# 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 # User all available cores # It seems useless in the context of this script n_cores = str(os.cpu_count()) os.environ['OMP_NUM_THREADS'] = n_cores os.environ['MKL_NUM_THREADS'] = n_cores # Define directory to store the samples cwd = os.getcwd() sample_dir = str(cwd) + '/wavs/' # Create the wavs dir if it does not exist if not os.path.isdir('wavs'): os.makedirs('wavs') # All columns from the parquet file except the one with the audio numpy arrays (it is huge) columns = ['ytid', 'ytid_seg', 'start', 'end', 'sentiment', 'happiness', 'sadness', 'anger', 'fear', 'disgust', 'surprise'] # Read the parquet file with polars df = pl.read_parquet('sqe_messai.parquet', columns = columns) # Replace the generic path with the actual path bad_dir = df.row(0)[1].rsplit('/', 1)[0] + '/' df = df.with_columns(pl.col('ytid_seg').str.replace_all(bad_dir, sample_dir)) # Export the csv file (excluding the last column) df.write_csv('sqe_messai_nowav.csv') print(df) # Now we are only interested on the column with the paths and the audio numpy arrays columns2 = ['ytid_seg', 'wav2numpy'] # Read the parquet file with polars (this will take a while) df2 = pl.read_parquet('sqe_messai.parquet', use_pyarrow=False, columns = columns2) # Replace the generic path with the actual path bad_dir = df2.row(0)[0].rsplit('/', 1)[0] + '/' df2 = df2.with_columns(pl.col('ytid_seg').str.replace_all(bad_dir, sample_dir)) # Function to convert the numpy arrays to wav files stored in the wavs folders def numpy2wav(row): segment = os.path.splitext(os.path.basename(os.path.normpath(row[0])))[0] print('PROCESSED:', segment) write(sample_dir + segment + '.wav', 16000, np.array(row[1]).astype(np.int16)) return segment # Apply the function (this will take a while) df2.apply(lambda x: numpy2wav(x))