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Set the meta parameters

notebooks/dataset_analysis/CheckDatasetSNR.ipynb

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This notebook computes the average SNR a given Voice Dataset. If the SNR is too low, that might reduce the performance or prevent model to learn. SNR paper can be seen here: https://www.cs.cmu.edu/~robust/Papers/KimSternIS08.pdf

To use this notebook, you need:

python
import os
import glob
import subprocess
import IPython
import soundfile as sf
import numpy as np
from tqdm import tqdm
from multiprocessing import Pool
from matplotlib import pylab as plt
%matplotlib inline
python
# Set the meta parameters
DATA_PATH = "/home/erogol/Data/m-ai-labs/de_DE/by_book/female/eva_k/"
NUM_PROC = 1
CURRENT_PATH = os.getcwd()
python
def compute_file_snr(file_path):
    """ Convert given file to required format with FFMPEG and process with WADA."""
    _, sr = sf.read(file_path)
    new_file = file_path.replace(".wav", "_tmp.wav")
    if sr != 16000:
        command = f'ffmpeg -i "{file_path}" -ac 1 -acodec pcm_s16le -y -ar 16000 "{new_file}"'
    else:
        command = f'cp "{file_path}" "{new_file}"'
    os.system(command)
    command = [f'"{CURRENT_PATH}/WadaSNR/Exe/WADASNR"', f'-i "{new_file}"', f'-t "{CURRENT_PATH}/WadaSNR/Exe/Alpha0.400000.txt"', '-ifmt mswav']
    output = subprocess.check_output(" ".join(command), shell=True)
    try:
        output = float(output.split()[-3].decode("utf-8"))
    except:
        raise RuntimeError(" ".join(command))
    os.system(f'rm "{new_file}"')
    return output, file_path
python
wav_file = "/home/erogol/Data/LJSpeech-1.1/wavs/LJ001-0001.wav"
output = compute_file_snr(wav_file)
python
wav_files = glob.glob(f"{DATA_PATH}/**/*.wav", recursive=True)
print(f" > Number of wav files {len(wav_files)}")
python
if NUM_PROC == 1:
    file_snrs = [None] * len(wav_files) 
    for idx, wav_file in tqdm(enumerate(wav_files)):
        tup = compute_file_snr(wav_file)
        file_snrs[idx] = tup
else:
    with Pool(NUM_PROC) as pool:
        file_snrs = list(tqdm(pool.imap(compute_file_snr, wav_files), total=len(wav_files)))
python
snrs = [tup[0] for tup in file_snrs]

error_idxs = np.where(np.isnan(snrs) == True)[0]
error_files = [wav_files[idx] for idx in error_idxs]

file_snrs = [i for j, i in enumerate(file_snrs) if j not in error_idxs]
file_names = [tup[1] for tup in file_snrs]
snrs = [tup[0] for tup in file_snrs]
file_idxs = np.argsort(snrs)


print(f" > Average SNR of the dataset:{np.mean(snrs)}")
python
def output_snr_with_audio(idx):
    file_idx = file_idxs[idx]
    file_name = file_names[file_idx]
    wav, sr = sf.read(file_name)
    # multi channel to single channel
    if len(wav.shape) == 2:
        wav = wav[:, 0]
    print(f" > {file_name} - snr:{snrs[file_idx]}")
    IPython.display.display(IPython.display.Audio(wav, rate=sr))
python
# find worse SNR files
N = 10  # number of files to fetch
for i in range(N):
    output_snr_with_audio(i)
python
# find best recordings
N = 10  # number of files to fetch
for i in range(N):
    output_snr_with_audio(-i-1)
python
plt.hist(snrs, bins=100)