IBM Code Model Asset Exchange: Audio Embedding Generator

This repository contains code to instantiate and deploy an audio embedding model. This model recognizes a signed 16-bit PCM wav file as an input, generates embeddings, applies PCA transformation/quantization, and outputs the result as arrays of 1 second embeddings. The model was trained on AudioSet. As described in the code this model is intended to be used an example and perhaps as a stepping stone for more complex models. See the Usage heading in the tensorflow/models Github page for more ideas about potential usages.

The model files are hosted on IBM Cloud Object Storage. The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the IBM Code Model Asset Exchange and the public API is powered by IBM Cloud.

Model Metadata

Domain	Application	Industry	Framework	Training Data	Input Data Format
Audio	Embeddings	Multi	TensorFlow	Google AudioSet	signed 16-bit PCM WAV audio file

References

J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, "Audio set: An ontology and human-labeled dataset for audio events", in IEEE ICASSP, 2017.
S. Hershey, S. Chaudhuri, D. P. W. Ellis, J. F. Gemmeke, A. Jansen, R. C. Moore, M. Plakal, D. Platt, R. A. Saurous, B. Seybold et al., "CNN architectures for large-scale audio classification", arXiv preprint arXiv:1609.09430, 2016.

Licenses

Component	License	Link
This repository	Apache 2.0	LICENSE
Model Files	Apache 2.0	AudioSet
Model Code	Apache 2.0	AudioSet
Test samples	Various	Sample README

Pre-requisites:

docker: The Docker command-line interface. Follow the installation instructions for your system.
The minimum recommended resources for this model is 8 GB Memory and 4 CPUs.
If you are on x86-64/AMD64, your CPU must support AVX at the minimum.

Deployment options

Deploy from Quay
Deploy on Red Hat OpenShift
Deploy on Kubernetes
Run Locally

Deploy from Quay

To run the docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 quay.io/codait/max-audio-embedding-generator

This will pull a pre-built image from the Quay.io container registry (or use an existing image if already cached locally) and run it. If you'd rather checkout and build the model locally you can follow the run locally steps below.

Deploy on Red Hat OpenShift

You can deploy the model-serving microservice on Red Hat OpenShift by following the instructions for the OpenShift web console or the OpenShift Container Platform CLI in this tutorial, specifying quay.io/codait/max-audio-embedding-generator as the image name.

Deploy on Kubernetes

You can also deploy the model on Kubernetes using the latest docker image on Quay.

On your Kubernetes cluster, run the following commands:

$ kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Audio-Embedding-Generator/master/max-audio-embedding-generator.yaml

The model will be available internally at port 5000, but can also be accessed externally through the NodePort.

A more elaborate tutorial on how to deploy this MAX model to production on IBM Cloud can be found here.

1. Build the Model

Clone this repository locally. In a terminal, run the following command:

$ git clone https://github.com/IBM/MAX-Audio-Embedding-Generator.git

Change directory into the repository base folder:

$ cd MAX-Audio-Embedding-Generator

To build the Docker image locally, run:

$ docker build -t max-audio-embedding-generator .

All required model assets will be downloaded during the build process. Note that currently this Docker image is CPU only (we will add support for GPU images later).

2. Deploy the Model

To run the Docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 max-audio-embedding-generator

3. Use the Model

The API server automatically generates an interactive Swagger documentation page. Go to http://localhost:5000 to load it. From there you can explore the API and also create test requests.

Use the model/predict endpoint to load a signed 16-bit PCM wav audio file (you can use the car-horn.wav file located in the samples folder) and get embeddings from the API.

You can also test it on the command line, for example:

$ curl -F "audio=@samples/car-horn.wav" -XPOST http://localhost:5000/model/predict

You should see a JSON response like that below:

{
  "status": "ok",
  "embedding": [
    [
      158,
      23,
      150,
      ...
    ],
    ...,
    ...,
    [
      163,
      29,
      178,
      ...
    ]
  ]
}

4. Run the Notebook

Once the model server is running, you can see how to use it by walking through the demo notebook. Note the demo requires jupyter, numpy, sklearn and matplotlib.

Run the following command from the model repo base folder, in a new terminal window (leaving the model server running in the other terminal window):

jupyter notebook

This will start the notebook server. You can open the demo notebook by clicking on demo.ipynb.

5. Development

To run the Flask API app in debug mode, edit config.py to set DEBUG = True under the application settings. You will then need to rebuild the Docker image (see step 1).

6. Cleanup

To stop the Docker container, type CTRL + C in your terminal.

Resources and Contributions

If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions here.

CPU4004/MAX-Audio-Embedding-Generator