Tigrinya Interview Transcription for Researchers & Journalists
Turn hours of recorded Tigrinya interviews into verbatim, speaker-separated text with millisecond timestamps. Stop manual typing and start analyzing your qualitative fieldwork.
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Livelihood & Community Recovery Interview (Mekelle, Tigray)
Tigrinya (ትግርኛ) • Central Tigray • 01:20
እቲ ዝተጀመረ ናይ ደቀቕቲ ንግዳዊ ንጥፈታት ሓገዝ ነተን ስድራቤታት ኣብ ምርግጋእ ናብራ እንታይ ኣበርክቶ ገይሩ?
“What contribution has the microenterprise support initiative made in stabilizing families' livelihoods?”
ናይ ምግቢ ንግዲ ብምጅማረይ ንደቀይ ዝኸውን ኣታዊ ክረክብን ህይወትና ናብ ንቡር ክምለስን ዓቢይ መሰረት ኮይኑና እዩ።
“By starting a small food business, it has provided an income for my children and served as a strong foundation for our lives returning to normal.”
Engineered for Tigrinya Interview Realities
Field recordings are conversational, spontaneous, and often recorded in challenging environments. Andebet was trained specifically for authentic Northern Horn of Africa speech.
Multi-Speaker Separation
Distinguishes between interviewer and interviewee even during overlapping dialogue and conversational affirmations (እወ, ሕራይ).
Ge'ez Fidel Orthography
Formats text in standard Ge'ez fidel syllabary including all Tigrinya characters (ቐ, ሸ, ቸ, ኘ, ዠ, ጀ) and traditional punctuation marks.
Parallel English Translation
Generate parallel English transcripts aligned sentence-by-sentence with original Ge'ez text and timestamps for international studies.
Audio Timestamp Verification
Click any quote to jump directly to that exact second in the recording. Ensure zero-hallucination citations during publication.
From Audio Recording to Verbatim Interview Transcript
Four simple steps to transcribe and analyze Tigrinya fieldwork interviews.
Upload Interview Audio
Upload WAV, MP3, M4A, or smartphone voice notes up to 2GB. Supports field recorders, Zoom interviews, and broadcast archives.
Supports all major audio formats
Neural Diarization & Transcription
Our acoustic engine identifies speakers, breaks down speech turns, and generates accurate Ge'ez fidel text.
Transcribes 1 hour in under 2 minutes
Review & Label Speakers
Listen with synchronized text playback. Rename generic speaker labels to research participant codes or study pseudonyms.
Interactive audio workspace
Export for Research or Media
Download verified transcripts formatted for Microsoft Word (.docx), NVivo, ATLAS.ti, or SRT video subtitles.
Ready for publication or coding
Who Transcribes Tigrinya Interviews with Andebet?
Trusted by researchers, journalists, and humanitarian organizations.
Academic Field Researchers
Mekelle University, international think tanks, PhD candidatesTranscribe in-depth informant interviews and thematic fieldwork with verbatim accuracy for qualitative coding.
Humanitarian & NGO Field Teams
UN agencies, international NGOs, evaluatorsDocument community feedback and key informant evaluations with parallel English translation for international reports.
Broadcast Journalists & Media
Broadcasters, independent newsrooms, podcastersExtract verified, time-coded quotes from recorded phone calls and press conferences with zero risk of misquoting.
Oral Historians & Archivists
Cultural preservation institutes, historical archivesPreserve authentic oral narratives and cultural traditions with verbatim acoustic backing and timestamped citations.
Frequently Asked Questions: Tigrinya Interview Transcription
Diarization, dialects, translation, and export compatibility.
Yes! With a single click, Andebet produces a parallel English translation aligned sentence-by-sentence with the original Ge'ez fidel transcript and audio timestamps, allowing non-Tigrinya speaking researchers to analyze qualitative findings directly.
Yes. Our models are trained on acoustic data spanning Northern Ethiopia (Central, Eastern, Southern Tigray) and Eritrea (Asmara, Seraye), accurately recognizing regional vocabulary and pronunciation.
Yes! Our export templates format speaker headings and timestamps to match the exact syntax required by NVivo, ATLAS.ti, and MAXQDA for immediate qualitative coding.
All uploads are encrypted in transit and at rest with 256-bit AES encryption. Data is strictly confidential and never used to train public machine learning models.
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