GitHub Repository: keras-team/keras-io
Path: blob/master/examples/audio/md/vocal_track_separation.md
³⁵⁰⁸ views

Vocal Track Separation with Encoder-Decoder Architecture

Author: Joaquin Jimenez
Date created: 2024/12/10
Last modified: 2024/12/10
Description: Train a model to separate vocal tracks from music mixtures.

View in Colab • GitHub source

Introduction

In this tutorial, we build a vocal track separation model using an encoder-decoder architecture in Keras 3.

We train the model on the MUSDB18 dataset, which provides music mixtures and isolated tracks for drums, bass, other, and vocals.

Key concepts covered:

Audio data preprocessing using the Short-Time Fourier Transform (STFT).
Audio data augmentation techniques.
Implementing custom encoders and decoders specialized for audio data.
Defining appropriate loss functions and metrics for audio source separation tasks.

The model architecture is derived from the TFC_TDF_Net model described in:

W. Choi, M. Kim, J. Chung, D. Lee, and S. Jung, “Investigating U-Nets with various intermediate blocks for spectrogram-based singing voice separation,” in the 21st International Society for Music Information Retrieval Conference, 2020.

For reference code, see: GitHub: ws-choi/ISMIR2020_U_Nets_SVS.

The data processing and model training routines are partly derived from: ZFTurbo/Music-Source-Separation-Training.

Setup

Import and install all the required dependencies.

!pip install -qq audiomentations soundfile ffmpeg-binaries
!pip install -qq "keras==3.7.0"
!sudo -n apt-get install -y graphviz >/dev/null 2>&1  # Required for plotting the model

import glob
import os

os.environ["KERAS_BACKEND"] = "jax"  # or "tensorflow" or "torch"

import random
import subprocess
import tempfile
import typing
from os import path

import audiomentations as aug
import ffmpeg
import keras
import numpy as np
import soundfile as sf
from IPython import display
from keras import callbacks, layers, ops, saving
from matplotlib import pyplot as plt

Configuration

The following constants define configuration parameters for audio processing and model training, including dataset paths, audio chunk sizes, Short-Time Fourier Transform (STFT) parameters, and training hyperparameters.

# MUSDB18 dataset configuration
MUSDB_STREAMS = {"mixture": 0, "drums": 1, "bass": 2, "other": 3, "vocals": 4}
TARGET_INSTRUMENTS = {track: MUSDB_STREAMS[track] for track in ("vocals",)}
N_INSTRUMENTS = len(TARGET_INSTRUMENTS)
SOURCE_INSTRUMENTS = tuple(k for k in MUSDB_STREAMS if k != "mixture")

# Audio preprocessing parameters for Short-Time Fourier Transform (STFT)
N_SUBBANDS = 4  # Number of subbands into which frequencies are split
CHUNK_SIZE = 65024  # Number of amplitude samples per audio chunk (~4 seconds)
STFT_N_FFT = 2048  # FFT points used in STFT
STFT_HOP_LENGTH = 512  # Hop length for STFT

# Training hyperparameters
N_CHANNELS = 64  # Base channel count for the model
BATCH_SIZE = 3
ACCUMULATION_STEPS = 2
EFFECTIVE_BATCH_SIZE = BATCH_SIZE * (ACCUMULATION_STEPS or 1)

# Paths
TMP_DIR = path.expanduser("~/.keras/tmp")
DATASET_DIR = path.expanduser("~/.keras/datasets")
MODEL_PATH = path.join(TMP_DIR, f"model_{keras.backend.backend()}.keras")
CSV_LOG_PATH = path.join(TMP_DIR, f"training_{keras.backend.backend()}.csv")
os.makedirs(DATASET_DIR, exist_ok=True)
os.makedirs(TMP_DIR, exist_ok=True)

# Set random seed for reproducibility
keras.utils.set_random_seed(21)

``` WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1734318393.806217 81028 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1734318393.809885 81028 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

</div>
---
## MUSDB18 Dataset

The MUSDB18 dataset is a standard benchmark for music source separation, containing
150 full-length music tracks along with isolated drums, bass, other, and vocals.
The dataset is stored in .mp4 format, and each .mp4 file includes multiple audio
streams (mixture and individual tracks).

### Download and Conversion

The following utility function downloads MUSDB18 and converts its .mp4 files to
.wav files for each instrument track, resampled to 16 kHz.


```python

def download_musdb18(out_dir=None):
    """Download and extract the MUSDB18 dataset, then convert .mp4 files to .wav files.

    MUSDB18 reference:
    Rafii, Z., Liutkus, A., Stöter, F.-R., Mimilakis, S. I., & Bittner, R. (2017).
    MUSDB18 - a corpus for music separation (1.0.0) [Data set]. Zenodo.
    """
    ffmpeg.init()
    from ffmpeg import FFMPEG_PATH

    # Create output directories
    os.makedirs((base := out_dir or tempfile.mkdtemp()), exist_ok=True)
    if path.exists((out_dir := path.join(base, "musdb18_wav"))):
        print("MUSDB18 dataset already downloaded")
        return out_dir

    # Download and extract the dataset
    download_dir = keras.utils.get_file(
        fname="musdb18",
        origin="https://zenodo.org/records/1117372/files/musdb18.zip",
        extract=True,
    )

    # ffmpeg command template: input, stream index, output
    ffmpeg_args = str(FFMPEG_PATH) + " -v error -i {} -map 0:{} -vn -ar 16000 {}"

    # Convert each mp4 file to multiple .wav files for each track
    for split in ("train", "test"):
        songs = os.listdir(path.join(download_dir, split))
        for i, song in enumerate(songs):
            if i % 10 == 0:
                print(f"{split.capitalize()}: {i}/{len(songs)} songs processed")

            mp4_path_orig = path.join(download_dir, split, song)
            mp4_path = path.join(tempfile.mkdtemp(), split, song.replace(" ", "_"))
            os.makedirs(path.dirname(mp4_path), exist_ok=True)
            os.rename(mp4_path_orig, mp4_path)

            wav_dir = path.join(out_dir, split, path.basename(mp4_path).split(".")[0])
            os.makedirs(wav_dir, exist_ok=True)

            for track in SOURCE_INSTRUMENTS:
                out_path = path.join(wav_dir, f"{track}.wav")
                stream_index = MUSDB_STREAMS[track]
                args = ffmpeg_args.format(mp4_path, stream_index, out_path).split()
                assert subprocess.run(args).returncode == 0, "ffmpeg conversion failed"
    return out_dir


# Download and prepare the MUSDB18 dataset
songs = download_musdb18(out_dir=DATASET_DIR)

``` MUSDB18 dataset already downloaded

</div>
### Custom Dataset

We define a custom dataset class to generate random audio chunks and their corresponding
labels. The dataset does the following:

1. Selects a random chunk from a random song and instrument.
2. Applies optional data augmentations.
3. Combines isolated tracks to form new synthetic mixtures.
4. Prepares features (mixtures) and labels (vocals) for training.

This approach allows creating an effectively infinite variety of training examples
through randomization and augmentation.


```python

class Dataset(keras.utils.PyDataset):
    def __init__(
        self,
        songs,
        batch_size=BATCH_SIZE,
        chunk_size=CHUNK_SIZE,
        batches_per_epoch=1000 * ACCUMULATION_STEPS,
        augmentation=True,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.augmentation = augmentation
        self.vocals_augmentations = [
            aug.PitchShift(min_semitones=-5, max_semitones=5, p=0.1),
            aug.SevenBandParametricEQ(-9, 9, p=0.25),
            aug.TanhDistortion(0.1, 0.7, p=0.1),
        ]
        self.other_augmentations = [
            aug.PitchShift(p=0.1),
            aug.AddGaussianNoise(p=0.1),
        ]
        self.songs = songs
        self.sizes = {song: self.get_track_set_size(song) for song in self.songs}
        self.batch_size = batch_size
        self.chunk_size = chunk_size
        self.batches_per_epoch = batches_per_epoch

    def get_track_set_size(self, song: str):
        """Return the smallest track length in the given song directory."""
        sizes = [len(sf.read(p)[0]) for p in glob.glob(path.join(song, "*.wav"))]
        if max(sizes) != min(sizes):
            print(f"Warning: {song} has different track lengths")
        return min(sizes)

    def random_chunk_of_instrument_type(self, instrument: str):
        """Extract a random chunk for the specified instrument from a random song."""
        song, size = random.choice(list(self.sizes.items()))
        track = path.join(song, f"{instrument}.wav")

        if self.chunk_size <= size:
            start = np.random.randint(size - self.chunk_size + 1)
            audio = sf.read(track, self.chunk_size, start, dtype="float32")[0]
            audio_mono = np.mean(audio, axis=1)
        else:
            # If the track is shorter than chunk_size, pad the signal
            audio_mono = np.mean(sf.read(track, dtype="float32")[0], axis=1)
            audio_mono = np.pad(audio_mono, ((0, self.chunk_size - size),))

        # If the chunk is almost silent, retry
        if np.mean(np.abs(audio_mono)) < 0.01:
            return self.random_chunk_of_instrument_type(instrument)

        return self.data_augmentation(audio_mono, instrument)

    def data_augmentation(self, audio: np.ndarray, instrument: str):
        """Apply data augmentation to the audio chunk, if enabled."""

        def coin_flip(x, probability: float, fn: typing.Callable):
            return fn(x) if random.uniform(0, 1) < probability else x

        if self.augmentation:
            augmentations = (
                self.vocals_augmentations
                if instrument == "vocals"
                else self.other_augmentations
            )
            # Loudness augmentation
            audio *= np.random.uniform(0.5, 1.5, (len(audio),)).astype("float32")
            # Random reverse
            audio = coin_flip(audio, 0.1, lambda x: np.flip(x))
            # Random polarity inversion
            audio = coin_flip(audio, 0.5, lambda x: -x)
            # Apply selected augmentations
            for aug_ in augmentations:
                aug_.randomize_parameters(audio, sample_rate=16000)
                audio = aug_(audio, sample_rate=16000)
        return audio

    def random_mix_of_tracks(self) -> dict:
        """Create a random mix of instruments by summing their individual chunks."""
        tracks = {}
        for instrument in SOURCE_INSTRUMENTS:
            # Start with a single random chunk
            mixup = [self.random_chunk_of_instrument_type(instrument)]

            # Randomly add more chunks of the same instrument (mixup augmentation)
            if self.augmentation:
                for p in (0.2, 0.02):
                    if random.uniform(0, 1) < p:
                        mixup.append(self.random_chunk_of_instrument_type(instrument))

            tracks[instrument] = np.mean(mixup, axis=0, dtype="float32")
        return tracks

    def __len__(self):
        return self.batches_per_epoch

    def __getitem__(self, idx):
        # Generate a batch of random mixtures
        batch = [self.random_mix_of_tracks() for _ in range(self.batch_size)]

        # Features: sum of all tracks
        batch_x = ops.sum(
            np.array([list(track_set.values()) for track_set in batch]), axis=1
        )

        # Labels: isolated target instruments (e.g., vocals)
        batch_y = np.array(
            [[track_set[t] for t in TARGET_INSTRUMENTS] for track_set in batch]
        )

        return batch_x, ops.convert_to_tensor(batch_y)


# Create train and validation datasets
train_ds = Dataset(glob.glob(path.join(songs, "train", "*")))
val_ds = Dataset(
    glob.glob(path.join(songs, "test", "*")),
    batches_per_epoch=int(0.1 * train_ds.batches_per_epoch),
    augmentation=False,
)

Visualize a Sample

Let's visualize a random mixed audio chunk and its corresponding isolated vocals. This helps to understand the nature of the preprocessed input data.


def visualize_audio_np(audio: np.ndarray, rate=16000, name="mixup"):
    """Plot and display an audio waveform and also produce an Audio widget."""
    plt.figure(figsize=(10, 6))
    plt.plot(audio)
    plt.title(f"Waveform: {name}")
    plt.xlim(0, len(audio))
    plt.ylabel("Amplitude")
    plt.show()
    # plt.savefig(f"tmp/{name}.png")

    # Normalize and display audio
    audio_norm = (audio - np.min(audio)) / (np.max(audio) - np.min(audio) + 1e-8)
    audio_norm = (audio_norm * 2 - 1) * 0.6
    display.display(display.Audio(audio_norm, rate=rate))
    # sf.write(f"tmp/{name}.wav", audio_norm, rate)


sample_batch_x, sample_batch_y = val_ds[None]  # Random batch
visualize_audio_np(ops.convert_to_numpy(sample_batch_x[0]))
visualize_audio_np(ops.convert_to_numpy(sample_batch_y[0, 0]), name="vocals")

Your browser does not support the audio element.

Model

Preprocessing

The model operates on STFT representations rather than raw audio. We define a preprocessing model to compute STFT and a corresponding inverse transform (iSTFT).


def stft(inputs, fft_size=STFT_N_FFT, sequence_stride=STFT_HOP_LENGTH):
    """Compute the STFT for the input audio and return the real and imaginary parts."""
    real_x, imag_x = ops.stft(inputs, fft_size, sequence_stride, fft_size)
    real_x, imag_x = ops.expand_dims(real_x, -1), ops.expand_dims(imag_x, -1)
    x = ops.concatenate((real_x, imag_x), axis=-1)

    # Drop last freq sample for convenience
    return ops.split(x, [x.shape[2] - 1], axis=2)[0]


def inverse_stft(inputs, fft_size=STFT_N_FFT, sequence_stride=STFT_HOP_LENGTH):
    """Compute the inverse STFT for the given STFT input."""
    x = inputs

    # Pad back dropped freq sample if using torch backend
    if keras.backend.backend() == "torch":
        x = ops.pad(x, ((0, 0), (0, 0), (0, 1), (0, 0)))

    real_x, imag_x = ops.split(x, 2, axis=-1)
    real_x = ops.squeeze(real_x, axis=-1)
    imag_x = ops.squeeze(imag_x, axis=-1)

    return ops.istft((real_x, imag_x), fft_size, sequence_stride, fft_size)

Model Architecture

The model uses a custom encoder-decoder architecture with Time-Frequency Convolution (TFC) and Time-Distributed Fully Connected (TDF) blocks. They are grouped into a TimeFrequencyTransformBlock, i.e. "TFC_TDF" in the original paper by Choi et al.

We then define an encoder-decoder network with multiple scales. Each encoder scale applies TFC_TDF blocks followed by downsampling, while decoder scales apply TFC_TDF blocks over the concatenation of upsampled features and associated encoder outputs.


@saving.register_keras_serializable()
class TimeDistributedDenseBlock(layers.Layer):
    """Time-Distributed Fully Connected layer block.

    Applies frequency-wise dense transformations across time frames with instance
    normalization and GELU activation.
    """

    def __init__(self, bottleneck_factor, fft_dim, **kwargs):
        super().__init__(**kwargs)
        self.fft_dim = fft_dim
        self.hidden_dim = fft_dim // bottleneck_factor

    def build(self, *_):
        self.group_norm_1 = layers.GroupNormalization(groups=-1)
        self.group_norm_2 = layers.GroupNormalization(groups=-1)
        self.dense_1 = layers.Dense(self.hidden_dim, use_bias=False)
        self.dense_2 = layers.Dense(self.fft_dim, use_bias=False)

    def call(self, x):
        # Apply normalization and dense layers frequency-wise
        x = ops.gelu(self.group_norm_1(x))
        x = ops.swapaxes(x, -1, -2)
        x = self.dense_1(x)

        x = ops.gelu(self.group_norm_2(ops.swapaxes(x, -1, -2)))
        x = ops.swapaxes(x, -1, -2)
        x = self.dense_2(x)
        return ops.swapaxes(x, -1, -2)


@saving.register_keras_serializable()
class TimeFrequencyConvolution(layers.Layer):
    """Time-Frequency Convolutional layer.

    Applies a 2D convolution over time-frequency representations and applies instance
    normalization and GELU activation.
    """

    def __init__(self, channels, **kwargs):
        super().__init__(**kwargs)
        self.channels = channels

    def build(self, *_):
        self.group_norm = layers.GroupNormalization(groups=-1)
        self.conv = layers.Conv2D(self.channels, 3, padding="same", use_bias=False)

    def call(self, x):
        return self.conv(ops.gelu(self.group_norm(x)))


@saving.register_keras_serializable()
class TimeFrequencyTransformBlock(layers.Layer):
    """Implements TFC_TDF block for encoder-decoder architecture.

    Repeatedly apply Time-Frequency Convolution and Time-Distributed Dense blocks as
    many times as specified by the `length` parameter.
    """

    def __init__(
        self, channels, length, fft_dim, bottleneck_factor, in_channels=None, **kwargs
    ):
        super().__init__(**kwargs)
        self.channels = channels
        self.length = length
        self.fft_dim = fft_dim
        self.bottleneck_factor = bottleneck_factor
        self.in_channels = in_channels or channels
        self.blocks = []

    def build(self, *_):
        # Add blocks in a flat list to avoid nested structures
        for i in range(self.length):
            in_channels = self.channels if i > 0 else self.in_channels
            self.blocks.append(TimeFrequencyConvolution(in_channels))
            self.blocks.append(
                TimeDistributedDenseBlock(self.bottleneck_factor, self.fft_dim)
            )
            self.blocks.append(TimeFrequencyConvolution(self.channels))
            # Residual connection
            self.blocks.append(layers.Conv2D(self.channels, 1, 1, use_bias=False))

    def call(self, inputs):
        x = inputs
        # Each block consists of 4 layers:
        # 1. Time-Frequency Convolution
        # 2. Time-Distributed Dense
        # 3. Time-Frequency Convolution
        # 4. Residual connection
        for i in range(0, len(self.blocks), 4):
            tfc_1 = self.blocks[i](x)
            tdf = self.blocks[i + 1](x)
            tfc_2 = self.blocks[i + 2](tfc_1 + tdf)
            x = tfc_2 + self.blocks[i + 3](x)  # Residual connection
        return x


@saving.register_keras_serializable()
class Downscale(layers.Layer):
    """Downscale time-frequency dimensions using a convolution."""

    conv_cls = layers.Conv2D

    def __init__(self, channels, scale, **kwargs):
        super().__init__(**kwargs)
        self.channels = channels
        self.scale = scale

    def build(self, *_):
        self.conv = self.conv_cls(self.channels, self.scale, self.scale, use_bias=False)
        self.norm = layers.GroupNormalization(groups=-1)

    def call(self, inputs):
        return self.norm(ops.gelu(self.conv(inputs)))


@saving.register_keras_serializable()
class Upscale(Downscale):
    """Upscale time-frequency dimensions using a transposed convolution."""

    conv_cls = layers.Conv2DTranspose


def build_model(
    inputs,
    n_instruments=N_INSTRUMENTS,
    n_subbands=N_SUBBANDS,
    channels=N_CHANNELS,
    fft_dim=(STFT_N_FFT // 2) // N_SUBBANDS,
    n_scales=4,
    scale=(2, 2),
    block_size=2,
    growth=128,
    bottleneck_factor=2,
    **kwargs,
):
    """Build the TFC_TDF encoder-decoder model for source separation."""
    # Compute STFT
    x = stft(inputs)

    # Split mixture into subbands as separate channels
    mix = ops.reshape(x, (-1, x.shape[1], x.shape[2] // n_subbands, 2 * n_subbands))
    first_conv_out = layers.Conv2D(channels, 1, 1, use_bias=False)(mix)
    x = first_conv_out

    # Encoder path
    encoder_outs = []
    for _ in range(n_scales):
        x = TimeFrequencyTransformBlock(
            channels, block_size, fft_dim, bottleneck_factor
        )(x)
        encoder_outs.append(x)
        fft_dim, channels = fft_dim // scale[0], channels + growth
        x = Downscale(channels, scale)(x)

    # Bottleneck
    x = TimeFrequencyTransformBlock(channels, block_size, fft_dim, bottleneck_factor)(x)

    # Decoder path
    for _ in range(n_scales):
        fft_dim, channels = fft_dim * scale[0], channels - growth
        x = ops.concatenate([Upscale(channels, scale)(x), encoder_outs.pop()], axis=-1)
        x = TimeFrequencyTransformBlock(
            channels, block_size, fft_dim, bottleneck_factor, in_channels=x.shape[-1]
        )(x)

    # Residual connection and final convolutions
    x = ops.concatenate([mix, x * first_conv_out], axis=-1)
    x = layers.Conv2D(channels, 1, 1, use_bias=False, activation="gelu")(x)
    x = layers.Conv2D(n_instruments * n_subbands * 2, 1, 1, use_bias=False)(x)

    # Reshape back to instrument-wise STFT
    x = ops.reshape(x, (-1, x.shape[1], x.shape[2] * n_subbands, n_instruments, 2))
    x = ops.transpose(x, (0, 3, 1, 2, 4))
    x = ops.reshape(x, (-1, n_instruments, x.shape[2], x.shape[3] * 2))

    return keras.Model(inputs=inputs, outputs=x, **kwargs)

Loss and Metrics

We define:

spectral_loss: Mean absolute error in STFT domain.
sdr: Signal-to-Distortion Ratio, a common source separation metric.


def prediction_to_wave(x, n_instruments=N_INSTRUMENTS):
    """Convert STFT predictions back to waveform."""
    x = ops.reshape(x, (-1, x.shape[2], x.shape[3] // 2, 2))
    x = inverse_stft(x)
    return ops.reshape(x, (-1, n_instruments, x.shape[1]))


def target_to_stft(y):
    """Convert target waveforms to their STFT representations."""
    y = ops.reshape(y, (-1, CHUNK_SIZE))
    y_real, y_imag = ops.stft(y, STFT_N_FFT, STFT_HOP_LENGTH, STFT_N_FFT)
    y_real, y_imag = y_real[..., :-1], y_imag[..., :-1]
    y = ops.stack([y_real, y_imag], axis=-1)
    return ops.reshape(y, (-1, N_INSTRUMENTS, y.shape[1], y.shape[2] * 2))


@saving.register_keras_serializable()
def sdr(y_true, y_pred):
    """Signal-to-Distortion Ratio metric."""
    y_pred = prediction_to_wave(y_pred)
    # Add epsilon for numerical stability
    num = ops.sum(ops.square(y_true), axis=-1) + 1e-8
    den = ops.sum(ops.square(y_true - y_pred), axis=-1) + 1e-8
    return 10 * ops.log10(num / den)


@saving.register_keras_serializable()
def spectral_loss(y_true, y_pred):
    """Mean absolute error in the STFT domain."""
    y_true = target_to_stft(y_true)
    return ops.mean(ops.absolute(y_true - y_pred))

Training

Visualize Model Architecture

# Load or create the model
if path.exists(MODEL_PATH):
    model = saving.load_model(MODEL_PATH)
else:
    model = build_model(keras.Input(sample_batch_x.shape[1:]), name="tfc_tdf_net")

# Display the model architecture
model.summary()
img = keras.utils.plot_model(model, path.join(TMP_DIR, "model.png"), show_shapes=True)
display.display(img)

Model: "tfc_tdf_net"

┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ input_layer         │ (None, 65024)     │          0 │ -                 │
│ (InputLayer)        │                   │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ stft (STFT)         │ [(None, 128,      │          0 │ input_layer[0][0] │
│                     │ 1025), (None,     │            │                   │
│                     │ 128, 1025)]       │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ expand_dims         │ (None, 128, 1025, │          0 │ stft[0][0]        │
│ (ExpandDims)        │ 1)                │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ expand_dims_1       │ (None, 128, 1025, │          0 │ stft[0][1]        │
│ (ExpandDims)        │ 1)                │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ concatenate         │ (None, 128, 1025, │          0 │ expand_dims[0][0… │
│ (Concatenate)       │ 2)                │            │ expand_dims_1[0]… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ split (Split)       │ [(None, 128,      │          0 │ concatenate[0][0] │
│                     │ 1024, 2), (None,  │            │                   │
│                     │ 128, 1, 2)]       │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ reshape (Reshape)   │ (None, 128, 256,  │          0 │ split[0][0]       │
│                     │ 8)                │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2d (Conv2D)     │ (None, 128, 256,  │        512 │ reshape[0][0]     │
│                     │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ time_frequency_tra… │ (None, 128, 256,  │    287,744 │ conv2d[0][0]      │
│ (TimeFrequencyTran… │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ downscale           │ (None, 64, 128,   │     49,536 │ time_frequency_t… │
│ (Downscale)         │ 192)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ time_frequency_tra… │ (None, 64, 128,   │  1,436,672 │ downscale[0][0]   │
│ (TimeFrequencyTran… │ 192)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ downscale_1         │ (None, 32, 64,    │    246,400 │ time_frequency_t… │
│ (Downscale)         │ 320)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ time_frequency_tra… │ (None, 32, 64,    │  3,904,512 │ downscale_1[0][0] │
│ (TimeFrequencyTran… │ 320)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ downscale_2         │ (None, 16, 32,    │    574,336 │ time_frequency_t… │
│ (Downscale)         │ 448)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ time_frequency_tra… │ (None, 16, 32,    │  7,635,968 │ downscale_2[0][0] │
│ (TimeFrequencyTran… │ 448)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ downscale_3         │ (None, 8, 16,     │  1,033,344 │ time_frequency_t… │
│ (Downscale)         │ 576)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ time_frequency_tra… │ (None, 8, 16,     │ 12,617,216 │ downscale_3[0][0] │
│ (TimeFrequencyTran… │ 576)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ upscale (Upscale)   │ (None, 16, 32,    │  1,033,088 │ time_frequency_t… │
│                     │ 448)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ concatenate_1       │ (None, 16, 32,    │          0 │ upscale[0][0],    │
│ (Concatenate)       │ 896)              │            │ time_frequency_t… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ time_frequency_tra… │ (None, 16, 32,    │ 15,065,600 │ concatenate_1[0]… │
│ (TimeFrequencyTran… │ 448)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ upscale_1 (Upscale) │ (None, 32, 64,    │    574,080 │ time_frequency_t… │
│                     │ 320)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ concatenate_2       │ (None, 32, 64,    │          0 │ upscale_1[0][0],  │
│ (Concatenate)       │ 640)              │            │ time_frequency_t… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ time_frequency_tra… │ (None, 32, 64,    │  7,695,872 │ concatenate_2[0]… │
│ (TimeFrequencyTran… │ 320)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ upscale_2 (Upscale) │ (None, 64, 128,   │    246,144 │ time_frequency_t… │
│                     │ 192)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ concatenate_3       │ (None, 64, 128,   │          0 │ upscale_2[0][0],  │
│ (Concatenate)       │ 384)              │            │ time_frequency_t… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ time_frequency_tra… │ (None, 64, 128,   │  2,802,176 │ concatenate_3[0]… │
│ (TimeFrequencyTran… │ 192)              │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ upscale_3 (Upscale) │ (None, 128, 256,  │     49,280 │ time_frequency_t… │
│                     │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ concatenate_4       │ (None, 128, 256,  │          0 │ upscale_3[0][0],  │
│ (Concatenate)       │ 128)              │            │ time_frequency_t… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ time_frequency_tra… │ (None, 128, 256,  │    439,808 │ concatenate_4[0]… │
│ (TimeFrequencyTran… │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ multiply (Multiply) │ (None, 128, 256,  │          0 │ time_frequency_t… │
│                     │ 64)               │            │ conv2d[0][0]      │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ concatenate_5       │ (None, 128, 256,  │          0 │ reshape[0][0],    │
│ (Concatenate)       │ 72)               │            │ multiply[0][0]    │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2d_59 (Conv2D)  │ (None, 128, 256,  │      4,608 │ concatenate_5[0]… │
│                     │ 64)               │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ conv2d_60 (Conv2D)  │ (None, 128, 256,  │        512 │ conv2d_59[0][0]   │
│                     │ 8)                │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ reshape_1 (Reshape) │ (None, 128, 1024, │          0 │ conv2d_60[0][0]   │
│                     │ 1, 2)             │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ transpose           │ (None, 1, 128,    │          0 │ reshape_1[0][0]   │
│ (Transpose)         │ 1024, 2)          │            │                   │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ reshape_2 (Reshape) │ (None, 1, 128,    │          0 │ transpose[0][0]   │
│                     │ 2048)             │            │                   │
└─────────────────────┴───────────────────┴────────────┴───────────────────┘

 Total params: 222,789,634 (849.88 MB)

 Trainable params: 55,697,408 (212.47 MB)

 Non-trainable params: 0 (0.00 B)

 Optimizer params: 167,092,226 (637.41 MB)

Compile and Train the Model

# Compile the model
optimizer = keras.optimizers.Adam(5e-05, gradient_accumulation_steps=ACCUMULATION_STEPS)
model.compile(optimizer=optimizer, loss=spectral_loss, metrics=[sdr])

# Define callbacks
cbs = [
    callbacks.ModelCheckpoint(MODEL_PATH, "val_sdr", save_best_only=True, mode="max"),
    callbacks.ReduceLROnPlateau(factor=0.95, patience=2),
    callbacks.CSVLogger(CSV_LOG_PATH),
]

if not path.exists(MODEL_PATH):
    model.fit(train_ds, validation_data=val_ds, epochs=10, callbacks=cbs, shuffle=False)
else:
    # Demonstration of a single epoch of training when model already exists
    model.fit(train_ds, validation_data=val_ds, epochs=1, shuffle=False, verbose=2)

``` 2000/2000 - 490s - 245ms/step - loss: 0.2977 - sdr: 5.6497 - val_loss: 0.1720 - val_sdr: 6.0508

</div>
---
## Evaluation

Evaluate the model on the validation dataset and visualize predicted vocals.


```python
model.evaluate(val_ds, verbose=2)
y_pred = model.predict(sample_batch_x, verbose=2)
y_pred = prediction_to_wave(y_pred)
visualize_audio_np(ops.convert_to_numpy(y_pred[0, 0]), name="vocals_pred")

``` 200/200 - 8s - 41ms/step - loss: 0.1747 - sdr: 5.9374

1/1 - 4s - 4s/step

</div>
    
![png](/img/examples/audio/vocal_track_separation/vocal_track_separation_24_2.png)
    




<audio  controls="controls" >
    <source src="data:audio/wav;base64,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" type="audio/wav" />
    Your browser does not support the audio element.
</audio>



---
## Conclusion

We built and trained a vocal track separation model using an encoder-decoder
architecture with custom blocks applied to the MUSDB18 dataset. We demonstrated
STFT-based preprocessing, data augmentation, and a source separation metric (SDR).

**Next steps:**

- Train for more epochs and refine hyperparameters.
- Separate multiple instruments simultaneously.
- Enhance the model to handle instruments not present in the mixture.