torchaudio.load

torchaudio.load(uri: Union[BinaryIO, str, 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[Tensor, int]

Load audio data from source.

By default (normalize=True, channels_first=True), this function returns Tensor with float32 dtype, and the shape of [channel, time].

Note

The formats this function can handle depend on the availability of backends. Please use the following functions to fetch the supported formats.

• FFmpeg: torchaudio.utils.ffmpeg_utils.get_audio_decoders()

• Sox: torchaudio.utils.sox_utils.list_read_formats()

• SoundFile: Refer to the official document.

Warning

normalize argument does not perform volume normalization. It only converts the sample type to torch.float32 from the native sample type.

When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit signed integer, 24-bit signed integer, and 8-bit unsigned integer, 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. Since torch does not support int24 dtype, 24-bit signed PCM are converted to int32 tensors.

normalize argument 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.

Parameters:

• uri (path-like object or file-like object) – Source of audio data.

• frame_offset (int, optional) – Number of frames to skip before start reading data.

• num_frames (int, optional) – 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, optional) –

  When True, this function converts the native sample type to float32. Default: True.

  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, optional) – When True, the returned Tensor has dimension [channel, time]. Otherwise, the returned Tensor’s dimension is [time, channel].

• format (str or None, optional) – If not None, interpreted as hint that may allow backend to override the detected format. (Default: None)

• buffer_size (int, optional) – Size of buffer to use when processing file-like objects, in bytes. (Default: 4096)

• backend (str or None, optional) –

  I/O backend to use. If None, function selects backend given input and available backends. Otherwise, must be one of ["ffmpeg", "sox", "soundfile"], with the corresponding backend being available. (Default: None)

  See also

  Backend and Dispatcher

Returns:

Resulting Tensor and sample rate.

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].

Return type:

(torch.Tensor, int)

Tutorials using load:

StreamWriter Advanced Usage

StreamWriter Advanced Usage

Speech Recognition with Wav2Vec2

Speech Recognition with Wav2Vec2

AudioEffector Usages

AudioEffector Usages

ASR Inference with CUDA CTC Decoder

ASR Inference with CUDA CTC Decoder

StreamWriter Basic Usage

StreamWriter Basic Usage

Torchaudio-Squim: Non-intrusive Speech Assessment in TorchAudio

Torchaudio-Squim: Non-intrusive Speech Assessment in TorchAudio

Music Source Separation with Hybrid Demucs

Music Source Separation with Hybrid Demucs

Audio I/O

Audio I/O

Speech Enhancement with MVDR Beamforming

Speech Enhancement with MVDR Beamforming

CTC forced alignment API tutorial

CTC forced alignment API tutorial

Audio Feature Extractions

Audio Feature Extractions

Audio Data Augmentation

Audio Data Augmentation

ASR Inference with CTC Decoder

ASR Inference with CTC Decoder

Forced alignment for multilingual data

Forced alignment for multilingual data

Forced Alignment with Wav2Vec2

Forced Alignment with Wav2Vec2