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https://github.com/ksangeeta2429/edgel3
Generate audio embedding out of pruned L3
https://github.com/ksangeeta2429/edgel3
audio-embedding deep-learning edge pruning
Last synced: about 2 months ago
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Generate audio embedding out of pruned L3
- Host: GitHub
- URL: https://github.com/ksangeeta2429/edgel3
- Owner: ksangeeta2429
- License: mit
- Created: 2019-04-29T20:46:48.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-08-02T16:23:56.000Z (5 months ago)
- Last Synced: 2024-10-06T09:12:13.531Z (3 months ago)
- Topics: audio-embedding, deep-learning, edge, pruning
- Language: Python
- Size: 540 MB
- Stars: 4
- Watchers: 1
- Forks: 5
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# edgel3
[](https://pypi.python.org/pypi/edgel3)
[](https://choosealicense.com/licenses/mit/)
[](https://travis-ci.com/ksangeeta2429/edgel3)
[](https://coveralls.io/github/ksangeeta2429/edgel3?branch=master)
[](https://edgel3.readthedocs.io/en/latest/?badge=latest)
Look, Listen, and Learn (L3) [4] Audio subnetwork produces generic audio representations that can be used for myriad downstream tasks. However, L3-Net Audio requires 18 MB and 12 MB of static and dynamic memory respectively, making it infeasible for small edge devices with a single microcontroller. [EdgeL3](https://github.com/ksangeeta2429/Publications/raw/master/EdgeL3_Compressing_L3_Net_for_Mote_Scale.pdf) [2] is competetive with L3 Audio while being 95.45% sparse. However, it still has a high activation memory requirement.
To jointly handle both static and dynamic memory, we introduce [Specialized Embedding Approximation](https://github.com/ksangeeta2429/Publications/raw/master/SEA.pdf)[1], a teacher-student learning paradigm where the student audio embedding model is trained to approximate only the part of the teacher's embedding manifold which is relevant to the target data-domain. Notice the difference between data-domain and dataset. Restricting the specialization on a particular downstream dataset would compromise intra-domain generalizability.
``edgel3`` is an open-source Python library for downloading the smaller versions of L3 models and computing deep audio embeddings from such models.
- The ``sea`` models are specialized for [SONYC-UST](https://zenodo.org/record/2590742#.YGlc1i1h2Tc) [5] data domain. Training pipelines can be found [[here](https://github.com/ksangeeta2429/embedding-approx)].
- The ``sparse`` models provided have been re-trained using two different mechanisms: fine-tuning ``ft`` and knowledge distillation ``kd``. Training pipelines can be found [[here](https://github.com/ksangeeta2429/l3embedding/tree/dcompression)].
For non-compressed L3-Net, please refer to [OpenL3](https://github.com/marl/openl3) [3]
# Installing edgel3
Dependencies
------------
#### Tensorflow
``edgel3`` has been tested with Tensorflow 2.0 and Keras 2.3.1.
pip install tensorflow==2.0.0
#### libsndfile
**edgel3** depends on the `pysoundfile` module to load audio files, which depends on the non-Python library ``libsndfile``. On Windows and macOS, these will be installed via ``pip`` and you can therefore skip this step.
However, on Linux this must be installed manually via your platform's package manager.
For Debian-based distributions (such as Ubuntu), this can be done by simply running
apt-get install libsndfile1
For more detailed information, please consult the ``pysoundfile`` [installation documentation](https://pysoundfile.readthedocs.io/en/0.9.0/#installation>).
Installing edgel3
-----------------
The simplest way to install edgel3 is by using ``pip``, which will also install the additional required dependencies
if needed. To install edgel3 using ``pip``, simply run
pip install edgel3
NOTE: Use `v0.2.1` for both ``sparse`` and ``sea`` models. `v0.1.0` has only ``sparse`` models.
To install the latest version of edgel3 from source:
1. Clone or pull the lastest version:
git clone https://github.com/ksangeeta2429/edgel3.git
2. Install using pip to handle python dependencies:
cd edgel3
pip install -e .
# Getting started with edgel3
Load a SONYC-UST specialized L3 audio (reduced input represenation and reduced architecture) that outputs an embedding of length 128
```python
model = edgel3.models.load_embedding_model(model_type='sea', emb_dim=128)
```
Load a 95.45% sparse L3 audio re-trained with fine-tuning
```python
model = edgel3.models.load_embedding_model(model_type='sparse', retrain_type='ft', sparsity=95.45)
```
Load a 87.0% sparse L3 audio re-trained with knowledge distillation
```python
model = edgel3.models.load_embedding_model(model_type='sparse', retrain_type='kd', sparsity=87.0)
```
For more examples, please see the [tutorial](https://edgel3.readthedocs.io/en/latest/tutorial.html) and [module usage](https://edgel3.readthedocs.io/en/latest/edgel3.html).
# References
If you use the SEA/EdgeL3 Github repos or the pre-trained models, please cite the relevant work:
[1] **[Specialized Embedding Approximation for Edge Intelligence: A case study in Urban Sound Classification](https://github.com/ksangeeta2429/Publications/raw/master/SEA.pdf)**
Sangeeta Srivastava, Dhrubojyoti Roy, Mark Cartwright, Juan Pablo Bello, and Anish Arora.
To be published in IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Canada, June 2021.
[2] **[EdgeL3: Compressing L3-Net for Mote-Scale Urban Noise Monitoring](https://github.com/ksangeeta2429/Publications/raw/master/EdgeL3_Compressing_L3_Net_for_Mote_Scale.pdf)**
Sangeeta Kumari, Dhrubojyoti Roy, Mark Cartwright, Juan Pablo Bello, and Anish Arora.
Parallel AI and Systems for the Edge (PAISE), Rio de Janeiro, Brazil, May 2019.
[3] **Look, Listen and Learn More: Design Choices for Deep Audio Embeddings**
Jason Cramer, Ho-Hsiang Wu, Justin Salamon, and Juan Pablo Bello.
IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pages 3852–3856, Brighton, UK, May 2019.
[4] **Look, Listen and Learn**
Relja Arandjelović and Andrew Zisserman
IEEE International Conference on Computer Vision (ICCV), Venice, Italy, Oct. 2017.
[5] **SONYC Urban Sound Tagging (SONYC-UST): a multilabel dataset from an urban acoustic sensor network**
Mark Cartwright, Ana Elisa Mendez Mendez, Graham Dove, Jason Cramer et al. 2019.