soni_translate/mdx_net.py

import gc
import hashlib
import os
import queue
import threading
import json
import shlex
import sys
import subprocess
import librosa
import numpy as np
import soundfile as sf
import torch
from tqdm import tqdm

try:
    from .utils import (
        remove_directory_contents,
        create_directories,
    )
except:  # noqa
    from utils import (
        remove_directory_contents,
        create_directories,
    )
from .logging_setup import logger

try:
    import onnxruntime as ort
except Exception as error:
    logger.error(str(error))
# import warnings
# warnings.filterwarnings("ignore")

stem_naming = {
    "Vocals": "Instrumental",
    "Other": "Instruments",
    "Instrumental": "Vocals",
    "Drums": "Drumless",
    "Bass": "Bassless",
}


class MDXModel:
    def __init__(
        self,
        device,
        dim_f,
        dim_t,
        n_fft,
        hop=1024,
        stem_name=None,
        compensation=1.000,
    ):
        self.dim_f = dim_f
        self.dim_t = dim_t
        self.dim_c = 4
        self.n_fft = n_fft
        self.hop = hop
        self.stem_name = stem_name
        self.compensation = compensation

        self.n_bins = self.n_fft // 2 + 1
        self.chunk_size = hop * (self.dim_t - 1)
        self.window = torch.hann_window(
            window_length=self.n_fft, periodic=True
        ).to(device)

        out_c = self.dim_c

        self.freq_pad = torch.zeros(
            [1, out_c, self.n_bins - self.dim_f, self.dim_t]
        ).to(device)

    def stft(self, x):
        x = x.reshape([-1, self.chunk_size])
        x = torch.stft(
            x,
            n_fft=self.n_fft,
            hop_length=self.hop,
            window=self.window,
            center=True,
            return_complex=True,
        )
        x = torch.view_as_real(x)
        x = x.permute([0, 3, 1, 2])
        x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
            [-1, 4, self.n_bins, self.dim_t]
        )
        return x[:, :, : self.dim_f]

    def istft(self, x, freq_pad=None):
        freq_pad = (
            self.freq_pad.repeat([x.shape[0], 1, 1, 1])
            if freq_pad is None
            else freq_pad
        )
        x = torch.cat([x, freq_pad], -2)
        # c = 4*2 if self.target_name=='*' else 2
        x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
            [-1, 2, self.n_bins, self.dim_t]
        )
        x = x.permute([0, 2, 3, 1])
        x = x.contiguous()
        x = torch.view_as_complex(x)
        x = torch.istft(
            x,
            n_fft=self.n_fft,
            hop_length=self.hop,
            window=self.window,
            center=True,
        )
        return x.reshape([-1, 2, self.chunk_size])


class MDX:
    DEFAULT_SR = 44100
    # Unit: seconds
    DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
    DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR

    def __init__(
        self, model_path: str, params: MDXModel, processor=0
    ):
        # Set the device and the provider (CPU or CUDA)
        self.device = (
            torch.device(f"cuda:{processor}")
            if processor >= 0
            else torch.device("cpu")
        )
        self.provider = (
            ["CUDAExecutionProvider"]
            if processor >= 0
            else ["CPUExecutionProvider"]
        )

        self.model = params

        # Load the ONNX model using ONNX Runtime
        self.ort = ort.InferenceSession(model_path, providers=self.provider)
        # Preload the model for faster performance
        self.ort.run(
            None,
            {"input": torch.rand(1, 4, params.dim_f, params.dim_t).numpy()},
        )
        self.process = lambda spec: self.ort.run(
            None, {"input": spec.cpu().numpy()}
        )[0]

        self.prog = None

    @staticmethod
    def get_hash(model_path):
        try:
            with open(model_path, "rb") as f:
                f.seek(-10000 * 1024, 2)
                model_hash = hashlib.md5(f.read()).hexdigest()
        except: # noqa
            model_hash = hashlib.md5(open(model_path, "rb").read()).hexdigest()

        return model_hash

    @staticmethod
    def segment(
        wave,
        combine=True,
        chunk_size=DEFAULT_CHUNK_SIZE,
        margin_size=DEFAULT_MARGIN_SIZE,
    ):
        """
        Segment or join segmented wave array

        Args:
            wave: (np.array) Wave array to be segmented or joined
            combine: (bool) If True, combines segmented wave array.
                If False, segments wave array.
            chunk_size: (int) Size of each segment (in samples)
            margin_size: (int) Size of margin between segments (in samples)

        Returns:
            numpy array: Segmented or joined wave array
        """

        if combine:
            # Initializing as None instead of [] for later numpy array concatenation
            processed_wave = None
            for segment_count, segment in enumerate(wave):
                start = 0 if segment_count == 0 else margin_size
                end = None if segment_count == len(wave) - 1 else -margin_size
                if margin_size == 0:
                    end = None
                if processed_wave is None:  # Create array for first segment
                    processed_wave = segment[:, start:end]
                else:  # Concatenate to existing array for subsequent segments
                    processed_wave = np.concatenate(
                        (processed_wave, segment[:, start:end]), axis=-1
                    )

        else:
            processed_wave = []
            sample_count = wave.shape[-1]

            if chunk_size <= 0 or chunk_size > sample_count:
                chunk_size = sample_count

            if margin_size > chunk_size:
                margin_size = chunk_size

            for segment_count, skip in enumerate(
                range(0, sample_count, chunk_size)
            ):
                margin = 0 if segment_count == 0 else margin_size
                end = min(skip + chunk_size + margin_size, sample_count)
                start = skip - margin

                cut = wave[:, start:end].copy()
                processed_wave.append(cut)

                if end == sample_count:
                    break

        return processed_wave

    def pad_wave(self, wave):
        """
        Pad the wave array to match the required chunk size

        Args:
            wave: (np.array) Wave array to be padded

        Returns:
            tuple: (padded_wave, pad, trim)
                - padded_wave: Padded wave array
                - pad: Number of samples that were padded
                - trim: Number of samples that were trimmed
        """
        n_sample = wave.shape[1]
        trim = self.model.n_fft // 2
        gen_size = self.model.chunk_size - 2 * trim
        pad = gen_size - n_sample % gen_size

        # Padded wave
        wave_p = np.concatenate(
            (
                np.zeros((2, trim)),
                wave,
                np.zeros((2, pad)),
                np.zeros((2, trim)),
            ),
            1,
        )

        mix_waves = []
        for i in range(0, n_sample + pad, gen_size):
            waves = np.array(wave_p[:, i:i + self.model.chunk_size])
            mix_waves.append(waves)

        mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(
            self.device
        )

        return mix_waves, pad, trim

    def _process_wave(self, mix_waves, trim, pad, q: queue.Queue, _id: int):
        """
        Process each wave segment in a multi-threaded environment

        Args:
            mix_waves: (torch.Tensor) Wave segments to be processed
            trim: (int) Number of samples trimmed during padding
            pad: (int) Number of samples padded during padding
            q: (queue.Queue) Queue to hold the processed wave segments
            _id: (int) Identifier of the processed wave segment

        Returns:
            numpy array: Processed wave segment
        """
        mix_waves = mix_waves.split(1)
        with torch.no_grad():
            pw = []
            for mix_wave in mix_waves:
                self.prog.update()
                spec = self.model.stft(mix_wave)
                processed_spec = torch.tensor(self.process(spec))
                processed_wav = self.model.istft(
                    processed_spec.to(self.device)
                )
                processed_wav = (
                    processed_wav[:, :, trim:-trim]
                    .transpose(0, 1)
                    .reshape(2, -1)
                    .cpu()
                    .numpy()
                )
                pw.append(processed_wav)
        processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
        q.put({_id: processed_signal})
        return processed_signal

    def process_wave(self, wave: np.array, mt_threads=1):
        """
        Process the wave array in a multi-threaded environment

        Args:
            wave: (np.array) Wave array to be processed
            mt_threads: (int) Number of threads to be used for processing

        Returns:
            numpy array: Processed wave array
        """
        self.prog = tqdm(total=0)
        chunk = wave.shape[-1] // mt_threads
        waves = self.segment(wave, False, chunk)

        # Create a queue to hold the processed wave segments
        q = queue.Queue()
        threads = []
        for c, batch in enumerate(waves):
            mix_waves, pad, trim = self.pad_wave(batch)
            self.prog.total = len(mix_waves) * mt_threads
            thread = threading.Thread(
                target=self._process_wave, args=(mix_waves, trim, pad, q, c)
            )
            thread.start()
            threads.append(thread)
        for thread in threads:
            thread.join()
        self.prog.close()

        processed_batches = []
        while not q.empty():
            processed_batches.append(q.get())
        processed_batches = [
            list(wave.values())[0]
            for wave in sorted(
                processed_batches, key=lambda d: list(d.keys())[0]
            )
        ]
        assert len(processed_batches) == len(
            waves
        ), "Incomplete processed batches, please reduce batch size!"
        return self.segment(processed_batches, True, chunk)


def run_mdx(
    model_params,
    output_dir,
    model_path,
    filename,
    exclude_main=False,
    exclude_inversion=False,
    suffix=None,
    invert_suffix=None,
    denoise=False,
    keep_orig=True,
    m_threads=2,
    device_base="cuda",
):
    if device_base == "cuda":
        device = torch.device("cuda:0")
        processor_num = 0
        device_properties = torch.cuda.get_device_properties(device)
        vram_gb = device_properties.total_memory / 1024**3
        m_threads = 1 if vram_gb < 8 else 2
    else:
        device = torch.device("cpu")
        processor_num = -1
        m_threads = 1

    if os.environ.get("ZERO_GPU") == "TRUE":
        duration = librosa.get_duration(filename=filename)

        if duration < 60:
            pass
        elif duration >= 60 and duration <= 900:
            m_threads = 4
        elif duration > 900:
            m_threads = 16

    logger.info(f"MDX-NET Threads: {m_threads}, duration {duration}")
    
    model_hash = MDX.get_hash(model_path)
    mp = model_params.get(model_hash)
    model = MDXModel(
        device,
        dim_f=mp["mdx_dim_f_set"],
        dim_t=2 ** mp["mdx_dim_t_set"],
        n_fft=mp["mdx_n_fft_scale_set"],
        stem_name=mp["primary_stem"],
        compensation=mp["compensate"],
    )

    mdx_sess = MDX(model_path, model, processor=processor_num)
    wave, sr = librosa.load(filename, mono=False, sr=44100)
    # normalizing input wave gives better output
    peak = max(np.max(wave), abs(np.min(wave)))
    wave /= peak
    if denoise:
        wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (
            mdx_sess.process_wave(wave, m_threads)
        )
        wave_processed *= 0.5
    else:
        wave_processed = mdx_sess.process_wave(wave, m_threads)
    # return to previous peak
    wave_processed *= peak
    stem_name = model.stem_name if suffix is None else suffix

    main_filepath = None
    if not exclude_main:
        main_filepath = os.path.join(
            output_dir,
            f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav",
        )
        sf.write(main_filepath, wave_processed.T, sr)

    invert_filepath = None
    if not exclude_inversion:
        diff_stem_name = (
            stem_naming.get(stem_name)
            if invert_suffix is None
            else invert_suffix
        )
        stem_name = (
            f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
        )
        invert_filepath = os.path.join(
            output_dir,
            f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav",
        )
        sf.write(
            invert_filepath,
            (-wave_processed.T * model.compensation) + wave.T,
            sr,
        )

    if not keep_orig:
        os.remove(filename)

    del mdx_sess, wave_processed, wave
    gc.collect()
    torch.cuda.empty_cache()
    return main_filepath, invert_filepath


MDX_DOWNLOAD_LINK = "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/"
UVR_MODELS = [
    "UVR-MDX-NET-Voc_FT.onnx",
    "UVR_MDXNET_KARA_2.onnx",
    "Reverb_HQ_By_FoxJoy.onnx",
    "UVR-MDX-NET-Inst_HQ_4.onnx",
]
BASE_DIR = "."  # os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
mdxnet_models_dir = os.path.join(BASE_DIR, "mdx_models")
output_dir = os.path.join(BASE_DIR, "clean_song_output")


def convert_to_stereo_and_wav(audio_path):
    wave, sr = librosa.load(audio_path, mono=False, sr=44100)

    # check if mono
    if type(wave[0]) != np.ndarray or audio_path[-4:].lower() != ".wav": # noqa
        stereo_path = f"{os.path.splitext(audio_path)[0]}_stereo.wav"
        stereo_path = os.path.join(output_dir, stereo_path)

        command = shlex.split(
            f'ffmpeg -y -loglevel error -i "{audio_path}" -ac 2 -f wav "{stereo_path}"'
        )
        sub_params = {
            "stdout": subprocess.PIPE,
            "stderr": subprocess.PIPE,
            "creationflags": subprocess.CREATE_NO_WINDOW
            if sys.platform == "win32"
            else 0,
        }
        process_wav = subprocess.Popen(command, **sub_params)
        output, errors = process_wav.communicate()
        if process_wav.returncode != 0 or not os.path.exists(stereo_path):
            raise Exception("Error processing audio to stereo wav")

        return stereo_path
    else:
        return audio_path


def process_uvr_task(
    orig_song_path: str = "aud_test.mp3",
    main_vocals: bool = False,
    dereverb: bool = True,
    song_id: str = "mdx",  # folder output name
    only_voiceless: bool = False,
    remove_files_output_dir: bool = False,
):
    if os.environ.get("SONITR_DEVICE") == "cpu":
        device_base = "cpu"
    else:
        device_base = "cuda" if torch.cuda.is_available() else "cpu"

    if remove_files_output_dir:
        remove_directory_contents(output_dir)

    with open(os.path.join(mdxnet_models_dir, "data.json")) as infile:
        mdx_model_params = json.load(infile)

    song_output_dir = os.path.join(output_dir, song_id)
    create_directories(song_output_dir)
    orig_song_path = convert_to_stereo_and_wav(orig_song_path)

    logger.debug(f"onnxruntime device >> {ort.get_device()}")

    if only_voiceless:
        logger.info("Voiceless Track Separation...")
        return run_mdx(
            mdx_model_params,
            song_output_dir,
            os.path.join(mdxnet_models_dir, "UVR-MDX-NET-Inst_HQ_4.onnx"),
            orig_song_path,
            suffix="Voiceless",
            denoise=False,
            keep_orig=True,
            exclude_inversion=True,
            device_base=device_base,
        )

    logger.info("Vocal Track Isolation and Voiceless Track Separation...")
    vocals_path, instrumentals_path = run_mdx(
        mdx_model_params,
        song_output_dir,
        os.path.join(mdxnet_models_dir, "UVR-MDX-NET-Voc_FT.onnx"),
        orig_song_path,
        denoise=True,
        keep_orig=True,
        device_base=device_base,
    )

    if main_vocals:
        logger.info("Main Voice Separation from Supporting Vocals...")
        backup_vocals_path, main_vocals_path = run_mdx(
            mdx_model_params,
            song_output_dir,
            os.path.join(mdxnet_models_dir, "UVR_MDXNET_KARA_2.onnx"),
            vocals_path,
            suffix="Backup",
            invert_suffix="Main",
            denoise=True,
            device_base=device_base,
        )
    else:
        backup_vocals_path, main_vocals_path = None, vocals_path

    if dereverb:
        logger.info("Vocal Clarity Enhancement through De-Reverberation...")
        _, vocals_dereverb_path = run_mdx(
            mdx_model_params,
            song_output_dir,
            os.path.join(mdxnet_models_dir, "Reverb_HQ_By_FoxJoy.onnx"),
            main_vocals_path,
            invert_suffix="DeReverb",
            exclude_main=True,
            denoise=True,
            device_base=device_base,
        )
    else:
        vocals_dereverb_path = main_vocals_path

    return (
        vocals_path,
        instrumentals_path,
        backup_vocals_path,
        main_vocals_path,
        vocals_dereverb_path,
    )


if __name__ == "__main__":
    from utils import download_manager

    for id_model in UVR_MODELS:
        download_manager(
            os.path.join(MDX_DOWNLOAD_LINK, id_model), mdxnet_models_dir
        )
    (
        vocals_path_,
        instrumentals_path_,
        backup_vocals_path_,
        main_vocals_path_,
        vocals_dereverb_path_,
    ) = process_uvr_task(
        orig_song_path="aud.mp3",
        main_vocals=True,
        dereverb=True,
        song_id="mdx",
        remove_files_output_dir=True,
    )