sox_io_backend.py

from typing import Tuple, Optional

import torch
from torchaudio._internal import (
    module_utils as _mod_utils,
)


class AudioMetaData:
    """Data class to be returned by :py:func:`~torchaudio.backend.sox_io_backend.info`.

    :ivar int sample_rate: Sample rate
    :ivar int num_frames: The number of frames
    :ivar int num_channels: The number of channels
    """
    def __init__(self, sample_rate: int, num_frames: int, num_channels: int):
        self.sample_rate = sample_rate
        self.num_frames = num_frames
        self.num_channels = num_channels


@_mod_utils.requires_module('torchaudio._torchaudio')
def info(filepath: str) -> AudioMetaData:
    """Get signal information of an audio file.

    Args:
        filepath (str): Path to audio file

    Returns:
        AudioMetaData: meta data of the given audio.
    """
    sinfo = torch.ops.torchaudio.sox_io_get_info(filepath)
    return AudioMetaData(sinfo.get_sample_rate(), sinfo.get_num_frames(), sinfo.get_num_channels())


@_mod_utils.requires_module('torchaudio._torchaudio')
def load(
        filepath: str,
        frame_offset: int = 0,
        num_frames: int = -1,
        normalize: bool = True,
        channels_first: bool = True,
) -> Tuple[torch.Tensor, int]:
    """Load audio data from file.

    Note:
        This function can handle all the codecs that underlying libsox can handle,
        however it is tested on the following formats;

        * WAV

            * 32-bit floating-point
            * 32-bit signed integer
            * 16-bit signed integer
            * 8-bit unsigned integer

        * MP3
        * FLAC
        * OGG/VORBIS
        * OPUS
        * SPHERE

        To load ``MP3``, ``FLAC``, ``OGG/VORBIS``, ``OPUS`` and other codecs ``libsox`` does not
        handle natively, your installation of ``torchaudio`` has to be linked to ``libsox``
        and corresponding codec libraries such as ``libmad`` or ``libmp3lame`` etc.

    By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with
    ``float32`` dtype and the shape of ``[channel, time]``.
    The samples are normalized to fit in the range of ``[-1.0, 1.0]``.

    When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit
    signed integer and 8-bit unsigned integer (24-bit signed integer is not supported),
    by providing ``normalize=False``, this function can return integer Tensor, where the samples
    are expressed within the whole range of the corresponding dtype, that is, ``int32`` tensor
    for 32-bit signed PCM, ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM.

    ``normalize`` parameter has no effect on 32-bit floating-point WAV and other formats, such as
    ``flac`` and ``mp3``.
    For these formats, this function always returns ``float32`` Tensor with values normalized to
    ``[-1.0, 1.0]``.

    Args:
        filepath (str):
            Path to audio file
        frame_offset (int):
            Number of frames to skip before start reading data.
        num_frames (int):
            Maximum number of frames to read. ``-1`` reads all the remaining samples,
            starting from ``frame_offset``.
            This function may return the less number of frames if there is not enough
            frames in the given file.
        normalize (bool):
            When ``True``, this function always return ``float32``, and sample values are
            normalized to ``[-1.0, 1.0]``.
            If input file is integer WAV, giving ``False`` will change the resulting Tensor type to
            integer type.
            This argument has no effect for formats other than integer WAV type.
        channels_first (bool):
            When True, the returned Tensor has dimension ``[channel, time]``.
            Otherwise, the returned Tensor's dimension is ``[time, channel]``.

    Returns:
        torch.Tensor:
            If the input file has integer wav format and normalization is off, then it has
            integer type, else ``float32`` type. If ``channels_first=True``, it has
            ``[channel, time]`` else ``[time, channel]``.
    """
    signal = torch.ops.torchaudio.sox_io_load_audio_file(
        filepath, frame_offset, num_frames, normalize, channels_first)
    return signal.get_tensor(), signal.get_sample_rate()


@_mod_utils.requires_module('torchaudio._torchaudio')
def save(
        filepath: str,
        src: torch.Tensor,
        sample_rate: int,
        channels_first: bool = True,
        compression: Optional[float] = None,
):
    """Save audio data to file.

    Note:
        Supported formats are;

        * WAV

            * 32-bit floating-point
            * 32-bit signed integer
            * 16-bit signed integer
            * 8-bit unsigned integer

        * MP3
        * FLAC
        * OGG/VORBIS
        * SPHERE

        To save ``MP3``, ``FLAC``, ``OGG/VORBIS``, and other codecs ``libsox`` does not
        handle natively, your installation of ``torchaudio`` has to be linked to ``libsox``
        and corresponding codec libraries such as ``libmad`` or ``libmp3lame`` etc.

    Args:
        filepath (str): Path to save file.
        tensor (torch.Tensor): Audio data to save. must be 2D tensor.
        sample_rate (int): sampling rate
        channels_first (bool):
            If ``True``, the given tensor is interpreted as ``[channel, time]``,
            otherwise ``[time, channel]``.
        compression (Optional[float]):
            Used for formats other than WAV. This corresponds to ``-C`` option of ``sox`` command.

                * | ``MP3``: Either bitrate (in ``kbps``) with quality factor, such as ``128.2``, or
                  | VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5``.
                * | ``FLAC``: compression level. Whole number from ``0`` to ``8``.
                  | ``8`` is default and highest compression.
                * | ``OGG/VORBIS``: number from ``-1`` to ``10``; ``-1`` is the highest compression
                  | and lowest quality. Default: ``3``.

            See the detail at http://sox.sourceforge.net/soxformat.html.
    """
    if compression is None:
        ext = str(filepath).split('.')[-1].lower()
        if ext in ['wav', 'sph']:
            compression = 0.
        elif ext == 'mp3':
            compression = -4.5
        elif ext == 'flac':
            compression = 8.
        elif ext in ['ogg', 'vorbis']:
            compression = 3.
        else:
            raise RuntimeError(f'Unsupported file type: "{ext}"')
    signal = torch.classes.torchaudio.TensorSignal(src, sample_rate, channels_first)
    torch.ops.torchaudio.sox_io_save_audio_file(filepath, signal, compression)


@_mod_utils.requires_module('torchaudio._torchaudio')
def load_wav(
        filepath: str,
        frame_offset: int = 0,
        num_frames: int = -1,
        channels_first: bool = True,
) -> Tuple[torch.Tensor, int]:
    """Load wave file.


    This function is defined only for the purpose of compatibility against other backend
    for simple usecases, such as ``torchaudio.load_wav(filepath)``.
    The implementation is same as :py:func:`load`.
    """
    return load(filepath, frame_offset, num_frames, normalize=False, channels_first=channels_first)