GITNUXSOFTWARE ADVICE
Communication MediaTop 10 Best Speech Analytics Software of 2026
Top 10 speech analytics software ranked for contact centers, with comparisons of features and tradeoffs for buyers evaluating Uniphore, Verint, CallMiner.
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
Uniphore is the strongest fit for enterprise QA teams that need governed interaction scoring and review automation built around conversation emotion and compliance checks, whereas Dialpad works best when contact centers want transcript-driven speech analytics tied to supervisor coaching workflows.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Uniphore
Automated interaction scoring tied to configurable review workflows with evidence capture for QA decisions.
Built for fits when enterprise QA teams need interaction scoring plus review automation tied to governance..
Verint
Editor pickQuality monitoring workflows that turn transcription into standardized interaction scoring across large call programs.
Built for fits when contact centers need governed QA scoring and enterprise integration for reliable speech analytics..
CallMiner
Editor pickInteraction scoring rules that connect conversation signals to QA review categories and KPI dashboards.
Built for fits when contact centers need governed interaction scoring tied to QA workflows..
Related reading
Comparison Table
Uniphore
enterpriseConversational AI platform with speech analytics and emotion detection.
Automated interaction scoring tied to configurable review workflows with evidence capture for QA decisions.
Uniphore ingests call audio and generates searchable transcripts, detected issues, and interaction-level scores used for quality monitoring. Review teams can validate findings, prioritize call replays, and use dashboards to monitor trends across channels and teams. The automation surface supports workflow actions like flagging conversations, assigning cases, and collecting evidence for review decisions.
A tradeoff appears when teams need deep domain specificity because interaction scoring depends on a maintained rules and model configuration. Uniphore fits best when call programs already define measurable behaviors, escalation criteria, and review processes that can be mapped into repeatable quality signals.
- +Conversation analytics outputs actionable scores for quality monitoring
- +Review workflows support evidence-backed tagging and case assignment
- +Searchable transcripts reduce time to diagnose customer and agent issues
- +Governance controls support controlled access and auditability
- –Scoring quality depends on ongoing configuration for each call program
- –More effort required to align findings with internal QA rubrics
- –Dataset onboarding can require tuning for channel and microphone variability
- –Complex programs may need tighter process discipline to stay consistent
Contact center QA leads
Score calls against behavior rubrics
Faster QA coverage and consistency
Operations analytics teams
Track performance KPIs by team
Clearer operational focus areas
Show 2 more scenarios
Compliance monitoring teams
Flag risky or missing commitments
Reduced compliance review lag
Detect compliance-relevant conversation events and create review queues for follow-up.
Call center managers
Improve agent performance through coaching
Improved agent execution
Use interaction analytics to pinpoint friction points and guide targeted coaching sessions.
Best for: Fits when enterprise QA teams need interaction scoring plus review automation tied to governance.
More related reading
Verint
enterpriseEnterprise customer engagement platform with speech analytics as a core capability.
Quality monitoring workflows that turn transcription into standardized interaction scoring across large call programs.
Verint targets speech-to-text and conversation analytics workflows where call teams need more than keyword spotting, including interaction scoring and quality monitoring outputs. The fit is strongest for organizations that already run enterprise governance, because configuration and review workflows can be standardized across lines of business. Integration depth is a key signal because analytics outputs must link back to customer and agent context for operational use.
A tradeoff is that fully operationalizing analytics often requires disciplined configuration of scoring rules and taxonomy so dashboards match QA expectations. Verint fits best for post-call analysis where search and trend reporting drive coaching cycles, not for experimental insights that need frequent model changes. Teams that want rapid, self-serve analytics iteration may find the governance layer slows iteration compared with lighter-weight deployments.
- +Conversation scoring for consistent QA across call programs
- +Searchable transcripts for fast review and coaching prep
- +Governance-focused monitoring workflows for regulated environments
- +Integration options for linking analytics to business systems
- –Scoring and taxonomy configuration needs careful upfront design
- –Iterating analytics rules can be slower than self-serve tools
- –Advanced analytics workflows can increase admin workload
- –Depth can depend on add-on modules for specific compliance needs
QA and quality assurance leads
Automate interaction scoring and review routing
Fewer manual review hours
Compliance and risk teams
Monitor calls for policy breaches
Faster policy investigations
Show 2 more scenarios
Contact center operations managers
Trend customer issues by topic
Targeted improvement plans
Verint supports reporting views that summarize themes across conversations for coaching priorities.
Enterprise systems integration teams
Connect analytics to CRM and ticketing
Closed-loop operational workflows
Verint integration options help route analytics signals into existing operational systems for action.
Best for: Fits when contact centers need governed QA scoring and enterprise integration for reliable speech analytics.
CallMiner
enterpriseDedicated speech analytics platform for contact center conversation intelligence.
Interaction scoring rules that connect conversation signals to QA review categories and KPI dashboards.
CallMiner targets organizations that need conversation analytics plus operational review workflows, including interaction scoring and QA surfacing for specific coaching criteria. The system uses natural language processing to extract themes and sentiments from transcripts, then ties results to configurable rules for reporting and performance measurement. This depth matters when the organization runs structured coaching programs and wants consistency across reviewers and teams.
A key tradeoff is that building high-precision scoring and category logic requires deliberate configuration work and iterative tuning of the rule set. CallMiner fits best when transcripts are already reliably captured and leadership needs auditable standards for agent performance monitoring across channels. It is less suited to teams wanting ad hoc, low-effort tagging without ongoing governance of scoring logic.
- +Configurable interaction scoring aligned to coaching rubrics
- +QA review views map conversation insights to agent actions
- +Conversation KPIs stay consistent across teams
- +API and exports support downstream analytics pipelines
- –High-precision scoring needs iterative configuration cycles
- –Advanced analytics workflows take admin attention
- –บาง reporting configurations can feel rigid at first
- –Onboarding integrations can require contact-center process mapping
Contact center QA leaders
Standardize coaching feedback across reviewers
More consistent agent coaching
Customer experience operations
Track drivers of deflection and churn
Clear drivers by segment
Show 2 more scenarios
Revenue operations analysts
Audit calls for compliance risks
Faster risk triage
Analysts use searchable transcripts and scoring flags to isolate high-risk customer interactions.
Contact center administrators
Integrate analytics into enterprise tools
Unified operational reporting
Admins push analytics results via API and exports into reporting and ticket workflows.
Best for: Fits when contact centers need governed interaction scoring tied to QA workflows.
NICE
enterpriseAI-powered speech analytics via Enlighten for customer experience and compliance.
Real-time and post-call evaluation workflows that feed interaction scoring results into QA and coaching processes.
NICE is a speech analytics software suite used for turning recorded and live customer calls into searchable conversations, agent performance views, and compliance-focused insights. Its core capabilities include call transcription, interaction scoring, and conversation-level analytics that support quality monitoring workflows at scale.
NICE also fits contact centers that need operational controls around who can view and act on analytics results, plus configurable monitoring rules for consistent evaluations. Integration and automation are central through documented APIs and event-driven workflows that connect analytics output to quality management, workforce tools, and reporting systems.
- +Conversation search ties transcripts to playback for faster QA review loops
- +Interaction scoring supports repeatable coaching across large agent populations
- +Extensible integrations support routing of analytics outputs into external workflows
- +Admin controls support governance over analytics viewing and evaluation access
- –Deep configuration takes time for scoring rules and monitoring coverage
- –Some advanced analytics workflows depend on additional components beyond core transcription
- –Real-time use requires careful sizing of processing and routing paths
- –Reporting customization can be constrained for highly tailored KPI definitions
Best for: Fits when contact centers need conversation analytics with repeatable scoring and governed QA workflows tied to external systems.
Genesys
enterpriseCloud contact center platform with speech and text analytics built in.
Analytics signals feed Genesys workflow automation so interaction insights can drive operational actions, not just reporting.
Genesys performs conversation analytics by ingesting recorded customer interactions and producing searchable transcripts, summaries, and agent performance signals. It uses Genesys Cloud’s orchestration stack to connect speech processing to routing workflows and to surface analytics inside operational queues.
Admin controls focus on managing user access for analytics views and configuration changes across contact center environments. Integration depth centers on API-driven data access and event-driven automation around captured interaction records.
- +Tight coupling between interaction capture and agent performance analytics in Genesys Cloud
- +Extensible automation options via Genesys APIs for analytics-driven workflows
- +Operational analytics views align with contact center monitoring needs
- +Governed access patterns support role-based control of analytics exposure
- –Speech analytics depth depends on the Genesys deployment pattern and configuration choices
- –Conversation search and scoring can feel constrained without careful analytics schema setup
- –Cross-environment reporting requires deliberate data plumbing between systems
- –Large-scale transcription workloads may require tuning to meet throughput targets
Best for: Fits when contact centers need analytics tied to routing and agent QA workflows in Genesys Cloud.
Observe.AI
enterpriseContact center AI platform specializing in speech analytics and agent coaching.
Real-time interaction scoring plus evidence-linked transcript review for QA workflows.
Observe.AI is a call and conversation speech analytics system built around agent performance analytics, quality monitoring, and conversation insights. It uses speech-to-text outputs to drive conversation search, interaction scoring, and KPI dashboarding across large call volumes.
Conversation playback assurance and structured highlights support post-call review for training and compliance monitoring. Administrative controls focus on enabling teams to configure workflows and govern access to analytics outputs.
- +Conversation search and scoring work off transcripts with clickable evidence
- +Agent performance analytics includes trend views across teams and skills
- +Quality monitoring workflows support repeatable rubric-based reviews
- +Playback assurance keeps reviewers aligned with what the caller said
- –Advanced automation requires workflow configuration effort beyond basic tagging
- –Some governance gaps appear when many teams need isolated reporting views
- –Reporting setup can become cumbersome with complex filters and segments
- –Custom analysis use cases may depend on integration work with existing systems
Best for: Fits when contact centers need transcript-based conversation analytics with repeatable QA and search for coaching.
Dialpad
SMBUCaaS and contact center platform with built-in voice intelligence speech analytics.
Dialpad Conversation Insights and coaching workflows turn call transcripts into review tasks for managers, not just dashboards.
Dialpad focuses on converting call recordings and transcripts into review workflows that supervisors can operationalize during quality monitoring.
Speech-to-text output supports transcription, conversation summary generation, and conversation search for faster issue isolation.
Admin and integration capabilities support extensibility and automation across voice systems, with configuration applied at organizational scope.
- +Actionable coaching workflows linked to real call evidence
- +Conversation search over transcripts reduces time-to-find issues
- +Conversation summaries compress long calls into review-ready notes
- +Strong integration options for embedding analytics into existing stacks
- –Customization of scoring logic can require careful process mapping
- –Speaker diarization quality varies by call audio conditions
- –Some advanced analytics outputs depend on enabling specific modules
- –Reporting depth for niche compliance formats can be limiting
Best for: Fits when contact centers want transcript-driven analytics plus supervisor coaching workflows.
Talkdesk
mid-marketCloud contact center platform with AI-powered speech analytics via Talkdesk IQ.
Built-in conversation search that pairs transcripts with playback and QA context for targeted coaching sessions.
Talkdesk combines conversation analytics with QA and call-center workflows so teams can measure interactions and act on findings. Conversation search and playback support target coaching, while configurable scoring and insights connect analytics to quality monitoring.
Speech-to-text output underpins downstream topics, summaries, and agent performance views across channels. Administration features focus on access control and auditability for governance of monitoring and reporting.
- +Conversation search links results to specific calls for faster QA review
- +Quality monitoring workflows align coaching tasks with measured interaction outcomes
- +Automation and configuration options support repeatable analytics-to-ops processes
- +Role-based access features help control who can view monitoring outputs
- –Deep scoring and tagging often requires careful configuration to match operations
- –Real-time insights coverage can lag behind post-call reporting depending on setup
- –Advanced analytics use cases depend on data readiness from integrations
- –Global governance requires more admin time when multiple teams share dashboards
Best for: Fits when contact centers need speech analytics tied to QA workflows and controlled access for multiple teams.
Invoca
mid-marketCall tracking and analytics platform with AI speech analytics for marketing teams.
Call-to-conversion attribution workflows that tie conversation outcomes back to marketing and routing performance.
Invoca analyzes phone calls to connect voice signals to marketing and customer outcomes. It combines call transcription with conversation analytics to surface what was said, what was decided, and where calls succeed or fail.
Configuration centers on defining call metadata and routing analytics back to teams for review and reporting. The main differentiation is the tight linkage of call intelligence to business KPIs through integration workflows and APIs.
- +Strong attribution-oriented workflows that connect call events to business KPIs
- +Conversation analytics supports actionable review of what was said on calls
- +Extensibility through APIs supports custom integrations and downstream automation
- +Operational controls help teams manage analysis coverage across call streams
- –Setup requires careful mapping of call metadata to analytics goals
- –Real-time inspection is limited compared with post-call analysis depth
- –Conversation search quality depends on consistent speech and labeling inputs
- –Advanced governance needs more admin effort than simpler transcript-only tools
Best for: Fits when call-driven attribution and conversation analytics must feed marketing and support KPIs.
Marchex
mid-marketCall analytics platform with conversation speech analytics for multi-location businesses.
Conversation search that links matched customer moments back to call audio for operational review and QA.
Marchex focuses on telecom-scale call intelligence with reporting built around call outcomes and audio artifacts. Speech-to-text transcription is paired with conversation search so teams can find relevant moments across large volumes.
Automated conversation analytics supports performance measurement for contact-center workflows, including keyword-style retrieval and thematic review of calls. Admin governance centers on managing data access for reporting and operational users tied to contact-center operations.
- +Conversation search helps locate audio segments tied to specific customer contexts
- +Call reporting is oriented around operational outcomes, not just transcript browsing
- +Transcription supports downstream QA and agent coaching workflows
- +Integration focus aligns with telecom and contact-center call flows
- –Setup typically requires tight alignment to existing call routing and identifiers
- –Automation depth for advanced NLU style scoring is less transparent
- –Some review workflows depend on curated analytics views rather than ad-hoc modeling
- –Extensibility is more constrained than general purpose transcription pipelines
Best for: Fits when contact centers need telecom-scale call intelligence and fast conversation search over transcripts and audio.
Conclusion
After evaluating 10 communication media, Uniphore 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 speech analytics software
This guide covers how speech analytics software turns call audio into searchable transcripts, conversation insights, and QA-ready scoring workflows across Uniphore, Verint, CallMiner, NICE, Genesys, Observe.AI, Dialpad, Talkdesk, Invoca, and Marchex.
It also explains how to evaluate automation and integration depth, governance controls for who can view and act on analytics, and configuration effort for scoring and monitoring rules.
Speech analytics platforms that score and operationalize call conversations
Speech analytics software uses speech-to-text transcription to create searchable call transcripts and conversation-level insights that contact centers and call-driven teams can use for QA, coaching, and performance measurement. It also typically links transcription outputs to interaction scoring, conversation search, and monitoring workflows that produce repeatable evaluation results.
Tools like Verint and NICE show what this looks like in practice by turning transcription into standardized interaction scoring and governed monitoring workflows. Uniphore demonstrates the same workflow pattern with automated interaction scoring tied to configurable review workflows and evidence capture for QA decisions.
Evaluation criteria for scoring, search, workflow automation, and governance
Speech analytics tools differ most in how reliably they convert transcripts into consistent scoring, how quickly reviewers can locate evidence, and how directly analytics can feed operational actions.
Teams also need to compare automation and API surface depth because interaction scoring and review workflows only stay trustworthy when teams can control configuration, access, and data plumbing across environments.
Configurable interaction scoring tied to QA review workflows
Uniphore, Verint, and CallMiner each focus on interaction scoring that connects conversation signals to QA review categories so scoring outcomes can map to internal rubrics. Uniphore adds evidence capture so QA decisions tie back to what was said in each call segment.
Conversation search that links transcripts to playback and review context
Talkdesk and NICE both pair conversation search with playback so reviewers can jump from a matched transcript moment to the audio they need for coaching. Uniphore also emphasizes searchable transcripts for diagnosing customer and agent issues faster.
Real-time or post-call evaluation workflows with operational handoff
NICE and Observe.AI support evaluation workflows that deliver interaction scoring plus evidence-linked transcript review for QA processes. NICE additionally targets both real-time and post-call evaluation paths so scoring can feed coaching workflows across different review cadences.
Transcript-based agent performance analytics with trend and team views
Observe.AI includes agent performance analytics with trend views across teams and skills so supervisors can track changes in conversation outcomes. Genesys pairs interaction capture with agent performance analytics inside operational queues so analytics stay tied to routing and daily monitoring.
Workflow automation signals that drive actions beyond reporting
Genesys is built around analytics signals feeding Genesys workflow automation so interaction insights can drive operational actions, not only dashboards. Dialpad also turns transcripts into structured insights that become review tasks for managers, which changes how quickly teams can respond to issues.
Governance and access controls for analytics viewing and evaluation
Verint, Uniphore, and Talkdesk all emphasize governed monitoring workflows and role-based access so analytics viewing and evaluation can match regulated or multi-team environments. Uniphore adds auditability and controlled access patterns around monitoring and governance for enterprise QA teams.
Decision framework for selecting speech analytics software by workflow fit
A useful selection process starts by mapping where interaction scoring and review outputs must land in daily operations. The next step is matching the tool to the way the organization handles automation, access control, and evidence capture.
Finally, configuration effort should be treated as part of the choice because tools like NICE and Uniphore can require more scoring-rule alignment to stay consistent with internal QA rubrics.
Choose the workflow pattern: evidence-linked QA tasks vs operational signals
If daily work is based on QA reviewers tagging outcomes and assigning cases, Uniphore fits because interaction scoring is tied to configurable review workflows with evidence capture. If daily work is based on supervisors acting inside operational queues, Genesys fits because analytics signals feed Genesys workflow automation and agent performance views.
Decide scoring control depth and expected configuration cadence
For teams that want standardized scoring across many call programs, Verint fits because quality monitoring workflows turn transcription into consistent interaction scoring at scale. For teams that need scoring rules mapped to coaching categories and KPI dashboards, CallMiner fits because interaction scoring rules connect conversation signals to QA review categories and KPI dashboards.
Pick the review speed requirement: conversation search plus playback assurance
If reviewers must move from a found moment to the exact audio fast, Talkdesk and NICE both provide conversation search paired with playback. If reviewers must stay aligned during post-call review, Observe.AI provides playback assurance so evidence-linked transcript review matches what the caller said.
Match real-time evaluation needs to processing and routing constraints
If real-time evaluation must feed coaching and scoring workflows, NICE supports real-time and post-call evaluation workflows that feed interaction scoring into QA and coaching. If the primary goal is post-call analysis for coaching and compliance, tools like CallMiner and Observe.AI still support repeatable scoring and structured QA workflows based on transcripts.
Validate governance and cross-team reporting access requirements
For regulated environments or enterprise monitoring where analytics access must be governed, Verint and Uniphore focus on controlled access patterns and auditability. For multi-team use where isolated views must be maintained, Talkdesk and Observe.AI both provide role-based access features, but governance effort can increase with shared dashboards.
Assess platform fit to your ecosystem and channel mix
If voice intelligence must embed into a broader contact center stack, Dialpad fits because it pairs speech-to-text and summaries with searchable transcripts and coaching workflows. If analytics must connect to marketing and routing outcomes, Invoca fits because it links conversation outcomes back to marketing and routing performance through call-to-conversion attribution workflows.
Which organizations should buy speech analytics software
Speech analytics software is most valuable when transcripts and conversation signals must feed quality monitoring, coaching, compliance monitoring, or KPI-driven decision-making. The right tool depends on whether the organization is contact-center focused, routing focused, or attribution focused.
The audience fit below maps directly to each tool’s stated best-for use.
Enterprise QA teams running governed scoring and review automation
Uniphore fits this segment because automated interaction scoring is tied to configurable review workflows with evidence capture and governance controls for auditability. The tool also emphasizes searchable transcripts to reduce time to diagnose customer and agent issues.
Large contact centers that need standardized scoring across many programs
Verint fits because quality monitoring workflows turn transcription into standardized interaction scoring across large call programs with governed monitoring patterns. NICE also fits when repeatable scoring must feed QA and coaching processes with both real-time and post-call evaluation workflows.
Contact centers that want scoring rules mapped to coaching categories and KPI dashboards
CallMiner fits this audience because interaction scoring rules connect conversation signals to QA review categories and KPI dashboards, which keeps coaching and metrics aligned. Genesys also fits teams that want analytics tied to routing and agent QA workflows inside Genesys Cloud.
Operations teams that need transcript-based coaching tasks and fast evidence lookup
Dialpad fits because it turns call transcripts and conversation insights into structured coaching workflows tied to real call evidence for managers. Talkdesk fits when conversation search must pair transcripts with playback and QA context for targeted coaching sessions.
Marketing and support organizations that tie calls to business outcomes
Invoca fits because call-to-conversion attribution workflows connect conversation outcomes back to marketing and routing performance. Marchex fits telecom-scale operations that need conversation search over transcripts and audio artifacts for operational review and QA.
Common buying pitfalls in speech analytics projects
Mistakes typically happen when scoring rules and monitoring coverage are treated as a one-time setup instead of an ongoing configuration cycle. Another common failure is assuming conversation search alone can replace evidence-linked QA workflows.
The pitfalls below reflect the specific cons cited across the tools in this set.
Underestimating configuration work to keep scoring consistent
Verint and Uniphore both require careful upfront design and ongoing configuration so scoring stays aligned across call programs. CallMiner and NICE also depend on scoring-rule alignment and monitoring coverage configuration to avoid inconsistent evaluation results.
Assuming all governance needs are solved by basic access controls
Observe.AI and Talkdesk can require additional admin time when many teams share dashboards or need isolated reporting views. Uniphore and Verint are better aligned to governed enterprise monitoring with auditability and controlled access patterns.
Picking a tool that cannot surface evidence quickly during QA review
Tools that focus on analytics without tight playback linkage slow down reviewers, which can reduce QA throughput. NICE and Talkdesk both pair conversation search with playback so reviewers can move from transcripts to audio moments for coaching.
Expecting real-time analytics depth without planning processing and routing setup
NICE requires careful sizing of processing and routing paths for real-time coverage, while Dialpad real-time depth and performance depend on module enablement for some advanced outputs. Invoca limits real-time inspection compared with post-call analysis depth, so the workflow should reflect that tradeoff.
Skipping process mapping for identity, metadata, or internal rubrics
Dialpad and Marchex both note that diarization or call routing alignment can affect review quality and setup effort, so call metadata and identifiers should be mapped to internal workflows. Invoca also needs careful mapping of call metadata to analytics goals to ensure attribution outcomes match business KPIs.
How We Selected and Ranked These Tools
We evaluated Uniphore, Verint, CallMiner, NICE, Genesys, Observe.AI, Dialpad, Talkdesk, Invoca, and Marchex using criteria-based scoring across features, ease of use, and value. Features carried the most weight in the overall score, while ease of use and value each carried equal weight, with those three factors making up the full ranking. This editorial research used the tool capabilities and workflow behaviors described in the supplied product details and scored them on how directly they support interaction scoring, conversation search, automation, and governance.
Uniphore set itself apart by pairing automated interaction scoring with configurable review workflows and evidence capture for QA decisions, and that concrete workflow automation increased its features score and helped it rank highest overall. Its governance controls for controlled access and auditability also mapped to enterprise monitoring needs, which supported both features and ease of use.
Frequently Asked Questions About speech analytics software
How do speech analytics tools typically integrate with contact-center recording and CRM systems?
What API and extensibility options matter when teams automate QA and reporting pipelines?
When is SSO and RBAC support a deciding factor for speech analytics administrators?
How does data migration work when moving from legacy transcription or QA scoring tools?
What admin controls are required to manage configuration changes and evidence retention?
What breaks if a team only collects transcripts and skips structured conversation analytics?
Which tools fit real-time evaluation versus post-call review workflows?
How do teams translate speech analytics into supervisor review tasks for coaching?
Where does speaker diarization, sentiment, or intent analysis fall short for specific use cases?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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