https://github.com/talkuhulk/music-genres-classification
Tensorflow implementation of music-genres-classification with InceptionResnetV2
https://github.com/talkuhulk/music-genres-classification
audio-classification classification cnn-tensorflow genres-classification inception-resnet-v2 librosa python tensorflow
Last synced: 1 day ago
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Tensorflow implementation of music-genres-classification with InceptionResnetV2
- Host: GitHub
- URL: https://github.com/talkuhulk/music-genres-classification
- Owner: TalkUHulk
- Created: 2019-06-23T08:21:50.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T22:59:11.000Z (over 2 years ago)
- Last Synced: 2025-02-26T21:45:09.356Z (8 months ago)
- Topics: audio-classification, classification, cnn-tensorflow, genres-classification, inception-resnet-v2, librosa, python, tensorflow
- Language: Python
- Homepage:
- Size: 779 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# music-genres-classification
## Abstract
The demo is to develop a deep learning model that will identify the genres from music.The model adopt Inception-ResnetV2 from slim.And the datasat used GTZAN Music Genre Dataset, which is a collection of 1000 songs in 10 genres, is the most widely used dataset.If you wanna train a model by yourself, download it from [GTZAN dataset](http://opihi.cs.uvic.ca/sound/genres.tar.gz).
Below table shows the result on test set:
Accuracy | Value
--------- | ---------
Top-1 | 69.70%
Top-3 | 92.50%
Below picture shows the training-process on tensorboard:
Loss
Accuracy
## Pre-Trained Testing
* Step1: download [pre_trained](https://pan.baidu.com/s/1Pg7UH5rj_xCv77Wz4sCy_A) model,and put it into `models/` folder.
* Step2: test by executing the following command:
```python
python test.py
```
## Training
* Step1: download Dataset GTZAN,and put it into `GTZAN/` folder.
* Step2: create the tfrecords by executing the following command:
```Bash
usage: create_data_to_train.py [-h] [--data_path DATA_PATH]
[--train_path TRAIN_PATH]
[--test_path TEST_PATH] [--test_size TEST_SIZE]
[--aug AUG]
```
```Bash
optional arguments:
-h, --help show this help message and exit
--data_path DATA_PATH
data_sets path.
--train_path TRAIN_PATH
train tfrecords save path.
--test_path TEST_PATH
test tfrecords save path.
--test_size TEST_SIZE
Proportion of test data that between [0, 1]
--aug AUG the size of data sets up to (arg) times as original. 1
means not augmentation.
```
* Step3: train the model by executing the following command:
```Bash
usage: train.py [-h] [--train_data TRAIN_DATA] [--decay_rate DECAY_RATE]
[--global_steps GLOBAL_STEPS] [--decay_steps DECAY_STEPS]
[--learning_rate LEARNING_RATE] [--eval_step EVAL_STEP]
[--batch_size BATCH_SIZE] [--model_path MODEL_PATH]
[--summary_path SUMMARY_PATH] [--load_model LOAD_MODEL]
```
```Bash
optional arguments:
-h, --help show this help message and exit
--train_data TRAIN_DATA
train_data path.
--decay_rate DECAY_RATE
learning rate decay rate.
--global_steps GLOBAL_STEPS
global steps
--decay_steps DECAY_STEPS
learning rate decay steps.
--learning_rate LEARNING_RATE
learning rate.
--eval_step EVAL_STEP
evaluation steps.
--batch_size BATCH_SIZE
batch size.
--model_path MODEL_PATH
tensorflow model path.
--summary_path SUMMARY_PATH
tensorflow summary path.
--load_model LOAD_MODEL
whether you wish to continue training.
```
## Testing
Run the following command to simple test the test-dataset:
```python
python test.py
```
## References
[1] https://github.com/deepsound-project/genre-recognition