NVIDIA Media and Entertainment United States

Insider

Insider needed a faster and more cost-effective way to localize video content for non-English-speaking audiences because traditional dubbing was too slow and expensive and subtitles delivered lower engagement.

100 million views
5-10X training workload speedups
hundreds of millions of additional viewers

Reported impact

Business outcome

Insider reached hundreds of millions of additional viewers, gained 100 million views on its Spanish YouTube channel within weeks, and expanded international audience engagement with dubbed content that sometimes outperformed the English originals.

  • Customer Experience
  • Revenue Growth
  • Scalability

Solution

Insider localized its video catalog by plugging Papercup's AI dubbing platform into existing publishing workflows instead of relying on slow studio dubbing or subtitles alone. Papercup trains and serves its speech models on NVIDIA A100 GPUs, using GPU acceleration for both model training and production voice synthesis. As clips move through the workflow, Papercup automatically translates dialogue, segments audio, generates human-sounding AI voice tracks, and routes the output through quality-control steps with human translators before release. NVIDIA Triton Inference Server is being adopted to raise synthesis throughput, simplify support for inference frameworks such as ONNX and TensorRT, and improve end-to-end latency and GPU utilization. This architecture let Insider dub back-catalog news videos quickly enough to keep content relevant while expanding distribution into Spanish, Latin American, and European channels.

Data flow

Existing Insider videos enter Papercup's dubbing workflow, dialogue is translated and segmented, AI voices are generated on NVIDIA GPU infrastructure, human translators verify quality, and localized videos are published to regional channels.

Solution architecture

4 components · 3 layers
  1. Compute
    • NVIDIA A100 GPUs Accelerate Papercup's training and inference workloads for AI dubbing.
  2. Serving
    • NVIDIA Triton Inference Server Improves synthesis throughput, inference flexibility, and end-to-end latency in production voice generation.
    • NVIDIA TensorRT Supports inference optimization as part of Papercup's production speech stack.
  3. Orchestration
    • Papercup AI dubbing platform Automates translation, segmentation, AI voice generation, and quality-control workflows for dubbed videos.

Architecture clues

  • Papercup's AI voices are verified by human translators before release
  • The workflow targets faster localization of back-catalog news and informational video content
  • Voice synthesis in production takes place on NVIDIA GPUs in the cloud

Evidence from the source

In a matter of weeks, Insider gained 100 million views on its Spanish YouTube channel.
Insider uses Papercup's AI solution, which runs on NVIDIA A100 GPUs, to localize news and existing videos in a quick and cost-effective way.
Moving their models in production to the Triton Inference Server is a key part of the strategy towards improved performance, flexibility, and utilization of GPUs during voice synthesis.
Papercup's AI solution helps make videos watchable in any language, using AI voices trained through their own machine learning algorithms.
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. NVIDIA

    Insider customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/accelerating-the-language-dubbing-process-with-nvidia-powered-ai-solutions/
    Source type
    Vendor-published customer story
    Published
    Oct 29, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Oct 29, 2025 · NVIDIA · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.