#
# We begin by investigating the dataset that will be used to train and evaluate your pipeline. [LibriSpeech](http://www.danielpovey.com/files/2015_icassp_librispeech.pdf) is a large corpus of English-read speech, designed for training and evaluating models for ASR. The dataset contains 1000 hours of speech derived from audiobooks. We will work with a small subset in this project, since larger-scale data would take a long while to train. However, after completing this project, if you are interested in exploring further, you are encouraged to work with more of the data that is provided [online](http://www.openslr.org/12/).
#
# In the code cells below, you will use the `vis_train_features` module to visualize a training example. The supplied argument `index=0` tells the module to extract the first example in the training set. (You are welcome to change `index=0` to point to a different training example, if you like, but please **DO NOT** amend any other code in the cell.) The returned variables are:
# - `vis_text` - transcribed text (label) for the training example.
# - `vis_raw_audio` - raw audio waveform for the training example.
# - `vis_mfcc_feature` - mel-frequency cepstral coefficients (MFCCs) for the training example.
# - `vis_spectrogram_feature` - spectrogram for the training example.
# - `vis_audio_path` - the file path to the training example.
# In[1]:
from data_generator import vis_train_features
# extract label and audio features for a single training example
vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path = vis_train_features(
)
# The following code cell visualizes the audio waveform for your chosen example, along with the corresponding transcript. You also have the option to play the audio in the notebook!
# In[23]:
from IPython.display import Markdown, display
from data_generator import vis_train_features, plot_raw_audio
from IPython.display import Audio
get_ipython().run_line_magic('matplotlib', 'inline')
# plot audio signal
plot_raw_audio(vis_raw_audio)
# print length of audio signal
display(Markdown('**Shape of Audio Signal** : ' + str(vis_raw_audio.shape)))
# print transcript corresponding to audio clip
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)
def load_data():
vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path = vis_train_features(
)
return vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path