Source code for torchaudio
import os
from typing import BinaryIO, Optional, Tuple, Union
import torch
# Initialize extension and backend first
from . import _extension # noqa # usort: skip
from . import compliance, datasets, functional, models, pipelines, transforms, utils # noqa: F401
from ._torchcodec import load_with_torchcodec, save_with_torchcodec
try:
from .version import __version__, git_version # noqa: F401
except ImportError:
pass
[docs]def load(
uri: Union[BinaryIO, str, os.PathLike],
frame_offset: int = 0,
num_frames: int = -1,
normalize: bool = True,
channels_first: bool = True,
format: Optional[str] = None,
buffer_size: int = 4096,
backend: Optional[str] = None,
) -> Tuple[torch.Tensor, int]:
"""Load audio data from source using TorchCodec's AudioDecoder.
.. note::
As of TorchAudio 2.9, this function relies on TorchCodec's decoding capabilities under the hood. It is
provided for convenience, but we do recommend that you port your code to
natively use ``torchcodec``'s ``AudioDecoder`` class for better
performance:
https://docs.pytorch.org/torchcodec/stable/generated/torchcodec.decoders.AudioDecoder.
Because of the reliance on Torchcodec, the parameters ``normalize``, ``buffer_size``, and
``backend`` are ignored and accepted only for backwards compatibility.
To install torchcodec, follow the instructions at https://github.com/pytorch/torchcodec#installing-torchcodec.
Args:
uri (path-like object or file-like object):
Source of audio data. The following types are accepted:
* ``path-like``: File path or URL.
* ``file-like``: Object with ``read(size: int) -> bytes`` method.
frame_offset (int, optional):
Number of samples to skip before start reading data.
num_frames (int, optional):
Maximum number of samples to read. ``-1`` reads all the remaining samples,
starting from ``frame_offset``.
normalize (bool, optional):
TorchCodec always returns normalized float32 samples. This parameter
is ignored and a warning is issued if set to False.
Default: ``True``.
channels_first (bool, optional):
When True, the returned Tensor has dimension `[channel, time]`.
Otherwise, the returned Tensor's dimension is `[time, channel]`.
format (str or None, optional):
Format hint for the decoder. May not be supported by all TorchCodec
decoders. (Default: ``None``)
buffer_size (int, optional):
Not used by TorchCodec AudioDecoder. Provided for API compatibility.
backend (str or None, optional):
Not used by TorchCodec AudioDecoder. Provided for API compatibility.
Returns:
(torch.Tensor, int): Resulting Tensor and sample rate.
Always returns float32 tensors. If ``channels_first=True``, shape is
`[channel, time]`, otherwise `[time, channel]`.
Raises:
ImportError: If torchcodec is not available.
ValueError: If unsupported parameters are used.
RuntimeError: If TorchCodec fails to decode the audio.
Note:
- TorchCodec always returns normalized float32 samples, so the ``normalize``
parameter has no effect.
- The ``buffer_size`` and ``backend`` parameters are ignored.
- Not all audio formats supported by torchaudio backends may be supported
by TorchCodec.
"""
return load_with_torchcodec(
uri,
frame_offset=frame_offset,
num_frames=num_frames,
normalize=normalize,
channels_first=channels_first,
format=format,
buffer_size=buffer_size,
backend=backend,
)
[docs]def save(
uri: Union[str, os.PathLike],
src: torch.Tensor,
sample_rate: int,
channels_first: bool = True,
format: Optional[str] = None,
encoding: Optional[str] = None,
bits_per_sample: Optional[int] = None,
buffer_size: int = 4096,
backend: Optional[str] = None,
compression: Optional[Union[float, int]] = None,
) -> None:
"""Save audio data to file using TorchCodec's AudioEncoder.
.. note::
As of TorchAudio 2.9, this function relies on TorchCodec's encoding capabilities under the hood.
It is provided for convenience, but we do recommend that you port your code to
natively use ``torchcodec``'s ``AudioEncoder`` class for better
performance:
https://docs.pytorch.org/torchcodec/stable/generated/torchcodec.encoders.AudioEncoder.
Because of the reliance on Torchcodec, the parameters ``format``, ``encoding``,
``bits_per_sample``, ``buffer_size``, and ``backend``, are ignored and accepted only for
backwards compatibility.
To install torchcodec, follow the instructions at https://github.com/pytorch/torchcodec#installing-torchcodec.
Args:
uri (path-like object):
Path to save the audio file. The file extension determines the format.
src (torch.Tensor):
Audio data to save. Must be a 1D or 2D tensor with float32 values
in the range [-1, 1]. If 2D, shape should be [channel, time] when
channels_first=True, or [time, channel] when channels_first=False.
sample_rate (int):
Sample rate of the audio data.
channels_first (bool, optional):
Indicates whether the input tensor has channels as the first dimension.
If True, expects [channel, time]. If False, expects [time, channel].
Default: True.
format (str or None, optional):
Audio format hint. Not used by TorchCodec (format is determined by
file extension). A warning is issued if provided.
Default: None.
encoding (str or None, optional):
Audio encoding. Not fully supported by TorchCodec AudioEncoder.
A warning is issued if provided. Default: None.
bits_per_sample (int or None, optional):
Bits per sample. Not directly supported by TorchCodec AudioEncoder.
A warning is issued if provided. Default: None.
buffer_size (int, optional):
Not used by TorchCodec AudioEncoder. Provided for API compatibility.
A warning is issued if not default value. Default: 4096.
backend (str or None, optional):
Not used by TorchCodec AudioEncoder. Provided for API compatibility.
A warning is issued if provided. Default: None.
compression (float, int or None, optional):
Compression level or bit rate. Maps to bit_rate parameter in
TorchCodec AudioEncoder. Default: None.
Raises:
ImportError: If torchcodec is not available.
ValueError: If input parameters are invalid.
RuntimeError: If TorchCodec fails to encode the audio.
Note:
- TorchCodec AudioEncoder expects float32 samples in [-1, 1] range.
- Some parameters (format, encoding, bits_per_sample, buffer_size, backend)
are not used by TorchCodec but are provided for API compatibility.
- The output format is determined by the file extension in the uri.
- TorchCodec uses FFmpeg under the hood for encoding.
"""
return save_with_torchcodec(
uri,
src,
sample_rate,
channels_first=channels_first,
format=format,
encoding=encoding,
bits_per_sample=bits_per_sample,
buffer_size=buffer_size,
backend=backend,
compression=compression,
)
__all__ = [
"load",
"load_with_torchcodec",
"save_with_torchcodec",
"save",
"compliance",
"datasets",
"functional",
"models",
"pipelines",
"utils",
"transforms",
]
