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Improve docs for building and installation steps #101
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| Original file line number | Diff line number | Diff line change |
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| # Building | ||
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| Building can be a bit tricky because of the many dependencies. It is recommended to follow the [building locally guide](#building-locally) for building the project. However, there is also a provided [devcontainer](#devcontainer) approach that allows for easy development. | ||
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| ## Devcontainer | ||
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| For convenient development in VSCode, a [devcontainer](https://code.visualstudio.com/docs/remote/containers) can be used. Simply open the project in VSCode and hit `F1`, type `Remote-Containers: Reopen in Container` and wait for the container to build. | ||
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| The provided [`.devcontainer/devcontainer.json`](.devcontainer/devcontainer.json) contains all required dependencies and also mounts the source code into the container, so one can immediately start coding. When the container starts, a development server is started that can be reached at [localhost:8080](http://localhost:8080). The server automatically reloads the application on any code changes. | ||
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| Note: Ensure Docker and VSCode with [Remote Development](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.vscode-remote-extensionpack) extension are installed. | ||
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| ## Building locally | ||
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| ### Install prerequisites | ||
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| #### Install TensorFlow Lite C library | ||
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| Download precompiled TensorFlow Lite C library for Linux from https://github.com/tphakala/tflite_c/releases/tag/v2.14.0 | ||
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| Copy libtensorflowlite_c.so to /usr/local/lib and run ```ldconfig``` | ||
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| ```bash | ||
| sudo cp libtensorflowlite_c.so /usr/local/lib | ||
| sudo ldconfig | ||
| ``` | ||
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| Clone tensorflow repository, this is required for header files to be present while compiling with CGO | ||
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| ```bash | ||
| mkdir ~/src | ||
| cd ~/src | ||
| git clone https://github.com/tensorflow/tensorflow.git | ||
| ``` | ||
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| Checkout TensorFlow v2.14.0 release | ||
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| ```bash | ||
| cd tensorflow | ||
| git checkout tags/v2.14.0 | ||
| ``` | ||
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| ### Building BirdNET-Go | ||
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| Clone BirdNET-Go repository | ||
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| ```bash | ||
| git clone https://github.com/tphakala/BirdNET-Go.git | ||
| ``` | ||
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| Build BirdNET-Go by make, compiled binary will be placed in go-birdnet/bin directory | ||
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| ```bash | ||
| cd BirdNET-Go | ||
| make | ||
| ``` | ||
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| #### Compiling for Windows | ||
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| Windows build is cross compiled on Linux, for this you need MinGW-w64 on your build system | ||
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| ```bash | ||
| sudo apt install mingw-w64-tools gcc-mingw-w64-x86-64 gcc-mingw-w64-i686 | ||
| ``` | ||
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| Download precompiled TensorFlow Lite C library for Windows from https://github.com/tphakala/tflite_c/releases/tag/v2.14.0 | ||
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| Copy **libtensorflowlite_c.dll** to /usr/x86_64-w64-mingw32/lib/ | ||
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| ```bash | ||
| sudo cp libtensorflowlite_c.dll /usr/x86_64-w64-mingw32/lib/ | ||
| ``` | ||
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| Clone tensorflow repository, this is required for header files to be present while compiling with CGO | ||
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| ```bash | ||
| mkdir ~/src | ||
| cd ~/src | ||
| git clone https://github.com/tensorflow/tensorflow.git | ||
| ``` | ||
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| ### Cross #compiling BirdNET-Go | ||
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| Clone BirdNET-Go repository | ||
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| ```bash | ||
| git clone https://github.com/tphakala/BirdNET-Go.git | ||
| ``` | ||
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| Build BirdNET-Go by running make windows | ||
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| ```bash | ||
| cd BirdNET-Go | ||
| make windows | ||
| ``` | ||
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| Windows executable is in **bin/birdnet.exe**, copy this and **libtensorflowlite_c.so** to your Windows system, library file must be in PATH for birdnet.exe to run properly. | ||
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| Yes it is correct that you need **libtensorflowlite_c.dll** in /usr/x86_64-w64-mingw32/lib/ for compile process, and on Windows you need **libtensorflowlite_c.so** for runtime. This sounds backwards but this is how it works. | ||
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| # Installation | ||
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| BirdNET-Go can be installed either using Docker or binary provided releases. Docker is the | ||
| preferred method, as it provides a self-contained and easily reproducible | ||
| environment. However, binary releases are convenient option for users who prefer not to install Docker. | ||
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| ## Docker | ||
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| **Note**: Docker is currently only supported when running inside a Linux based | ||
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| host system. | ||
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| ### Installing Docker | ||
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| To install Docker, follow the instructions in the [official installation guide](https://docs.docker.com/engine/install) for your operating system. | ||
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| ### Running BirdNET-GO with Docker - Simple setup | ||
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| The command below will start a container with the latest version BirdNET-Go: | ||
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| > docker run -ti -p 8080:8080 --device /dev/snd ghcr.io/tphakala/birdnet-go:latest | ||
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| Once executed, the service can be reached at [localhost:8080](http://localhost:8080). | ||
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| ### Running BirdNET-GO with Docker - Normal setup | ||
| While the [simple](##Running-BirdNET-GO-with-Docker-Simple) example above works, it is highly likely that customizing the runtime settings more as well as enabling persistent storage is desirable. The docker run snippet below offers many more options: | ||
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| ``` | ||
| docker run -ti \ | ||
| -p 8080:8080 \ | ||
| --env ALSA_CARD=<index/name> | ||
| --env TZ="<TZ identifier>" | ||
| --device /dev/snd \ | ||
| -v /path/to/config:/config \ | ||
| -v /path/to/data:/data \ | ||
| ghcr.io/tphakala/birdnet-go:latest | ||
| ``` | ||
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| Summary of parameters: | ||
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| | Parameter | Function | | ||
| | :----: | --- | | ||
| | `-p 8080` | BirdNET-GO webserver port. | | ||
| | `--env ALSA_CARD=<index/name>` | ALSA capture device to use. Find index/name of desired device by executing `arecord -l` on the host. [More info.](#deciding-alsa_card-value)| | ||
| | `--env TZ="TZ identifier"` | Timezone to use. See [wikipedia article](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones#List) to find TZ identifier.| | ||
| | `--device /dev/snd` | Mounts in audio devices to the container. | | ||
| | `-v /config` | Config directory in the container. | | ||
| | `-v /data` | Data such as database and recordings. | | ||
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| #### Example setup | ||
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| To start BirdNET-GO, simply fill in the values of the parameters. Below is an example of how it might look: | ||
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| ``` | ||
| docker run -ti \ | ||
| -p 8080:8080 \ | ||
| --env ALSA_CARD=0 | ||
| --env TZ="Europe/Stockholm" | ||
| --device /dev/snd \ | ||
| -v $HOME/BirdNET-Go-Volumes/config:/config \ | ||
| -v $HOME/BirdNET-Go-Volumes/data:/data \ | ||
| ghcr.io/tphakala/birdnet-go:latest | ||
| ``` | ||
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| #### Deciding ALSA_CARD value | ||
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| Within the BirdNET-Go container, knowledge of the designated microphone is absent. Consequently, it is necessary to specify the appropriate ALSA_CARD environment variable. Determining the correct value for this variable involves the following steps on the host computer: | ||
| 1. Open a terminal and execute the command `arecord -l` to list all available capture devices. | ||
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| ``` | ||
| > arecord -l | ||
| **** List of CAPTURE Hardware Devices **** | ||
| card 0: PCH [Generic Analog], device 0: Analog [Analog] | ||
| Subdevices: 1/1 | ||
| Subdevice #0: subdevice #0 | ||
| card 0: PCH [Generic Analog], device 2: Alt Analog [Alt Analog] | ||
| Subdevices: 1/1 | ||
| Subdevice #0: subdevice #0 | ||
| card 1: Microphone [USB Microphone], device 0: USB Audio [USB Audio] | ||
| Subdevices: 1/1 | ||
| Subdevice #0: subdevice #0 | ||
| ``` | ||
| 2. Identify the desired capture device. In the example above, cards 0 and 1 are available. | ||
| 3. Specify the ALSA_CARD value when running the BirdNET-Go container. For instance, if the USB Microphone device is chosen, set `ALSA_CARD` to either `ALSA_CARD=1` or `ALSA_CARD=Microphone`. | ||
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| ## Binary releases | ||
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| Ready to run binaries can be found in [releases](https://github.com/tphakala/BirdNET-Go/releases/) section. Unfortunately, not everything is contained inside the binary itself, meaning that certain dependencies must be installed on the host system first. One of them being TensorFlow Lite C library, see this [guide](building.md#install-tensorflow-lite-c-library) for more information. | ||
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Correct the explanation regarding the use of
libtensorflowlite_c.dllandlibtensorflowlite_c.sofor Windows compilation and runtime. It seems there might be a mix-up in the explanation. Clarify thatlibtensorflowlite_c.dllis needed for runtime on Windows, notlibtensorflowlite_c.so.Committable suggestion