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Version: 3.4.0

Emotion Recognition

Phonexia emotion-recognition is a tool for recognition of emotions of speakers in audio recordings. At this time, following emotions are supported : angry, sad, happy, neutral.

Versioning​

We use Semantic Versioning.

Quick reference​

How to use this image​

Getting the image​

You can easily obtain the docker image from docker hub. There are 2 variants of the image. One for CPU, one for GPU with suffix -gpu in a tag.

docker pull phonexia/emotion-recognition:latest

To get the latest GPU image, run:

docker pull phonexia/emotion-recognition:gpu

Running the image​

info

The preferred way to deploy microservice to a production environment is to use Helm Chart. See the Helm chart deployment for more information.

Docker​

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

docker run --rm -it phonexia/emotion-recognition:latest --help

The output should look like this:

Usage: emotion-recognition [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 that 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/emotion-recognition:latest --model /models/emotion_recognition-generic-1.0.0.model --license_key ${license-key}

Replace the /opt/phx/models, generic-1.0.0.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:
  emotion-recognition:
    image: phonexia/emotion-recognition:latest
    environment:
      - PHX_MODEL_PATH=/models/emotion_recognition-generic-1.0.0.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 emotion_recognition-generic-1.0.0.model with the actual name of a model.

Run a microservice:

$ docker compose up

GPU​

The GPU images has a suffix -gpu in the image tag (e.g. 1.0.0-gpu) or you can use a tag gpu to get the latest version. In these images, the most computationally demanding tasks are handled by the GPU. The prerequisites are NVIDIA GPU with drivers and nvidia-container-toolkit installed (see the Installing the NVIDIA Container Toolkit for more info).

To run GPU images you will need to make the GPU available inside the docker container. This is done by --gpus parameter (typically --gpus all), see the Access an NVIDIA GPU chapter for more info, for example:

docker run --rm -it --gpus all -v /opt/phx/models:/models -p 8080:8080 phonexia/emotion-recognition:gpu --model /models/emotion-recognition-generic-1.0.0.model --license_key ${license-key}

Or use a docker compose file:

version: '3'
services:
  voiceprint-extraction:
    image: phonexia/emotion-recognition:gpu
    environment:
      - PHX_MODEL_PATH=/models/emotion_recognition-generic-1.0.0.model
      - PHX_LICENSE_KEY=<license-key>
    ports:
      - 8080:8080
    volumes:
      - ./models:/models/
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Performance optimization​

The emotion-recognition 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 emotion-recognition microservice defines a EmotionRecognition service with remote procedure called Recognize. This procedure accepts an argument (also referred to as "message") called RecognizeRequest, which contains the audio as an array of bytes, together with an optional config argument.

This RecognizeRequest 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 Recognize procedure returns a message called RecognizeResponse which consists of the resulting emotion scores.

Connecting to microservice​

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

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.
from phonexia.grpc.common.core_pb2 import Audio, RawAudioConfig, TimeRange
from phonexia.grpc.technologies.emotion_recognition.v1.emotion_recognition_pb2 import RecognizeConfig, RecognizeRequest, RecognizeResponse
from phonexia.grpc.technologies.emotion_recognition.v1.emotion_recognition_pb2_grpc import EmotionRecognitionStub
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.

Using existing clients​

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-emotion-recognition-client

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

emotion_recognition_client --help
grpcurl client​

If you need a simple tool for testing the microservice on the command line, you can use grpcurl. This tool can serialize and send a request for you, if you provide the request body in JSON format and specify the endpoint.

The audio content in the body must be encoded in Base64. The request also cannot exceed 4 MiB, therefore it's necessary to split bigger files to multiple chunks. You can use jq tool to generate JSON input for grpcurl.

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. Replace ${path_to_audio_file} with corresponding value.

base64 -w 4000000 ${path_to_audio_file} | jq -cnR '{"audio":{"content":inputs}}' | grpcurl -plaintext -use-reflection -d @ localhost:8080 phonexia.grpc.technologies.emotion_recognition.v1.EmotionRecognition/Recognize

The grpcurl automatically serializes the response to this request into JSON including the voiceprint with the billed time.

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.