Gender identification
Phonexia gender-identification is a tool for calculating the probability of a male or a female voice appearing in a voiceprint. To learn more, visit the technology's home page.
Installation
- Docker image
- Docker compose
- Helm chart
Getting gender identification docker image
You can easily obtain the gender identification docker image from docker hub. Just run:
docker pull phonexia/gender-identification:latest
Running the image
Docker
You can start the microservice and list all the supported options by running:
docker run --rm -it phonexia/gender-identification:latest --help
The output should look like this:
Usage: gender-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.
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/gender-identification:latest --model /models/gender_identification-xl-5.0.0.model --license_key ${license-key}
Replace the /opt/phx/models
, gender_identification-xl-5.0.0.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.
Docker compose
Create a docker-compose.yml
file:
version: '3'
services:
gender-identification:
image: phonexia/gender-identification:latest
environment:
- PHX_MODEL_PATH=/models/gender_identification-xl-5.0.0.model
- PHX_LICENSE_KEY=<license-key>
ports:
- 8080:8080
volumes:
- ./models:/models/
Create a models
folder in the same directory as the docker-compose.yml
file and place a model file in it. Replace <license-key>
with your license key and gender_identification-xl-5.0.0.model
with the actual name of a model.
The model and license_key options are required. To obtain the model and license, contact Phonexia.
You can than start the microservice by running:
$ docker compose up
The optimal way for large scale deployment is by using container orchestration system. Take a look at out Helm chart deployment page for deployment using Kubernetes.
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 gender-identification microservice defines a GenderIdentification
service with remote procedures called Identify
. This procedure accepts an argument (also referred to as "message") called IdentifyRequest
, which contains an array of voiceprints. This IdentifyRequest
argument is streamed, meaning that it may be received in multiple requests, each containing a part of the voiceprints. Once all requests have been received and processed, the Identify
procedure returns a message called IdentifyResponse
, which consists of the resulting array of IdentifyResult
messages that correspond to each voiceprint on the input. The result has a probability score for both male and female appearing in the voiceprint.
Connecting to microservice
There are multiple ways how you can communicate with our microservices.
- Generated library
- Python client
- grpcurl client
- GUI clients
Using generated library
The most common way how to communicate with the microservices is via a programming language using a generated library.
Python library
If you use Python as your programming language, you can use our official gRPC Python library.
To install the package using pip
, run:
pip install phonexia-grpc
You can then import:
- Specific libraries for each microservice that provide the message wrappers.
- stubs for the
gRPC
clients.
# phx_core contains classes common for multiple microservices like `Voiceprint`.
import phonexia.grpc.common.core_pb2 as phx_core
# gender_identification_pb2 contains `IdentifyRequest` and `IdentifyResponse`.
import phonexia.grpc.technologies.gender_identification.v1.gender_identification_pb2 as gid
# gender_identification_pb2_grpc contains `GenderIdentificationStub` needed to make the requests.
import phonexia.grpc.technologies.gender_identification.v1.gender_identification_pb2_grpc as gid_grpc
Generate library for programming language of your choice
For the definition of microservice interfaces, we use the standard way of protocol buffers. The services
, together with the procedures
and messages
that they expose, are defined in the so-called proto
files.
The proto
files can be used to generate client libraries in many programming languages. Take a look at protobuf tutorials for how to get started with generating the library in the languages of your choice using the protoc
tool.
You can find the proto
files developed by Phonexia in this repository.
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-gender-identification-client
After the successful installation, run the following command to see the client options:
gender_identification_client --help
grpcurl client
If you need a simple tool for testing the microservice on cmd line, you can use grpcurl. This tool will serialize and send a body that we define in json to an endpoint that we specify.
The body for grpcurl
request should look somewhat like this:
{
"voiceprints": [
{
"content": "${voiceprint_1}"
},
{
"content": "${voiceprint_2}"
},
...
]
}
For this we already need to have the voiceprints
. If you don't have any, you can obtain some using the Phonexia voiceprint-extraction
microservice.
In the request, there may be multiple voiceprints in the voiceprints
array.
You can obtain such a request body with the following command.
echo -n '{"voiceprints": [{"content": "'$(cat ${path_to_voiceprint_1})'"}, {"content": "'$(cat ${path_to_voiceprint_2})'"}]}' > ${path_to_body}
Replace path_to_voiceprint_1
, path_to_voiceprint_2
and path_to_body
with corresponding values.
Now you can make the request. The microservice supports reflection. That means that you don't need to know the API in advance to make a request.
grpcurl -plaintext -use-reflection -d @ localhost:8080 phonexia.grpc.technologies.gender_identification.v1.GenderIdentification/Identify < ${path_to_body}
The grpcurl
should automatically serialize the response to this query into JSON
including the scores in the matrix.
GUI clients
If you'd prefer to use a GUI client like Postman or Warthog to test the microservice, take a look at the GUI Client page in our documentation. Note that you will still need to convert the audio into the Base64 format manually as those tools do not support it by default either.
Further links
- Maintained by Phonexia
- Contact us via e-mail, or open a ticket at the Phonexia Service Desk
- File an issue
- See list of licenses
- See the terms of use
Versioning
We use Semantic Versioning.