High-accuracy audio and video transcription for speech recognition models, voice AI platforms, and multilingual NLP datasets. Our native-speaker teams deliver timestamped, speaker-diarised transcripts in 15+ languages — including African languages underrepresented in global speech datasets.
Speech recognition and voice AI systems are trained on massive libraries of accurately transcribed audio. We convert your audio and video files into clean, structured transcripts — with word-level timestamps, speaker labels, and noise/accent annotations that make your model more robust.
We cover a broad language portfolio including English, Swahili, Amharic, Hausa, Arabic, French, Somali, Luganda, and many more — giving you access to rare training data that global transcription providers don't offer.
Our team includes native speakers of East, West, and North African languages alongside global languages. We handle accented speech, overlapping speakers, domain jargon, and noisy environments that automated tools fail on.
ASR training data in target languages and acoustic conditions. We produce clean, timestamped transcripts that train your speech-to-text models to handle real-world audio with accents, noise, and domain vocabulary.
Wake-word and utterance transcription for conversational AI. We transcribe user commands, dialogue turns, and voice interactions with intent and entity annotations to train robust voice AI systems.
Parallel corpora and translated transcripts for multilingual models. Our native speaker teams produce aligned transcripts across language pairs for machine translation and cross-lingual NLP research.
Clinical consultation and dictation transcription for healthcare AI. Our trained transcriptionists handle medical terminology, drug names, and clinical procedures with HIPAA-compliant data handling protocols.
Court hearing, interview, and broadcast transcription with speaker identification and timestamp accuracy. We support legal discovery, media archiving, and content accessibility workflows.
Call recording transcription for quality monitoring and NLU training. We transcribe customer-agent interactions with speaker labels, sentiment markers, and topic annotations for analytics platforms.
Review audio quality, language mix, speaker count, and domain vocabulary before scoping. We assess acoustic conditions and language requirements to assign the right native speaker teams.
Define verbatim vs. clean read, timestamp granularity, speaker naming convention, and noise annotation rules. We create detailed transcription guidelines with worked examples for every project.
Audio assigned to native speakers with domain familiarity; reviewed by a second annotator. Dual-pass transcription ensures accuracy even in challenging acoustic environments.
Accuracy scoring, timestamp alignment check, and delivery in TXT, SRT, VTT, JSON, or CSV. Every transcript passes QA review before delivery to your pipeline.
African and low-resource languages that major providers don't support. Our native speaker network covers languages underrepresented in global speech datasets — essential for building truly inclusive AI.
Human transcription with second-pass review outperforms ASR significantly. Our dual-pass quality process ensures every transcript meets production-grade accuracy standards.
48-hour pilot, scalable to thousands of audio hours per week. Our parallel team structure means your project timelines are met without compromising transcription quality.
Medical, legal, and call recordings protected under ISO 27001 protocols. All audio data is processed in secure, access-controlled environments with encrypted transfer and audit trails.
SRT, VTT, TXT, JSON, custom XML — ready for your pipeline. We deliver in any format your ASR training system or content platform requires, with schema documentation included.
Native-speaker quality at African talent rates. Access professional transcription at 40-60% below US and EU providers — making large-scale speech dataset creation affordable.
Send us a sample audio file and we'll return a pilot transcript within 48 hours.