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Version: 4.0.0-rc1

Language identification

Phonexia language-identification is a tool for calculating the probabilities of individual languages appearing in an audio recording. To learn more about the technology, visit this link.

Installation​

Getting the image​

You can easily obtain the language identification image from docker hub. There are 2 variants of the image. For CPU and for GPU.

You can get the CPU image by specifying a direct version in the tag (e.g. 1.0.0) or latest for the latest image:

docker pull phonexia/language-identification:latest

Running the image​

You can start the microservice and list all the supported options by running:

docker run --rm -it phonexia/language-identification:latest --help

The output should look like this:


language-identification [OPTIONS]


OPTIONS:
  -h,     --help              Print this help message and exit 
  -m,     --model file REQUIRED (Env:PHX_MODEL_PATH) 
                              Path to a model file. 
  -k,     --license_key string REQUIRED (Env:PHX_LICENSE_KEY) 
                              License key. 
  -a,     --listening_address address [[::]]  (Env:PHX_LISTENING_ADDRESS) 
                              Address on which the server will be listening. Address '[::]' 
                              also accepts IPv4 connections. 
  -p,     --port number [8080]  (Env:PHX_PORT) 
                              Port on which the server will be listening. 
  -l,     --log_level level:{error,warning,info,debug,trace} [info]  (Env:PHX_LOG_LEVEL) 
                              Logging level. Possible values: error, warning, info, debug, 
                              trace. 
          --keepalive_time_s number:[0, max_int] [60]  (Env:PHX_KEEPALIVE_TIME_S) 
                              Time between 2 consecutive keep-alive messages, that are sent if 
                              there is no activity from the client. If set to 0, the default 
                              gRPC configuration (2hr) will be set (note, that this may get the 
                              microservice into unresponsive state). 
          --keepalive_timeout_s number:[1, max int] [20]  (Env:PHX_KEEPALIVE_TIMEOUT_S) 
                              Time to wait for keep alive acknowledgement until the connection 
                              is dropped by the server. 
          --device TEXT:{cpu,cuda} [cpu]  (Env:PHX_DEVICE) 
                              Compute device used for inference. 
          --num_threads_per_instance NUM (Env:PHX_NUM_THREADS_PER_INSTANCE) 
                              Number of threads per instance (applies to CPU processing only). 
                              Use N CPU threads in the microservice for each request. Number of 
                              threads is automatically detected if set to 0. 
          --num_instances_per_device NUM:UINT > 0 (Env:PHX_NUM_INSTANCES_PER_DEVICE) 
                              Number of instances per device (both CPU and GPU processing). 
                              Microservice can process requests concurrently if value is >1. 
          --device_index ID (Env:PHX_DEVICE_INDEX) 
                              Device identifier
note

The model and license_key options are required. To obtain the model and license, contact Phonexia.

You can specify the options either via command line arguments or via environmental variables.

Run the container with the mandatory parameters:

docker run --rm -it -v /opt/phx/models:/models -p 8080:8080 phonexia/language-identification:latest --model /models/language_identification-xl-5.4.0.model --license_key ${license-key}

Replace the /opt/phx/models, language_identification-xl-5.4.0.model and license-key with the corresponding values.

With this command, the container will start, and the microservice will be listening on port 8080 on localhost.

Performance optimization​

The language-identification microservice supports GPU acceleration.

In the docker images with GPU support, the GPU acceleration is enabled by default. While GPU acceleration will be used primarily, certain processing tasks will still rely on CPU resources.

For better performance, multiple microservices can share a GPU unit. The number of microservice instances per GPU depends on the hardware used.

Microservice communication​

gRPC API​

For communication, our microservices use gRPC, which is a high-performance, open-source Remote Procedure Call (RPC) framework that enables efficient communication between distributed systems using a variety of programming languages. We use an interface definition language to specify a common interface and contracts between components. This is primarily achieved by specifying methods with parameters and return types.

Take a look at our gRPC API documentation. The language-identification microservice defines a LanguageIdentification service with remote procedures called Identify and ListSupportedLanguages. The Identify procedure accepts an argument (also referred to as "message") called IdentifyRequest, which contains the audio as an array of bytes, together with an optional config argument.

The config argument is used for controlling the identification parameters. In this argument, we can specify the amount of speech that will be used for the identification as well as all the languages from the set of supported languages that will be included in the identification. This way we can select only a subset of all the available languages. Finally, we can define a language groups, that group several languages and assigns them common identifier.

This IdentifyRequest argument is streamed, meaning that it may be received in multiple requests, each containing a part of the audio. If specified, the optional config argument must be sent only with the first request. Once all requests have been received and processed, the Identify procedure returns a message called IdentifyResponse which consists of the resulting language probability scores.

Connecting to microservice​

There are multiple ways how you can communicate with our microservices.

Phonexia Python client​

The easiest way to get started with testing is to use our simple Python client. To get it, run:

pip install phonexia-language-identification-client

After the successful installation, run the following command to see the client options:

language_identification_client --help

Versioning​

We use Semantic Versioning.