📑 TensorFlow Workout 03. Recognizing Keywords

🌐   tensorflow_03.html

📑 TensorFlow Workout 03

Code Modules

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#!python3 -m pip install tensorflow==2.6.0 \

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#--user --quiet --no-warn-script-location

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path='/home/sc_work/.sage/local/lib/python3.9/site-packages'

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import sys,warnings; sys.path.append(path)

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warnings.filterwarnings('ignore')

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import sys,warnings,os,shutil,zipfile,h5py,urllib

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import pathlib,pylab as pl,seaborn as sb

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import numpy as np,pandas as pd,tensorflow as tf

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from tensorflow.keras.layers.experimental \

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import preprocessing as tkp

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import tensorflow.keras.layers as tkl,\

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tensorflow.keras.models as tkm

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from IPython.display import display,Audio

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if not sys.warnoptions: warnings.simplefilter('ignore')

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rs=int(42); tf.random.set_seed(rs); np.random.seed(rs)

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AUTOTUNE=tf.data.AUTOTUNE

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File Processing

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file_path='http://storage.googleapis.com/'+\

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          'download.tensorflow.org/data/'

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file_name='mini_speech_commands.zip'

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file_dir='mini_speech_commands/'

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input_file=urllib.request.urlopen(file_path+file_name)

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output_file=open(file_name,'wb')

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output_file.write(input_file.read())

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output_file.close(); input_file.close()

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with zipfile.ZipFile(file_name,'r') as z:

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    list_names=\

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    [x for x in z.namelist() 

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     if (file_dir in x) and ('__MACOSX' not in x) \

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     and ('README.md' not in x)]

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    z.close()

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dir_names=[x for x in list_names \

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if ('.wav' not in x) and (len(x)>21)]

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files=[x for x in list_names if ('.wav' in x)]

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names=[x[21:-1] for x in dir_names]

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files=tf.random.shuffle(files)

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num_samples=len(files)

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print('labels: ',names)

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print('number of total examples: ',num_samples)

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print('examples of the tensor array: \n',files[:int(10)].numpy())

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train_files=files[:int(.8*num_samples)]

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valid_files=files[int(.8*num_samples):int(.9*num_samples)]

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test_files=files[-int(.1*num_samples):]

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def get_audio_and_label(file_path):

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    file_path_split=tf.strings.split(file_path,os.path.sep)

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    file_path=file_path.numpy().decode()

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    file=file_path_split[-int(1)].numpy().decode()

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    label=file_path_split[-int(2)]

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    with zipfile.ZipFile(file_name) as z:

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        with open(file,'wb') as f:

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            f.write(z.read(file_path)); f.close()

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        z.close()

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    audio_raw=tf.io.read_file(file)

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    audio,_=tf.audio.decode_wav(audio_raw)

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    audio=tf.squeeze(audio,axis=-int(1))

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    os.remove(file)

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    return audio,label

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def get_spectrogram(

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    audio,num=int(16000),

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    frame_length=int(255),frame_step=int(128)):

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    zeros=tf.zeros([num]-tf.shape(audio),dtype=tf.float32)

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    audio=tf.cast(audio,tf.float32)

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    equal_length=tf.concat([audio,zeros],int(0))

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    # a Fourier transform with time information

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    spectrogram=tf.signal.stft(

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        equal_length,frame_length=\

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        frame_length,frame_step=frame_step)

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    # the magnitude

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    spectrogram=tf.abs(spectrogram)

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    return spectrogram

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def get_spectrogram_and_label_id(audio,label):

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    spectrogram=get_spectrogram(audio)

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    spectrogram=tf.expand_dims(spectrogram,-int(1))

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    label_id=tf.argmax(label==names)

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    return spectrogram,label_id

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Data Representation

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for i in range(3):

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    n=randint(1,1000)

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    audio,label=get_audio_and_label(files[int(i+n)])

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    spectrogram,label_id=\

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    get_spectrogram_and_label_id(audio,label)

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    display(table([('label','shape of audio tensors',

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                    'spectrogram shape'),

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                   (label.numpy().decode('utf-8'),

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                    audio.shape,spectrogram.shape)]))

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    display(Audio(audio,rate=int(16000)))

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rows,cols=int(2),int(4)

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fig,axes=pl.subplots(rows,cols,figsize=(6,3))

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for i in range(rows*cols):

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    audio,label=get_audio_and_label(files[int(i)])

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    ax=axes[i//cols][i%cols]

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    ax.plot(audio.numpy(),color='slategray',alpha=.7)

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    ax.set_yticks(np.arange(-1.2,1.2,.2))

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    label=label.numpy().decode('utf-8')

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    ax.set_title('$\\mathbb{'+label+'}$',

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                 color='slategray',fontsize='large')

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pl.tight_layout(); pl.show()

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def plot_spectrogram(spectrogram,ax):

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    log_spec=np.log(spectrogram.T)

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    height=log_spec.shape[0]

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    width=log_spec.shape[1]

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    x=np.linspace(0,np.size(spectrogram),num=width,dtype=int)

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    y=range(height)

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    ax.pcolormesh(x,y,log_spec,cmap='bone',shading='auto')

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fig,axes=pl.subplots(2,figsize=(6,3))

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timescale=np.arange(audio.shape[0])

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axes[0].plot(timescale,audio.numpy(),alpha=.7,color='slategray')

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axes[0].set_title('$\mathbb{'+'waveform'+'}$',

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                  color='slategray',fontsize='large')

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axes[0].set_xlim([0,16000])

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plot_spectrogram(np.squeeze(spectrogram.numpy()),axes[1])

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axes[1].set_title('$\mathbb{'+'spectrogram'+'}$',

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                  color='slategray',fontsize='large')

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pl.tight_layout(); pl.show()

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rows,cols=int(2),int(5)

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fig,axes=pl.subplots(rows,cols,figsize=(6,3))

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for i in range(rows*cols):

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    audio,label=get_audio_and_label(files[int(i)])

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    spectrogram,label_id=get_spectrogram_and_label_id(audio,label)

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    ax=axes[i//cols][i%cols]

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    plot_spectrogram(np.squeeze(spectrogram.numpy()+.1**10),ax)

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    ax.set_title('$\mathbb{'+names[label_id.numpy()]+'}$',

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                 color='slategray',fontsize='large')

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    ax.axis('off')

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pl.tight_layout(); pl.show()

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Data H5Storing

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def get_arrays(file_list):

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    spectr_arr,label_arr=[],[]

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    for i in range(len(file_list)):

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        audio,label=get_audio_and_label(file_list[int(i)])

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        spectrogram,label_id=\

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        get_spectrogram_and_label_id(audio,label)

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        spectr_arr.append(spectrogram.numpy())

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        label_arr.append(label_id.numpy())

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    return np.array(spectr_arr),np.array(label_arr,dtype='int32')

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spectr_train,label_train=get_arrays(train_files[:int(200)])

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spectr_valid,label_valid=get_arrays(valid_files[:int(20)])

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spectr_test,label_test=get_arrays(test_files[:int(20)])

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Messages

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print(' train: \n',

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    spectr_train.shape,spectr_train.dtype,

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    label_train.shape,label_train.dtype,'\n valid: \n',

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    spectr_valid.shape,spectr_valid.dtype,

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    label_valid.shape,label_valid.dtype,'\n test: \n',

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    spectr_test.shape,spectr_test.dtype,

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    label_test.shape,label_test.dtype)

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Messages

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rows,cols=int(2),int(5)

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fig,axes=pl.subplots(rows,cols,figsize=(6,3))

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for i in range(rows*cols):

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    ax=axes[i//cols][i%cols]

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    plot_spectrogram(np.squeeze(spectr_test[i]+.1**10),ax)

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    ax.set_title('$\mathbb{'+names[label_test[i]]+'}$',

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                 color='slategray',fontsize='xx-large')

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    ax.axis('off')

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pl.show()

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h5f='SpectrCommands124129.h5'

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with h5py.File(h5f,'w') as f:

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    f.create_dataset('spectr_train',data=spectr_train,

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                     compression='gzip')

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    f.create_dataset('label_train',data=label_train,

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                     compression='gzip')

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    f.create_dataset('spectr_valid',data=spectr_valid,

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                     compression='gzip')

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    f.create_dataset('label_valid',data=label_valid,

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                     compression='gzip')

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    f.create_dataset('spectr_test',data=spectr_test,

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                     compression='gzip')

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    f.create_dataset('label_test',data=label_test,

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                     compression='gzip')

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    f.close()

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pretty_print('file size: %s'%list(os.stat(h5f))[int(6)])

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