환경 소리 분류를 위한 YAMNet을 사용한 전이 학습

pip install tensorflow_io

import os

from IPython import display
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_io as tfio

2022-12-14 22:43:37.220589: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-14 22:43:37.220708: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-14 22:43:37.220718: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

yamnet_model_handle = 'https://tfhub.dev/google/yamnet/1'
yamnet_model = hub.load(yamnet_model_handle)

testing_wav_file_name = tf.keras.utils.get_file('miaow_16k.wav',
                                                'https://storage.googleapis.com/audioset/miaow_16k.wav',
                                                cache_dir='./',
                                                cache_subdir='test_data')

print(testing_wav_file_name)

Downloading data from https://storage.googleapis.com/audioset/miaow_16k.wav
215546/215546 [==============================] - 0s 0us/step
./test_data/miaow_16k.wav

# Utility functions for loading audio files and making sure the sample rate is correct.

@tf.function
def load_wav_16k_mono(filename):
    """ Load a WAV file, convert it to a float tensor, resample to 16 kHz single-channel audio. """
    file_contents = tf.io.read_file(filename)
    wav, sample_rate = tf.audio.decode_wav(
          file_contents,
          desired_channels=1)
    wav = tf.squeeze(wav, axis=-1)
    sample_rate = tf.cast(sample_rate, dtype=tf.int64)
    wav = tfio.audio.resample(wav, rate_in=sample_rate, rate_out=16000)
    return wav

testing_wav_data = load_wav_16k_mono(testing_wav_file_name)

_ = plt.plot(testing_wav_data)

# Play the audio file.
display.Audio(testing_wav_data, rate=16000)

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
Instructions for updating:
Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
Instructions for updating:
Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
WARNING:tensorflow:Using a while_loop for converting IO>AudioResample cause there is no registered converter for this op.
WARNING:tensorflow:Using a while_loop for converting IO>AudioResample cause there is no registered converter for this op.

class_map_path = yamnet_model.class_map_path().numpy().decode('utf-8')
class_names =list(pd.read_csv(class_map_path)['display_name'])

for name in class_names[:20]:
  print(name)
print('...')

Speech
Child speech, kid speaking
Conversation
Narration, monologue
Babbling
Speech synthesizer
Shout
Bellow
Whoop
Yell
Children shouting
Screaming
Whispering
Laughter
Baby laughter
Giggle
Snicker
Belly laugh
Chuckle, chortle
Crying, sobbing
...

scores, embeddings, spectrogram = yamnet_model(testing_wav_data)
class_scores = tf.reduce_mean(scores, axis=0)
top_class = tf.math.argmax(class_scores)
inferred_class = class_names[top_class]

print(f'The main sound is: {inferred_class}')
print(f'The embeddings shape: {embeddings.shape}')

The main sound is: Animal
The embeddings shape: (13, 1024)

_ = tf.keras.utils.get_file('esc-50.zip',
                        'https://github.com/karoldvl/ESC-50/archive/master.zip',
                        cache_dir='./',
                        cache_subdir='datasets',
                        extract=True)

Downloading data from https://github.com/karoldvl/ESC-50/archive/master.zip
   8192/Unknown - 0s 0us/step

esc50_csv = './datasets/ESC-50-master/meta/esc50.csv'
base_data_path = './datasets/ESC-50-master/audio/'

pd_data = pd.read_csv(esc50_csv)
pd_data.head()

my_classes = ['dog', 'cat']
map_class_to_id = {'dog':0, 'cat':1}

filtered_pd = pd_data[pd_data.category.isin(my_classes)]

class_id = filtered_pd['category'].apply(lambda name: map_class_to_id[name])
filtered_pd = filtered_pd.assign(target=class_id)

full_path = filtered_pd['filename'].apply(lambda row: os.path.join(base_data_path, row))
filtered_pd = filtered_pd.assign(filename=full_path)

filtered_pd.head(10)

filenames = filtered_pd['filename']
targets = filtered_pd['target']
folds = filtered_pd['fold']

main_ds = tf.data.Dataset.from_tensor_slices((filenames, targets, folds))
main_ds.element_spec

(TensorSpec(shape=(), dtype=tf.string, name=None),
 TensorSpec(shape=(), dtype=tf.int64, name=None),
 TensorSpec(shape=(), dtype=tf.int64, name=None))

def load_wav_for_map(filename, label, fold):
  return load_wav_16k_mono(filename), label, fold

main_ds = main_ds.map(load_wav_for_map)
main_ds.element_spec

WARNING:tensorflow:Using a while_loop for converting IO>AudioResample cause there is no registered converter for this op.
WARNING:tensorflow:Using a while_loop for converting IO>AudioResample cause there is no registered converter for this op.
(TensorSpec(shape=<unknown>, dtype=tf.float32, name=None),
 TensorSpec(shape=(), dtype=tf.int64, name=None),
 TensorSpec(shape=(), dtype=tf.int64, name=None))

# applies the embedding extraction model to a wav data
def extract_embedding(wav_data, label, fold):
  ''' run YAMNet to extract embedding from the wav data '''
  scores, embeddings, spectrogram = yamnet_model(wav_data)
  num_embeddings = tf.shape(embeddings)[0]
  return (embeddings,
            tf.repeat(label, num_embeddings),
            tf.repeat(fold, num_embeddings))

# extract embedding
main_ds = main_ds.map(extract_embedding).unbatch()
main_ds.element_spec

(TensorSpec(shape=(1024,), dtype=tf.float32, name=None),
 TensorSpec(shape=(), dtype=tf.int64, name=None),
 TensorSpec(shape=(), dtype=tf.int64, name=None))

cached_ds = main_ds.cache()
train_ds = cached_ds.filter(lambda embedding, label, fold: fold < 4)
val_ds = cached_ds.filter(lambda embedding, label, fold: fold == 4)
test_ds = cached_ds.filter(lambda embedding, label, fold: fold == 5)

# remove the folds column now that it's not needed anymore
remove_fold_column = lambda embedding, label, fold: (embedding, label)

train_ds = train_ds.map(remove_fold_column)
val_ds = val_ds.map(remove_fold_column)
test_ds = test_ds.map(remove_fold_column)

train_ds = train_ds.cache().shuffle(1000).batch(32).prefetch(tf.data.AUTOTUNE)
val_ds = val_ds.cache().batch(32).prefetch(tf.data.AUTOTUNE)
test_ds = test_ds.cache().batch(32).prefetch(tf.data.AUTOTUNE)

my_model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(1024), dtype=tf.float32,
                          name='input_embedding'),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(len(my_classes))
], name='my_model')

my_model.summary()

Model: "my_model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense (Dense)               (None, 512)               524800    
                                                                 
 dense_1 (Dense)             (None, 2)                 1026      
                                                                 
=================================================================
Total params: 525,826
Trainable params: 525,826
Non-trainable params: 0
_________________________________________________________________

my_model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                 optimizer="adam",
                 metrics=['accuracy'])

callback = tf.keras.callbacks.EarlyStopping(monitor='loss',
                                            patience=3,
                                            restore_best_weights=True)

history = my_model.fit(train_ds,
                       epochs=20,
                       validation_data=val_ds,
                       callbacks=callback)

Epoch 1/20
15/15 [==============================] - 6s 45ms/step - loss: 0.8358 - accuracy: 0.8542 - val_loss: 0.6317 - val_accuracy: 0.8687
Epoch 2/20
15/15 [==============================] - 0s 4ms/step - loss: 0.4559 - accuracy: 0.8708 - val_loss: 0.8692 - val_accuracy: 0.8687
Epoch 3/20
15/15 [==============================] - 0s 4ms/step - loss: 0.5520 - accuracy: 0.9083 - val_loss: 0.5947 - val_accuracy: 0.8750
Epoch 4/20
15/15 [==============================] - 0s 4ms/step - loss: 0.4196 - accuracy: 0.8979 - val_loss: 0.3989 - val_accuracy: 0.9062
Epoch 5/20
15/15 [==============================] - 0s 4ms/step - loss: 0.6085 - accuracy: 0.9208 - val_loss: 0.2268 - val_accuracy: 0.9125
Epoch 6/20
15/15 [==============================] - 0s 4ms/step - loss: 0.2139 - accuracy: 0.9125 - val_loss: 0.2261 - val_accuracy: 0.8813
Epoch 7/20
15/15 [==============================] - 0s 4ms/step - loss: 0.1438 - accuracy: 0.9229 - val_loss: 0.2437 - val_accuracy: 0.8625
Epoch 8/20
15/15 [==============================] - 0s 4ms/step - loss: 0.1358 - accuracy: 0.9208 - val_loss: 0.3345 - val_accuracy: 0.8687
Epoch 9/20
15/15 [==============================] - 0s 4ms/step - loss: 0.1831 - accuracy: 0.9187 - val_loss: 0.2614 - val_accuracy: 0.9125
Epoch 10/20
15/15 [==============================] - 0s 4ms/step - loss: 0.2672 - accuracy: 0.9062 - val_loss: 0.8230 - val_accuracy: 0.8750
Epoch 11/20
15/15 [==============================] - 0s 4ms/step - loss: 0.3867 - accuracy: 0.9208 - val_loss: 0.3918 - val_accuracy: 0.8750

loss, accuracy = my_model.evaluate(test_ds)

print("Loss: ", loss)
print("Accuracy: ", accuracy)

5/5 [==============================] - 0s 7ms/step - loss: 0.4896 - accuracy: 0.8062
Loss:  0.48958301544189453
Accuracy:  0.8062499761581421

scores, embeddings, spectrogram = yamnet_model(testing_wav_data)
result = my_model(embeddings).numpy()

inferred_class = my_classes[result.mean(axis=0).argmax()]
print(f'The main sound is: {inferred_class}')

The main sound is: cat

class ReduceMeanLayer(tf.keras.layers.Layer):
  def __init__(self, axis=0, **kwargs):
    super(ReduceMeanLayer, self).__init__(**kwargs)
    self.axis = axis

  def call(self, input):
    return tf.math.reduce_mean(input, axis=self.axis)

saved_model_path = './dogs_and_cats_yamnet'

input_segment = tf.keras.layers.Input(shape=(), dtype=tf.float32, name='audio')
embedding_extraction_layer = hub.KerasLayer(yamnet_model_handle,
                                            trainable=False, name='yamnet')
_, embeddings_output, _ = embedding_extraction_layer(input_segment)
serving_outputs = my_model(embeddings_output)
serving_outputs = ReduceMeanLayer(axis=0, name='classifier')(serving_outputs)
serving_model = tf.keras.Model(input_segment, serving_outputs)
serving_model.save(saved_model_path, include_optimizer=False)

WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.
WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
INFO:tensorflow:Assets written to: ./dogs_and_cats_yamnet/assets
INFO:tensorflow:Assets written to: ./dogs_and_cats_yamnet/assets

tf.keras.utils.plot_model(serving_model)

reloaded_model = tf.saved_model.load(saved_model_path)

reloaded_results = reloaded_model(testing_wav_data)
cat_or_dog = my_classes[tf.math.argmax(reloaded_results)]
print(f'The main sound is: {cat_or_dog}')

The main sound is: cat

serving_results = reloaded_model.signatures['serving_default'](testing_wav_data)
cat_or_dog = my_classes[tf.math.argmax(serving_results['classifier'])]
print(f'The main sound is: {cat_or_dog}')

The main sound is: cat

test_pd = filtered_pd.loc[filtered_pd['fold'] == 5]
row = test_pd.sample(1)
filename = row['filename'].item()
print(filename)
waveform = load_wav_16k_mono(filename)
print(f'Waveform values: {waveform}')
_ = plt.plot(waveform)

display.Audio(waveform, rate=16000)

./datasets/ESC-50-master/audio/5-231762-A-0.wav
WARNING:tensorflow:Using a while_loop for converting IO>AudioResample cause there is no registered converter for this op.
WARNING:tensorflow:Using a while_loop for converting IO>AudioResample cause there is no registered converter for this op.
Waveform values: [ 0.0000000e+00  3.3992906e-10 -3.0525005e-10 ...  0.0000000e+00
  0.0000000e+00  0.0000000e+00]

# Run the model, check the output.
scores, embeddings, spectrogram = yamnet_model(waveform)
class_scores = tf.reduce_mean(scores, axis=0)
top_class = tf.math.argmax(class_scores)
inferred_class = class_names[top_class]
top_score = class_scores[top_class]
print(f'[YAMNet] The main sound is: {inferred_class} ({top_score})')

reloaded_results = reloaded_model(waveform)
your_top_class = tf.math.argmax(reloaded_results)
your_inferred_class = my_classes[your_top_class]
class_probabilities = tf.nn.softmax(reloaded_results, axis=-1)
your_top_score = class_probabilities[your_top_class]
print(f'[Your model] The main sound is: {your_inferred_class} ({your_top_score})')

[YAMNet] The main sound is: Silence (0.7000001668930054)
[Your model] The main sound is: dog (0.9935454726219177)