# -*- coding: utf-8 -*-
"""
Audio Data Augmentation
=======================

**Author**: `Moto Hira <moto@meta.com>`__

``torchaudio`` provides a variety of ways to augment audio data.

In this tutorial, we look into a way to apply effects, filters,
RIR (room impulse response) and codecs.

At the end, we synthesize noisy speech over phone from clean speech.
"""

import torch
import torchaudio
import torchaudio.functional as F

print(torch.__version__)
print(torchaudio.__version__)

######################################################################
# Preparation
# -----------
#
# First, we import the modules and download the audio assets we use in this tutorial.
#

import math

from IPython.display import Audio
import matplotlib.pyplot as plt

from torchaudio.utils import download_asset

SAMPLE_WAV = download_asset("tutorial-assets/steam-train-whistle-daniel_simon.wav")
SAMPLE_RIR = download_asset("tutorial-assets/Lab41-SRI-VOiCES-rm1-impulse-mc01-stu-clo-8000hz.wav")
SAMPLE_SPEECH = download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042-8000hz.wav")
SAMPLE_NOISE = download_asset("tutorial-assets/Lab41-SRI-VOiCES-rm1-babb-mc01-stu-clo-8000hz.wav")


######################################################################
# Applying effects and filtering
# ------------------------------
#
# :py:func:`torchaudio.sox_effects` allows for directly applying filters similar to
# those available in ``sox`` to Tensor objects and file object audio sources.
#
# There are two functions for this:
#
# -  :py:func:`torchaudio.sox_effects.apply_effects_tensor` for applying effects
#    to Tensor.
# -  :py:func:`torchaudio.sox_effects.apply_effects_file` for applying effects to
#    other audio sources.
#
# Both functions accept effect definitions in the form
# ``List[List[str]]``.
# This is mostly consistent with how ``sox`` command works, but one caveat is
# that ``sox`` adds some effects automatically, whereas ``torchaudio``’s
# implementation does not.
#
# For the list of available effects, please refer to `the sox
# documentation <http://sox.sourceforge.net/sox.html>`__.
#
# **Tip** If you need to load and resample your audio data on the fly,
# then you can use :py:func:`torchaudio.sox_effects.apply_effects_file`
# with effect ``"rate"``.
#
# **Note** :py:func:`torchaudio.sox_effects.apply_effects_file` accepts a
# file-like object or path-like object.
# Similar to :py:func:`torchaudio.load`, when the audio format cannot be
# inferred from either the file extension or header, you can provide
# argument ``format`` to specify the format of the audio source.
#
# **Note** This process is not differentiable.
#

# Load the data
waveform1, sample_rate1 = torchaudio.load(SAMPLE_WAV)

# Define effects
effects = [
    ["lowpass", "-1", "300"],  # apply single-pole lowpass filter
    ["speed", "0.8"],  # reduce the speed
    # This only changes sample rate, so it is necessary to
    # add `rate` effect with original sample rate after this.
    ["rate", f"{sample_rate1}"],
    ["reverb", "-w"],  # Reverbration gives some dramatic feeling
]

# Apply effects
waveform2, sample_rate2 = torchaudio.sox_effects.apply_effects_tensor(waveform1, sample_rate1, effects)

print(waveform1.shape, sample_rate1)
print(waveform2.shape, sample_rate2)

######################################################################
# Note that the number of frames and number of channels are different from
# those of the original after the effects are applied. Let’s listen to the
# audio.
#

def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None):
    waveform = waveform.numpy()

    num_channels, num_frames = waveform.shape
    time_axis = torch.arange(0, num_frames) / sample_rate

    figure, axes = plt.subplots(num_channels, 1)
    if num_channels == 1:
        axes = [axes]
    for c in range(num_channels):
        axes[c].plot(time_axis, waveform[c], linewidth=1)
        axes[c].grid(True)
        if num_channels > 1:
            axes[c].set_ylabel(f"Channel {c+1}")
        if xlim:
            axes[c].set_xlim(xlim)
    figure.suptitle(title)
    plt.show(block=False)

######################################################################
#

def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
    waveform = waveform.numpy()

    num_channels, _ = waveform.shape

    figure, axes = plt.subplots(num_channels, 1)
    if num_channels == 1:
        axes = [axes]
    for c in range(num_channels):
        axes[c].specgram(waveform[c], Fs=sample_rate)
        if num_channels > 1:
            axes[c].set_ylabel(f"Channel {c+1}")
        if xlim:
            axes[c].set_xlim(xlim)
    figure.suptitle(title)
    plt.show(block=False)

######################################################################
# Original:
# ~~~~~~~~~
#

plot_waveform(waveform1, sample_rate1, title="Original", xlim=(-0.1, 3.2))
plot_specgram(waveform1, sample_rate1, title="Original", xlim=(0, 3.04))
Audio(waveform1, rate=sample_rate1)

######################################################################
# Effects applied:
# ~~~~~~~~~~~~~~~~
#

plot_waveform(waveform2, sample_rate2, title="Effects Applied", xlim=(-0.1, 3.2))
plot_specgram(waveform2, sample_rate2, title="Effects Applied", xlim=(0, 3.04))
Audio(waveform2, rate=sample_rate2)

######################################################################
# Doesn’t it sound more dramatic?
#

######################################################################
# Simulating room reverberation
# -----------------------------
#
# `Convolution
# reverb <https://en.wikipedia.org/wiki/Convolution_reverb>`__ is a
# technique that's used to make clean audio sound as though it has been
# produced in a different environment.
#
# Using Room Impulse Response (RIR), for instance, we can make clean speech
# sound as though it has been uttered in a conference room.
#
# For this process, we need RIR data. The following data are from the VOiCES
# dataset, but you can record your own — just turn on your microphone
# and clap your hands.
#

rir_raw, sample_rate = torchaudio.load(SAMPLE_RIR)
plot_waveform(rir_raw, sample_rate, title="Room Impulse Response (raw)")
plot_specgram(rir_raw, sample_rate, title="Room Impulse Response (raw)")
Audio(rir_raw, rate=sample_rate)

######################################################################
# First, we need to clean up the RIR. We extract the main impulse, normalize
# the signal power, then flip along the time axis.
#

rir = rir_raw[:, int(sample_rate * 1.01) : int(sample_rate * 1.3)]
rir = rir / torch.norm(rir, p=2)
RIR = torch.flip(rir, [1])

plot_waveform(rir, sample_rate, title="Room Impulse Response")

######################################################################
# Then, we convolve the speech signal with the RIR filter.
#

speech, _ = torchaudio.load(SAMPLE_SPEECH)

speech_ = torch.nn.functional.pad(speech, (RIR.shape[1] - 1, 0))
augmented = torch.nn.functional.conv1d(speech_[None, ...], RIR[None, ...])[0]

######################################################################
# Original:
# ~~~~~~~~~
#

plot_waveform(speech, sample_rate, title="Original")
plot_specgram(speech, sample_rate, title="Original")
Audio(speech, rate=sample_rate)

######################################################################
# RIR applied:
# ~~~~~~~~~~~~
#

plot_waveform(augmented, sample_rate, title="RIR Applied")
plot_specgram(augmented, sample_rate, title="RIR Applied")
Audio(augmented, rate=sample_rate)


######################################################################
# Adding background noise
# -----------------------
#
# To add background noise to audio data, you can simply add a noise Tensor to
# the Tensor representing the audio data. A common method to adjust the
# intensity of noise is changing the Signal-to-Noise Ratio (SNR).
# [`wikipedia <https://en.wikipedia.org/wiki/Signal-to-noise_ratio>`__]
#
# $$ \\mathrm{SNR} = \\frac{P_{signal}}{P_{noise}} $$
#
# $$ \\mathrm{SNR_{dB}} = 10 \\log _{{10}} \\mathrm {SNR} $$
#

speech, _ = torchaudio.load(SAMPLE_SPEECH)
noise, _ = torchaudio.load(SAMPLE_NOISE)
noise = noise[:, : speech.shape[1]]

speech_rms = speech.norm(p=2)
noise_rms = noise.norm(p=2)

snr_dbs = [20, 10, 3]
noisy_speeches = []
for snr_db in snr_dbs:
    snr = 10 ** (snr_db / 20)
    scale = snr * noise_rms / speech_rms
    noisy_speeches.append((scale * speech + noise) / 2)

######################################################################
# Background noise:
# ~~~~~~~~~~~~~~~~~
#

plot_waveform(noise, sample_rate, title="Background noise")
plot_specgram(noise, sample_rate, title="Background noise")
Audio(noise, rate=sample_rate)

######################################################################
# SNR 20 dB:
# ~~~~~~~~~~
#

snr_db, noisy_speech = snr_dbs[0], noisy_speeches[0]
plot_waveform(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
plot_specgram(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
Audio(noisy_speech, rate=sample_rate)

######################################################################
# SNR 10 dB:
# ~~~~~~~~~~
#

snr_db, noisy_speech = snr_dbs[1], noisy_speeches[1]
plot_waveform(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
plot_specgram(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
Audio(noisy_speech, rate=sample_rate)

######################################################################
# SNR 3 dB:
# ~~~~~~~~~
#

snr_db, noisy_speech = snr_dbs[2], noisy_speeches[2]
plot_waveform(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
plot_specgram(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]")
Audio(noisy_speech, rate=sample_rate)


######################################################################
# Applying codec to Tensor object
# -------------------------------
#
# :py:func:`torchaudio.functional.apply_codec` can apply codecs to
# a Tensor object.
#
# **Note** This process is not differentiable.
#


waveform, sample_rate = torchaudio.load(SAMPLE_SPEECH)

configs = [
    {"format": "wav", "encoding": "ULAW", "bits_per_sample": 8},
    {"format": "gsm"},
    {"format": "vorbis", "compression": -1},
]
waveforms = []
for param in configs:
    augmented = F.apply_codec(waveform, sample_rate, **param)
    waveforms.append(augmented)

######################################################################
# Original:
# ~~~~~~~~~
#

plot_waveform(waveform, sample_rate, title="Original")
plot_specgram(waveform, sample_rate, title="Original")
Audio(waveform, rate=sample_rate)

######################################################################
# 8 bit mu-law:
# ~~~~~~~~~~~~~
#

plot_waveform(waveforms[0], sample_rate, title="8 bit mu-law")
plot_specgram(waveforms[0], sample_rate, title="8 bit mu-law")
Audio(waveforms[0], rate=sample_rate)

######################################################################
# GSM-FR:
# ~~~~~~~
#

plot_waveform(waveforms[1], sample_rate, title="GSM-FR")
plot_specgram(waveforms[1], sample_rate, title="GSM-FR")
Audio(waveforms[1], rate=sample_rate)

######################################################################
# Vorbis:
# ~~~~~~~
#

plot_waveform(waveforms[2], sample_rate, title="Vorbis")
plot_specgram(waveforms[2], sample_rate, title="Vorbis")
Audio(waveforms[2], rate=sample_rate)

######################################################################
# Simulating a phone recoding
# ---------------------------
#
# Combining the previous techniques, we can simulate audio that sounds
# like a person talking over a phone in a echoey room with people talking
# in the background.
#

sample_rate = 16000
original_speech, sample_rate = torchaudio.load(SAMPLE_SPEECH)

plot_specgram(original_speech, sample_rate, title="Original")

# Apply RIR
speech_ = torch.nn.functional.pad(original_speech, (RIR.shape[1] - 1, 0))
rir_applied = torch.nn.functional.conv1d(speech_[None, ...], RIR[None, ...])[0]

plot_specgram(rir_applied, sample_rate, title="RIR Applied")

# Add background noise
# Because the noise is recorded in the actual environment, we consider that
# the noise contains the acoustic feature of the environment. Therefore, we add
# the noise after RIR application.
noise, _ = torchaudio.load(SAMPLE_NOISE)
noise = noise[:, : rir_applied.shape[1]]

snr_db = 8
scale = (10 ** (snr_db / 20)) * noise.norm(p=2) / rir_applied.norm(p=2)
bg_added = (scale * rir_applied + noise) / 2

plot_specgram(bg_added, sample_rate, title="BG noise added")

# Apply filtering and change sample rate
filtered, sample_rate2 = torchaudio.sox_effects.apply_effects_tensor(
    bg_added,
    sample_rate,
    effects=[
        ["lowpass", "4000"],
        [
            "compand",
            "0.02,0.05",
            "-60,-60,-30,-10,-20,-8,-5,-8,-2,-8",
            "-8",
            "-7",
            "0.05",
        ],
        ["rate", "8000"],
    ],
)

plot_specgram(filtered, sample_rate2, title="Filtered")

# Apply telephony codec
codec_applied = F.apply_codec(filtered, sample_rate2, format="gsm")

plot_specgram(codec_applied, sample_rate2, title="GSM Codec Applied")


######################################################################
# Original speech:
# ~~~~~~~~~~~~~~~~
#

Audio(original_speech, rate=sample_rate)

######################################################################
# RIR applied:
# ~~~~~~~~~~~~
#

Audio(rir_applied, rate=sample_rate)

######################################################################
# Background noise added:
# ~~~~~~~~~~~~~~~~~~~~~~~
#

Audio(bg_added, rate=sample_rate)

######################################################################
# Filtered:
# ~~~~~~~~~
#

Audio(filtered, rate=sample_rate2)

######################################################################
# Codec applied:
# ~~~~~~~~~~~~~~
#

Audio(codec_applied, rate=sample_rate2)
