GITNUXSOFTWARE ADVICE
AI In IndustryTop 10 Best Transcription AI Software of 2026
Ranked roundup of transcription ai software with key feature comparisons and tradeoffs for teams, including Fireflies, Happy Scribe, and Sonix.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Fireflies is the best fit for teams that want meeting transcripts with speaker labeling and repeatable follow-up workflows, while Deepgram stands out if you’re building API-driven transcription into automated downstream pipelines with diarization and timestamps.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Fireflies
Meeting content search tied to timestamped, speaker-labeled transcripts for fast review and handoff.
Built for fits when teams need searchable, speaker-labeled meeting transcripts with repeatable follow-up workflows..
Happy Scribe
Editor pickTranscript editor with project workflows that support iterative correction, then direct export to caption and document formats.
Built for fits when teams need editable transcripts from batches of audio and video, with caption-ready exports..
Sonix
Editor pickAPI-based job processing that pairs transcription results with automated downstream exports and review workflows.
Built for fits when teams need repeatable transcript exports for meetings and content pipelines with automation support..
Related reading
Comparison Table
Fireflies
SMBAI notetaker joining meetings to transcribe, summarize, and search conversation content.
Meeting content search tied to timestamped, speaker-labeled transcripts for fast review and handoff.
Fireflies focuses on meeting-to-text workflows with diarization so speakers stay labeled through long recordings. It provides timestamped transcripts that make it easier to jump to the exact moment during review and handoff. The product also emphasizes meeting context retrieval so teams can locate specific decisions, questions, and handoffs from past calls.
A tradeoff is that very custom transcription behavior, like narrow domain vocabulary tuning and advanced acoustic customization, is less prominent than workflow automation and transcript usability. Fireflies fits best when teams need consistent transcript review and searchable meeting artifacts across recurring meetings, not when they only need raw, offline transcription output.
- +Speaker-labeled transcripts with navigable timestamps
- +Search-first workflow for reusing past meeting content
- +Exports readable transcript formats for distribution
- +Automation helps convert meetings into action-ready artifacts
- –Advanced domain vocabulary tuning is not the central strength
- –Overlapping speech can still reduce diarization clarity in edge cases
- –Integrations depend on meeting capture sources being supported
- –Deep post-processing customization needs extra workflow effort
Customer success teams
Review call outcomes and next steps
Faster case documentation
Revenue operations teams
Audit meeting decisions across accounts
Lower review time
Show 2 more scenarios
Sales teams
Re-use deal insights from recordings
More consistent follow-through
Searchable transcripts help teams find objections, answers, and promised deliverables.
Product managers
Synthesize recurring customer conversations
Quicker iteration planning
Conversation artifacts reduce manual note-taking for recurring feedback themes.
Best for: Fits when teams need searchable, speaker-labeled meeting transcripts with repeatable follow-up workflows.
More related reading
Happy Scribe
SMBAI and human transcription platform with interactive editing and subtitle tools.
Transcript editor with project workflows that support iterative correction, then direct export to caption and document formats.
Happy Scribe supports audio and video ingestion with batch transcription, which fits teams that process recordings in volume without building a custom ASR pipeline. Transcripts can be exported in common caption and document formats like SRT, WebVTT, TXT, and DOCX, which reduces downstream conversion work. The transcript editor supports iterative correction, and projects keep related files and outputs together for review workflows.
A tradeoff is that diarization and speaker labeling quality depends on the input audio clarity, and mixed or overlapping speech may require manual cleanup in the editor. The best fit is post-production transcription for interviews, recorded training sessions, or meetings where humans review and correct text before delivery.
- +Transcript editor supports fast human corrections before final export
- +Exports include SRT and WebVTT for caption and player workflows
- +Project-based organization keeps batch jobs and outputs manageable
- +Multilingual transcription supports mixed-language content review
- –Overlapping speech often needs manual cleanup in the editor
- –Diarization output can degrade on low clarity or far-field audio
- –API automation depends on external workflow orchestration for approvals
- –Less suited for interactive low-latency transcription sessions
Video production teams
Caption creation from recorded interviews
Faster captioning with fewer re-edits
Training and learning teams
Publish lecture transcripts for archives
Consistent transcripts across courses
Show 2 more scenarios
Market research teams
Review call recordings for insights
Clearer verbatim records for analysis
Batch transcribe interviews and correct text before sharing with stakeholders.
Product ops teams
Automate transcription ingestion pipelines
Reduced manual transcription work
Use the API to submit files and collect outputs for downstream systems.
Best for: Fits when teams need editable transcripts from batches of audio and video, with caption-ready exports.
Sonix
SMBAutomated transcription, translation, and subtitle generation with an in-browser editor.
API-based job processing that pairs transcription results with automated downstream exports and review workflows.
Sonix handles batch-style transcription where users upload media, receive text results, and then refine them in a transcript editor. It outputs transcripts in common document and caption formats like TXT, DOCX, SRT, and WebVTT. Multilingual transcription and word-level timestamps help reviewers align text with audio during editing and handoffs.
A tradeoff appears in review capacity. Quality improves with careful transcript inspection, and speaker-related accuracy can vary on noisy recordings with overlapping speech. Sonix fits best when teams need repeatable transcription outputs for meetings, interviews, and content libraries where exported files must match downstream formatting needs.
- +Exports transcripts as DOCX plus caption files SRT and WebVTT
- +Word-level timestamps speed alignment during transcript review
- +API supports automated transcription jobs and post-processing workflows
- +Transcript editor supports iterative corrections to final output
- –Speaker diarization accuracy can degrade on overlapping speech
- –Higher-quality results require manual review on sensitive transcripts
- –Setup for automation workflows needs engineering time
- –Advanced tuning options are less granular than specialist editors
Content operations teams
Caption file generation for published media
Faster publication-ready caption delivery
Research and interview teams
Multilingual interview transcription with review
Quicker researcher annotation
Show 2 more scenarios
Customer success teams
Meeting transcription for searchable records
Improved knowledge retrieval
Transforms recorded calls into editable transcripts for internal documentation and recall.
Media production engineers
Automated transcription pipeline via API
Lower manual processing overhead
Runs transcription through API-driven jobs and exports files for downstream tools.
Best for: Fits when teams need repeatable transcript exports for meetings and content pipelines with automation support.
Otter
SMBAI meeting assistant providing real-time transcription, speaker identification, and automated summaries.
Inline transcript editing with speaker-aware playback ties corrections to the original audio moment without redoing the session.
Otter turns recorded meetings into searchable transcripts with inline editing and collaborative playback controls. It emphasizes fast cleanup workflows through a transcript editor, speaker-aware output, and export-ready files for follow-up.
The core experience centers on transcription, punctuation and capitalization restoration, and time-aligned reading that helps users verify meaning. Otter also supports automation via API access for ingestion and transcript retrieval, which matters for teams standardizing capture across recurring meetings.
- +Transcript editor supports quick word-level corrections during review
- +Speaker-aware output reduces manual labeling for recurring meetings
- +Exports support common document workflows like DOCX and plain text
- +API supports programmatic transcription retrieval for connected apps
- –Overlapping speech handling can degrade when multiple people talk
- –Real-time transcription quality varies more across accents and mic setups
- –Bulk transcription management is less granular than enterprise capture tools
- –Advanced customization like custom vocab is limited versus research-grade ASR
Best for: Fits when teams need clean meeting transcripts fast, plus API access for integrating capture workflows.
Descript
SMBAudio and video editor with AI transcription, text-based editing, and overdub features.
Script-to-media editing, where transcript edits drive corresponding changes in the audio and video timeline.
Descript turns audio and video into editable transcripts so edits in text immediately rewrite the underlying media. It supports transcription workflows with word-level timestamps and punctuation and capitalization restoration, which helps turn raw ASR output into publication-ready text.
The editor model also enables speaker diarization for conversations and exports transcripts into common document and caption formats for downstream use. Human-in-the-loop review is supported through the transcript editor so teams can correct recognition errors before finalizing deliverables.
- +Text-based editing rewrites audio and video to match transcript changes
- +Word-level timestamps make it faster to locate and fix specific speech
- +Speaker diarization helps attribute dialogue in multi-person recordings
- +Export formats cover transcripts for documents and captions workflows
- –Overlapping speech can still produce fragmented speaker attributions
- –Automation and API depth is limited compared with transcription-first vendors
Best for: Fits when teams need transcript-first editing for audio or video, with timestamped fixes and speaker attribution.
Deepgram
API-firstVoice AI platform providing real-time and batch transcription via a developer API.
API-first transcription with configurable delivery that includes word-level timestamps for tight synchronization use cases.
Deepgram targets teams that need ASR results delivered through an API with production-grade latency and control. Core capabilities include real-time and batch transcription, word-level timestamps, and punctuation plus capitalization restoration.
Speaker diarization and confidence scores support review workflows, while export formats cover common subtitle and document needs. The differentiator is the depth of integration surfaces for custom pipelines rather than a manual transcript-first editor experience.
- +API transcription supports real-time and batch workflows
- +Word-level timestamps make downstream alignment straightforward
- +Speaker diarization and confidence scores aid quality review
- +Subtitle and document exports fit common publishing needs
- –Browser-based transcript editing is limited versus transcription-first tools
- –Advanced workflows require more integration effort
- –Overlapping speech handling can reduce diarization clarity
- –Higher throughput setups increase operational complexity
Best for: Fits when engineering teams need API-driven transcription with timestamps and diarization for automated downstream workflows.
Trint
enterpriseAI transcription and collaboration platform for video and audio content with multi-language support.
In-editor timestamped revision workflow that keeps edits aligned for SRT and WebVTT exports.
Trint pairs automated transcription with an editor built for review workflows, not just text output. It handles word-level alignment and exports finalized transcripts into common caption and document formats.
Multilingual transcription includes language detection so a single workflow can cover mixed inputs. Its core differentiation is tight transcript editing around timestamps and export-ready outputs.
- +Transcript editor links text edits to timestamps for fast review
- +Export support covers SRT, WebVTT, TXT, and DOCX use cases
- +Multilingual transcription with language detection reduces pre-sorting work
- +Word-level timestamps improve navigation during corrections
- –Overlapping-speech quality can require manual cleanup on dense audio
- –API transcription support and webhook depth may be insufficient for strict automation needs
- –Speaker separation is less granular than diarization-first competitors
- –Batch throughput can bottleneck when projects include long, multi-file uploads
Best for: Fits when editorial teams need timestamped transcripts with an editor-centered review workflow and common export formats.
Notta
SMBAI transcription and summarization tool for meetings, recordings, and live conversations.
Speaker-aware transcripts paired with an editor that keeps text fixes tied to the exact audio segment.
Notta turns audio and video inputs into readable transcripts, with a workflow built around reviewing and editing text. It provides automated transcription, speaker labeling for multi-speaker recordings, and timestamped output suitable for review and export.
Notta also supports collaboration through shareable transcripts and uses a transcript editor that keeps re-listening and text fixes tightly connected. Its standout capability is turning recorded calls and meetings into structured artifacts that teams can act on without rebuilding the workflow from scratch.
- +Fast transcript review with inline editing and re-listen controls
- +Speaker labeling for multi-speaker recordings to reduce manual cleanup
- +Exports transcripts in common formats for downstream use
- +Collaboration via shareable transcript links for lightweight review
- –Limited control over ASR vocabulary and domain adaptation
- –Overlapping speech handling can degrade word accuracy
- –API support is less prominent than UI-based transcription workflows
- –Batch ingestion and large-volume throughput need careful planning
Best for: Fits when teams need quick transcript review with speaker labeling and simple sharing for meetings or calls.
Transkriptor
SMBBrowser and mobile transcription app converting audio and video to text across multiple languages.
Speaker diarization with an editor workflow that keeps corrected timestamps and text aligned during review.
Transkriptor turns audio and video into text transcripts with punctuation and capitalization restoration, and it supports speaker diarization for multi-speaker recordings. It provides a browser-first transcript editor for reviewing output and exporting transcripts into common caption and document formats.
Transkriptor also supports multilingual transcription and language identification to route mixed-language audio to the right decoding pipeline. The product’s core workflow centers on ingestion, automated transcription, review, and structured export for downstream use.
- +Speaker diarization separates contributions for meetings and interviews
- +Transcript editor supports fast corrections after automated punctuation and casing
- +Multilingual transcription and language identification handle mixed-language audio
- +Exports cover caption-style and document-style output formats
- –No clearly documented real-time transcription workflow for live streams
- –Automation depth via API and webhooks is limited for large-scale pipelines
- –Overlapping speech accuracy can degrade in dense turn-taking segments
- –File-level configuration for domain tuning is not exposed for everyday users
Best for: Fits when teams need diarized, edited transcripts from recordings and straightforward export for sharing.
Azure AI Speech
API-firstAzure AI Speech provides speech-to-text APIs with real-time recognition, diarization, and custom speech models.
Speaker diarization labeling and word-level timestamps together support downstream segment alignment in one transcription pass.
Azure AI Speech provides transcription via Azure Speech to Text APIs and audio-to-text models deployed inside Microsoft Azure. It supports real-time and batch transcription workflows, including word-level timestamps and punctuation and casing restoration.
The solution is designed for integration into existing Azure apps, with configurable speech settings and buildable automation around the REST API surface. Speaker diarization is available as a way to label who spoke during a single audio stream.
- +Production-grade REST API for batch and real-time transcription workflows
- +Word-level timestamps for aligning transcript tokens to audio segments
- +Punctuation and capitalization restoration for readability without extra tooling
- +Speaker diarization to label multiple speakers in long-form audio
- –Tuning speech configuration is required for noisy or mixed-domain audio
- –Custom vocabulary and phrase boosting add complexity to deployment
- –Transcript outputs often require an ingestion pipeline for downstream formats
- –Streaming latency and throughput depend on client setup and audio chunking
Best for: Fits when teams already run Azure and need API-driven transcription for batch jobs and live streams.
Conclusion
After evaluating 10 ai in industry, Fireflies stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right transcription ai software
This buyer's guide covers how to choose transcription AI software for meeting notes, content pipelines, and production APIs. Tools covered include Fireflies, Happy Scribe, Sonix, Otter, Descript, Deepgram, Trint, Notta, Transkriptor, and Azure AI Speech.
The guide connects concrete capabilities like speaker-labeled transcripts, word-level timestamps, caption exports, and API-first delivery to the practical workflows each tool supports. It also maps common failure points like overlapping speech diarization and limited automation depth to specific alternatives.
Transcription AI software that turns audio and video into editable, timestamped text and downstream-ready outputs
Transcription AI software converts audio and video into text using automatic speech recognition, with punctuation and capitalization restoration and time alignment for review and export. Many tools add speaker diarization so transcripts attribute dialogue and help users navigate long recordings.
Teams use this software to turn recordings into searchable meeting archives, caption files like SRT or WebVTT, and document-ready transcripts for publishing workflows. Fireflies is an example focused on meeting content search tied to timestamped, speaker-labeled transcripts, while Deepgram centers on API-driven real-time and batch transcription delivery with word-level timestamps.
Evaluation criteria for transcription AI tools: control, alignment, and workflow integration
Transcription accuracy is only one part of the decision because the real cost often comes from editing time, alignment friction, and how easily transcripts move into existing workflows. Fireflies, Sonix, and Trint differentiate through timestamped exports and review loops, while Deepgram and Azure AI Speech differentiate through API-first delivery.
Automation and integration depth matter when transcription output must feed downstream systems without manual exports. Sonix pairs API-based job processing with automated downstream export workflows, while Deepgram and Azure AI Speech deliver timestamps and diarization through developer interfaces for production pipelines.
Timestamped transcripts that speed editing and alignment
Word-level or time-aligned timestamps reduce the time spent locating errors and matching transcript text to the original audio. Sonix provides word-level timestamps for faster transcript review, while Trint links in-editor revisions to timestamps for aligned SRT and WebVTT exports.
Speaker labeling and diarization for multi-person recordings
Speaker diarization helps attribute dialogue and supports review workflows when multiple voices talk in one recording. Fireflies outputs speaker-labeled transcripts with navigable timestamps, while Azure AI Speech pairs speaker diarization labeling with word-level timestamps for segment alignment in one transcription pass.
Caption and document export formats that match publishing workflows
Export formats determine how quickly transcripts can become captions, documents, or shareable artifacts. Happy Scribe exports SRT and WebVTT for caption workflows, and Sonix exports DOCX plus SRT and WebVTT for document and subtitle pipelines.
Transcript editor workflows that support iterative correction
Interactive editing reduces turnaround when transcripts require human fixes before final delivery. Happy Scribe uses a transcript editor with project workflows for iterative correction, while Otter provides inline transcript editing with speaker-aware playback that ties edits to the exact audio moment.
API-first transcription output for automated pipelines
An API-driven transcription interface reduces manual steps when transcripts must be produced at scale and routed into other systems. Deepgram is API-first with configurable delivery that includes word-level timestamps, and Sonix offers API-based job processing that pairs transcription results with automated downstream exports and review workflows.
Meeting-to-knowledge workflows that turn transcripts into reusable artifacts
Some tools add retrieval and action workflows so recordings become a searchable knowledge base rather than one-time text. Fireflies stands out with meeting content search tied to timestamped, speaker-labeled transcripts for fast review and handoff, and Notta couples speaker-aware transcripts with an editor that keeps text fixes tied to the exact audio segment for repeatable meeting review.
Decision framework for selecting transcription AI software for real workflows
Start by identifying whether the primary workflow is editing for publishing, meeting follow-up, or automated ingestion through an API. That choice steers toward tools like Happy Scribe and Trint for editor-centered revision, Fireflies and Otter for meeting review, or Deepgram and Azure AI Speech for production API pipelines.
Next, map the quality risks in each candidate workflow to the tool's known diarization behavior under overlapping speech. Many tools struggle when multiple people talk, so the selection should hinge on whether inline editing and timestamp navigation are available to clean up edge cases.
Choose the transcription delivery model: transcript-first editing or API-first production
If the workflow requires iterative review inside an editor, tools like Happy Scribe, Sonix, and Trint provide interactive transcript editing with timestamp-aligned corrections. If the workflow requires automated ingestion and programmatic output, tools like Deepgram and Azure AI Speech focus on REST API delivery for real-time and batch transcription with word-level timestamps.
Validate diarization behavior against the meeting reality of turn-taking
For multi-speaker meetings with frequent overlaps, prioritize tools that already provide strong speaker labeling and timestamp navigation for cleanup. Fireflies delivers speaker-labeled transcripts with navigable timestamps, while Azure AI Speech provides speaker diarization labeling with word-level timestamps that support downstream segment alignment when diarization must drive automation.
Lock in export targets before choosing a tool
If captions and subtitles are required, confirm the tool outputs SRT and WebVTT and that the editor keeps edits aligned to timestamps for clean exports. Happy Scribe exports SRT and WebVTT, and Trint exports finalized transcripts into SRT and WebVTT-focused workflows.
Pick the automation surface that matches the handoff workflow
For teams that must turn transcription into repeatable downstream actions, select tooling that pairs automation with retrieval or job-style processing. Sonix provides API-based job processing tied to automated downstream exports and review workflows, while Fireflies connects meeting content search to timestamped speaker-labeled transcripts for handoff.
Test overlap-heavy audio with an editing loop, not just a single transcript export
When overlapping speech reduces diarization clarity, the practical fix is often fast editing tied to the original audio moment. Otter supports inline transcript editing with speaker-aware playback, and Descript enables transcript-first editing where transcript edits rewrite the underlying media timeline to keep fixes consistent.
Who should buy which transcription AI tool based on actual workflow needs
Transcription AI tools split into two common buying patterns: editorial teams who need timestamped exports and editing speed, and engineering or operations teams who need API-driven transcription output. The best choice depends on whether the workflow centers on review and export or on automation and integration.
Meeting-specific tools also differ in how they reduce repeated work. Fireflies and Notta focus on structured meeting artifacts, while Deepgram and Azure AI Speech focus on developer interfaces for production pipelines.
Teams that need searchable meeting transcripts with repeatable follow-up workflows
Fireflies fits teams that want meeting content search tied to timestamped, speaker-labeled transcripts so users can find prior decisions without scanning audio. Otter is a close fit when speed of transcript cleanup matters alongside speaker-aware playback.
Editorial and media teams that need editable transcripts and caption-ready exports
Happy Scribe and Sonix fit teams that produce batch transcripts and must correct recognition errors before exporting SRT, WebVTT, and document formats. Trint fits editor-centered timestamped revisions when maintaining alignment for SRT and WebVTT exports is the core workflow.
Engineering teams building automated transcription into apps and pipelines
Deepgram fits engineering teams that need API-driven transcription with word-level timestamps for downstream synchronization use cases. Azure AI Speech fits teams already running Azure that need REST API access for real-time and batch transcription plus diarization.
Content creators and teams that want transcript edits to drive media edits
Descript fits teams that need transcript-first editing where changes in the transcript rewrite the audio and video timeline. This is a stronger fit than editor-only transcript export tools when the goal includes editing the recording itself.
Common pitfalls when choosing transcription AI software for production use
Many teams underestimate how much overlapping speech impacts diarization and editing workload. Several tools show that overlap-heavy sessions can reduce diarization clarity, which forces manual cleanup in the transcript editor.
Another frequent mistake is choosing a tool for transcript exports but discovering the automation surface is too shallow for the actual pipeline. Deepgram and Sonix are built for API-driven workflows, while other tools emphasize UI review and may require additional orchestration for strict automation.
Assuming speaker diarization will stay clean in overlapping turn-taking
Avoid treating diarization as fully reliable for dense conversations since Fireflies, Sonix, Otter, and Deepgram can see diarization clarity drop when multiple people talk. Use tools with strong timestamp navigation and an editing loop like Trint or Otter so speaker attribution fixes stay fast.
Choosing a transcript tool without matching export formats to the publishing system
Avoid picking a tool that only outputs plain text when the target workflow needs caption files or document formats. Happy Scribe and Sonix explicitly support SRT and WebVTT exports, and Trint supports SRT, WebVTT, TXT, and DOCX-focused workflows.
Ignoring the cost of integration when automation must feed downstream steps
Avoid selecting tools without an API-first path when transcription output must feed automated systems. Deepgram offers API-driven transcription for real-time and batch workflows, and Sonix provides API-based job processing that pairs transcription results with automated downstream exports.
Expecting real-time transcription quality and workflow parity across all tools
Avoid assuming every transcription editor tool provides a stable real-time workflow. Otter is built around real-time transcription experiences, while tools like Happy Scribe are described as less suited for interactive low-latency transcription sessions.
Relying on domain vocabulary tuning without an editing fallback
Avoid expecting advanced domain vocabulary tuning to compensate for tough audio. Fireflies and Notta limit domain vocabulary tuning as a central strength, so require an editor workflow like Happy Scribe or Trint to correct domain-specific terms after transcription.
How We Selected and Ranked These Tools
We evaluated Fireflies, Happy Scribe, Sonix, Otter, Descript, Deepgram, Trint, Notta, Transkriptor, and Azure AI Speech on transcription and editing capabilities, ease of use, and overall value for the workflows described in their tool summaries. Features carried the most weight at 40% because it most directly affects correction time, while ease of use and value each accounted for 30% because both impact throughput after transcripts are produced.
Fireflies separated itself from lower-ranked tools through meeting content search tied to timestamped, speaker-labeled transcripts, which directly improved downstream review and handoff speed and raised the features score more than editor-only or API-only strengths.
Frequently Asked Questions About transcription ai software
Which tools handle speaker diarization and speaker identification for multi-speaker recordings?
How does each tool deliver word-level timestamps for transcript synchronization?
When do teams choose an editor-first workflow versus API-driven transcription?
What breaks if a transcription workflow relies only on punctuation and capitalization restoration for readability?
Which tools support automation through an API or webhook-style retrieval of transcription results?
How do data migration and schema mapping work when moving transcripts between tools and systems?
Which tools provide administrative controls like RBAC and audit logs for team governance?
How do tools handle language identification and code-switching in multilingual audio?
What integration approach works best for caption outputs like SRT and WebVTT?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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