perch-runner

A docker container that runs Perch.

Commands

This container has three basic functions:

1. Generate Perch embeddings for a given audio file
2. To classify the embeddings given an audio file and one or more additional linear classifier
3. To measure distance between a given sample and a given audio file (Not implemented Yet)

Each of these output a result per 5 second chunks

Generate embeddings

The generate command accepts one input file or one input folder and one output directory.

• One file will be produced containing the embeddings for the input file.
• The file MUST be in a self-describing format.
• Each result in the file must have at least
  • a start value representing the start of the record in seconds
  • an embedding value representing the embedding for the record
    • For CSV this should be a Base64 encoded string of the embedding vector
  • a source_separation_channel representing the virtual channel the record came from
  • and other columns are allowed as needed
• Files should include provenance metadata in their headers
• Note: audio recordings IDs (or other source identifying information) are not needed. The assumption is that this container is run by an orchestrator that knows what audio file is being processed.

Usage

generate <input-file> <output-directory>
    <input-file>                The file to generate embeddings for
    <output-directory>          The directory to write the embeddings to
    -F|--format [csv|parquet]   The format of the embeddings file: `CSV`` or `Parquet``
    -c|--config <config-file>   An optional configuration file to use

Examples

cd /data
curl -o audio.wav https://api.ecosounds.org/audio_recordings/123.wav
docker run -v /data:/data perch-runner generate /data/audio.wav /data/output

Classify

The classify command accepts one input file, one output directory, and one or more classifiers. The classifiers are provided in a config file.

• One file will be produced containing the classifications for the input file.
• The file MUST be in a self-describing format.
• The file should return results in <insert link to our event common format>
• Files should include provenance metadata in their headers

Usage

classify [-F|--format] [-c|--config <config-file> ] <input-file> <output-directory>
    <input-file>                The file to generate embeddings for
    <output-directory>          The directory to write the embeddings to
    -c|--config <config-file>   An optional configuration file to use

Configuration file

classifier: keras_saved_model_path

Batch Mode

The "generate" and "inference" commands can be also be used in batch mode by altering the entrypoint to /app/src/batch.py

Tests

Host tests

To test the docker container works as expected from the host:

1. Into a virtual environment install the testing dependencies with pip install -r requirements-host.txt
2. Run `pytest