value-investors-tts/voice-training-sets/berkshire-meetings/1994/noise_cleanup.py

from pathlib import Path

import os
import subprocess
import soundfile as sf
import pyloudnorm as pyln
import sys

homedir = os.environ['HOME']

src = sys.argv[1]
rnn = f"{homedir}/src/rnnoise/examples/rnnoise_demo"

paths = Path(src).glob("**/*.wav")

for filepath in paths:
    target_filepath=Path(str(filepath).replace("original", "wavs"))
    target_dir=os.path.dirname(target_filepath)

    if (str(filepath) == str(target_filepath)):
        raise ValueError("Source and target path are identical: " + str(target_filepath))
    if (os.path.exists(target_filepath)):
        print(f"Output already exists, skipping {target_filepath}")

    print("From: " + str(filepath))
    print("To: " + str(target_filepath))

    # Stereo to Mono; upsample to 48000Hz
    subprocess.run(["sox", filepath, "48k.wav", "remix", "-", "rate", "48000"])
    subprocess.run(["sox", "48k.wav", "-c", "1", "-r", "48000", "-b", "16", "-e", "signed-integer", "-t", "raw", "temp.raw"]) # convert wav to raw
    subprocess.run([rnn, "temp.raw", "rnn.raw"]) # apply rnnoise
    subprocess.run(["sox", "-r", "48k", "-b", "16", "-e", "signed-integer", "rnn.raw", "-t", "wav", "rnn.wav"]) # convert raw back to wav

    subprocess.run(["mkdir", "-p", str(target_dir)])
    subprocess.run(["sox", "rnn.wav", str(target_filepath), "remix", "-", "highpass", "100", "lowpass", "7000", "rate", "22050"]) # apply high/low pass filter and change sr to 22050Hz

    data, rate = sf.read(target_filepath)

    try:
        # TODO I don't fully understand why peak loudness could cause an error
        # but perhaps really loud clips are too distorted to be useful
        # We might need to catch this and handle it separately than just skipping, like
        # clips that are too small
        # peak normalize audio to -1 dB
        peak_normalized_audio = pyln.normalize.peak(data, -1.0)

        # measure the loudness first
        meter = pyln.Meter(rate) # create BS.1770 meter
        loudness = meter.integrated_loudness(data)

        # loudness normalize audio to -25 dB LUFS
        loudness_normalized_audio = pyln.normalize.loudness(data, loudness, -25.0)
    except ValueError as e:
        print(f"ValueError: ${str(e)}")

    sf.write(target_filepath, data=loudness_normalized_audio, samplerate=22050)

    print("")