Skip to content

Latest commit

 

History

History
96 lines (82 loc) · 3.05 KB

README.md

File metadata and controls

96 lines (82 loc) · 3.05 KB
Raw

(简体中文|English)

Audio Tagging

Introduction

Audio tagging is the task of labeling an audio clip with one or more labels or tags, including 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

see installation.

You can choose one way from easy, meduim and hard to install paddlespeech.

2. Prepare Input File

The 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): The audio file to tag.
    • model: Model type of tagging task. Default: panns_cnn14.
    • config: Config of tagging task. Use a pretrained model when it is None. Default: None.
    • ckpt_path: Model checkpoint. Use a pretrained model when it is None. Default: None.
    • label_file: Label file of tagging task. Use audio set labels when it is None. Default: None.
    • topk: Show topk tagging labels of the result. Default: 1.
    • device: Choose the device to execute model inference. Default: default device of paddlepaddle in the 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