Lingvanex
Enterprise-grade Neural Machine Translation with local data residency and 100+ language support.

Enterprise-grade speech recognition framework for ultra-low latency, high-accuracy multilingual transcription.
FunASR is a fundamental speech recognition toolkit developed by Alibaba DAMO Academy’s Speech Lab, engineered to bridge the gap between academic research and production-grade industrial applications. Positioned as a market leader in 2026 for multilingual processing, its core architecture utilizes the Paraformer model—a non-autoregressive transformer that achieves state-of-the-art accuracy while significantly reducing inference latency compared to traditional RNN-T or Whisper-based models. The framework is highly modular, integrating Voice Activity Detection (VAD) via FSMN-VAD, punctuation restoration through CT-Transformer, and speaker diarization using the CAM++ model. FunASR is specifically optimized for long-form audio processing and real-time streaming, offering unique features like hotword customization (Seaco-Paraformer) to handle technical jargon and proper nouns. By supporting deployment across ONNX, TensorRT, and various edge devices, it provides enterprises with a privacy-first, self-hosted alternative to proprietary APIs. It is particularly dominant in the Asia-Pacific market due to its superior handling of Mandarin-English code-switching and diverse Chinese dialects, making it a critical asset for global enterprises targeting cross-border communication and localized customer service automation.
A non-autoregressive end-to-end speech recognition model that predicts all tokens in parallel.
Enterprise-grade Neural Machine Translation with local data residency and 100+ language support.
A high-performance Python library for speech data representation, manipulation, and efficient deep learning pipelines.
Enterprise-Grade Conversational Voice AI for Seamless Human-Like Interactions.
AI-driven transcription and subtitling engine for high-speed content localization.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A specialized bias mechanism allowing the model to prioritize specific keywords/entities provided at runtime.
Controllable Time-delay Transformer for real-time punctuation and inverse text normalization.
Feed-forward Sequential Memory Network based Voice Activity Detection.
Context-Aware Masking based speaker embedding extraction for 'who spoke when' identification.
Native support for exporting models to optimized inference engines.
Unified modeling of mixed-language audio streams, particularly Mandarin and English.
High latency in traditional ASR makes live captions distractingly slow.
Registry Updated:2/7/2026
Difficulty identifying different speakers in a board room setting.
Processing massive volumes of call data for quality assurance.