Datasets:
Tasks:
Audio Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
< 1K
Tags:
SER
Speech Emotion Recognition
Speech Emotion Classification
Audio Classification
Audio
Emotion
License:
# REQUIREMENTS | |
# $ pip install scipy | |
# $ pip install "polars[all]" | |
# You may need to install snappy in order to run this script: | |
# $ sudo pacman -S snappy | |
# $ pip install python-snappy | |
import polars as pl | |
import numpy as np | |
from scipy.io.wavfile import write | |
import os | |
n_cores = str(os.cpu_count()) | |
os.environ['OMP_NUM_THREADS'] = n_cores | |
os.environ['MKL_NUM_THREADS'] = n_cores | |
if not os.path.isdir('wavs'): | |
os.makedirs('wavs') | |
columns = ['ytid', 'ytid_seg', 'start', 'end', 'sentiment', 'happiness', 'sadness', 'anger', 'fear', 'disgust', 'surprise'] | |
df = pl.read_parquet('sqe_messai.parquet', columns = columns) | |
df.write_csv('sqe_messai_nowav.csv') | |
print(df) | |
columns2 = ['ytid_seg', 'wav2numpy'] | |
df2 = pl.read_parquet('sqe_messai.parquet', use_pyarrow=False, columns = columns2) | |
def numpy2wav(row): | |
segment = os.path.splitext(os.path.basename(os.path.normpath(row[0])))[0] | |
print('PROCESSED:', segment) | |
write('wavs/' + segment + '.wav', 16000, np.array(row[1])) | |
return segment | |
df2.apply(lambda x: numpy2wav(x)) | |