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README.md
(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 https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md.
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 |