Oodles builds enterprise-grade Automatic Speech Recognition systems using Python-based backends, real-time streaming architectures, and deep learning speech models to deliver accurate, secure, and scalable speech-to-text solutions.
Automatic Speech Recognition (ASR), also known as Speech-to-Text (STT), is a technology that converts spoken audio into structured, machine-readable text using neural acoustic and language models.
At Oodles, ASR systems are engineered using transformer-based deep learning models, Python and C++ inference engines, and GPU-accelerated pipelines to handle accents, noisy environments, and domain-specific terminology.
Low-latency ASR pipelines using WebSockets and streaming speech engines.
Speech-to-text support for 100+ languages using pre-trained and fine-tuned models.
Automatic speaker identification and segmentation in multi-speaker audio.
Domain-specific speech model fine-tuning for healthcare, legal, and enterprise use.
On-premise and private cloud ASR systems for sensitive audio data.
Punctuation, timestamps, and formatting for clean speech transcripts.
Live transcription, compliance monitoring, and agent assistance.
Clinical documentation with medical vocabulary-trained ASR models.
Low-latency subtitles for broadcasts, webinars, and events.
Speech recognition for conversational IVR and voice-enabled systems.
Multi-speaker transcription with timestamps and diarization.
Lecture transcription, subtitles, and searchable learning content.
Oodles builds Automatic Speech Recognition software using proven programming languages, deep learning frameworks, and scalable infrastructure.
OpenAI Whisper, NVIDIA NeMo ASR, Mozilla DeepSpeech, transformer-based speech models
Python, C++, JavaScript for ASR inference, APIs, and real-time streaming
PyTorch, TensorFlow, Hugging Face Transformers, Kaldi
Docker, Kubernetes, GPU acceleration, AWS, Azure, on-premise servers