Techmo ASR Documentation

Features

• Age recognition
  

• Gender recognition

• Language recognition

• ASR Studio
  

Technology

• Ready to use Python client implementation

• Utilizing gRPC technology

• Specifically tailored for the MRCPv2 protocol

• TLS/mTLS encryption for confidentiality and integrity of data

On-Premises

• Container management with Kubernetes technology

• Containerization management with Docker technology

• No needs for GPU cluster to generate company voice

• > 2 simultaneous streaming channels from one cpu core

• Over 1000000 hours per year in customer service

• Billing by channels or characters and minutes

• Monitor and system performance analysis with Prometheus

• Working on Red Hat family OS distributions

• Simple to be used on Ubuntu

Guide to Techmo ASR API

• Introduction
• Quick start navigation
• Q&A

Details

• Naming and versioning
  • Notation
  • Namespace
  • Version
• Releases
  • Release notes
• Feature comparison
• Features
  • Recognition engine
  • Recognition request messages
  • Recognition result content
  • Recognition result types
  • Recognition timeouts
  • Recognition timeouts control

Client applications

• ASR Studio
• Guide to Techmo ASR API usage for Python
  • Introduction
  • Code generation tutorial
  • Generated code usage tutorial

ASR gRPC API

• Protocol Documentation
  • techmo/asr/api/v1p1/asr.proto
  • Scalar Value Types

On-premises configuration

• System requirements
• System architecture
• Directory structure of the deployment package
• Docker setup
  • Loading docker image
  • Service options and configuration
  • Licensing data generation
  • TLS authentication
  • Starting the Service
• MRCP environment
  • MRCP proxy
  • Configuration of MRCP environment

Management and tracking services

• Service logs
  • gRPC health-check
• Event monitoring with Prometheus
  • Prometheus

References

• Partners