"""
StreamWriter Basic Usage
========================

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

This tutorial shows how to use :py:class:`torchaudio.io.StreamWriter` to
encode and save audio/video data into various formats/destinations.

"""

######################################################################
#
# .. note::
#
#    This tutorial requires torchaudio nightly build and FFmpeg libraries (>=4.1, <4.4).
#
#    To install torchaudio nightly build, please refer to
#    https://pytorch.org/get-started/locally/ .
#
#    There are multiple ways to install FFmpeg libraries.
#    If you are using Anaconda Python distribution,
#    ``conda install 'ffmpeg<4.4'`` will install the required FFmpeg libraries.
#

######################################################################
#
# .. warning::
#
#    TorchAudio dynamically loads compatible FFmpeg libraries
#    installed on the system.
#    The types of supported formats (media format, encoder, encoder
#    options, etc) depend on the libraries.
#
#    To check the available muxers and encoders, you can use the
#    following command
#
#    .. code-block:: console
#
#       ffmpeg -muxers
#       ffmpeg -encoders

######################################################################
#
# Preparation
# -----------

import torch
import torchaudio

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

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

try:
    from torchaudio.io import StreamWriter
except ImportError:
    try:
        import google.colab

        print(
            """
            To enable running this notebook in Google Colab, install nightly
            torch and torchaudio builds by adding the following code block to the top
            of the notebook before running it:
            !pip3 uninstall -y torch torchvision torchaudio
            !pip3 install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
            """
        )
    except ModuleNotFoundError:
        pass
    raise

print("FFmpeg library versions")
for k, v in torchaudio.utils.ffmpeg_utils.get_versions().items():
    print(f"  {k}: {v}")

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

import io
import os
import tempfile

from torchaudio.utils import download_asset
from IPython.display import Audio, Video

SAMPLE_PATH = download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav")
WAVEFORM, SAMPLE_RATE = torchaudio.load(SAMPLE_PATH, channels_first=False)
NUM_FRAMES, NUM_CHANNELS = WAVEFORM.shape

_BASE_DIR = tempfile.TemporaryDirectory()


def get_path(filename):
    return os.path.join(_BASE_DIR.name, filename)


######################################################################
#
# The basic usage
# ---------------
#
# To save Tensor data into media formats with StreamWriter, there
# are three necessary steps
#
# 1. Specify the output
# 2. Configure streams
# 3. Write data
#
# The following code illustrates how to save audio data as WAV file.
#

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

# 1. Define the destination. (local file in this case)
path = get_path("test.wav")
s = StreamWriter(path)

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

# 2. Configure the stream. (8kHz, Stereo WAV)
s.add_audio_stream(
    sample_rate=SAMPLE_RATE,
    num_channels=NUM_CHANNELS,
)

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

# 3. Write the data
with s.open():
    s.write_audio_chunk(0, WAVEFORM)

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

Audio(path)

######################################################################
#
# Now we look into each step in more detail.

######################################################################
#
# Write destination
# -----------------
#
# StreamWriter supports different types of write destinations
#
# 1. Local files
# 2. File-like objects
# 3. Streaming protocols (such as RTMP and UDP)
# 4. Media devices (speakers and video players) †
#
# † For media devices, please refer to
# `StreamWriter Advanced Usages <./streamwriter_advanced.html>`__.
#

######################################################################
# Local files
# ~~~~~~~~~~~
#
# StreamWriter supports saving media to local files.
#
#
# .. code::
#
#    StreamWriter(dst="audio.wav")
#
#    StreamWriter(dst="audio.mp3")
#
# This works for still images and videos as well.
#
# .. code::
#
#    StreamWriter(dst="image.jpeg")
#
#    StreamWriter(dst="video.mpeg")
#

######################################################################
# File-like objects
# ~~~~~~~~~~~~~~~~~
#
# You can also pass a file-lie object. A file-like object must implement
# ``write`` method conforming to :py:attr:`io.RawIOBase.write`.
#
# .. code::
#
#    # Open the local file as fileobj
#    with open("audio.wav", "wb") as dst:
#        StreamWriter(dst=dst)
#
# .. code::
#
#    # In-memory encoding
#    buffer = io.BytesIO()
#    StreamWriter(dst=buffer)
#

######################################################################
# Streaming protocols
# ~~~~~~~~~~~~~~~~~~~
#
# You can stream the media with streaming protocols
#
# .. code::
#
#    # Real-Time Messaging Protocol
#    StreamWriter(dst="rtmp://localhost:1234/live/app", format="flv")
#
#    # UDP
#    StreamWriter(dst="udp://localhost:48550", format="mpegts")
#

######################################################################
#
# Configuring output streams
# --------------------------
#
# Once the destination is specified, the next step is to configure the streams.
# For typical audio and still image cases, only one stream is required,
# but for video with audio, at least two streams (one for audio and the other
# for video) need to be configured.
#

######################################################################
# Audio Stream
# ~~~~~~~~~~~~
#
# An audio stream can be added with
# :py:meth:`~torchaudio.io.StreamWriter.add_audio_stream` method.
#
# For writing regular audio files, at minimum ``sample_rate`` and ``num_channels``
# are required.
#
# .. code::
#
#    s = StreamWriter("audio.wav")
#    s.add_audio_stream(sample_rate=8000, num_channels=2)
#
# By default, audio streams expect the input waveform tensors to be ``torch.float32`` type.
# If the above case, the data will be encoded into the detault encoding format of WAV format,
# which is 16-bit signed integer Linear PCM. StreamWriter converts the sample format internally.
#
# If the encoder supports multiple sample formats and you want to change the encoder sample format,
# you can use ``encoder_format`` option.
#
# In the following example, the StreamWriter expects the data type of the input waveform Tensor
# to be ``torch.float32``, but it will convert the sample to 16-bit signed integer when encoding.
#
# .. code::
#
#    s = StreamWriter("audio.mp3")
#    s.add_audio_stream(
#        ...,
#        encoder="libmp3lame",   # "libmp3lame" is often the default encoder for mp3,
#                                # but specifying it manually, for the sake of illustration.
#
#        encoder_format="s16p",  # "libmp3lame" encoder supports the following sample format.
#                                #  - "s16p" (16-bit signed integer)
#                                #  - "s32p" (32-bit signed integer)
#                                #  - "fltp" (32-bit floating point)
#    )
#
# If the data type of your waveform Tensor is something other than ``torch.float32``,
# you can provide ``format`` option to change the expected data type.
#
# The following example configures StreamWriter to expect Tensor of ``torch.int16`` type.
#
# .. code::
#
#    # Audio data passed to StreamWriter must be torch.int16
#    s.add_audio_stream(..., format="s16")
#
# The following figure illustrates how ``format`` and ``encoder_format`` options work
# for audio streams.
#
# .. image:: https://download.pytorch.org/torchaudio/tutorial-assets/streamwriter-format-audio.png
#

######################################################################
# Video Stream
# ~~~~~~~~~~~~
#
# To add a still image or a video stream, you can use
# :py:meth:`~torchaudio.io.StreamWriter.add_video_stream` method.
#
# At minimum, ``frame_rate``, ``height`` and ``width`` are required.
#
# .. code::
#
#    s = StreamWriter("video.mp4")
#    s.add_video_stream(frame_rate=10, height=96, width=128)
#
# For still images, please use ``frame_rate=1``.
#
# .. code::
#
#    s = StreamWriter("image.png")
#    s.add_video_stream(frame_rate=1, ...)
#
# Similar to the audio stream, you can provide ``format`` and ``encoder_format``
# option to controll the format of input data and encoding.
#
# The following example encodes video data in YUV422 format.
#
# .. code::
#
#    s = StreamWriter("video.mov")
#    s.add_video_stream(
#        ...,
#        encoder="libx264",  # libx264 supports different YUV formats, such as
#                            # yuv420p yuvj420p yuv422p yuvj422p yuv444p yuvj444p nv12 nv16 nv21
#
#        encoder_format="yuv422p",  # StreamWriter will convert the input data to YUV422 internally
#    )
#
# YUV formats are commonly used in video encoding. Many YUV formats are composed of chroma
# channel of different plane size than that of luma channel. This makes it difficult to
# directly express it as ``torch.Tensor`` type.
# Therefore, StreamWriter will automatically convert the input video Tensor into the target format.
#
# StreamWriter expects the input image tensor to be 4-D (`time`, `channel`, `height`, `width`)
# and ``torch.uint8`` type.
#
# The default color channel is RGB. That is three color channels corresponding red, green and blue.
# If your input has different color channel, such as BGR and YUV, you can specify it with
# ``format`` option.
#
# The following example specifies BGR format.
#
# .. code::
#
#    s.add_video_stream(..., format="bgr24")
#                       # Image data passed to StreamWriter must have
#                       # three color channels representing Blue Green Red.
#                       #
#                       # The shape of the input tensor has to be
#                       # (time, channel==3, height, width)
#
#
# The following figure illustrates how ``format`` and ``encoder_format`` options work for
# video streams.
#
# .. image:: https://download.pytorch.org/torchaudio/tutorial-assets/streamwriter-format-video.png
#

######################################################################
#
# Write data
# ----------
#
# Once streams are configured, the next step is to open the output location
# and start writing data.
#
# Use :py:meth:`~torchaudio.io.StreamWriter.open` method to open the
# destination, and then write data with :py:meth:`~torchaudio.io.StreamWriter.write_audio_chunk`
# and/or :py:meth:`~torchaudio.io.StreamWriter.write_video_chunk`.
#
# Audio tensors are expected to have the shape of `(time, channels)`,
# and video/image tensors are expected to have the shape of `(time, channels, height, width)`.
#
# Channels, height and width must match the configuration of the corresponding
# stream, specified with ``"format"`` option.
#
# Tensor representing a still image must have only one frame in time dimension,
# but audio and video tensors can have arbitral number of frames in time dimension.
#
# The following code snippet illustrates this;
#

######################################################################
# Ex) Audio
# ~~~~~~~~~
#

# Configure stream
s = StreamWriter(dst=get_path("audio.wav"))
s.add_audio_stream(sample_rate=SAMPLE_RATE, num_channels=NUM_CHANNELS)

# Write data
with s.open():
    s.write_audio_chunk(0, WAVEFORM)

######################################################################
# Ex) Image
# ~~~~~~~~~
#

# Image config
height = 96
width = 128

# Configure stream
s = StreamWriter(dst=get_path("image.png"))
s.add_video_stream(frame_rate=1, height=height, width=width, format="rgb24")

# Generate image
chunk = torch.randint(256, (1, 3, height, width), dtype=torch.uint8)

# Write data
with s.open():
    s.write_video_chunk(0, chunk)


######################################################################
# Ex) Video without audio
# ~~~~~~~~~~~~~~~~~~~~~~~
#

# Video config
frame_rate = 30
height = 96
width = 128

# Configure stream
s = StreamWriter(dst=get_path("video.mp4"))
s.add_video_stream(frame_rate=frame_rate, height=height, width=width, format="rgb24")

# Generate video chunk (3 seconds)
time = int(frame_rate * 3)
chunk = torch.randint(256, (time, 3, height, width), dtype=torch.uint8)

# Write data
with s.open():
    s.write_video_chunk(0, chunk)

######################################################################
# Ex) Video with audio
# ~~~~~~~~~~~~~~~~~~~~
#
# To write video with audio, separate streams have to be configured.
#

# Configure stream
s = StreamWriter(dst=get_path("video.mp4"))
s.add_audio_stream(sample_rate=SAMPLE_RATE, num_channels=NUM_CHANNELS)
s.add_video_stream(frame_rate=frame_rate, height=height, width=width, format="rgb24")

# Generate audio/video chunk (3 seconds)
time = int(SAMPLE_RATE * 3)
audio_chunk = torch.randn((time, NUM_CHANNELS))
time = int(frame_rate * 3)
video_chunk = torch.randint(256, (time, 3, height, width), dtype=torch.uint8)

# Write data
with s.open():
    s.write_audio_chunk(0, audio_chunk)
    s.write_video_chunk(1, video_chunk)


######################################################################
# Writing data chunk by chunk
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# When writing data, it is possible to split data along time dimension and
# write them by smaller chunks.
#

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

# Write data in one-go
dst1 = io.BytesIO()
s = StreamWriter(dst=dst1, format="mp3")
s.add_audio_stream(SAMPLE_RATE, NUM_CHANNELS)
with s.open():
    s.write_audio_chunk(0, WAVEFORM)

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

# Write data in smaller chunks
dst2 = io.BytesIO()
s = StreamWriter(dst=dst2, format="mp3")
s.add_audio_stream(SAMPLE_RATE, NUM_CHANNELS)
with s.open():
    for start in range(0, NUM_FRAMES, SAMPLE_RATE):
        end = start + SAMPLE_RATE
        s.write_audio_chunk(0, WAVEFORM[start:end, ...])

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

# Check that the contents are same
dst1.seek(0)
bytes1 = dst1.read()

print(f"bytes1: {len(bytes1)}")
print(f"{bytes1[:10]}...{bytes1[-10:]}\n")

dst2.seek(0)
bytes2 = dst2.read()

print(f"bytes2: {len(bytes2)}")
print(f"{bytes2[:10]}...{bytes2[-10:]}\n")

assert bytes1 == bytes2

######################################################################
# Note on slicing and AAC
# ~~~~~~~~~~~~~~~~~~~~~~~
#
# .. warning::
#
#    FFmpeg's native AAC encoder (which is used by default when
#    saving video with MP4 format) has a bug that affects the audibility.
#
#    Please refer to the examples bellow.
#

def test_slice(audio_encoder, slice_size, ext="mp4"):
    path = get_path(f"slice_{slice_size}.{ext}")

    s = StreamWriter(dst=path)
    s.add_audio_stream(SAMPLE_RATE, NUM_CHANNELS, encoder=audio_encoder)
    with s.open():
        for start in range(0, NUM_FRAMES, slice_size):
            end = start + slice_size
            s.write_audio_chunk(0, WAVEFORM[start:end, ...])
    return path

######################################################################
#
# This causes some artifacts.

# note:
# Chrome does not support playing AAC audio directly while Safari does.
# Using MP4 container and specifying AAC allows Chrome to play it.
Video(test_slice(audio_encoder="aac", slice_size=8000, ext="mp4"), embed=True)

######################################################################
#
# It is more noticeable when using smaller slice.
Video(test_slice(audio_encoder="aac", slice_size=512, ext="mp4"), embed=True)

######################################################################
#
# Lame MP3 encoder works fine for the same slice size.
Audio(test_slice(audio_encoder="libmp3lame", slice_size=512, ext="mp3"))

######################################################################
#
# Example - Spectrum Visualizer
# -----------------------------
#
# In this section, we use StreamWriter to create a spectrum visualization
# of audio and save it as a video file.
#
# To create spectrum visualization, we use
# :py:class:`torchaudio.transforms.Spectrogram`, to get spectrum presentation
# of audio, generate raster images of its visualization using matplotplib,
# then use StreamWriter to convert them to video with the original audio.

import torchaudio.transforms as T
import matplotlib.pyplot as plt

######################################################################
#
# Prepare Data
# ~~~~~~~~~~~~
#
# First, we prepare the spectrogram data.
# We use :py:class:`~torchaudio.transforms.Spectrogram`.
#
# We adjust ``hop_length`` so that one frame of the spectrogram corresponds
# to one video frame.
#

frame_rate = 20
n_fft = 4000

trans = T.Spectrogram(
    n_fft=n_fft,
    hop_length=SAMPLE_RATE // frame_rate,  # One FFT per one video frame
    normalized=True,
    power=1,
)
specs = trans(WAVEFORM.T)[0].T

######################################################################
#
# The resulting spectrogram looks like the following.
#

spec_db = T.AmplitudeToDB(stype="magnitude", top_db=80)(specs.T)
_ = plt.imshow(spec_db, aspect="auto", origin='lower')

######################################################################
#
# Prepare Canvas
# ~~~~~~~~~~~~~~
#
# We use ``matplotlib`` to visualize the spectrogram per frame.
# We create a helper function that plots the spectrogram data and
# generates a raster imager of the figure.
#

fig, ax = plt.subplots(figsize=[3.2, 2.4])
ax.set_position([0, 0, 1, 1])
ax.set_facecolor("black")
ncols, nrows = fig.canvas.get_width_height()


def _plot(data):
    ax.clear()
    x = list(range(len(data)))
    R, G, B = 238/255, 76/255, 44/255
    for coeff, alpha in [(0.8, 0.7), (1, 1)]:
        d = data ** coeff
        ax.fill_between(x, d, -d, color=[R, G, B, alpha])
    xlim = n_fft // 2 + 1
    ax.set_xlim([-1, n_fft // 2 + 1])
    ax.set_ylim([-1, 1])
    ax.text(
        xlim, 0.95,
        f"Created with TorchAudio\n{torchaudio.__version__}",
        color="white", ha="right", va="top", backgroundcolor="black")
    fig.canvas.draw()
    frame = torch.frombuffer(fig.canvas.tostring_rgb(), dtype=torch.uint8)
    return frame.reshape(nrows, ncols, 3).permute(2, 0, 1)

# sphinx_gallery_defer_figures

######################################################################
#
# Write Video
# ~~~~~~~~~~~
#
# Finally, we use StreamWriter and write video.
# We process one second of audio and video frames at a time.
#

s = StreamWriter(get_path("example.mp4"))
s.add_audio_stream(sample_rate=SAMPLE_RATE, num_channels=NUM_CHANNELS)
s.add_video_stream(frame_rate=frame_rate, height=nrows, width=ncols)

with s.open():
    i = 0
    # Process by second
    for t in range(0, NUM_FRAMES, SAMPLE_RATE):
        # Write audio chunk
        s.write_audio_chunk(0, WAVEFORM[t:t + SAMPLE_RATE, :])

        # write 1 second of video chunk
        frames = [_plot(spec) for spec in specs[i:i+frame_rate]]
        if frames:
            s.write_video_chunk(1, torch.stack(frames))
        i += frame_rate

# sphinx_gallery_defer_figures

######################################################################
#
# Result
# ~~~~~~
#
# The result looks like below.
#
#

Video(get_path("example.mp4"), embed=True)

# sphinx_gallery_defer_figures

######################################################################
#
# Carefully watching the video, it can be
# observed that the sound of "s" (curio\ **si**\ ty, be\ **si**\ des, thi\ **s**\ ) has
# more energy allocated on higher frequency side (right side of the video).

######################################################################
#
# Tag: :obj:`torchaudio.io`
#
