PaddleSpeech/demos/audio_tagging

Audio Tagging

Introduction

Audio tagging is the task of labelling an audio clip with one or more labels or tags, includeing music tagging, acoustic scene classification, audio event classification, etc.

This demo is an implementation to tag an audio file with 527 AudioSet labels. It can be done by a single command or a few lines in python using PaddleSpeech.

Usage

1. Installation

pip install paddlespeech

2. Prepare Input File

Input of this demo should be a WAV file(.wav).

Here are sample files for this demo that can be downloaded:

wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/cat.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/dog.wav

3. Usage

• Command Line(Recommended)

  paddlespeech cls --input ./cat.wav --topk 10
  

  Usage:

  paddlespeech cls --help
  

  Arguments:

  • input(required): Audio file to tag.
  • model: Model type of tagging task. Default: panns_cnn14.
  • config: Config of tagging task. Use pretrained model when it is None. Default: None.
  • ckpt_path: Model checkpoint. Use pretrained model when it is None. Default: None.
  • label_file: Label file of tagging task. Use audioset labels when it is None. Default: None.
  • topk: Show topk tagging labels of result. Default: 1.
  • device: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.

  Output:

  [2021-12-08 14:49:40,671] [    INFO] [utils.py] [L225] - CLS Result:
  Cat: 0.8991316556930542
  Domestic animals, pets: 0.8806838393211365
  Meow: 0.8784668445587158
  Animal: 0.8776564598083496
  Caterwaul: 0.2232048511505127
  Speech: 0.03101264126598835
  Music: 0.02870696596801281
  Inside, small room: 0.016673989593982697
  Purr: 0.008387474343180656
  Bird: 0.006304860580712557
  

• Python API

  import paddle
  from paddlespeech.cli import CLSExecutor
  
  cls_executor = CLSExecutor()
  result = cls_executor(
      model='panns_cnn14',
      config=None,  # Set `config` and `ckpt_path` to None to use pretrained model.
      label_file=None,
      ckpt_path=None,
      audio_file='./cat.wav',
      topk=10,
      device=paddle.get_device())
  print('CLS Result: \n{}'.format(result))
  

  Output:

  CLS Result:
  Cat: 0.8991316556930542
  Domestic animals, pets: 0.8806838393211365
  Meow: 0.8784668445587158
  Animal: 0.8776564598083496
  Caterwaul: 0.2232048511505127
  Speech: 0.03101264126598835
  Music: 0.02870696596801281
  Inside, small room: 0.016673989593982697
  Purr: 0.008387474343180656
  Bird: 0.006304860580712557
  

4.Pretrained Models

Here is a list of pretrained models released by PaddleSpeech that can be used by command and python api:

Model	Sample Rate
panns_cnn6	32000
panns_cnn10	32000
panns_cnn14	32000