speaker_recognition_using_cnn.ipynb

Kernel: Python 3

Speaker Recognition

Author: Fadi Badine
Date created: 14/06/2020
Last modified: 19/07/2023
Description: Classify speakers using Fast Fourier Transform (FFT) and a 1D Convnet.

Introduction

This example demonstrates how to create a model to classify speakers from the frequency domain representation of speech recordings, obtained via Fast Fourier Transform (FFT).

It shows the following:

• How to use tf.data to load, preprocess and feed audio streams into a model

• How to create a 1D convolutional network with residual connections for audio classification.

Our process:

• We prepare a dataset of speech samples from different speakers, with the speaker as label.

• We add background noise to these samples to augment our data.

• We take the FFT of these samples.

• We train a 1D convnet to predict the correct speaker given a noisy FFT speech sample.

Note:

• This example should be run with TensorFlow 2.3 or higher, or tf-nightly.

• The noise samples in the dataset need to be resampled to a sampling rate of 16000 Hz before using the code in this example. In order to do this, you will need to have installed ffmpg.

Setup

import os

os.environ["KERAS_BACKEND"] = "tensorflow"

import shutil
import numpy as np

import tensorflow as tf
import keras

from pathlib import Path
from IPython.display import display, Audio

# Get the data from https://www.kaggle.com/kongaevans/speaker-recognition-dataset/
# and save it to ./speaker-recognition-dataset.zip
# then unzip it to ./16000_pcm_speeches

!kaggle datasets download -d kongaevans/speaker-recognition-dataset
!unzip -qq speaker-recognition-dataset.zip

DATASET_ROOT = "16000_pcm_speeches"

# The folders in which we will put the audio samples and the noise samples
AUDIO_SUBFOLDER = "audio"
NOISE_SUBFOLDER = "noise"

DATASET_AUDIO_PATH = os.path.join(DATASET_ROOT, AUDIO_SUBFOLDER)
DATASET_NOISE_PATH = os.path.join(DATASET_ROOT, NOISE_SUBFOLDER)

# Percentage of samples to use for validation
VALID_SPLIT = 0.1

# Seed to use when shuffling the dataset and the noise
SHUFFLE_SEED = 43

# The sampling rate to use.
# This is the one used in all the audio samples.
# We will resample all the noise to this sampling rate.
# This will also be the output size of the audio wave samples
# (since all samples are of 1 second long)
SAMPLING_RATE = 16000

# The factor to multiply the noise with according to:
#   noisy_sample = sample + noise * prop * scale
#      where prop = sample_amplitude / noise_amplitude
SCALE = 0.5

BATCH_SIZE = 128
EPOCHS = 1  # For a real training run, use EPOCHS = 100

Data preparation

The dataset is composed of 7 folders, divided into 2 groups:

• Speech samples, with 5 folders for 5 different speakers. Each folder contains 1500 audio files, each 1 second long and sampled at 16000 Hz.

• Background noise samples, with 2 folders and a total of 6 files. These files are longer than 1 second (and originally not sampled at 16000 Hz, but we will resample them to 16000 Hz). We will use those 6 files to create 354 1-second-long noise samples to be used for training.

Let's sort these 2 categories into 2 folders:

• An audio folder which will contain all the per-speaker speech sample folders

• A noise folder which will contain all the noise samples

Before sorting the audio and noise categories into 2 folders, we have the following directory structure:

main_directory/
...speaker_a/
...speaker_b/
...speaker_c/
...speaker_d/
...speaker_e/
...other/
..._background_noise_/

After sorting, we end up with the following structure:

main_directory/
...audio/
......speaker_a/
......speaker_b/
......speaker_c/
......speaker_d/
......speaker_e/
...noise/
......other/
......_background_noise_/

for folder in os.listdir(DATASET_ROOT):
    if os.path.isdir(os.path.join(DATASET_ROOT, folder)):
        if folder in [AUDIO_SUBFOLDER, NOISE_SUBFOLDER]:
            # If folder is `audio` or `noise`, do nothing
            continue
        elif folder in ["other", "_background_noise_"]:
            # If folder is one of the folders that contains noise samples,
            # move it to the `noise` folder
            shutil.move(
                os.path.join(DATASET_ROOT, folder),
                os.path.join(DATASET_NOISE_PATH, folder),
            )
        else:
            # Otherwise, it should be a speaker folder, then move it to
            # `audio` folder
            shutil.move(
                os.path.join(DATASET_ROOT, folder),
                os.path.join(DATASET_AUDIO_PATH, folder),
            )

Noise preparation

In this section:

• We load all noise samples (which should have been resampled to 16000)

• We split those noise samples to chunks of 16000 samples which correspond to 1 second duration each

# Get the list of all noise files
noise_paths = []
for subdir in os.listdir(DATASET_NOISE_PATH):
    subdir_path = Path(DATASET_NOISE_PATH) / subdir
    if os.path.isdir(subdir_path):
        noise_paths += [
            os.path.join(subdir_path, filepath)
            for filepath in os.listdir(subdir_path)
            if filepath.endswith(".wav")
        ]
if not noise_paths:
    raise RuntimeError(f"Could not find any files at {DATASET_NOISE_PATH}")
print(
    "Found {} files belonging to {} directories".format(
        len(noise_paths), len(os.listdir(DATASET_NOISE_PATH))
    )
)

Resample all noise samples to 16000 Hz

command = (
    "for dir in `ls -1 " + DATASET_NOISE_PATH + "`; do "
    "for file in `ls -1 " + DATASET_NOISE_PATH + "/$dir/*.wav`; do "
    "sample_rate=`ffprobe -hide_banner -loglevel panic -show_streams "
    "$file | grep sample_rate | cut -f2 -d=`; "
    "if [ $sample_rate -ne 16000 ]; then "
    "ffmpeg -hide_banner -loglevel panic -y "
    "-i $file -ar 16000 temp.wav; "
    "mv temp.wav $file; "
    "fi; done; done"
)
os.system(command)


# Split noise into chunks of 16,000 steps each
def load_noise_sample(path):
    sample, sampling_rate = tf.audio.decode_wav(
        tf.io.read_file(path), desired_channels=1
    )
    if sampling_rate == SAMPLING_RATE:
        # Number of slices of 16000 each that can be generated from the noise sample
        slices = int(sample.shape[0] / SAMPLING_RATE)
        sample = tf.split(sample[: slices * SAMPLING_RATE], slices)
        return sample
    else:
        print("Sampling rate for {} is incorrect. Ignoring it".format(path))
        return None


noises = []
for path in noise_paths:
    sample = load_noise_sample(path)
    if sample:
        noises.extend(sample)
noises = tf.stack(noises)

print(
    "{} noise files were split into {} noise samples where each is {} sec. long".format(
        len(noise_paths), noises.shape[0], noises.shape[1] // SAMPLING_RATE
    )
)

Dataset generation

def paths_and_labels_to_dataset(audio_paths, labels):
    """Constructs a dataset of audios and labels."""
    path_ds = tf.data.Dataset.from_tensor_slices(audio_paths)
    audio_ds = path_ds.map(
        lambda x: path_to_audio(x), num_parallel_calls=tf.data.AUTOTUNE
    )
    label_ds = tf.data.Dataset.from_tensor_slices(labels)
    return tf.data.Dataset.zip((audio_ds, label_ds))


def path_to_audio(path):
    """Reads and decodes an audio file."""
    audio = tf.io.read_file(path)
    audio, _ = tf.audio.decode_wav(audio, 1, SAMPLING_RATE)
    return audio


def add_noise(audio, noises=None, scale=0.5):
    if noises is not None:
        # Create a random tensor of the same size as audio ranging from
        # 0 to the number of noise stream samples that we have.
        tf_rnd = tf.random.uniform(
            (tf.shape(audio)[0],), 0, noises.shape[0], dtype=tf.int32
        )
        noise = tf.gather(noises, tf_rnd, axis=0)

        # Get the amplitude proportion between the audio and the noise
        prop = tf.math.reduce_max(audio, axis=1) / tf.math.reduce_max(noise, axis=1)
        prop = tf.repeat(tf.expand_dims(prop, axis=1), tf.shape(audio)[1], axis=1)

        # Adding the rescaled noise to audio
        audio = audio + noise * prop * scale

    return audio


def audio_to_fft(audio):
    # Since tf.signal.fft applies FFT on the innermost dimension,
    # we need to squeeze the dimensions and then expand them again
    # after FFT
    audio = tf.squeeze(audio, axis=-1)
    fft = tf.signal.fft(
        tf.cast(tf.complex(real=audio, imag=tf.zeros_like(audio)), tf.complex64)
    )
    fft = tf.expand_dims(fft, axis=-1)

    # Return the absolute value of the first half of the FFT
    # which represents the positive frequencies
    return tf.math.abs(fft[:, : (audio.shape[1] // 2), :])


# Get the list of audio file paths along with their corresponding labels

class_names = os.listdir(DATASET_AUDIO_PATH)
print(
    "Our class names: {}".format(
        class_names,
    )
)

audio_paths = []
labels = []
for label, name in enumerate(class_names):
    print(
        "Processing speaker {}".format(
            name,
        )
    )
    dir_path = Path(DATASET_AUDIO_PATH) / name
    speaker_sample_paths = [
        os.path.join(dir_path, filepath)
        for filepath in os.listdir(dir_path)
        if filepath.endswith(".wav")
    ]
    audio_paths += speaker_sample_paths
    labels += [label] * len(speaker_sample_paths)

print(
    "Found {} files belonging to {} classes.".format(len(audio_paths), len(class_names))
)

# Shuffle
rng = np.random.RandomState(SHUFFLE_SEED)
rng.shuffle(audio_paths)
rng = np.random.RandomState(SHUFFLE_SEED)
rng.shuffle(labels)

# Split into training and validation
num_val_samples = int(VALID_SPLIT * len(audio_paths))
print("Using {} files for training.".format(len(audio_paths) - num_val_samples))
train_audio_paths = audio_paths[:-num_val_samples]
train_labels = labels[:-num_val_samples]

print("Using {} files for validation.".format(num_val_samples))
valid_audio_paths = audio_paths[-num_val_samples:]
valid_labels = labels[-num_val_samples:]

# Create 2 datasets, one for training and the other for validation
train_ds = paths_and_labels_to_dataset(train_audio_paths, train_labels)
train_ds = train_ds.shuffle(buffer_size=BATCH_SIZE * 8, seed=SHUFFLE_SEED).batch(
    BATCH_SIZE
)

valid_ds = paths_and_labels_to_dataset(valid_audio_paths, valid_labels)
valid_ds = valid_ds.shuffle(buffer_size=32 * 8, seed=SHUFFLE_SEED).batch(32)


# Add noise to the training set
train_ds = train_ds.map(
    lambda x, y: (add_noise(x, noises, scale=SCALE), y),
    num_parallel_calls=tf.data.AUTOTUNE,
)

# Transform audio wave to the frequency domain using `audio_to_fft`
train_ds = train_ds.map(
    lambda x, y: (audio_to_fft(x), y), num_parallel_calls=tf.data.AUTOTUNE
)
train_ds = train_ds.prefetch(tf.data.AUTOTUNE)

valid_ds = valid_ds.map(
    lambda x, y: (audio_to_fft(x), y), num_parallel_calls=tf.data.AUTOTUNE
)
valid_ds = valid_ds.prefetch(tf.data.AUTOTUNE)

Model Definition

def residual_block(x, filters, conv_num=3, activation="relu"):
    # Shortcut
    s = keras.layers.Conv1D(filters, 1, padding="same")(x)
    for i in range(conv_num - 1):
        x = keras.layers.Conv1D(filters, 3, padding="same")(x)
        x = keras.layers.Activation(activation)(x)
    x = keras.layers.Conv1D(filters, 3, padding="same")(x)
    x = keras.layers.Add()([x, s])
    x = keras.layers.Activation(activation)(x)
    return keras.layers.MaxPool1D(pool_size=2, strides=2)(x)


def build_model(input_shape, num_classes):
    inputs = keras.layers.Input(shape=input_shape, name="input")

    x = residual_block(inputs, 16, 2)
    x = residual_block(x, 32, 2)
    x = residual_block(x, 64, 3)
    x = residual_block(x, 128, 3)
    x = residual_block(x, 128, 3)

    x = keras.layers.AveragePooling1D(pool_size=3, strides=3)(x)
    x = keras.layers.Flatten()(x)
    x = keras.layers.Dense(256, activation="relu")(x)
    x = keras.layers.Dense(128, activation="relu")(x)

    outputs = keras.layers.Dense(num_classes, activation="softmax", name="output")(x)

    return keras.models.Model(inputs=inputs, outputs=outputs)


model = build_model((SAMPLING_RATE // 2, 1), len(class_names))

model.summary()

# Compile the model using Adam's default learning rate
model.compile(
    optimizer="Adam",
    loss="sparse_categorical_crossentropy",
    metrics=["accuracy"],
)

# Add callbacks:
# 'EarlyStopping' to stop training when the model is not enhancing anymore
# 'ModelCheckPoint' to always keep the model that has the best val_accuracy
model_save_filename = "model.keras"

earlystopping_cb = keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True)
mdlcheckpoint_cb = keras.callbacks.ModelCheckpoint(
    model_save_filename, monitor="val_accuracy", save_best_only=True
)

Training

history = model.fit(
    train_ds,
    epochs=EPOCHS,
    validation_data=valid_ds,
    callbacks=[earlystopping_cb, mdlcheckpoint_cb],
)

Evaluation

print(model.evaluate(valid_ds))

We get ~ 98% validation accuracy.

Demonstration

Let's take some samples and:

• Predict the speaker

• Compare the prediction with the real speaker

• Listen to the audio to see that despite the samples being noisy, the model is still pretty accurate

SAMPLES_TO_DISPLAY = 10

test_ds = paths_and_labels_to_dataset(valid_audio_paths, valid_labels)
test_ds = test_ds.shuffle(buffer_size=BATCH_SIZE * 8, seed=SHUFFLE_SEED).batch(
    BATCH_SIZE
)

test_ds = test_ds.map(
    lambda x, y: (add_noise(x, noises, scale=SCALE), y),
    num_parallel_calls=tf.data.AUTOTUNE,
)

for audios, labels in test_ds.take(1):
    # Get the signal FFT
    ffts = audio_to_fft(audios)
    # Predict
    y_pred = model.predict(ffts)
    # Take random samples
    rnd = np.random.randint(0, BATCH_SIZE, SAMPLES_TO_DISPLAY)
    audios = audios.numpy()[rnd, :, :]
    labels = labels.numpy()[rnd]
    y_pred = np.argmax(y_pred, axis=-1)[rnd]

    for index in range(SAMPLES_TO_DISPLAY):
        # For every sample, print the true and predicted label
        # as well as run the voice with the noise
        print(
            "Speaker:\33{} {}\33[0m\tPredicted:\33{} {}\33[0m".format(
                "[92m" if labels[index] == y_pred[index] else "[91m",
                class_names[labels[index]],
                "[92m" if labels[index] == y_pred[index] else "[91m",
                class_names[y_pred[index]],
            )
        )
        display(Audio(audios[index, :, :].squeeze(), rate=SAMPLING_RATE))