This software captures local audio data and streams it to AWS S3 buckets -- both as lossy (AAC-encoded) data in HLS segments for live-listening and as a lossless (FLAC-encoded) for archiving and/or acoustic analysis. There are branches for both arm32v7 and amd64 architectures, though the majority of initial development has been on the ARM-based Raspberry Pi3b. The current branches use ffmpeg+s3fs calle by a bash script; an improved upload script is under development using ffmpeg+boto within a Python script.
Background & motivation
This code was developed for source nodes on the Orcasound hydrophone network (WA, USA) -- thus the repository names! Our primary motivation is to make it easy for lots of folks to listen for whales using their favorite device/OS/browser.
We also aspire to use open-source software as much as possible. A long-term goal is to stream lossless FLAC-encoded data within DASH segments to a player that works optimally on as many listening devices as possible.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
If you want to set up your hardware to host a hydrophone within the Orcasound network, take a look at how to join Orcasound and our prototype built from a Raspberry Pi3b with the Pisound Hat.
Audio data is acquired within a Docker container by ALSA/FFmpeg, written to /tmp directories, transfered to /mnt directories by rsync, and transferred to AWS S3 buckets by s3fs. Errors/etc are logged to LogDNA via a separate Docker container.
An ARM or X86 device with a sound card (or other audio input device) connected to the Internet (via wireless network or ethernet cable) that has Docker-compose installed and an AWS account with some S3 buckets set up.
Choose the branch that is appropriate for your architecture. Clone that branch and create an .env file that contains the following:
AWSACCESSKEYID=YourAWSaccessKey AWSSECRETACCESSKEY=YourAWSsecretAccessKey NODE_NAME=YourNodeName NODE_TYPE=hls-only AUDIO_HW_ID=1,0 CHANNELS=2 FLAC_DURATION=30 SEGMENT_DURATION=10 SYSLOG_URL=syslog+tls://syslog-a.logdna.com:YourLogDNAPort SYSLOG_STRUCTURED_DATA='logdna@YourLogDNAnumber key="YourLogDNAKey" tag="docker"
- NODE_NAME should indicate your device and it's location, ideally in the form
device_location(e.g. we call our Raspberry Pi staging device in Seattle
- NODE_TYPE determines what audio data formats will be generated and transferred to their respective AWS buckets.
- AUDIO_HW_ID is the card,device providing the audio data.
- CHANNELS indicates the number of audio channels to expect (1 or 2).
- FLAC_DURATION is the amount of seconds you want in each archvied lossless file.
- SEGMENT_DURATION is the amount of seconds you want in each streamed lossy segment.
Running local tests
In the repository directory (where you also put your .env file) run
docker-compose up -d. Watch what happens using
htop. If you want to verify files are being written to /tmp or /mnt directories, get the name of your streaming service using
docker-compose ps (in this case
orcanode_streaming_1) and then do
docker exec -it orcanode_streaming_1 /bin/bash to get a bash shell within the running container.
Running an end-to-end test
Once you've verified that s3fs is transferring files to your S3 buckets (with public read access), you can test the stream using a browser-based reference player. For example, with [Bitmovin HLS/MPEG/DASH player] you can use the drop-down menu to select HLS and then paste the URL for your current S3-based m3u8 manifest file into it to listen to the stream.
Your URL should look something like this:
If you would like to add a node to the Orcasound hydrophone network, deployment of the current code to new devices is handled via Resin.io and you should contact Scott for guidance on how to participate.
- FFmpeg - Uses ALSA to acquire audio data, then generates lossy streams and/or lossless archive files
- rsync - Transfers files locally from /tmp to /mnt directories
- s3fs - Used to transfer audio data from local device to S3 bucket(s)
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
- Paul Cretu - Lead developer - Paul on Github
- Scott Veirs - Project manager - Scott on Github
- Steve Hicks - Raspberry Pi expert - Steve on Github
- Val Veirs - Hydrophone expert - Val on Github
See also the list of contributors who participated in this project.
This project is licensed under the GNU Affero General Public License v3.0 - see the LICENSE.md file for details
- Thanks to the backers of the 2017 Kickstarter that funded the development of this open-source code.
- Thanks to the makers of the Raspberry Pi and the Pisound HAT.
- Thanks to the many friends and backers who helped improve maintain nodes and improve the Orcasound app.