How much do audio transcription and labeling services cost?
Pricing depends on language mix, audio quality, timestamp granularity, and whether you need diarization, event spans, or PII tagging. For human work, Abaka pricing commonly maps to skill level—STEM Generalist work is $12/hr and LLM Math/Coding specialist work is $18/hr, with other task types priced separately. Platform usage is available via Abaka Forge credits at $0.20 USD each. After a short sample review, we propose a scoped pilot with a clear cost range and acceptance criteria.
How long does a typical transcription + labeling project take?
Most teams start with a pilot so we can calibrate guidelines and confirm output formats. Many pilots can be delivered in 2–3 weeks depending on scope, language coverage, and review cycles. After the pilot, production timelines depend on volume and complexity—speaker overlap and heavy jargon increase review needs. We set weekly delivery targets, provide progress reporting, and scale capacity up or down as your roadmap changes so you can keep model training and evaluation moving.
What audio formats and export formats do you support?
We support common audio containers and sample rates and can work with your existing capture pipeline. On the output side, we deliver transcripts and labels in structured formats designed for ML training and analytics—JSON/JSONL, CSV, and subtitle formats like SRT/VTT for time-aligned text. For diarization, we can provide RTTM-style speaker turn outputs, and for phonetic or alignment workflows we can deliver TextGrid when needed. We align the exact schema to your downstream tooling.
What accuracy can you achieve for transcripts and speaker diarization?
Accuracy depends on audio conditions (noise, overlap, compression), language variety, and domain jargon. Abaka targets high-quality outputs using calibrated rubrics, multi-layer QA, and adjudication on ambiguous segments, and can operate to audited accuracy targets up to 99% for many labeling scopes. For diarization, we focus on consistent speaker turns and role tagging, with specific attention to overlap handling and long-call drift. We recommend starting with a pilot to set measurable acceptance thresholds.
How do you handle security and sensitive recordings?
Abaka runs secure delivery under strict NDAs with segregated secure pipelines and role-based access controls. We support governance requirements aligned to SOC 2, ISO 27001, GDPR, and CCPA. For sensitive audio, we can add PII annotation to produce redaction-ready outputs tied to timestamps and provide audit-friendly logs and provenance. Your data remains exclusively yours—never repurposed, resold, or shared—and we do not build models that compete with you.
Can you support multilingual transcription and code-switching audio?
Yes. Abaka supports multilingual delivery across 50+ countries and can handle code-switching and locale-specific normalization rules. We align transcripts to your requirements—verbatim vs normalized, punctuation conventions, numerals, and domain terms—then enforce consistency through reviewer calibration and QA sampling. If you need parallel outputs (e.g., transcript plus translation), we can structure the dataset to preserve alignment and metadata so you can train and evaluate multilingual ASR and voice agents reliably.
How are you different from other transcription and labeling vendors?
Abaka is built for frontier AI dataset production, not generic transcription. You get secure, versioned workflows in Abaka Forge, multi-layer QA with adjudication, and access to specialized reviewer pools across domains. We also provide broader modalities (text, RLHF, video, 3D) so you can unify pipelines when audio is part of a multimodal product. Most importantly, we never build models that compete with you, and your data is exclusively yours—no repurposing, resale, or sharing.
What if we need changes to the labeling taxonomy mid-project?
Change requests are normal—new intents, revised event categories, updated diarization policies, or different timestamp granularity. We manage changes through versioned guidelines and controlled rollouts so new labels don’t silently mix with old ones. When needed, we can backfill previous batches or create mapping logic so your training pipeline remains stable. Weekly reporting includes taxonomy issues and edge cases, helping you decide whether to refine definitions, add examples, or escalate ambiguous categories.
Can we start with a small pilot before committing to scale?
Yes—starting with a pilot is recommended. We typically begin with a representative sample that includes your hardest cases (overlaps, noise, jargon, multilingual segments). The pilot validates guidelines, export formats, QA gates, and turnaround expectations. You’ll receive labeled outputs plus a QA summary and error taxonomy so you can assess usefulness for training and evaluation. After sign-off, we scale volume while keeping the same rubric and versioning so results remain consistent.
Who owns the transcripts and labels that are produced?
You do. Your audio, transcripts, labels, and derived artifacts are exclusively yours and are not repurposed, resold, or shared. Abaka’s operating model is designed for long-term trust—no competing model-building incentives and no acquisition pressure. We maintain lineage and provenance so you can trace outputs to their sources and document how they were produced. If you require specific contractual terms around IP, retention, and deletion, we can align during onboarding.
What tools do you use to manage audio transcription and labeling?
We use Abaka Forge—an all-in-one platform for collection, cleaning, annotation, and production across modalities. For audio, it supports queue management, reviewer calibration, adjudication, QA sampling, and export automation, with full versioning and change logs. Large-model automation accelerates repetitive steps so humans can focus on edge cases like overlaps and jargon. If your team already uses internal tools, we can integrate via structured exports and agreed schemas.
What is the minimum dataset size you can support?
There is no strict minimum. We can support small, high-leverage pilots (for example, a few dozen to a few hundred recordings) to validate guidelines and model impact, and we can scale to large ongoing queues when you’re ready. The right minimum depends on your goal—benchmarking, training, or production monitoring—and the diversity you need across accents, devices, and environments. We’ll recommend a pilot size that is statistically useful without wasting budget.