Source code for

import warnings
from sys import platform
from typing import Optional

import torch
import torchaudio

dict_format = {
    torch.uint8: "u8",
    torch.int16: "s16",
    torch.int32: "s32",
    torch.int64: "s64",
    torch.float32: "flt",
    torch.float64: "dbl",

[docs]@torchaudio._extension.fail_if_no_ffmpeg def play_audio( waveform: torch.Tensor, sample_rate: Optional[float], device: Optional[str] = None, ) -> None: """Plays audio through specified or available output device. .. warning:: This function is currently only supported on MacOS, and requires libavdevice (FFmpeg) with ``audiotoolbox`` output device. .. note:: This function can play up to two audio channels. Args: waveform: Tensor containing the audio to play. Expected shape: `(time, num_channels)`. sample_rate: Sample rate of the audio to play. device: Output device to use. If None, the default device is used. """ if platform == "darwin": device = device or "audiotoolbox" path = "-" else: raise ValueError(f"This function only supports MacOS, but current OS is {platform}") available_devices = list(torchaudio.utils.ffmpeg_utils.get_output_devices().keys()) if device not in available_devices: raise ValueError(f"Device {device} is not available. Available devices are: {available_devices}") if waveform.dtype not in dict_format: raise ValueError(f"Unsupported type {waveform.dtype}. The list of supported types is: {dict_format.keys()}") format = dict_format[waveform.dtype] if waveform.ndim != 2: raise ValueError(f"Expected 2D tensor with shape `(time, num_channels)`, got {waveform.ndim}D tensor instead") time, num_channels = waveform.size() if num_channels > 2: warnings.warn( f"Expected up to 2 channels, got {num_channels} channels instead. Only the first 2 channels will be played." ) # Write to speaker device s =, format=device) s.add_audio_stream(sample_rate, num_channels, format=format) # write audio to the device block_size = 256 with for i in range(0, time, block_size): s.write_audio_chunk(0, waveform[i : i + block_size, :])


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources