def plot_audio_visualizations(index=0):
# plot audio visualizations
vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path = vis_train_features(
index=index)
plot_spectrogram_feature(vis_spectrogram_feature)
plot_mfcc_feature(vis_mfcc_feature)
plot_raw_audio(vis_raw_audio)
# For this project, you won't use the raw audio waveform as input to your model. Instead, we provide code that first performs a pre-processing step to convert the raw audio to a feature representation that has historically proven successful for ASR models. Your acoustic model will accept the feature representation as input.
#
# In this project, you will explore two possible feature representations. _After completing the project_, if you'd like to read more about deep learning architectures that can accept raw audio input, you are encouraged to explore this [research paper](https://pdfs.semanticscholar.org/a566/cd4a8623d661a4931814d9dffc72ecbf63c4.pdf).
#
# ### Spectrograms
#
# The first option for an audio feature representation is the [spectrogram](https://www.youtube.com/watch?v=_FatxGN3vAM). In order to complete this project, you will **not** need to dig deeply into the details of how a spectrogram is calculated; but, if you are curious, the code for calculating the spectrogram was borrowed from [this repository](https://github.com/baidu-research/ba-dls-deepspeech). The implementation appears in the `utils.py` file in your repository.
#
# The code that we give you returns the spectrogram as a 2D tensor, where the first (_vertical_) dimension indexes time, and the second (_horizontal_) dimension indexes frequency. To speed the convergence of your algorithm, we have also normalized the spectrogram. (You can see this quickly in the visualization below by noting that the mean value hovers around zero, and most entries in the tensor assume values close to zero.)
# In[3]:
from data_generator import plot_spectrogram_feature
# plot normalized spectrogram
plot_spectrogram_feature(vis_spectrogram_feature)
# print shape of spectrogram
display(
Markdown('**Shape of Spectrogram** : ' +
str(vis_spectrogram_feature.shape)))
# ### Mel-Frequency Cepstral Coefficients (MFCCs)
#
# The second option for an audio feature representation is [MFCCs](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). You do **not** need to dig deeply into the details of how MFCCs are calculated, but if you would like more information, you are welcome to peruse the [documentation](https://github.com/jameslyons/python_speech_features) of the `python_speech_features` Python package. Just as with the spectrogram features, the MFCCs are normalized in the supplied code.
#
# The main idea behind MFCC features is the same as spectrogram features: at each time window, the MFCC feature yields a feature vector that characterizes the sound within the window. Note that the MFCC feature is much lower-dimensional than the spectrogram feature, which could help an acoustic model to avoid overfitting to the training dataset.
# In[4]:
from data_generator import plot_mfcc_feature