rvc/train/preprocess/preprocess.py

import os
import sys
import time
from scipy import signal
from scipy.io import wavfile
import numpy as np
import concurrent.futures
from tqdm import tqdm
import json
from distutils.util import strtobool
import librosa
import multiprocessing

now_directory = os.getcwd()
sys.path.append(now_directory)

from rvc.lib.utils import load_audio
from rvc.train.preprocess.slicer import Slicer

# Remove colab logs
import logging

logging.getLogger("numba.core.byteflow").setLevel(logging.WARNING)
logging.getLogger("numba.core.ssa").setLevel(logging.WARNING)
logging.getLogger("numba.core.interpreter").setLevel(logging.WARNING)

# Constants
OVERLAP = 0.3
MAX_AMPLITUDE = 0.9
ALPHA = 0.75
HIGH_PASS_CUTOFF = 48
SAMPLE_RATE_16K = 16000


class PreProcess:
    def __init__(self, sr: int, exp_dir: str, per: float):
        self.slicer = Slicer(
            sr=sr,
            threshold=-42,
            min_length=1500,
            min_interval=400,
            hop_size=15,
            max_sil_kept=500,
        )
        self.sr = sr
        self.b_high, self.a_high = signal.butter(
            N=5, Wn=HIGH_PASS_CUTOFF, btype="high", fs=self.sr
        )
        self.per = per
        self.exp_dir = exp_dir
        self.device = "cpu"
        self.gt_wavs_dir = os.path.join(exp_dir, "sliced_audios")
        self.wavs16k_dir = os.path.join(exp_dir, "sliced_audios_16k")
        os.makedirs(self.gt_wavs_dir, exist_ok=True)
        os.makedirs(self.wavs16k_dir, exist_ok=True)

    def _normalize_audio(self, audio: np.ndarray):
        tmp_max = np.abs(audio).max()
        if tmp_max > 2.5:
            return None
        return (audio / tmp_max * (MAX_AMPLITUDE * ALPHA)) + (1 - ALPHA) * audio

    def process_audio_segment(
        self,
        audio_segment: np.ndarray,
        idx0: int,
        idx1: int,
        process_effects: bool,
    ):
        normalized_audio = (
            self._normalize_audio(audio_segment) if process_effects else audio_segment
        )
        if normalized_audio is None:
            print(f"{idx0}-{idx1}-filtered")
            return
        wavfile.write(
            os.path.join(self.gt_wavs_dir, f"{idx0}_{idx1}.wav"),
            self.sr,
            normalized_audio.astype(np.float32),
        )
        audio_16k = librosa.resample(
            normalized_audio, orig_sr=self.sr, target_sr=SAMPLE_RATE_16K
        )
        wavfile.write(
            os.path.join(self.wavs16k_dir, f"{idx0}_{idx1}.wav"),
            SAMPLE_RATE_16K,
            audio_16k.astype(np.float32),
        )

    def process_audio(
        self,
        path: str,
        idx0: int,
        cut_preprocess: bool,
        process_effects: bool,
    ):
        audio_length = 0
        try:
            audio = load_audio(path, self.sr)
            audio_length = librosa.get_duration(y=audio, sr=self.sr)
            if process_effects:
                audio = signal.lfilter(self.b_high, self.a_high, audio)
            idx1 = 0
            if cut_preprocess:
                for audio_segment in self.slicer.slice(audio):
                    i = 0
                    while True:
                        start = int(self.sr * (self.per - OVERLAP) * i)
                        i += 1
                        if len(audio_segment[start:]) > (self.per + OVERLAP) * self.sr:
                            tmp_audio = audio_segment[
                                start : start + int(self.per * self.sr)
                            ]
                            self.process_audio_segment(
                                tmp_audio, idx0, idx1, process_effects
                            )
                            idx1 += 1
                        else:
                            tmp_audio = audio_segment[start:]
                            self.process_audio_segment(
                                tmp_audio, idx0, idx1, process_effects
                            )
                            idx1 += 1
                            break
            else:
                self.process_audio_segment(audio, idx0, idx1, process_effects)
        except Exception as error:
            print(f"Error processing audio: {error}")
        return audio_length


def format_duration(seconds):
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    seconds = int(seconds % 60)
    return f"{hours:02}:{minutes:02}:{seconds:02}"


def save_dataset_duration(file_path, dataset_duration):
    try:
        with open(file_path, "r") as f:
            data = json.load(f)
    except FileNotFoundError:
        data = {}

    formatted_duration = format_duration(dataset_duration)
    new_data = {
        "total_dataset_duration": formatted_duration,
        "total_seconds": dataset_duration,
    }
    data.update(new_data)

    with open(file_path, "w") as f:
        json.dump(data, f, indent=4)


def process_audio_wrapper(args):
    pp, file, cut_preprocess, process_effects = args
    file_path, idx0 = file
    return pp.process_audio(file_path, idx0, cut_preprocess, process_effects)


def preprocess_training_set(
    input_root: str,
    sr: int,
    num_processes: int,
    exp_dir: str,
    per: float,
    cut_preprocess: bool,
    process_effects: bool,
):
    start_time = time.time()
    pp = PreProcess(sr, exp_dir, per)
    print(f"Starting preprocess with {num_processes} processes...")

    files = [
        (os.path.join(input_root, f), idx)
        for idx, f in enumerate(os.listdir(input_root))
        if f.lower().endswith((".wav", ".mp3", ".flac", ".ogg"))
    ]
    # print(f"Number of files: {len(files)}")
    with concurrent.futures.ThreadPoolExecutor(max_workers=num_processes) as executor:
        audio_length = list(
            tqdm(
                executor.map(
                    process_audio_wrapper,
                    [(pp, file, cut_preprocess, process_effects) for file in files],
                ),
                total=len(files),
            )
        )
    audio_length = sum(audio_length)
    save_dataset_duration(
        os.path.join(exp_dir, "model_info.json"), dataset_duration=audio_length
    )
    elapsed_time = time.time() - start_time
    print(
        f"Preprocess completed in {elapsed_time:.2f} seconds on {format_duration(audio_length)} seconds of audio."
    )


if __name__ == "__main__":
    experiment_directory = str(sys.argv[1])
    input_root = str(sys.argv[2])
    sample_rate = int(sys.argv[3])
    percentage = float(sys.argv[4])
    num_processes = sys.argv[5]
    if num_processes.lower() == "none":
        num_processes = multiprocessing.cpu_count()
    else:
        num_processes = int(num_processes)
    cut_preprocess = strtobool(sys.argv[6])
    process_effects = strtobool(sys.argv[7])

    preprocess_training_set(
        input_root,
        sample_rate,
        num_processes,
        experiment_directory,
        percentage,
        cut_preprocess,
        process_effects,
    )