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Holistic Evaluation of Audio Representations (HEAR) 2021 -- Evaluation Kit

Project description



Evaluation kit for HEAR 2021 NeurIPS competition, using tasks from hear-preprocess.


Downstream evaluation on each task involves two steps:

  • computing audio embeddings
  • learning a shallow fully-connected predictor

The first step's speed depends upon a variety of factors. The second step's speed is relatively similar between models.

If you have any questions or comments:


Tested with Python 3.7 and 3.8. Python 3.9 is not officially supported because pip3 installs are very finicky, but it might work.

We officially support Torch 1.9 and Tensorflor 2.6.0, as well as Tensorflow 2.4.2 using the hack described in the Dockerfile README. We use CUDA 11.2. Other versions are possible, please contact us.

We test on 16GB GCP GPUs.


Here is a simple quickstart to evaluate hearbaseline using random projections and a tiny subset of the open tasks. More detailed instructions are below.

Open In Colab


There are 3 ways to run heareval:

  1. Locally, through pip3 install (or conda)
  2. Using Docker
  3. On the cloud

You are welcome to contact us if you have any questions or issues.

Local installation

pip3 install heareval


We have docker images containing the heareval environment. turian/heareval:stable contains the latest stable image with all dependencies bundled in.

Cloud GPUs

The easiest way to do evaluation is to launch a Spotty GCP instance. You can easily adapt Spotty also for AWS GPU instances.

Prepare a spotty.yaml file with the provided template file:

cp spotty.yaml.tmpl spotty.yaml

Change the instance name in the copied file. Specifically, change "USERNAME" suffix in instances: name to allow for multiple users in the same project to make separate gcp instances and volumes to avoid conflicts within the project.

Run spotty:

spotty start
spotty sh

This requires the heareval Docker image, which is pre-built and published on Dockerhub for your convenience.

Please refer to README.spotty for more details.

Download Open Tasks

If you are on GCP cloud, you can freely download open tasks as follows:

gsutil -m cp gs://hear2021/open-tasks/hear-2021.0.3-*-{SAMPLE_RATE}.gz . && for f in hear-*.gz; do tar zxf "$f"; done

where SAMPLE_RATE in {16000, 20050, 32000, 44100, 48000} is the sample rate your model desires.

If you are downloading from HTTPS, please only download open tasks once and mirror them internally, because cloud downloads are expensive for us. We are looking for longer-term hosting options.


for the following tasks:


where SAMPLE_RATE in {16000, 20050, 32000, 44100, 48000} is the sample rate your model desires.

Untar all the files.

Compute embeddings

time python3 -m heareval.embeddings.runner MODULE_NAME --model WEIGHTS_FILE --tasks-dir hear-2021.0.3/tasks/

where MODULE_NAME is your embedding model name.

This will create directories embeddings/MODULE_NAME/TASK/ with your embeddings. If you run the above command multiple times, it will skip tasks it has already performed embedding on. You can delete directories if you want to recompute embeddings.

There is an advanced option --model-options whereby you can pass a JSON string of parameters to the model. This is useful for experimenting with model hyperparameters. These options appear in the embeddings output directory name, so you can run several different model variations at once.

Evaluation over embeddings

You can then run final downstream evaluation on these embeddings as follows:

python3 -m heareval.predictions.runner embeddings/{MODULE_NAME}/*

This will run on a particular module, over all tasks, with determinism and the default number of grid points. Embeddings will be loaded into CPU memory, for speed of training. Logs will be sent to stdout and concise logs will be in logs/. If you run this multiple times, it should be deterministic, but will always start from scratch.

Ignore warnings about Leaking Caffe2 thread-pool after fork, this is a known torch bug.

More advanced flags allow different downstream training regimes

Final test scores are logged to stdout and also to {EMBEDDINGS_DIR}/{MODULE_NAME}/{TASK_NAME}/test.predicted-scores.json.

Note on Speed

Models with larger embeddings scale sub-linearly in training time (because of GPU optimizations) and linearly in hop-size (for event-based prediction tasks). The main hyperparameters controlling downstream training time are the maximum number of epochs and number of grid points for grid search.


If you are developing this repo, clone repo:

git clone
cd hear-eval-kit

Install in development mode:

pip3 install -e ".[dev]"

Make sure you have pre-commit hooks installed:

pre-commit install

Running tests:

python3 -m pytest

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