# -*- coding: utf-8 -*-
"""
Audio I/O
=========

``torchaudio`` integrates ``libsox`` and provides a rich set of audio I/O.
"""

# When running this tutorial in Google Colab, install the required packages
# with the following.
# !pip install torchaudio boto3

import torch
import torchaudio

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

######################################################################
# Preparing data and utility functions (skip this section)
# --------------------------------------------------------
#

# @title Prepare data and utility functions. {display-mode: "form"}
# @markdown
# @markdown You do not need to look into this cell.
# @markdown Just execute once and you are good to go.
# @markdown
# @markdown In this tutorial, we will use a speech data from [VOiCES dataset](https://iqtlabs.github.io/voices/),
# @markdown which is licensed under Creative Commos BY 4.0.


import io
import os
import requests
import tarfile

import boto3
from botocore import UNSIGNED
from botocore.config import Config
import matplotlib.pyplot as plt
from IPython.display import Audio, display


_SAMPLE_DIR = "_assets"
SAMPLE_WAV_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.wav"
SAMPLE_WAV_PATH = os.path.join(_SAMPLE_DIR, "steam.wav")

SAMPLE_MP3_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.mp3"
SAMPLE_MP3_PATH = os.path.join(_SAMPLE_DIR, "steam.mp3")

SAMPLE_GSM_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.gsm"
SAMPLE_GSM_PATH = os.path.join(_SAMPLE_DIR, "steam.gsm")

SAMPLE_WAV_SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"  # noqa: E501
SAMPLE_WAV_SPEECH_PATH = os.path.join(_SAMPLE_DIR, "speech.wav")

SAMPLE_TAR_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit.tar.gz"
SAMPLE_TAR_PATH = os.path.join(_SAMPLE_DIR, "sample.tar.gz")
SAMPLE_TAR_ITEM = "VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"

S3_BUCKET = "pytorch-tutorial-assets"
S3_KEY = "VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"


def _fetch_data():
    os.makedirs(_SAMPLE_DIR, exist_ok=True)
    uri = [
        (SAMPLE_WAV_URL, SAMPLE_WAV_PATH),
        (SAMPLE_MP3_URL, SAMPLE_MP3_PATH),
        (SAMPLE_GSM_URL, SAMPLE_GSM_PATH),
        (SAMPLE_WAV_SPEECH_URL, SAMPLE_WAV_SPEECH_PATH),
        (SAMPLE_TAR_URL, SAMPLE_TAR_PATH),
    ]
    for url, path in uri:
        with open(path, "wb") as file_:
            file_.write(requests.get(url).content)


_fetch_data()


def print_stats(waveform, sample_rate=None, src=None):
    if src:
        print("-" * 10)
        print("Source:", src)
        print("-" * 10)
    if sample_rate:
        print("Sample Rate:", sample_rate)
    print("Shape:", tuple(waveform.shape))
    print("Dtype:", waveform.dtype)
    print(f" - Max:     {waveform.max().item():6.3f}")
    print(f" - Min:     {waveform.min().item():6.3f}")
    print(f" - Mean:    {waveform.mean().item():6.3f}")
    print(f" - Std Dev: {waveform.std().item():6.3f}")
    print()
    print(waveform)
    print()


def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=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)
        if ylim:
            axes[c].set_ylim(ylim)
    figure.suptitle(title)
    plt.show(block=False)


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

    num_channels, num_frames = 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)


def play_audio(waveform, sample_rate):
    waveform = waveform.numpy()

    num_channels, num_frames = waveform.shape
    if num_channels == 1:
        display(Audio(waveform[0], rate=sample_rate))
    elif num_channels == 2:
        display(Audio((waveform[0], waveform[1]), rate=sample_rate))
    else:
        raise ValueError("Waveform with more than 2 channels are not supported.")


def _get_sample(path, resample=None):
    effects = [["remix", "1"]]
    if resample:
        effects.extend(
            [
                ["lowpass", f"{resample // 2}"],
                ["rate", f"{resample}"],
            ]
        )
    return torchaudio.sox_effects.apply_effects_file(path, effects=effects)


def get_sample(*, resample=None):
    return _get_sample(SAMPLE_WAV_PATH, resample=resample)


def inspect_file(path):
    print("-" * 10)
    print("Source:", path)
    print("-" * 10)
    print(f" - File size: {os.path.getsize(path)} bytes")
    print(f" - {torchaudio.info(path)}")


######################################################################
# Querying audio metadata
# -----------------------
#
# Function ``torchaudio.info`` fetches audio metadata. You can provide
# a path-like object or file-like object.
#


metadata = torchaudio.info(SAMPLE_WAV_PATH)
print(metadata)

######################################################################
# Where
#
# -  ``sample_rate`` is the sampling rate of the audio
# -  ``num_channels`` is the number of channels
# -  ``num_frames`` is the number of frames per channel
# -  ``bits_per_sample`` is bit depth
# -  ``encoding`` is the sample coding format
#
# ``encoding`` can take on one of the following values:
#
# -  ``"PCM_S"``: Signed integer linear PCM
# -  ``"PCM_U"``: Unsigned integer linear PCM
# -  ``"PCM_F"``: Floating point linear PCM
# -  ``"FLAC"``: Flac, `Free Lossless Audio
#    Codec <https://xiph.org/flac/>`__
# -  ``"ULAW"``: Mu-law,
#    [`wikipedia <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`__]
# -  ``"ALAW"``: A-law
#    [`wikipedia <https://en.wikipedia.org/wiki/A-law_algorithm>`__]
# -  ``"MP3"`` : MP3, MPEG-1 Audio Layer III
# -  ``"VORBIS"``: OGG Vorbis [`xiph.org <https://xiph.org/vorbis/>`__]
# -  ``"AMR_NB"``: Adaptive Multi-Rate
#    [`wikipedia <https://en.wikipedia.org/wiki/Adaptive_Multi-Rate_audio_codec>`__]
# -  ``"AMR_WB"``: Adaptive Multi-Rate Wideband
#    [`wikipedia <https://en.wikipedia.org/wiki/Adaptive_Multi-Rate_Wideband>`__]
# -  ``"OPUS"``: Opus [`opus-codec.org <https://opus-codec.org/>`__]
# -  ``"GSM"``: GSM-FR
#    [`wikipedia <https://en.wikipedia.org/wiki/Full_Rate>`__]
# -  ``"UNKNOWN"`` None of above
#

######################################################################
# **Note**
#
# -  ``bits_per_sample`` can be ``0`` for formats with compression and/or
#    variable bit rate (such as MP3).
# -  ``num_frames`` can be ``0`` for GSM-FR format.
#

metadata = torchaudio.info(SAMPLE_MP3_PATH)
print(metadata)

metadata = torchaudio.info(SAMPLE_GSM_PATH)
print(metadata)


######################################################################
# Querying file-like object
# ~~~~~~~~~~~~~~~~~~~~~~~~~
#
# ``info`` works on file-like objects.
#

print("Source:", SAMPLE_WAV_URL)
with requests.get(SAMPLE_WAV_URL, stream=True) as response:
    metadata = torchaudio.info(response.raw)
print(metadata)

######################################################################
# **Note** When passing a file-like object, ``info`` does not read
# all of the underlying data; rather, it reads only a portion
# of the data from the beginning.
# Therefore, for a given audio format, it may not be able to retrieve the
# correct metadata, including the format itself.
# The following example illustrates this.
#
# -  Use argument ``format`` to specify the audio format of the input.
# -  The returned metadata has ``num_frames = 0``
#

print("Source:", SAMPLE_MP3_URL)
with requests.get(SAMPLE_MP3_URL, stream=True) as response:
    metadata = torchaudio.info(response.raw, format="mp3")

    print(f"Fetched {response.raw.tell()} bytes.")
print(metadata)

######################################################################
# Loading audio data into Tensor
# ------------------------------
#
# To load audio data, you can use ``torchaudio.load``.
#
# This function accepts a path-like object or file-like object as input.
#
# The returned value is a tuple of waveform (``Tensor``) and sample rate
# (``int``).
#
# By default, the resulting tensor object has ``dtype=torch.float32`` and
# its value range is normalized within ``[-1.0, 1.0]``.
#
# For the list of supported format, please refer to `the torchaudio
# documentation <https://pytorch.org/audio>`__.
#

waveform, sample_rate = torchaudio.load(SAMPLE_WAV_SPEECH_PATH)

print_stats(waveform, sample_rate=sample_rate)
plot_waveform(waveform, sample_rate)
plot_specgram(waveform, sample_rate)
play_audio(waveform, sample_rate)


######################################################################
# Loading from file-like object
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# ``torchaudio``\ ’s I/O functions now support file-like objects. This
# allows for fetching and decoding audio data from locations
# within and beyond the local file system.
# The following examples illustrate this.
#

# Load audio data as HTTP request
with requests.get(SAMPLE_WAV_SPEECH_URL, stream=True) as response:
    waveform, sample_rate = torchaudio.load(response.raw)
plot_specgram(waveform, sample_rate, title="HTTP datasource")

# Load audio from tar file
with tarfile.open(SAMPLE_TAR_PATH, mode="r") as tarfile_:
    fileobj = tarfile_.extractfile(SAMPLE_TAR_ITEM)
    waveform, sample_rate = torchaudio.load(fileobj)
plot_specgram(waveform, sample_rate, title="TAR file")

# Load audio from S3
client = boto3.client("s3", config=Config(signature_version=UNSIGNED))
response = client.get_object(Bucket=S3_BUCKET, Key=S3_KEY)
waveform, sample_rate = torchaudio.load(response["Body"])
plot_specgram(waveform, sample_rate, title="From S3")


######################################################################
# Tips on slicing
# ~~~~~~~~~~~~~~~
#
# Providing ``num_frames`` and ``frame_offset`` arguments restricts
# decoding to the corresponding segment of the input.
#
# The same result can be achieved using vanilla Tensor slicing,
# (i.e. ``waveform[:, frame_offset:frame_offset+num_frames]``). However,
# providing ``num_frames`` and ``frame_offset`` arguments is more
# efficient.
#
# This is because the function will end data acquisition and decoding
# once it finishes decoding the requested frames. This is advantageous
# when the audio data are transferred via network as the data transfer will
# stop as soon as the necessary amount of data is fetched.
#
# The following example illustrates this.
#

# Illustration of two different decoding methods.
# The first one will fetch all the data and decode them, while
# the second one will stop fetching data once it completes decoding.
# The resulting waveforms are identical.

frame_offset, num_frames = 16000, 16000  # Fetch and decode the 1 - 2 seconds

print("Fetching all the data...")
with requests.get(SAMPLE_WAV_SPEECH_URL, stream=True) as response:
    waveform1, sample_rate1 = torchaudio.load(response.raw)
    waveform1 = waveform1[:, frame_offset: frame_offset + num_frames]
    print(f" - Fetched {response.raw.tell()} bytes")

print("Fetching until the requested frames are available...")
with requests.get(SAMPLE_WAV_SPEECH_URL, stream=True) as response:
    waveform2, sample_rate2 = torchaudio.load(
        response.raw, frame_offset=frame_offset, num_frames=num_frames
    )
    print(f" - Fetched {response.raw.tell()} bytes")

print("Checking the resulting waveform ... ", end="")
assert (waveform1 == waveform2).all()
print("matched!")


######################################################################
# Saving audio to file
# --------------------
#
# To save audio data in formats interpretable by common applications,
# you can use ``torchaudio.save``.
#
# This function accepts a path-like object or file-like object.
#
# When passing a file-like object, you also need to provide argument ``format``
# so that the function knows which format it should use. In the
# case of a path-like object, the function will infer the format from
# the extension. If you are saving to a file without an extension, you need
# to provide argument ``format``.
#
# When saving WAV-formatted data, the default encoding for ``float32`` Tensor
# is 32-bit floating-point PCM. You can provide arguments ``encoding`` and
# ``bits_per_sample`` to change this behavior. For example, to save data
# in 16-bit signed integer PCM, you can do the following.
#
# **Note** Saving data in encodings with lower bit depth reduces the
# resulting file size but also precision.
#


waveform, sample_rate = get_sample()
print_stats(waveform, sample_rate=sample_rate)

# Save without any encoding option.
# The function will pick up the encoding which
# the provided data fit
path = f"{_SAMPLE_DIR}/save_example_default.wav"
torchaudio.save(path, waveform, sample_rate)
inspect_file(path)

# Save as 16-bit signed integer Linear PCM
# The resulting file occupies half the storage but loses precision
path = f"{_SAMPLE_DIR}/save_example_PCM_S16.wav"
torchaudio.save(path, waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)
inspect_file(path)


######################################################################
# ``torchaudio.save`` can also handle other formats. To name a few:
#

waveform, sample_rate = get_sample(resample=8000)

formats = [
    "mp3",
    "flac",
    "vorbis",
    "sph",
    "amb",
    "amr-nb",
    "gsm",
]

for format in formats:
    path = f"{_SAMPLE_DIR}/save_example.{format}"
    torchaudio.save(path, waveform, sample_rate, format=format)
    inspect_file(path)


######################################################################
# Saving to file-like object
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# Similar to the other I/O functions, you can save audio to file-like
# objects. When saving to a file-like object, argument ``format`` is
# required.
#


waveform, sample_rate = get_sample()

# Saving to bytes buffer
buffer_ = io.BytesIO()
torchaudio.save(buffer_, waveform, sample_rate, format="wav")

buffer_.seek(0)
print(buffer_.read(16))
