Microsoft Nonprofit Global

Project Gutenberg

Document Processing

The oldest digital library and a leading distributor of free eBooks, Project Gutenberg wanted to make its collection more accessible to a broader community of members, including people who have visual impairments and those around the world who might not have access to traditional libraries. To achieve this goal, the organization collaborated with Microsoft on an AI-driven solution to create audiobooks at scale, creating nearly 5,000 new audio books.

Created in 1971, Project Gutenberg is the oldest digital library and the first organization to make electronic versions of public-domain books available via the internet.
“It creates audiobooks en masse, reducing days of volunteer labor to just 30 seconds per book.”

Reported impact

Business outcome

Compared to traditional audiobook production using voice actors or volunteer readers, the AI-driven solution provides dramatic time savings. “When we learned about this neural text to speech technology, the possibilities were obvious,” Newby says. “It creates audiobooks en masse, reducing days of volunteer labor to just 30 seconds per book.”

  • Cost Reduction
  • Scalability

Solution

The oldest digital library and a leading distributor of free eBooks, Project Gutenberg wanted to make its collection more accessible to a broader community of members, including people who have visual impairments and those around the world who might not have access to traditional libraries. To achieve this goal, the organization collaborated with Microsoft on an AI-driven solution to create audiobooks at scale, creating nearly 5,000 new audio books. Project Gutenberg used Azure AI Services, Azure Synapse Analytics, Microsoft Fabric to support document processing with a governed operating model that connected the relevant data sources, automation logic, and employee-facing experiences without custom point-to-point tooling.

Data flow

Data moves from connected source systems into Microsoft Fabric, where it is transformed, modeled, and stored for governed access before being served to business users through analytics applications.

Solution architecture

3 components · 2 layers
  1. Compute
    • Azure AI Services Supplies language, vision, and other AI APIs that classify, interpret, or enrich source content.
  2. Storage
    • Azure Synapse Analytics Provides data warehousing and large-scale analytical processing for unified reporting.
    • Microsoft Fabric Hosts the unified lake, warehouse, and analytics environment used to consolidate and model enterprise data.

Architecture clues

  • Azure AI Services: Supplies language, vision, and other AI APIs that classify, interpret, or enrich source content.
  • Azure Synapse Analytics: Provides data warehousing and large-scale analytical processing for unified reporting.
  • Created in 1971, Project Gutenberg is the oldest digital library and the first organization to make electronic versions of public-domain books available via the internet.
  • Microsoft Fabric: Hosts the unified lake, warehouse, and analytics environment used to consolidate and model enterprise data.
  • “It creates audiobooks en masse, reducing days of volunteer labor to just 30 seconds per book.”

Evidence from the source

A team from Microsoft approached Project Gutenberg about a collaboration to produce thousands of high-quality audiobooks using an AI-driven solution and then give them back to the Project Gutenberg community. These new audio recordings have made Project Gutenberg’s books more accessible to a wider audience of people around the world, including those facing accessibility challenges.
An AI-driven solution for rapid audiobook creation
Compared to traditional audiobook production using voice actors or volunteer readers, the AI-driven solution provides dramatic time savings. “When we learned about this neural text to speech technology, the possibilities were obvious,” Newby says. “It creates audiobooks en masse, reducing days of volunteer labor to just 30 seconds per book.”
Project Gutenberg first loads existing electronic books from its collection into Azure Synapse Analytics to allow working with large amounts of data. Then, they parse the books and use SynapseML distributed ML library to create audio recordings using the neural text to speech capability in Azure AI services
The oldest digital library and a leading distributor of free eBooks, Project Gutenberg wanted to make its collection more accessible to a broader community of members, including people who have visual impairments and those around the world who might not have access to traditional libraries. To achieve this goal, the organization collaborated with Microsoft on an AI-driven solution to create audiobooks at scale, creating nearly 5,000 new audio books.
Transparency Evidence & methodology 85/100 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 value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 impact15/25
    1 metric 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
    5 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. Microsoft

    Project Gutenberg customer story

    First-party
    https://www.microsoft.com/en/customers/story/1646266241611394912-project-gutenberg-nonprofit-azure-synapse-analytics-azure-ai-services
    Source type
    Vendor-published customer story
    Published
    Jul 18, 2023
    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 Jul 18, 2023 · Microsoft · 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.