Transgate

Converts live or pre-recorded audio streams into text using neural acoustic models with automatic language detection and support for 50+ languages. The system processes audio chunks incrementally, returning partial transcriptions in real-time while maintaining context across utterance boundaries for improved accuracy on continuous speech.

Applies spectral filtering and neural denoising to incoming audio before transcription, removing background noise, echo, and audio artifacts that degrade recognition accuracy. Uses frequency-domain analysis to isolate speech components and suppress non-speech signals, improving transcription accuracy in noisy environments by 15-25% without requiring manual noise profile training.

Returns granular timing information for each recognized word, including start/end timestamps accurate to 10ms precision and per-word confidence scores (0-100) indicating recognition certainty. Generates alignment metadata mapping audio frames to transcript tokens, enabling precise audio-to-text synchronization for subtitle generation, speaker highlighting, and error analysis.

Accepts multiple audio files (up to 100 files per batch) and processes them asynchronously via a job queue, returning results via webhook callbacks or polling a status endpoint. Implements exponential backoff retry logic for failed files, automatic chunking of large files (>500MB), and parallel processing across multiple workers to optimize throughput for non-real-time transcription workflows.

Identifies speaker boundaries in multi-speaker audio and tags transcript segments with speaker labels (Speaker 1, Speaker 2, etc.) using speaker embedding clustering and voice activity detection. Optionally integrates with speaker identification models to match speakers to known voice profiles, enabling automatic attribution of dialogue to specific participants in meetings or interviews.

Accepts custom word lists, acronyms, and domain-specific terminology to bias the speech recognition model toward recognizing specialized vocabulary. Integrates custom terms into the decoding process via a weighted language model, improving accuracy for industry jargon, product names, and technical terms that would otherwise be misrecognized or split into multiple words.

Provides REST API endpoints for submitting transcription jobs, polling job status, and retrieving results, with optional webhook callbacks for asynchronous result delivery. Implements standard HTTP authentication (API keys, OAuth 2.0), rate limiting with quota management, and detailed error responses with actionable remediation steps for integration into backend systems and CI/CD pipelines.