=1,
+                           padding=params.CONV_PADDING,
+                           use_bias=False,
+                           activation=None)(output)
+    output = _batch_norm(name='{}/pointwise_conv/bn'.format(name))(output)
+    output = layers.ReLU(name='{}/pointwise_conv/relu'.format(name))(output)
+    return output
+  return _separable_conv_layer
+
+
+_YAMNET_LAYER_DEFS = [
+    # (layer_function, kernel, stride, num_filters)
+    (_conv,          [3, 3], 2,   32),
+    (_separable_conv, [3, 3], 1,   64),
+    (_separable_conv, [3, 3], 2,  128),
+    (_separable_conv, [3, 3], 1,  128),
+    (_separable_conv, [3, 3], 2,  256),
+    (_separable_conv, [3, 3], 1,  256),
+    (_separable_conv, [3, 3], 2,  512),
+    (_separable_conv, [3, 3], 1,  512),
+    (_separable_conv, [3, 3], 1,  512),
+    (_separable_conv, [3, 3], 1,  512),
+    (_separable_conv, [3, 3], 1,  512),
+    (_separable_conv, [3, 3], 1,  512),
+    (_separable_conv, [3, 3], 2, 1024),
+    (_separable_conv, [3, 3], 1, 1024)
+]
+
+
+def yamnet(features):
+  """Define the core YAMNet mode in Keras."""
+  net = layers.Reshape(
+    (params.PATCH_FRAMES, params.PATCH_BANDS, 1),
+    input_shape=(params.PATCH_FRAMES, params.PATCH_BANDS))(features)
+  for (i, (layer_fun, kernel, stride, filters)) in enumerate(_YAMNET_LAYER_DEFS):
+    net = layer_fun('layer{}'.format(i + 1), kernel, stride, filters)(net)
+  net = layers.GlobalAveragePooling2D()(net)
+  logits = layers.Dense(units=params.NUM_CLASSES, use_bias=True)(net)
+  predictions = layers.Activation(
+    name=params.EXAMPLE_PREDICTIONS_LAYER_NAME,
+    activation=params.CLASSIFIER_ACTIVATION)(logits)
+  return predictions
+
+
+def yamnet_frames_model(feature_params):
+  """Defines the YAMNet waveform-to-class-scores model.
+
+  Args:
+    feature_params: An object with parameter fields to control the feature
+    calculation.
+
+  Returns:
+    A model accepting (1, num_samples) waveform input and emitting a
+    (num_patches, num_classes) matrix of class scores per time frame as
+    well as a (num_spectrogram_frames, num_mel_bins) spectrogram feature
+    matrix.
+  """
+  waveform = layers.Input(batch_shape=(1, None))
+  # Store the intermediate spectrogram features to use in visualization.
+  spectrogram = features_lib.waveform_to_log_mel_spectrogram(
+    tf.squeeze(waveform, axis=0), feature_params)
+  patches = features_lib.spectrogram_to_patches(spectrogram, feature_params)
+  predictions = yamnet(patches)
+  frames_model = Model(name='yamnet_frames', 
+                       inputs=waveform, outputs=[predictions, spectrogram])
+  return frames_model
+
+
+def class_names(class_map_csv):
+  """Read the class name definition file and return a list of strings."""
+  with open(class_map_csv) as csv_file:
+    reader = csv.reader(csv_file)
+    next(reader)   # Skip header
+    return np.array([display_name for (_, _, display_name) in reader])

research/audioset/yamnet/yamnet_class_map.csv

+index,mid,display_name
+0,/m/09x0r,Speech
+1,/m/0ytgt,"Child speech, kid speaking"
+2,/m/01h8n0,Conversation
+3,/m/02qldy,"Narration, monologue"
+4,/m/0261r1,Babbling
+5,/m/0brhx,Speech synthesizer
+6,/m/07p6fty,Shout
+7,/m/07q4ntr,Bellow
+8,/m/07rwj3x,Whoop
+9,/m/07sr1lc,Yell
+10,/t/dd00135,Children shouting
+11,/m/03qc9zr,Screaming
+12,/m/02rtxlg,Whispering
+13,/m/01j3sz,Laughter
+14,/t/dd00001,Baby laughter
+15,/m/07r660_,Giggle
+16,/m/07s04w4,Snicker
+17,/m/07sq110,Belly laugh
+18,/m/07rgt08,"Chuckle, chortle"
+19,/m/0463cq4,"Crying, sobbing"
+20,/t/dd00002,"Baby cry, infant cry"
+21,/m/07qz6j3,Whimper
+22,/m/07qw_06,"Wail, moan"
+23,/m/07plz5l,Sigh
+24,/m/015lz1,Singing
+25,/m/0l14jd,Choir
+26,/m/01swy6,Yodeling
+27,/m/02bk07,Chant
+28,/m/01c194,Mantra
+29,/t/dd00005,Child singing
+30,/t/dd00006,Synthetic singing
+31,/m/06bxc,Rapping
+32,/m/02fxyj,Humming
+33,/m/07s2xch,Groan
+34,/m/07r4k75,Grunt
+35,/m/01w250,Whistling
+36,/m/0lyf6,Breathing
+37,/m/07mzm6,Wheeze
+38,/m/01d3sd,Snoring
+39,/m/07s0dtb,Gasp
+40,/m/07pyy8b,Pant
+41,/m/07q0yl5,Snort
+42,/m/01b_21,Cough
+43,/m/0dl9sf8,Throat clearing
+44,/m/01hsr_,Sneeze
+45,/m/07ppn3j,Sniff
+46,/m/06h7j,Run
+47,/m/07qv_x_,Shuffle
+48,/m/07pbtc8,"Walk, footsteps"
+49,/m/03cczk,"Chewing, mastication"
+50,/m/07pdhp0,Biting
+51,/m/0939n_,Gargling
+52,/m/01g90h,Stomach rumble
+53,/m/03q5_w,"Burping, eructation"
+54,/m/02p3nc,Hiccup
+55,/m/02_nn,Fart
+56,/m/0k65p,Hands
+57,/m/025_jnm,Finger snapping
+58,/m/0l15bq,Clapping
+59,/m/01jg02,"Heart sounds, heartbeat"
+60,/m/01jg1z,Heart murmur
+61,/m/053hz1,Cheering
+62,/m/028ght,Applause
+63,/m/07rkbfh,Chatter
+64,/m/03qtwd,Crowd
+65,/m/07qfr4h,"Hubbub, speech noise, speech babble"
+66,/t/dd00013,Children playing
+67,/m/0jbk,Animal
+68,/m/068hy,"Domestic animals, pets"
+69,/m/0bt9lr,Dog
+70,/m/05tny_,Bark
+71,/m/07r_k2n,Yip
+72,/m/07qf0zm,Howl
+73,/m/07rc7d9,Bow-wow
+74,/m/0ghcn6,Growling
+75,/t/dd00136,Whimper (dog)
+76,/m/01yrx,Cat
+77,/m/02yds9,Purr
+78,/m/07qrkrw,Meow
+79,/m/07rjwbb,Hiss
+80,/m/07r81j2,Caterwaul
+81,/m/0ch8v,"Livestock, farm animals, working animals"
+82,/m/03k3r,Horse
+83,/m/07rv9rh,Clip-clop
+84,/m/07q5rw0,"Neigh, whinny"
+85,/m/01xq0k1,"Cattle, bovinae"
+86,/m/07rpkh9,Moo
+87,/m/0239kh,Cowbell
+88,/m/068zj,Pig
+89,/t/dd00018,Oink
+90,/m/03fwl,Goat
+91,/m/07q0h5t,Bleat
+92,/m/07bgp,Sheep
+93,/m/025rv6n,Fowl
+94,/m/09b5t,"Chicken, rooster"
+95,/m/07st89h,Cluck
+96,/m/07qn5dc,"Crowing, cock-a-doodle-doo"
+97,/m/01rd7k,Turkey
+98,/m/07svc2k,Gobble
+99,/m/09ddx,Duck
+100,/m/07qdb04,Quack
+101,/m/0dbvp,Goose
+102,/m/07qwf61,Honk
+103,/m/01280g,Wild animals
+104,/m/0cdnk,"Roaring cats (lions, tigers)"
+105,/m/04cvmfc,Roar
+106,/m/015p6,Bird
+107,/m/020bb7,"Bird vocalization, bird call, bird song"
+108,/m/07pggtn,"Chirp, tweet"
+109,/m/07sx8x_,Squawk
+110,/m/0h0rv,"Pigeon, dove"
+111,/m/07r_25d,Coo
+112,/m/04s8yn,Crow
+113,/m/07r5c2p,Caw
+114,/m/09d5_,Owl
+115,/m/07r_80w,Hoot
+116,/m/05_wcq,"Bird flight, flapping wings"
+117,/m/01z5f,"Canidae, dogs, wolves"
+118,/m/06hps,"Rodents, rats, mice"
+119,/m/04rmv,Mouse
+120,/m/07r4gkf,Patter
+121,/m/03vt0,Insect
+122,/m/09xqv,Cricket
+123,/m/09f96,Mosquito
+124,/m/0h2mp,"Fly, housefly"
+125,/m/07pjwq1,Buzz
+126,/m/01h3n,"Bee, wasp, etc."
+127,/m/09ld4,Frog
+128,/m/07st88b,Croak
+129,/m/078jl,Snake
+130,/m/07qn4z3,Rattle
+131,/m/032n05,Whale vocalization
+132,/m/04rlf,Music
+133,/m/04szw,Musical instrument
+134,/m/0fx80y,Plucked string instrument
+135,/m/0342h,Guitar
+136,/m/02sgy,Electric guitar
+137,/m/018vs,Bass guitar
+138,/m/042v_gx,Acoustic guitar
+139,/m/06w87,"Steel guitar, slide guitar"
+140,/m/01glhc,Tapping (guitar technique)
+141,/m/07s0s5r,Strum
+142,/m/018j2,Banjo
+143,/m/0jtg0,Sitar
+144,/m/04rzd,Mandolin
+145,/m/01bns_,Zither
+146,/m/07xzm,Ukulele
+147,/m/05148p4,Keyboard (musical)
+148,/m/05r5c,Piano
+149,/m/01s0ps,Electric piano
+150,/m/013y1f,Organ
+151,/m/03xq_f,Electronic organ
+152,/m/03gvt,Hammond organ
+153,/m/0l14qv,Synthesizer
+154,/m/01v1d8,Sampler
+155,/m/03q5t,Harpsichord
+156,/m/0l14md,Percussion
+157,/m/02hnl,Drum kit
+158,/m/0cfdd,Drum machine
+159,/m/026t6,Drum
+160,/m/06rvn,Snare drum
+161,/m/03t3fj,Rimshot
+162,/m/02k_mr,Drum roll
+163,/m/0bm02,Bass drum
+164,/m/011k_j,Timpani
+165,/m/01p970,Tabla
+166,/m/01qbl,Cymbal
+167,/m/03qtq,Hi-hat
+168,/m/01sm1g,Wood block
+169,/m/07brj,Tambourine
+170,/m/05r5wn,Rattle (instrument)
+171,/m/0xzly,Maraca
+172,/m/0mbct,Gong
+173,/m/016622,Tubular bells
+174,/m/0j45pbj,Mallet percussion
+175,/m/0dwsp,"Marimba, xylophone"
+176,/m/0dwtp,Glockenspiel
+177,/m/0dwt5,Vibraphone
+178,/m/0l156b,Steelpan
+179,/m/05pd6,Orchestra
+180,/m/01kcd,Brass instrument
+181,/m/0319l,French horn
+182,/m/07gql,Trumpet
+183,/m/07c6l,Trombone
+184,/m/0l14_3,Bowed string instrument
+185,/m/02qmj0d,String section
+186,/m/07y_7,"Violin, fiddle"
+187,/m/0d8_n,Pizzicato
+188,/m/01xqw,Cello
+189,/m/02fsn,Double bass
+190,/m/085jw,"Wind instrument, woodwind instrument"
+191,/m/0l14j_,Flute
+192,/m/06ncr,Saxophone
+193,/m/01wy6,Clarinet
+194,/m/03m5k,Harp
+195,/m/0395lw,Bell
+196,/m/03w41f,Church bell
+197,/m/027m70_,Jingle bell
+198,/m/0gy1t2s,Bicycle bell
+199,/m/07n_g,Tuning fork
+200,/m/0f8s22,Chime
+201,/m/026fgl,Wind chime
+202,/m/0150b9,Change ringing (campanology)
+203,/m/03qjg,Harmonica
+204,/m/0mkg,Accordion
+205,/m/0192l,Bagpipes
+206,/m/02bxd,Didgeridoo
+207,/m/0l14l2,Shofar
+208,/m/07kc_,Theremin
+209,/m/0l14t7,Singing bowl
+210,/m/01hgjl,Scratching (performance technique)
+211,/m/064t9,Pop music
+212,/m/0glt670,Hip hop music
+213,/m/02cz_7,Beatboxing
+214,/m/06by7,Rock music
+215,/m/03lty,Heavy metal
+216,/m/05r6t,Punk rock
+217,/m/0dls3,Grunge
+218,/m/0dl5d,Progressive rock
+219,/m/07sbbz2,Rock and roll
+220,/m/05w3f,Psychedelic rock
+221,/m/06j6l,Rhythm and blues
+222,/m/0gywn,Soul music
+223,/m/06cqb,Reggae
+224,/m/01lyv,Country
+225,/m/015y_n,Swing music
+226,/m/0gg8l,Bluegrass
+227,/m/02x8m,Funk
+228,/m/02w4v,Folk music
+229,/m/06j64v,Middle Eastern music
+230,/m/03_d0,Jazz
+231,/m/026z9,Disco
+232,/m/0ggq0m,Classical music
+233,/m/05lls,Opera
+234,/m/02lkt,Electronic music
+235,/m/03mb9,House music
+236,/m/07gxw,Techno
+237,/m/07s72n,Dubstep
+238,/m/0283d,Drum and bass
+239,/m/0m0jc,Electronica
+240,/m/08cyft,Electronic dance music
+241,/m/0fd3y,Ambient music
+242,/m/07lnk,Trance music
+243,/m/0g293,Music of Latin America
+244,/m/0ln16,Salsa music
+245,/m/0326g,Flamenco
+246,/m/0155w,Blues
+247,/m/05fw6t,Music for children
+248,/m/02v2lh,New-age music
+249,/m/0y4f8,Vocal music
+250,/m/0z9c,A capella
+251,/m/0164x2,Music of Africa
+252,/m/0145m,Afrobeat
+253,/m/02mscn,Christian music
+254,/m/016cjb,Gospel music
+255,/m/028sqc,Music of Asia
+256,/m/015vgc,Carnatic music
+257,/m/0dq0md,Music of Bollywood
+258,/m/06rqw,Ska
+259,/m/02p0sh1,Traditional music
+260,/m/05rwpb,Independent music
+261,/m/074ft,Song
+262,/m/025td0t,Background music
+263,/m/02cjck,Theme music
+264,/m/03r5q_,Jingle (music)
+265,/m/0l14gg,Soundtrack music
+266,/m/07pkxdp,Lullaby
+267,/m/01z7dr,Video game music
+268,/m/0140xf,Christmas music
+269,/m/0ggx5q,Dance music
+270,/m/04wptg,Wedding music
+271,/t/dd00031,Happy music
+272,/t/dd00033,Sad music
+273,/t/dd00034,Tender music
+274,/t/dd00035,Exciting music
+275,/t/dd00036,Angry music
+276,/t/dd00037,Scary music
+277,/m/03m9d0z,Wind
+278,/m/09t49,Rustling leaves
+279,/t/dd00092,Wind noise (microphone)
+280,/m/0jb2l,Thunderstorm
+281,/m/0ngt1,Thunder
+282,/m/0838f,Water
+283,/m/06mb1,Rain
+284,/m/07r10fb,Raindrop
+285,/t/dd00038,Rain on surface
+286,/m/0j6m2,Stream
+287,/m/0j2kx,Waterfall
+288,/m/05kq4,Ocean
+289,/m/034srq,"Waves, surf"
+290,/m/06wzb,Steam
+291,/m/07swgks,Gurgling
+292,/m/02_41,Fire
+293,/m/07pzfmf,Crackle
+294,/m/07yv9,Vehicle
+295,/m/019jd,"Boat, Water vehicle"
+296,/m/0hsrw,"Sailboat, sailing ship"
+297,/m/056ks2,"Rowboat, canoe, kayak"
+298,/m/02rlv9,"Motorboat, speedboat"
+299,/m/06q74,Ship
+300,/m/012f08,Motor vehicle (road)
+301,/m/0k4j,Car
+302,/m/0912c9,"Vehicle horn, car horn, honking"
+303,/m/07qv_d5,Toot
+304,/m/02mfyn,Car alarm
+305,/m/04gxbd,"Power windows, electric windows"
+306,/m/07rknqz,Skidding
+307,/m/0h9mv,Tire squeal
+308,/t/dd00134,Car passing by
+309,/m/0ltv,"Race car, auto racing"
+310,/m/07r04,Truck
+311,/m/0gvgw0,Air brake
+312,/m/05x_td,"Air horn, truck horn"
+313,/m/02rhddq,Reversing beeps
+314,/m/03cl9h,"Ice cream truck, ice cream van"
+315,/m/01bjv,Bus
+316,/m/03j1ly,Emergency vehicle
+317,/m/04qvtq,Police car (siren)
+318,/m/012n7d,Ambulance (siren)
+319,/m/012ndj,"Fire engine, fire truck (siren)"
+320,/m/04_sv,Motorcycle
+321,/m/0btp2,"Traffic noise, roadway noise"
+322,/m/06d_3,Rail transport
+323,/m/07jdr,Train
+324,/m/04zmvq,Train whistle
+325,/m/0284vy3,Train horn
+326,/m/01g50p,"Railroad car, train wagon"
+327,/t/dd00048,Train wheels squealing
+328,/m/0195fx,"Subway, metro, underground"
+329,/m/0k5j,Aircraft
+330,/m/014yck,Aircraft engine
+331,/m/04229,Jet engine
+332,/m/02l6bg,"Propeller, airscrew"
+333,/m/09ct_,Helicopter
+334,/m/0cmf2,"Fixed-wing aircraft, airplane"
+335,/m/0199g,Bicycle
+336,/m/06_fw,Skateboard
+337,/m/02mk9,Engine
+338,/t/dd00065,Light engine (high frequency)
+339,/m/08j51y,"Dental drill, dentist's drill"
+340,/m/01yg9g,Lawn mower
+341,/m/01j4z9,Chainsaw
+342,/t/dd00066,Medium engine (mid frequency)
+343,/t/dd00067,Heavy engine (low frequency)
+344,/m/01h82_,Engine knocking
+345,/t/dd00130,Engine starting
+346,/m/07pb8fc,Idling
+347,/m/07q2z82,"Accelerating, revving, vroom"
+348,/m/02dgv,Door
+349,/m/03wwcy,Doorbell
+350,/m/07r67yg,Ding-dong
+351,/m/02y_763,Sliding door
+352,/m/07rjzl8,Slam
+353,/m/07r4wb8,Knock
+354,/m/07qcpgn,Tap
+355,/m/07q6cd_,Squeak
+356,/m/0642b4,Cupboard open or close
+357,/m/0fqfqc,Drawer open or close
+358,/m/04brg2,"Dishes, pots, and pans"
+359,/m/023pjk,"Cutlery, silverware"
+360,/m/07pn_8q,Chopping (food)
+361,/m/0dxrf,Frying (food)
+362,/m/0fx9l,Microwave oven
+363,/m/02pjr4,Blender
+364,/m/02jz0l,"Water tap, faucet"
+365,/m/0130jx,Sink (filling or washing)
+366,/m/03dnzn,Bathtub (filling or washing)
+367,/m/03wvsk,Hair dryer
+368,/m/01jt3m,Toilet flush
+369,/m/012xff,Toothbrush
+370,/m/04fgwm,Electric toothbrush
+371,/m/0d31p,Vacuum cleaner
+372,/m/01s0vc,Zipper (clothing)
+373,/m/03v3yw,Keys jangling
+374,/m/0242l,Coin (dropping)
+375,/m/01lsmm,Scissors
+376,/m/02g901,"Electric shaver, electric razor"
+377,/m/05rj2,Shuffling cards
+378,/m/0316dw,Typing
+379,/m/0c2wf,Typewriter
+380,/m/01m2v,Computer keyboard
+381,/m/081rb,Writing
+382,/m/07pp_mv,Alarm
+383,/m/07cx4,Telephone
+384,/m/07pp8cl,Telephone bell ringing
+385,/m/01hnzm,Ringtone
+386,/m/02c8p,"Telephone dialing, DTMF"
+387,/m/015jpf,Dial tone
+388,/m/01z47d,Busy signal
+389,/m/046dlr,Alarm clock
+390,/m/03kmc9,Siren
+391,/m/0dgbq,Civil defense siren
+392,/m/030rvx,Buzzer
+393,/m/01y3hg,"Smoke detector, smoke alarm"
+394,/m/0c3f7m,Fire alarm
+395,/m/04fq5q,Foghorn
+396,/m/0l156k,Whistle
+397,/m/06hck5,Steam whistle
+398,/t/dd00077,Mechanisms
+399,/m/02bm9n,"Ratchet, pawl"
+400,/m/01x3z,Clock
+401,/m/07qjznt,Tick
+402,/m/07qjznl,Tick-tock
+403,/m/0l7xg,Gears
+404,/m/05zc1,Pulleys
+405,/m/0llzx,Sewing machine
+406,/m/02x984l,Mechanical fan
+407,/m/025wky1,Air conditioning
+408,/m/024dl,Cash register
+409,/m/01m4t,Printer
+410,/m/0dv5r,Camera
+411,/m/07bjf,Single-lens reflex camera
+412,/m/07k1x,Tools
+413,/m/03l9g,Hammer
+414,/m/03p19w,Jackhammer
+415,/m/01b82r,Sawing
+416,/m/02p01q,Filing (rasp)
+417,/m/023vsd,Sanding
+418,/m/0_ksk,Power tool
+419,/m/01d380,Drill
+420,/m/014zdl,Explosion
+421,/m/032s66,"Gunshot, gunfire"
+422,/m/04zjc,Machine gun
+423,/m/02z32qm,Fusillade
+424,/m/0_1c,Artillery fire
+425,/m/073cg4,Cap gun
+426,/m/0g6b5,Fireworks
+427,/g/122z_qxw,Firecracker
+428,/m/07qsvvw,"Burst, pop"
+429,/m/07pxg6y,Eruption
+430,/m/07qqyl4,Boom
+431,/m/083vt,Wood
+432,/m/07pczhz,Chop
+433,/m/07pl1bw,Splinter
+434,/m/07qs1cx,Crack
+435,/m/039jq,Glass
+436,/m/07q7njn,"Chink, clink"
+437,/m/07rn7sz,Shatter
+438,/m/04k94,Liquid
+439,/m/07rrlb6,"Splash, splatter"
+440,/m/07p6mqd,Slosh
+441,/m/07qlwh6,Squish
+442,/m/07r5v4s,Drip
+443,/m/07prgkl,Pour
+444,/m/07pqc89,"Trickle, dribble"
+445,/t/dd00088,Gush
+446,/m/07p7b8y,Fill (with liquid)
+447,/m/07qlf79,Spray
+448,/m/07ptzwd,Pump (liquid)
+449,/m/07ptfmf,Stir
+450,/m/0dv3j,Boiling
+451,/m/0790c,Sonar
+452,/m/0dl83,Arrow
+453,/m/07rqsjt,"Whoosh, swoosh, swish"
+454,/m/07qnq_y,"Thump, thud"
+455,/m/07rrh0c,Thunk
+456,/m/0b_fwt,Electronic tuner
+457,/m/02rr_,Effects unit
+458,/m/07m2kt,Chorus effect
+459,/m/018w8,Basketball bounce
+460,/m/07pws3f,Bang
+461,/m/07ryjzk,"Slap, smack"
+462,/m/07rdhzs,"Whack, thwack"
+463,/m/07pjjrj,"Smash, crash"
+464,/m/07pc8lb,Breaking
+465,/m/07pqn27,Bouncing
+466,/m/07rbp7_,Whip
+467,/m/07pyf11,Flap
+468,/m/07qb_dv,Scratch
+469,/m/07qv4k0,Scrape
+470,/m/07pdjhy,Rub
+471,/m/07s8j8t,Roll
+472,/m/07plct2,Crushing
+473,/t/dd00112,"Crumpling, crinkling"
+474,/m/07qcx4z,Tearing
+475,/m/02fs_r,"Beep, bleep"
+476,/m/07qwdck,Ping
+477,/m/07phxs1,Ding
+478,/m/07rv4dm,Clang
+479,/m/07s02z0,Squeal
+480,/m/07qh7jl,Creak
+481,/m/07qwyj0,Rustle
+482,/m/07s34ls,Whir
+483,/m/07qmpdm,Clatter
+484,/m/07p9k1k,Sizzle
+485,/m/07qc9xj,Clicking
+486,/m/07rwm0c,Clickety-clack
+487,/m/07phhsh,Rumble
+488,/m/07qyrcz,Plop
+489,/m/07qfgpx,"Jingle, tinkle"
+490,/m/07rcgpl,Hum
+491,/m/07p78v5,Zing
+492,/t/dd00121,Boing
+493,/m/07s12q4,Crunch
+494,/m/028v0c,Silence
+495,/m/01v_m0,Sine wave
+496,/m/0b9m1,Harmonic
+497,/m/0hdsk,Chirp tone
+498,/m/0c1dj,Sound effect
+499,/m/07pt_g0,Pulse
+500,/t/dd00125,"Inside, small room"
+501,/t/dd00126,"Inside, large room or hall"
+502,/t/dd00127,"Inside, public space"
+503,/t/dd00128,"Outside, urban or manmade"
+504,/t/dd00129,"Outside, rural or natural"
+505,/m/01b9nn,Reverberation
+506,/m/01jnbd,Echo
+507,/m/096m7z,Noise
+508,/m/06_y0by,Environmental noise
+509,/m/07rgkc5,Static
+510,/m/06xkwv,Mains hum
+511,/m/0g12c5,Distortion
+512,/m/08p9q4,Sidetone
+513,/m/07szfh9,Cacophony
+514,/m/0chx_,White noise
+515,/m/0cj0r,Pink noise
+516,/m/07p_0gm,Throbbing
+517,/m/01jwx6,Vibration
+518,/m/07c52,Television
+519,/m/06bz3,Radio
+520,/m/07hvw1,Field recording

research/audioset/yamnet/yamnet_test.py

+# Copyright 2019 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.
+# ==============================================================================
+
+"""Installation test for YAMNet."""
+
+import numpy as np
+import tensorflow as tf
+
+import params
+import yamnet
+
+class YAMNetTest(tf.test.TestCase):
+
+  _yamnet_graph = None
+  _yamnet = None
+  _yamnet_classes = None
+
+  @classmethod
+  def setUpClass(cls):
+    super(YAMNetTest, cls).setUpClass()
+    cls._yamnet_graph = tf.Graph()
+    with cls._yamnet_graph.as_default():
+      cls._yamnet = yamnet.yamnet_frames_model(params)
+      cls._yamnet.load_weights('yamnet.h5')
+      cls._yamnet_classes = yamnet.class_names('yamnet_class_map.csv')
+
+  def clip_test(self, waveform, expected_class_name, top_n=10):
+    """Run the model on the waveform, check that expected class is in top-n."""
+    with YAMNetTest._yamnet_graph.as_default():
+      prediction = np.mean(YAMNetTest._yamnet.predict(
+        np.reshape(waveform, [1, -1]), steps=1)[0], axis=0)
+      top_n_class_names = YAMNetTest._yamnet_classes[
+        np.argsort(prediction)[-top_n:]]
+      self.assertIn(expected_class_name, top_n_class_names)
+
+  def testZeros(self):
+    self.clip_test(
+        waveform=np.zeros((1, int(3 * params.SAMPLE_RATE))),
+        expected_class_name='Silence')
+
+  def testRandom(self):
+    np.random.seed(51773)  # Ensure repeatability.
+    self.clip_test(
+        waveform=np.random.uniform(-1.0, +1.0,
+                                   (1, int(3 * params.SAMPLE_RATE))),
+        expected_class_name='White noise')
+
+  def testSine(self):
+    self.clip_test(
+        waveform=np.reshape(
+            np.sin(2 * np.pi * 440 * np.linspace(
+                0, 3, int(3 *params.SAMPLE_RATE))),
+            [1, -1]),
+        expected_class_name='Sine wave')
+
+
+if __name__ == '__main__':
+  tf.test.main()

research/audioset/yamnet/yamnet_visualization.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2019 The TensorFlow Authors All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Visualization of the YAMNet audio event classification model.\n",
+    "# See https://github.com/tensorflow/models/tree/master/research/audioset/yamnet/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports.\n",
+    "import numpy as np\n",
+    "import soundfile as sf\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import params\n",
+    "import yamnet as yamnet_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W1121 08:50:31.581582 4453795264 deprecation.py:506] From /Applications/anaconda/envs/py3/lib/python3.7/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set up the YAMNet model.\n",
+    "params.PATCH_HOP_SECONDS = 0.1  # 10 Hz scores frame rate.\n",
+    "yamnet = yamnet_model.yamnet_frames_model(params)\n",
+    "yamnet.load_weights('yamnet.h5')\n",
+    "class_names = yamnet_model.class_names('yamnet_class_map.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in the audio.\n",
+    "# You can get this example waveform via:\n",
+    "# curl -O https://storage.googleapis.com/audioset/speech_whistling2.wav\n",
+    "wav_data, sr = sf.read('speech_whistling2.wav', dtype=np.int16)\n",
+    "waveform = wav_data / 32768.0\n",
+    "# Sampling rate should be 16000 Hz.\n",
+    "assert sr == 16000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run the model.\n",
+    "scores, spectrogram = yamnet.predict(np.reshape(waveform, [1, -1]), steps=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the results.\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "\n",
+    "# Plot the waveform.\n",
+    "plt.subplot(3, 1, 1)\n",
+    "plt.plot(waveform)\n",
+    "plt.xlim([0, len(waveform)])\n",
+    "# Plot the log-mel spectrogram (returned by the model).\n",
+    "plt.subplot(3, 1, 2)\n",
+    "plt.imshow(spectrogram.T, aspect='auto', interpolation='nearest', origin='bottom')\n",
+    "\n",
+    "# Plot and label the model output scores for the top-scoring classes.\n",
+    "mean_scores = np.mean(scores, axis=0)\n",
+    "top_N = 10\n",
+    "top_class_indices = np.argsort(mean_scores)[::-1][:top_N]\n",
+    "plt.subplot(3, 1, 3)\n",
+    "plt.imshow(scores[:, top_class_indices].T, aspect='auto', interpolation='nearest', cmap='gray_r')\n",
+    "# Compensate for the PATCH_WINDOW_SECONDS (0.96 s) context window to align with spectrogram.\n",
+    "patch_padding = (params.PATCH_WINDOW_SECONDS / 2) / params.PATCH_HOP_SECONDS\n",
+    "plt.xlim([-patch_padding, scores.shape[0] + patch_padding])\n",
+    "# Label the top_N classes.\n",
+    "yticks = range(0, top_N, 1)\n",
+    "plt.yticks(yticks, [class_names[top_class_indices[x]] for x in yticks])\n",
+    "_ = plt.ylim(-0.5 + np.array([top_N, 0]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}