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Source code for torchaudio.legacy

from __future__ import division, print_function
import os.path

import torch
import _torch_sox

import torchaudio


[docs]def load(filepath, out=None, normalization=None, num_frames=0, offset=0): r"""Loads an audio file from disk into a Tensor. The default options have changed as of torchaudio 0.2 and this function maintains option defaults from version 0.1. Args: filepath (str): Path to audio file out (torch.Tensor, optional): An output Tensor to use instead of creating one. (Default: ``None``) normalization (bool or number, optional): If boolean `True`, then output is divided by `1 << 31` (assumes 16-bit depth audio, and normalizes to `[0, 1]`. If `number`, then output is divided by that number. (Default: ``None``) num_frames (int, optional): Number of frames to load. -1 to load everything after the offset. (Default: ``0``) offset (int, optional): Number of frames from the start of the file to begin data loading. (Default: ``0``) Returns: Tuple[torch.Tensor, int]: The output tensor is of size `[L x C]` where L is the number of audio frames, C is the number of channels. The integer is sample-rate of the audio (as listed in the metadata of the file) Example >>> data, sample_rate = torchaudio.legacy.load('foo.mp3') >>> print(data.size()) torch.Size([278756, 2]) >>> print(sample_rate) 44100 """ return torchaudio.load(filepath, out, normalization, False, num_frames, offset)
[docs]def save(filepath, src, sample_rate, precision=32): r"""Saves a Tensor with audio signal to disk as a standard format like mp3, wav, etc. The default options have changed as of torchaudio 0.2 and this function maintains option defaults from version 0.1. Args: filepath (str): Path to audio file src (torch.Tensor): An input 2D Tensor of shape `[L x C]` where L is the number of audio frames, C is the number of channels sample_rate (int): The sample-rate of the audio to be saved precision (int, optional): The bit-precision of the audio to be saved. (Default: ``32``) Example >>> data, sample_rate = torchaudio.legacy.load('foo.mp3') >>> torchaudio.legacy.save('foo.wav', data, sample_rate) """ torchaudio.save(filepath, src, sample_rate, precision, False)

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