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

Generate embedding vectors from audio files
https://github.com/IBM/MAX-Audio-Embedding-Generator

docker-image machine-learning machine-learning-models tensorflow

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Generate embedding vectors from audio files

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README 

        
[![Build Status](https://travis-ci.com/IBM/MAX-Audio-Embedding-Generator.svg?branch=master)](https://travis-ci.com/IBM/MAX-Audio-Embedding-Generator) [![Website Status](https://img.shields.io/website/http/max-audio-embedding-generator.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud/swagger.json.svg?label=api+demo)](http://max-audio-embedding-generator.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud)



[](http://ibm.biz/max-to-ibm-cloud-tutorial) 



# 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](https://github.com/tensorflow/models/tree/master/research/audioset#output-embeddings),

and outputs the result as arrays of 1 second embeddings. The model was trained on

[AudioSet](https://research.google.com/audioset/). As described in the

[code](https://github.com/tensorflow/models/blob/master/research/audioset/vggish/vggish_inference_demo.py) this model is

intended to be used an example and perhaps as a stepping stone for more complex models. See the

[Usage](https://github.com/tensorflow/models/tree/master/research/audioset#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](https://developer.ibm.com/code/exchanges/models/) and the public API is powered by [IBM Cloud](https://ibm.biz/Bdz2XM).



## 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"](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45857.pdf),

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"](https://arxiv.org/pdf/1609.09430.pdf),

arXiv preprint arXiv:1609.09430, 2016.



## Licenses



| Component | License | Link  |

| ------------- | --------  | -------- |

| This repository | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [LICENSE](LICENSE) |

| Model Files | [Apache 2.0](https://github.com/tensorflow/models/blob/master/LICENSE) | [AudioSet](https://github.com/tensorflow/models/tree/master/research/audioset) |

| Model Code | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [AudioSet](https://github.com/tensorflow/models/tree/master/research/audioset) |

| Test samples | Various | [Sample README](samples/README.md) |



## Pre-requisites:



* `docker`: The [Docker](https://www.docker.com/) command-line interface. Follow the [installation instructions](https://docs.docker.com/install/) 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](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) at the minimum.



# Deployment options



* [Deploy from Quay](#deploy-from-quay)

* [Deploy on Red Hat OpenShift](#deploy-on-red-hat-openshift)

* [Deploy on Kubernetes](#deploy-on-kubernetes)

* [Run Locally](#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](#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](https://developer.ibm.com/tutorials/deploy-a-model-asset-exchange-microservice-on-red-hat-openshift/), 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](https://ibm.biz/Bdz2XM) can be found [here](http://ibm.biz/max-to-ibm-cloud-tutorial). 



## Run Locally



1. [Build the Model](#1-build-the-model)

2. [Deploy the Model](#2-deploy-the-model)

3. [Use the Model](#3-use-the-model)

4. [Run the Notebook](#4-run-the-notebook)

5. [Development](#5-development)

6. [Cleanup](#6-cleanup)



### 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.



![Swagger Doc Screenshot](docs/swagger-screenshot.png)



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](samples/demo.ipynb). _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](#1-build-the-model)).



### 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](https://github.com/CODAIT/max-central-repo).