How much does an Audio Annotation firm cost per hour or per dataset?
Pricing depends on complexity (clean read vs verbatim transcription, overlap density, diarization, event taxonomies, and PII handling) and the review level you choose. For reference, Abaka offers multilingual TTS dataset preparation at $7/hr, and platform usage via Abaka Forge credits at $0.20 USD each. For end-to-end audio annotation, we typically propose a pilot batch first, then finalize unit pricing after we measure edge cases and QA effort. Talk to an Expert and we’ll scope an exact plan.
How fast can you start and deliver the first audio-labeled pilot?
Most teams can begin with a Day 0–3 alignment to define the spec, exports, and acceptance tests. A first pilot is commonly delivered in Week 1–2, depending on audio length, language coverage, and whether diarization or safety labeling is included. If you already have guidelines and sample audio, we can accelerate setup by configuring Abaka Forge quickly and validating outputs against your existing training pipeline before scaling production.
What audio formats and annotation outputs do you support?
We work with common audio formats such as WAV and MP3 and can handle multi-channel recordings where available. Outputs can include time-stamped transcripts, turn tables, and diarization artifacts like RTTM, plus structured JSON/CSV for downstream training and analytics. If you need forced-alignment friendly segmentation, event spans, or language-ID markers, we incorporate those into the spec and deliver consistent schemas across batches through Abaka Forge.
What accuracy can you achieve for transcription and diarization labels?
Accuracy depends on audio conditions (noise, overlap, accents, channel quality) and how strict the conventions are. Abaka targets up to 99% accuracy under the agreed QA checks by using calibrated reviewers, sampling audits, and adjudication for ambiguous cases. We recommend defining measurable acceptance tests early—such as boundary tolerances for speaker turns and consistent rules for disfluencies—so “accuracy” maps to what improves training and evaluation, not subjective preferences.
How do you keep voice data secure and compliant?
Abaka operates with SOC 2 and ISO 27001 compliance and supports GDPR and CCPA requirements. We use strict NDAs, segregated secure pipelines, and controlled access in Abaka Forge. For sensitive programs, we implement redaction labeling (PII markers) and minimize exposure by limiting who can access raw audio. Your data is exclusively yours—never repurposed, resold, or shared—and we maintain full IP provenance, including 0% copyright risk on collected data.
Do you support multilingual audio annotation and code-switching?
Yes. Abaka supports delivery across 50+ countries, with routing to language-competent annotators and reviewers for dialects, accents, and mixed-language segments. We can label language-ID spans, code-switch boundaries, and region-specific orthography rules so transcripts remain consistent. Multilingual projects benefit from a gold set and calibration, which we run early to prevent drift between languages and to keep evaluation comparable across markets.
How are you different from other audio labeling vendors?
Two differences matter most for serious teams. First, trust: Abaka never builds models that compete with you, and your data is never repurposed or resold. Second, operational rigor: we design annotation specs with acceptance tests, run calibrated QA with adjudication, and deliver consistent exports through Abaka Forge. Many vendors focus on raw throughput; we focus on training- and eval-ready supervision that remains stable as you scale languages, domains, and volume.
Can we request guideline changes after the project starts?
Yes—change requests are common as you learn from early model results. We handle updates via versioned guidelines: we document the change, identify which batches are impacted, and decide whether to re-label or to branch the dataset. Abaka Forge keeps provenance and task versions so you can reproduce results. We also recommend weekly governance reviews to consolidate changes, avoid churn, and keep production moving without silently shifting label definitions.
Can you run a small paid pilot before we commit to scale?
Yes. We recommend a scoped pilot that reflects your hardest cases—overlap-heavy conversations, noisy channels, accents, and sensitive content—so the spec is validated under real conditions. The pilot produces deliverables you can use immediately for training or evaluation and includes a calibration report on disagreement categories. After you approve outputs and acceptance metrics, we scale production with the same workflow, avoiding the common “pilot looked great, scale fell apart” failure mode.
Who owns the labeled audio data and the resulting annotations?
You do. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared. We do not train competing models on customer data. We maintain full IP provenance for work we perform, and we can provide documentation of labeling processes, guideline versions, and dataset lineage. This ownership clarity is critical when audio data includes proprietary scripts, agent prompts, or regulated customer conversations.
What tooling do you use for audio annotation and QA workflows?
We use Abaka Forge, our all-in-one platform for collection, cleaning, annotation, and production. For audio programs, we configure task flows for transcription, diarization, event tagging, and safety/PII labeling with calibrated QA and adjudication. Exports are standardized and versioned, and access controls support secure programs. If you already have internal tooling, we can align exports to your schemas so you can keep existing training pipelines unchanged.
What is the minimum dataset size you can take on?
We support both small pilots and large production streams. Minimum size depends less on hours of audio and more on complexity: number of languages, overlap density, taxonomies, and security requirements. If you’re early, we can start with a representative pilot that includes your hardest slices and proves the spec and export compatibility. From there, we can scale to continuous delivery with predictable QA and weekly governance cadence.