10% Memory error; i need your help!

[user517]

I have 8+26 gb memory, 90% free… so there are 30GB free RAM memory… and this happens…
Same:

Tensorflow Allocation Memory: Allocation of 38535168 exceeds 10% of system memory

I did everything they suggest, … can anybody reproduce this=?

Same as:

import h5py
import os
import librosa
import wave
import numpy as np
import tensorflow as tf
import scipy
import numpy
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Conv3D, MaxPooling3D, Flatten, Dense
import subprocess
import mel_features
import math


import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from librosa import display
from tensorflow import keras
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'





def samples_to_numpy(file_path):
    sr, wav_file = scipy.io.wavfile.read(file_path)#capable of float32
    one_array = numpy.full((len(wav_file)), 0, dtype=np.int8)
    two_array = numpy.full((len(wav_file)), 0, dtype=np.int8)
    three_array = numpy.full((len(wav_file)), 0, dtype=np.int8)

    for i in range(len(wav_file)):
        float_32 = wav_file[i]
        #the trick
        one = np.int8(0)
        two = np.int8(0)
        three = np.int8(0)
        if float_32 > 0 or float_32 == 0:
            while (float_32 > 1) and (one < 128):
                one += np.int8(1) # weight 1
                float_32 -= 1
            while (float_32 > (1/128) and (two < 128)):
                two += np.int8(1) #weight 1/128
                float_32 -= (1/128)
            while (float_32 > (1/16384) and (three < 128)):
                three += np.int8(1) # weight 1/16384
                float_32 -= 1/16384
        elif float_32 < 0:
            while (float_32 < (-1) and (one > (-127))):
                one -= np.int8(1)#weight 1
                float_32 += 1
            while (float_32 < (-(1/128)) and (two > (-127))):
                two -= np.int8(1) #weight 1/128
                float_32 += (1/128)
            while (float_32 < (-(1/16384)) and (three > (-127))):
                three -= np.int8(1)#weight  1/16384
                float_32 += 1/16384
        one_array[i] = one
        two_array[i] = two
        three_array[i] = three
    return one_array, two_array, three_array

def normalize(samples):
#min/max
    j =  999
    k = -999
    for i in samples:
        if i < j:
            j = i
        if i > k:
            k = i
    if j >= k:
        print('ERROR, invalid bounds. Cannot Normalize!!!')
    print('what is wrong? +++')
    if k == 0 and j == 0:
        return
    factor1 = 128/k
    factor2 = (-127)/j
    normal = []
    if factor1 > factor2 and factor2 != 0:
        for i in samples:
            integer1 = round(i*factor2)
            normal.append(int(integer1))
    elif factor1 != 0:
        for i in samples:
            integer1 = round(i*factor1)
            normal.append(int(integer1))
    else:
        return

    norm = np.asarray(normal, dtype=np.int8)
    return norm
def compute_spectrogram(audio_samples, audio_sample_rate_hz):
    """Compute log-mel spectrogram and scale it to uint8."""
    samples = audio_samples.flatten() / float(2**15)
    #samples = normalize(audio_samples)
    spectrogram = 30 * (
        mel_features.log_mel_spectrogram(
            audio_samples,
            audio_sample_rate_hz,
            log_offset=0.001,
            window_length_secs=0.025,
            hop_length_secs=0.010,
            num_mel_bins=256,
            lower_edge_hertz=10,
            upper_edge_hertz=1000) - np.log(1e-3))
    return spectrogram


def load_and_preprocess_data(data_dir, classes, target_shape=(128, 128)):
    data = []
    labels = []
    for i, class_name in enumerate(classes):
        class_dir = os.path.join(data_dir, class_name)
        for filename in os.listdir(class_dir):
            if filename.endswith('.wav'):
                file_path = os.path.join(class_dir, filename)
                audio_data, sample_rate = librosa.load(file_path, sr=None)
                # Perform preprocessing (e.g., convert to Mel spectrogram and resize)
                mel_spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sample_rate)
                mel_spectrogram = resize(np.expand_dims(mel_spectrogram, axis=-1), target_shape)
                data.append(mel_spectrogram)
                labels.append(i)
    return np.array(data), np.array(labels)

def train2():
    feature_list = []
    label_list = []
    file = 'final0.wav'
    audio_path = os.path.join('/home/audio/', file)
    audio, sr = librosa.load(path=audio_path, sr=None)
    audio = librosa.effects.time_stretch(y=audio, rate=len(audio)/sr)
    # Calculate features and get the label from the filename
    mels = librosa.feature.melspectrogram(y=audio, sr=sr, n_fft=2048, hop_length=512)
    mels_db = librosa.power_to_db(S=mels, ref=1.0)
    feature_list.append(mels_db.reshape((256, 256, 1)))
    label_list.append(file[0])
    features = np.array(feature_list)
    labels = np.array(label_list)

    model = keras.Sequential(layers=[
            keras.layers.InputLayer(input_shape=features[0].shape),
            keras.layers.Conv2D(16, 3, padding='same', activation=keras.activations.relu),
            keras.layers.MaxPooling2D(),
            keras.layers.Conv2D(32, 3, padding='same', activation=keras.activations.relu),
            keras.layers.MaxPooling2D(),
            keras.layers.Flatten(),
            keras.layers.Dropout(0.3),
            keras.layers.Dense(64, activation=keras.activations.relu),
            keras.layers.Dense(10, activation=keras.activations.softmax)
        ])
    model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])
    print(model.summary())


    # Set parameters for data splitting and training
    TEST_SIZE = 0.3
    BATCH_SIZE = 64
    EPOCHS = 50

    # Encode Labels
    encoded_labels = tf.one_hot(indices=labels, depth=10)
    # Split dataset to train and test data
    X_train, X_test, y_train, y_test = train_test_split(features, encoded_labels.numpy(), test_size=TEST_SIZE)

    # Train the model
    early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
    history = model.fit(x=X_train, y=y_train, validation_split=TEST_SIZE, epochs=EPOCHS, batch_size=BATCH_SIZE, callbacks=[early_stopping])

    # Plot the training history
    fig, axs = plt.subplots(2)
    fig.set_size_inches(12, 8)
    fig.suptitle('Training History', fontsize=16)
    axs[0].plot(history.epoch, history.history['loss'], history.history['val_loss'])
    axs[0].set(title='Loss', xlabel='Epoch', ylabel='Loss')
    axs[0].legend(['loss', 'val_loss'])
    axs[1].plot(history.epoch, history.history['accuracy'], history.history['val_accuracy'])
    axs[1].set(title='Accuracy', xlabel='Epoch', ylabel='Accuracy')
    axs[1].legend(['accuracy', 'val_accuracy'])
    plt.show()







def train():
    #Split data into training and testing sets
    #data, labels = load_and_preprocess_data(data_dir, classes)
    no_epochs = 30
    learning_rate = 0.001
    no_classes = 10
    verbosity = 1
    batch_size = 1

    data1, data2, data3 = samples_to_numpy('/home/final0.wav')
    mel1 = compute_spectrogram(audio_samples=data1,audio_sample_rate_hz=192000)
    mel2 = compute_spectrogram(audio_samples=data2,audio_sample_rate_hz=192000)
    mel3 = compute_spectrogram(audio_samples=data3,audio_sample_rate_hz=192000)
    # Create a neural network model
    # Create the model)


    y = np.zeros(len(mel1))
    x = np.array(mel1,np.uint8)
    x = x.reshape(len(mel1), 256, -1 )
    for i in range(len(mel1)):
        y[i] = i

    model = Sequential(layers=[
            keras.layers.InputLayer(input_shape=x.shape),
            keras.layers.Conv2D(16,3, padding='same', activation=keras.activations.relu),
            keras.layers.MaxPooling2D(),
            keras.layers.Conv2D(32,3, padding='same', activation=keras.activations.relu),
            keras.layers.MaxPooling2D(),
            keras.layers.Flatten(),
            keras.layers.Dropout(0.3),
            keras.layers.Dense(64, activation=keras.activations.relu),
            keras.layers.Dense(10, activation=keras.activations.softmax)
        ])
    print(mel1)
    model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.categorical_crossentropy,experimental_run_tf_function = False, metrics=['accuracy'])
    history = model.fit(x,y, batch_size=batch_size, epochs=no_epochs, verbose=verbosity)
    model.save('/home/model_full.h5')
    model = tf.keras.models.load_model('/home/model_full.h5', compile=False)
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    tflite_model = converter.convert()
    with tf.io.gfile.GFile('/home/model.tflite', 'wb') as f:
        f.write(tflite_model)
def test_audio(file_path, model):
    # Load and preprocess the audio file
    audio_data, sample_rate = librosa.load(file_path, sr=None)
    mel_spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sample_rate)
    mel_spectrogram = resize(np.expand_dims(mel_spectrogram, axis=-1), target_shape)
    mel_spectrogram = tf.reshape(mel_spectrogram, (1,) + target_shape + (1,))

    # Make predictions
    predictions = model.predict(mel_spectrogram)

    # Get the class probabilities
    class_probabilities = predictions[0]

    # Get the predicted class index
    predicted_class_index = np.argmax(class_probabilities)
    return class_probabilities, predicted_class_index

# Test an audio file
#test_audio_file = 'dog_barking_4.wav'
#class_probabilities, predicted_class_index = test_audio(test_audio_file, model)

# Display results for all classes
def display():
    for i, class_label in enumerate(classes):
        probability = class_probabilities[i]
        print(f'Class: {class_label}, Probability: {probability:.4f}')

# Calculate and display the predicted class and accuracy
    predicted_class = classes[predicted_class_index]
    accuracy = class_probabilities[predicted_class_index]
    print(f'The audio is classified as: {predicted_class}')
    print(f'Accuracy: {accuracy:.4f}')


if __name__=="__main__":
    train()
    subprocess.run(["edgetpu_compiler", "/home/model.tflite"])

[quote="user517, post:1, topic:49620, full:true"]
'''
import h5py
import os
import librosa
import wave
import numpy as np
import tensorflow as tf
import scipy
import numpy
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Conv3D, MaxPooling3D, Flatten, Dense
import subprocess
import mel_features
import math


import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from librosa import display
from tensorflow import keras
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'





def samples_to_numpy(file_path):
    sr, wav_file = scipy.io.wavfile.read(file_path)#capable of float32
    one_array = numpy.full((len(wav_file)), 0, dtype=np.int8)
    two_array = numpy.full((len(wav_file)), 0, dtype=np.int8)
    three_array = numpy.full((len(wav_file)), 0, dtype=np.int8)

    for i in range(len(wav_file)):
        float_32 = wav_file[i]
        #the trick
        one = np.int8(0)
        two = np.int8(0)
        three = np.int8(0)
        if float_32 > 0 or float_32 == 0:
            while (float_32 > 1) and (one < 128):
                one += np.int8(1) # weight 1
                float_32 -= 1
            while (float_32 > (1/128) and (two < 128)):
                two += np.int8(1) #weight 1/128
                float_32 -= (1/128)
            while (float_32 > (1/16384) and (three < 128)):
                three += np.int8(1) # weight 1/16384
                float_32 -= 1/16384
        elif float_32 < 0:
            while (float_32 < (-1) and (one > (-127))):
                one -= np.int8(1)#weight 1
                float_32 += 1
            while (float_32 < (-(1/128)) and (two > (-127))):
                two -= np.int8(1) #weight 1/128
                float_32 += (1/128)
            while (float_32 < (-(1/16384)) and (three > (-127))):
                three -= np.int8(1)#weight  1/16384
                float_32 += 1/16384
        one_array[i] = one
        two_array[i] = two
        three_array[i] = three
    return one_array, two_array, three_array

def normalize(samples):
#min/max
    j =  999
    k = -999
    for i in samples:
        if i < j:
            j = i
        if i > k:
            k = i
    if j >= k:
        print('ERROR, invalid bounds. Cannot Normalize!!!')
    print('what is wrong? +++')
    if k == 0 and j == 0:
        return
    factor1 = 128/k
    factor2 = (-127)/j
    normal = []
    if factor1 > factor2 and factor2 != 0:
        for i in samples:
            integer1 = round(i*factor2)
            normal.append(int(integer1))
    elif factor1 != 0:
        for i in samples:
            integer1 = round(i*factor1)
            normal.append(int(integer1))
    else:
        return

    norm = np.asarray(normal, dtype=np.int8)
    return norm
def compute_spectrogram(audio_samples, audio_sample_rate_hz):
    """Compute log-mel spectrogram and scale it to uint8."""
    samples = audio_samples.flatten() / float(2**15)
    #samples = normalize(audio_samples)
    spectrogram = 30 * (
        mel_features.log_mel_spectrogram(
            audio_samples,
            audio_sample_rate_hz,
            log_offset=0.001,
            window_length_secs=0.025,
            hop_length_secs=0.010,
            num_mel_bins=256,
            lower_edge_hertz=10,
            upper_edge_hertz=1000) - np.log(1e-3))
    return spectrogram


def load_and_preprocess_data(data_dir, classes, target_shape=(128, 128)):
    data = []
    labels = []
    for i, class_name in enumerate(classes):
        class_dir = os.path.join(data_dir, class_name)
        for filename in os.listdir(class_dir):
            if filename.endswith('.wav'):
                file_path = os.path.join(class_dir, filename)
                audio_data, sample_rate = librosa.load(file_path, sr=None)
                # Perform preprocessing (e.g., convert to Mel spectrogram and resize)
                mel_spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sample_rate)
                mel_spectrogram = resize(np.expand_dims(mel_spectrogram, axis=-1), target_shape)
                data.append(mel_spectrogram)
                labels.append(i)
    return np.array(data), np.array(labels)

def train2():
    feature_list = []
    label_list = []
    file = 'final0.wav'
    audio_path = os.path.join('/home/audio/', file)
    audio, sr = librosa.load(path=audio_path, sr=None)
    audio = librosa.effects.time_stretch(y=audio, rate=len(audio)/sr)
    # Calculate features and get the label from the filename
    mels = librosa.feature.melspectrogram(y=audio, sr=sr, n_fft=2048, hop_length=512)
    mels_db = librosa.power_to_db(S=mels, ref=1.0)
    feature_list.append(mels_db.reshape((256, 256, 1)))
    label_list.append(file[0])
    features = np.array(feature_list)
    labels = np.array(label_list)

    model = keras.Sequential(layers=[
            keras.layers.InputLayer(input_shape=features[0].shape),
            keras.layers.Conv2D(16, 3, padding='same', activation=keras.activations.relu),
            keras.layers.MaxPooling2D(),
            keras.layers.Conv2D(32, 3, padding='same', activation=keras.activations.relu),
            keras.layers.MaxPooling2D(),
            keras.layers.Flatten(),
            keras.layers.Dropout(0.3),
            keras.layers.Dense(64, activation=keras.activations.relu),
            keras.layers.Dense(10, activation=keras.activations.softmax)
        ])
    model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])
    print(model.summary())


    # Set parameters for data splitting and training
    TEST_SIZE = 0.3
    BATCH_SIZE = 64
    EPOCHS = 50

    # Encode Labels
    encoded_labels = tf.one_hot(indices=labels, depth=10)
    # Split dataset to train and test data
    X_train, X_test, y_train, y_test = train_test_split(features, encoded_labels.numpy(), test_size=TEST_SIZE)

    # Train the model
    early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
    history = model.fit(x=X_train, y=y_train, validation_split=TEST_SIZE, epochs=EPOCHS, batch_size=BATCH_SIZE, callbacks=[early_stopping])

    # Plot the training history
    fig, axs = plt.subplots(2)
    fig.set_size_inches(12, 8)
    fig.suptitle('Training History', fontsize=16)
    axs[0].plot(history.epoch, history.history['loss'], history.history['val_loss'])
    axs[0].set(title='Loss', xlabel='Epoch', ylabel='Loss')
    axs[0].legend(['loss', 'val_loss'])
    axs[1].plot(history.epoch, history.history['accuracy'], history.history['val_accuracy'])
    axs[1].set(title='Accuracy', xlabel='Epoch', ylabel='Accuracy')
    axs[1].legend(['accuracy', 'val_accuracy'])
    plt.show()







def train():
    #Split data into training and testing sets
    #data, labels = load_and_preprocess_data(data_dir, classes)
    no_epochs = 30
    learning_rate = 0.001
    no_classes = 10
    verbosity = 1
    batch_size = 1

    data1, data2, data3 = samples_to_numpy('/home/final0.wav')
    mel1 = compute_spectrogram(audio_samples=data1,audio_sample_rate_hz=192000)
    mel2 = compute_spectrogram(audio_samples=data2,audio_sample_rate_hz=192000)
    mel3 = compute_spectrogram(audio_samples=data3,audio_sample_rate_hz=192000)
    # Create a neural network model
    # Create the model)


    y = np.zeros(len(mel1))
    x = np.array(mel1,np.uint8)
    x = x.reshape(len(mel1), 256, -1 )
    for i in range(len(mel1)):
        y[i] = i

    model = Sequential(layers=[
            keras.layers.InputLayer(input_shape=x.shape),
            keras.layers.Conv2D(16,3, padding='same', activation=keras.activations.relu),
            keras.layers.MaxPooling2D(),
            keras.layers.Conv2D(32,3, padding='same', activation=keras.activations.relu),
            keras.layers.MaxPooling2D(),
            keras.layers.Flatten(),
            keras.layers.Dropout(0.3),
            keras.layers.Dense(64, activation=keras.activations.relu),
            keras.layers.Dense(10, activation=keras.activations.softmax)
        ])
    print(mel1)
    model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.categorical_crossentropy,experimental_run_tf_function = False, metrics=['accuracy'])
    history = model.fit(x,y, batch_size=batch_size, epochs=no_epochs, verbose=verbosity)
    model.save('/home/model_full.h5')
    model = tf.keras.models.load_model('/home/model_full.h5', compile=False)
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    tflite_model = converter.convert()
    with tf.io.gfile.GFile('/home/model.tflite', 'wb') as f:
        f.write(tflite_model)
def test_audio(file_path, model):
    # Load and preprocess the audio file
    audio_data, sample_rate = librosa.load(file_path, sr=None)
    mel_spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sample_rate)
    mel_spectrogram = resize(np.expand_dims(mel_spectrogram, axis=-1), target_shape)
    mel_spectrogram = tf.reshape(mel_spectrogram, (1,) + target_shape + (1,))

    # Make predictions
    predictions = model.predict(mel_spectrogram)

    # Get the class probabilities
    class_probabilities = predictions[0]

    # Get the predicted class index
    predicted_class_index = np.argmax(class_probabilities)
    return class_probabilities, predicted_class_index

# Test an audio file
#test_audio_file = 'dog_barking_4.wav'
#class_probabilities, predicted_class_index = test_audio(test_audio_file, model)

# Display results for all classes
def display():
    for i, class_label in enumerate(classes):
        probability = class_probabilities[i]
        print(f'Class: {class_label}, Probability: {probability:.4f}')

# Calculate and display the predicted class and accuracy
    predicted_class = classes[predicted_class_index]
    accuracy = class_probabilities[predicted_class_index]
    print(f'The audio is classified as: {predicted_class}')
    print(f'Accuracy: {accuracy:.4f}')


if __name__=="__main__":
    train()
    subprocess.run(["edgetpu_compiler", "/home/model.tflite"])

Same Error:

import h5py
import keras
import os
import librosa
import wave
import numpy as np
import tensorflow as tf
import scipy
import numpy
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Conv3D, MaxPooling3D, Flatten, Dense
import subprocess
import mel_features

def samples_to_numpy(file_path):
    sr, wav_file = scipy.io.wavfile.read(file_path)#capable of float32
    one_array = numpy.full((len(wav_file)), 0, dtype=np.int8)
    two_array = numpy.full((len(wav_file)), 0, dtype=np.int8)
    three_array = numpy.full((len(wav_file)), 0, dtype=np.int8)

    for i in range(len(wav_file)):
        float_32 = wav_file[i]
        #the trick
        one = np.int8(0)
        two = np.int8(0)
        three = np.int8(0)
        if float_32 > 0 or float_32 == 0:
            while (float_32 > 1) and (one < 128):
                one += np.int8(1) # weight 1
                float_32 -= 1
            while (float_32 > (1/128) and (two < 128)):
                two += np.int8(1) #weight 1/128
                float_32 -= (1/128)
            while (float_32 > (1/16384) and (three < 128)):
                three += np.int8(1) # weight 1/16384
                float_32 -= 1/16384
        elif float_32 < 0:
            while (float_32 < (-1) and (one > (-127))):
                one -= np.int8(1)#weight 1
                float_32 += 1
            while (float_32 < (-(1/128)) and (two > (-127))):
                two -= np.int8(1) #weight 1/128
                float_32 += (1/128)
            while (float_32 < (-(1/16384)) and (three > (-127))):
                three -= np.int8(1)#weight  1/16384
                float_32 += 1/16384
        one_array[i] = one
        two_array[i] = two
        three_array[i] = three
    return one_array, two_array, three_array


def compute_spectrogram(audio_samples, audio_sample_rate_hz):
    """Compute log-mel spectrogram and scale it to uint8."""
    #samples = audio_samples.flatten() / float(2**15)
    spectrogram = 30 * (
        mel_features.log_mel_spectrogram(
            audio_samples,
            audio_sample_rate_hz,
            log_offset=0.001,
            window_length_secs=0.025,
            hop_length_secs=0.010,
            num_mel_bins=32,
            lower_edge_hertz=10,
            upper_edge_hertz=1000) - np.log(1e-3))
    return spectrogram


def load_and_preprocess_data(data_dir, classes, target_shape=(128, 128)):
    data = []
    labels = []
    for i, class_name in enumerate(classes):
        class_dir = os.path.join(data_dir, class_name)
        for filename in os.listdir(class_dir):
            if filename.endswith('.wav'):
                file_path = os.path.join(class_dir, filename)
                audio_data, sample_rate = librosa.load(file_path, sr=None)
                # Perform preprocessing (e.g., convert to Mel spectrogram and resize)
                mel_spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sample_rate)
                mel_spectrogram = resize(np.expand_dims(mel_spectrogram, axis=-1), target_shape)
                data.append(mel_spectrogram)
                labels.append(i)
    return np.array(data), np.array(labels)

def train():
    #Split data into training and testing sets
    #data, labels = load_and_preprocess_data(data_dir, classes)
    no_epochs = 30
    learning_rate = 0.001
    no_classes = 10
    verbosity = 1
    batch_size=100

    data1, data2, data3 = samples_to_numpy('/home/sound1.wav')
    mel1 = compute_spectrogram(audio_samples=data1,audio_sample_rate_hz=192000)
    mel2 = compute_spectrogram(audio_samples=data2,audio_sample_rate_hz=192000)
    mel3 = compute_spectrogram(audio_samples=data3,audio_sample_rate_hz=192000)
    sample_shape = (len(data1),len(data2),len(data3), 3)
    # Create a neural network model
    # Create the model
    model = Sequential()
    model.add(Conv3D(12, kernel_size=(3, 3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=sample_shape))
    model.add(MaxPooling3D(pool_size=(2, 2, 2)))
    model.compile(loss=tf.losses.categorical_crossentropy,
              optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
              metrics=['accuracy'])
    #dataset = tf.data.Dataset.from_tensor_slices([mel1,mel2,mel3])
    line = np.zeros((len(data1)))
    for i in range(len(data1)):
        line[i] = i
    xarrays = np.zeros((len(line,))*3)
    xarrays.reshape((3,len(line))).transpose()
    xarrays = numpy.insert(xarrays, 0, data1, axis=0)
    xarrays = numpy.insert(xarrays, 1, data2, axis=0)
    xarrays = numpy.insert(xarrays, 2, data3, axis=0)
    yarrays = np.zeros((len(line,))*3)
    yarrays.reshape((3,len(line))).transpose()
    yarrays = numpy.insert(yarrays, 0, line, axis=0)
    yarrays = numpy.insert(yarrays, 1, line, axis=0)
    yarrays = numpy.insert(yarrays, 2, line, axis=0)
    x_3d = xarrays
    y_3d = yarrays
    model.fit(x_3d, y_3d, batch_size=batch_size, epochs=no_epochs, verbose=verbosity)
    model.save('/home/model_full.h5')
    model = tf.keras.models.load_model('/home/model_full.h5', compile=False)
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    tflite_model = converter.convert()
    with tf.io.gfile.GFile('/home/model.tflite', 'wb') as f:
        f.write(tflite_model)
def test_audio(file_path, model):
    # Load and preprocess the audio file
    audio_data, sample_rate = librosa.load(file_path, sr=None)
    mel_spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sample_rate)
    mel_spectrogram = resize(np.expand_dims(mel_spectrogram, axis=-1), target_shape)
    mel_spectrogram = tf.reshape(mel_spectrogram, (1,) + target_shape + (1,))

    # Make predictions
    predictions = model.predict(mel_spectrogram)

    # Get the class probabilities
    class_probabilities = predictions[0]

    # Get the predicted class index
    predicted_class_index = np.argmax(class_probabilities)
    return class_probabilities, predicted_class_index

# Test an audio file
#test_audio_file = 'dog_barking_4.wav'
#class_probabilities, predicted_class_index = test_audio(test_audio_file, model)

# Display results for all classes
def display():
    for i, class_label in enumerate(classes):
        probability = class_probabilities[i]
        print(f'Class: {class_label}, Probability: {probability:.4f}')

# Calculate and display the predicted class and accuracy
    predicted_class = classes[predicted_class_index]
    accuracy = class_probabilities[predicted_class_index]
    print(f'The audio is classified as: {predicted_class}')
    print(f'Accuracy: {accuracy:.4f}')


if __name__=="__main__":
    train()
    subprocess.run(["edgetpu_compiler", "/home/model.tflite"])

[Kiran_Sai_Ramineni]

Hi @user517, Could you please try by manually enabling the GPU memory growth by using

gpus = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)

and let us know if you are facing the same issue or not. Thank You.

[user517]

ok, i do not have a cuda gpu – it is a simple laptop.

another issue is
unable to open file: libtensorflow_io.so

The memory issue, is about my laptops limitations…i assume…

2024-11-25 15:41:41.887846: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2024-11-25 15:41:43.112022: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2024-11-25 15:41:43.117004: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-11-25 15:41:45.340576: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
/usr/local/lib/python3.11/dist-packages/requests/__init__.py:86: RequestsDependencyWarning: Unable to find acceptable character detection dependency (chardet or charset_normalizer).
  warnings.warn(
2024-11-25 15:42:03.460600: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 3931635712 exceeds 10% of free system memory.
2024-11-25 15:42:07.692877: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 3931635712 exceeds 10% of free system memory.
2024-11-25 15:42:51.386053: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 3931635712 exceeds 10% of free system memory.
Traceback (most recent call last):
  File "/home/jon/train.py", line 85, in <module>
    train()
  File "/home/jon/train.py", line 63, in train
    tf.config.experimental.set_memory_growth(gpus[0], True)
                                             ~~~~^^^
IndexError: list index out of range

I just ordered a GPU cluster, so try on NVIDEA high performance cluster…
and let you know, what happens.

[user517]

_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 quantize_layer (QuantizeLay  (None, 998, 256, 1)      3         
 er)                                                             
                                                                 
 quant_conv2d (QuantizeWrapp  (None, 998, 256, 16)     195       
 erV2)                                                           
                                                                 
 quant_max_pooling2d (Quanti  (None, 499, 128, 16)     1         
 zeWrapperV2)                                                    
                                                                 
 quant_conv2d_1 (QuantizeWra  (None, 499, 128, 32)     4707      
 pperV2)                                                         
                                                                 
 quant_max_pooling2d_1 (Quan  (None, 249, 64, 32)      1         
 tizeWrapperV2)                                                  
                                                                 
 quant_flatten (QuantizeWrap  (None, 509952)           1         
 perV2)                                                          
                                                                 
 quant_dropout (QuantizeWrap  (None, 509952)           1         
 perV2)                                                          
                                                                 
 quant_dense (QuantizeWrappe  (None, 64)               32636997  
 rV2)                                                            
                                                                 
 quant_dense_1 (QuantizeWrap  (None, 10)               655       
 perV2)                                                          
                                                                 
=================================================================
Total params: 32,642,561
Trainable params: 32,642,442
Non-trainable params: 119
_________________________________________________________________
WARNING:tensorflow:AutoGraph could not transform <function NumpyIODataset.__init__.<locals>.f at 0x7f9fcc139da0> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: unable to open file: libtensorflow_io.so, from paths: ['/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so']
caused by: ['/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
Traceback (most recent call last):
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py", line 427, in converted_call
    converted_f = _convert_actual(target_entity, program_ctx)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py", line 269, in _convert_actual
    transformed, module, source_map = _TRANSPILER.transform(entity, program_ctx)
                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/pyct/transpiler.py", line 282, in transform
    return self.transform_function(obj, user_context)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/pyct/transpiler.py", line 466, in transform_function
    nodes, ctx = super(PyToPy, self).transform_function(fn, user_context)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/pyct/transpiler.py", line 359, in transform_function
    result = self.transform_ast(node, context)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py", line 240, in transform_ast
    node = directives.transform(node, ctx)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/converters/directives.py", line 177, in transform
    return DirectivesTransformer(ctx).visit(node)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/core/converter.py", line 314, in visit
    return super(Base, self).visit(node)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/pyct/transformer.py", line 441, in visit
    result = super(Base, self).visit(node)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 418, in visit
    return visitor(node)
           ^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 494, in generic_visit
    value = self.visit(value)
            ^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/core/converter.py", line 314, in visit
    return super(Base, self).visit(node)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/pyct/transformer.py", line 441, in visit
    result = super(Base, self).visit(node)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 418, in visit
    return visitor(node)
           ^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 494, in generic_visit
    value = self.visit(value)
            ^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/core/converter.py", line 314, in visit
    return super(Base, self).visit(node)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/pyct/transformer.py", line 441, in visit
    result = super(Base, self).visit(node)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 418, in visit
    return visitor(node)
           ^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 503, in generic_visit
    new_node = self.visit(old_value)
               ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/core/converter.py", line 314, in visit
    return super(Base, self).visit(node)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/pyct/transformer.py", line 441, in visit
    result = super(Base, self).visit(node)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 418, in visit
    return visitor(node)
           ^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 494, in generic_visit
    value = self.visit(value)
            ^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/core/converter.py", line 314, in visit
    return super(Base, self).visit(node)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/pyct/transformer.py", line 441, in visit
    result = super(Base, self).visit(node)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 418, in visit
    return visitor(node)
           ^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 503, in generic_visit
    new_node = self.visit(old_value)
               ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/core/converter.py", line 314, in visit
    return super(Base, self).visit(node)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/pyct/transformer.py", line 441, in visit
    result = super(Base, self).visit(node)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 418, in visit
    return visitor(node)
           ^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 503, in generic_visit
    new_node = self.visit(old_value)
               ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/core/converter.py", line 314, in visit
    return super(Base, self).visit(node)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/pyct/transformer.py", line 441, in visit
    result = super(Base, self).visit(node)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/ast.py", line 418, in visit
    return visitor(node)
           ^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/converters/directives.py", line 126, in visit_Attribute
    if hasattr(parent_val, node.attr):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/__init__.py", line 88, in __getattr__
    return getattr(self._load(), attrb)
                   ^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/__init__.py", line 84, in _load
    self._mod = _load_library(self._library)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/__init__.py", line 69, in _load_library
    raise NotImplementedError(
NotImplementedError: unable to open file: libtensorflow_io.so, from paths: ['/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so']
caused by: ['/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/jon/train.py", line 85, in <module>
    train()
  File "/home/jon/train.py", line 73, in train
    x_2d = tfio.experimental.IODataset.from_numpy(x).batch(2)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/experimental/io_dataset_ops.py", line 158, in from_numpy
    return numpy_dataset_ops.NumpyIODataset(a, internal=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/experimental/numpy_dataset_ops.py", line 68, in __init__
    dataset = dataset.map(f)
              ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/data/ops/dataset_ops.py", line 2240, in map
    return map_op._map_v2(
           ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/data/ops/map_op.py", line 37, in _map_v2
    return _MapDataset(
           ^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/data/ops/map_op.py", line 107, in __init__
    self._map_func = structured_function.StructuredFunctionWrapper(
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/data/ops/structured_function.py", line 261, in __init__
    self._function = fn_factory()
                     ^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py", line 232, in get_concrete_function
    concrete_function = self._get_concrete_function_garbage_collected(
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py", line 202, in _get_concrete_function_garbage_collected
    concrete_function, _ = self._maybe_define_concrete_function(args, kwargs)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py", line 166, in _maybe_define_concrete_function
    return self._maybe_define_function(args, kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py", line 396, in _maybe_define_function
    concrete_function = self._create_concrete_function(
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py", line 300, in _create_concrete_function
    func_graph_module.func_graph_from_py_func(
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/framework/func_graph.py", line 1214, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/data/ops/structured_function.py", line 238, in wrapped_fn
    ret = wrapper_helper(*args)
          ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/data/ops/structured_function.py", line 169, in wrapper_helper
    ret = autograph.tf_convert(self._func, ag_ctx)(*nested_args)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py", line 689, in wrapper
    return converted_call(f, args, kwargs, options=options)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py", line 434, in converted_call
    return _fall_back_unconverted(f, args, kwargs, options, e)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py", line 484, in _fall_back_unconverted
    return _call_unconverted(f, args, kwargs, options)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py", line 458, in _call_unconverted
    return f(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/experimental/numpy_dataset_ops.py", line 47, in f
    [
  File "/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/experimental/numpy_dataset_ops.py", line 48, in <listcomp>
    core_ops.io_numpy_read(
    ^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/__init__.py", line 88, in __getattr__
    return getattr(self._load(), attrb)
                   ^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/__init__.py", line 84, in _load
    self._mod = _load_library(self._library)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/__init__.py", line 69, in _load_library
    raise NotImplementedError(
NotImplementedError: unable to open file: libtensorflow_io.so, from paths: ['/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so']
caused by: ['/usr/local/lib/python3.11/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']