<jupyter_start><jupyter_text>Visualizing IEMOCAP Data Balance===This notebook tries to get a basic overview of the IEMOCAP dataset in terms of the data balance and distribution<jupyter_code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Read the audio_path_labels.csv file that is created by the label_extraction.py program
df = pd.read_csv('audio_paths_labels.csv')
df
# Get value counts of the different 'ground truth' emotion labels in the data set
df['emotion'].value_counts()
sns.countplot(x='emotion', data=df)
sns.distplot(df['valence'], kde=False, bins=100)
sns.distplot(df['activation'], kde=False, bins=8)
sns.distplot(df['dominance'], kde=False, bins=8)
max(df['valence'])
min(df['valence'])
max(df['activation'])
min(df['activation'])
df[df['emotion']=='ang']['valence'].mean()
df[df['emotion']=='hap']['valence'].mean()
main_categories = ['neu', 'ang', 'fru', 'sad', 'exc', 'hap']<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15756951596292482, 'fraction_numerical': 0.005149330587023687, 'mean_word_length': 8.08411214953271, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '552183', 'license': 'MIT', 'n_tokens_mistral': 345, 'n_tokens_neox': 327, 'n_words': 91, 'path': 'src/preprocessing/Visualizing_IEMOCAP_Labels.ipynb', 'repo_name': 'dem123456789/Speech-Emotion-Recognition-with-Dual-Sequence-LSTM-Architecture', 'size': '971'}