Document to Audiobook: EPUB, PDF, DOCX to MP3

What does Document to Audiobook do?

Document to Audiobook turns e-books, documents and web articles into audiobooks. Give it an EPUB, PDF, DOCX, MOBI, AZW3, ODT, RTF, TXT, PPTX, XLSX, HTML page or any of 176 file types, and you get an MP3 or M4B audiobook with chapters, the book title and author in the tags, and a timed SRT transcript.

• Batch: up to 100 URLs, or one ZIP / 7z / RAR / TAR with any number of documents (up to 200 per run), in the same or different formats. Each document becomes its own audiobook, or all of them become one audiobook with a chapter per document.
• Chapters: taken from the book's headings (EPUB, DOCX, Markdown, HTML ...), the PDF's bookmarks, or lines like "Chapter 3" in plain text. Slides become one chapter each.
• 322 natural neural voices in 75 languages, chosen automatically from the text's language, or the one you name. Speed, pitch and volume are adjustable.
• AI OCR, handwriting included: scanned PDFs, DjVu, photos of pages, handwritten letters and notes, and comic archives are read by an AI vision model that copes with cursive, stains and faded ink. Classic OCR (30 languages) takes over if the AI is unavailable. Handwriting is read best in Latin script; very dense cursive in other scripts (for example Arabic ruq'a) may not be read reliably.
• Web pages: give an article URL and only the main text is read, without menus and ads.
• $0.01 per minute of audio. Failed documents are free.

Who is it for?

• Readers and students: listen to e-books, papers, lecture notes and long articles on the go.
• Publishers and self-published authors: a quick audio edition or a preview of a manuscript, chapter by chapter.
• Accessibility teams: audio versions of reports, policies and handouts for people who prefer to listen.
• Language learners: text and timed SRT side by side, at a slower speed if you like.
• Developers and automations: "document in, audiobook out" over the Apify API, with files you can link to directly.

How to use it

1. Upload a file (or a ZIP with many), paste document or web page URLs, or paste text.
2. Leave Language on Auto-detect, or pick one; choose a female or male voice, or name a specific voice.
3. Pick MP3 (plays everywhere) or M4B (audiobook apps show chapters), then Start.
4. Open the Audiobooks tab of the output: each row has a link to the audio file, the SRT file and the chapter list.

Supported formats (176 extensions)

Family	Extensions
Documents and markup	.bib .creole .dbk .docbook .docx .dokuwiki .epub .fb2 .htm .html .ipynb .jats .latex .man .markdown .md .mdown .mediawiki .mkd .muse .odt .opml .org .pod .rst .rtf .t2t .tex .textile .tikiwiki .twiki .typ .wiki .xht .xhtml
E-books	.azw .azw3 .azw4 .chm .fbz .htmlz .kepub .lit .lrf .mobi .oeb .pdb .pml .pmlz .prc .rb .recipe .snb .tcr .txtz
Fixed layout	.djv .djvu .oxps .pdf .xps
Word processors	.abw .cwk .doc .docm .dot .dotm .dotx .fodt .hwp .lwp .mw .ott .pages .sdw .stw .sxw .uot .wpd .wps .wpt .wri .zabw
Presentations (one chapter per slide)	.fodp .key .odp .otp .pot .potx .pps .ppsx .ppt .pptm .pptx .sdd .sxi .uop
Spreadsheets (rows read as sentences)	.123 .dif .fods .numbers .ods .ots .slk .sxc .uos .wk1 .wks .xls .xlsb .xlsm .xlsx .xlt .xltx
Plain text and data	.adoc .asc .asciidoc .csv .ini .json .jsonl .log .me .ndjson .nfo .text .toml .tsv .txt .xml .yaml .yml
Subtitles and lyrics	.ass .lrc .sbv .srt .ssa .sub .vtt .webvtt
Email	.eml .mht .mhtml .msg
Images (OCR)	.bmp .gif .j2k .jfif .jp2 .jpe .jpeg .jpg .pbm .pgm .png .pnm .ppm .tif .tiff .webp
Comics (OCR)	.cb7 .cbr .cbt .cbz
Archives (every document inside)	.7z .bz2 .gz .iso .lz .rar .tar .tbz2 .tgz .txz .xz .zip .zst

The type is recognised from the file name, the server's content type and the file's first bytes, so links without an extension work too. Google Drive, Google Docs, Dropbox and OneDrive share links are turned into downloads (the file must be shared with "anyone with the link"). Password-protected and DRM-protected files cannot be read.

Output

One dataset row per audiobook, plus the files in the run's key-value store (public links in the row).

Field	Meaning
status	completed, failed (free) or info (a free note, for example files in a ZIP that are not documents)
title, author	From the book's metadata, the PDF info, the web page, or the file name
audioUrl, audioFormat	Link to the MP3 or M4B file
subtitlesUrl	Link to the SRT transcript (sentence by sentence)
chapters	{chapterNumber, chapter, start, startSeconds, durationSeconds} for each chapter
chapterFiles	One audio link per chapter, with "Also save one file per chapter"
durationSeconds, durationText	Length of the audiobook
language, voice	The voice that read it (voicesUsed lists more than one if the service dropped a voice during the run)
characters, words, ocrPages	What was read
truncated, message	True when only the beginning was read ("Max characters per document" or the run's maximum cost)
chargedAudioMinutes, chargedOcrPages	What this row cost
error, message	On failed rows: a short code (download-failed, unsupported-format, protected-document, no-text, speech-failed, BUDGET_LIMIT ...) and a plain explanation

How much does it cost?

Event	Price	When
Audio minute	$0.01	Per started minute of finished audio. A 10-page article is about 5 to 8 minutes ($0.05 to $0.08); a 300-page novel is about 8 to 10 hours ($5 to $6).
OCR page	$0.005	Per scanned page, photo or handwritten page read with AI OCR. Documents with a text layer never use OCR.

• Failed documents, unsupported files and info rows are free.
• Faster speech means fewer minutes: at Speed 25% a book costs about 20% less.
• "Max cost per run" is respected: if a document would go over it, only the beginning is read, and the row says so (truncated). Documents that no longer fit are skipped for free (BUDGET_LIMIT).

Input example

{
    "documentUrls": [
        "https://www.gutenberg.org/ebooks/1342.epub3.images",
        "https://example.com/blog/long-article",
        "https://drive.google.com/file/d/FILE_ID/view?usp=sharing"
    ],
    "language": "auto",
    "voiceGender": "female",
    "speed": 10,
    "audioFormat": "m4b",
    "subtitles": true,
    "chapterFiles": false
}

Voices

Leave Voice empty to pick one from the language and gender, or name one from this list (the (f) / (m) mark is the voice's gender). With Language: Auto-detect, each document gets a voice in its own language.

Locale	Voices
af-ZA (Afrikaans)	af-ZA-AdriNeural (f), af-ZA-WillemNeural (m)
am-ET (Amharic)	am-ET-AmehaNeural (m), am-ET-MekdesNeural (f)
ar-AE (Arabic)	ar-AE-FatimaNeural (f), ar-AE-HamdanNeural (m)
ar-BH (Arabic)	ar-BH-AliNeural (m), ar-BH-LailaNeural (f)
ar-DZ (Arabic)	ar-DZ-AminaNeural (f), ar-DZ-IsmaelNeural (m)
ar-EG (Arabic)	ar-EG-SalmaNeural (f), ar-EG-ShakirNeural (m)
ar-IQ (Arabic)	ar-IQ-BasselNeural (m), ar-IQ-RanaNeural (f)
ar-JO (Arabic)	ar-JO-SanaNeural (f), ar-JO-TaimNeural (m)
ar-KW (Arabic)	ar-KW-FahedNeural (m), ar-KW-NouraNeural (f)
ar-LB (Arabic)	ar-LB-LaylaNeural (f), ar-LB-RamiNeural (m)
ar-LY (Arabic)	ar-LY-ImanNeural (f), ar-LY-OmarNeural (m)
ar-MA (Arabic)	ar-MA-JamalNeural (m), ar-MA-MounaNeural (f)
ar-OM (Arabic)	ar-OM-AbdullahNeural (m), ar-OM-AyshaNeural (f)
ar-QA (Arabic)	ar-QA-AmalNeural (f), ar-QA-MoazNeural (m)
ar-SA (Arabic)	ar-SA-HamedNeural (m), ar-SA-ZariyahNeural (f)
ar-SY (Arabic)	ar-SY-AmanyNeural (f), ar-SY-LaithNeural (m)
ar-TN (Arabic)	ar-TN-HediNeural (m), ar-TN-ReemNeural (f)
ar-YE (Arabic)	ar-YE-MaryamNeural (f), ar-YE-SalehNeural (m)
az-AZ (Azerbaijani)	az-AZ-BabekNeural (m), az-AZ-BanuNeural (f)
bg-BG (Bulgarian)	bg-BG-BorislavNeural (m), bg-BG-KalinaNeural (f)
bn-BD (Bangla)	bn-BD-NabanitaNeural (f), bn-BD-PradeepNeural (m)
bn-IN (Bangla)	bn-IN-BashkarNeural (m), bn-IN-TanishaaNeural (f)
bs-BA (Bosnian)	bs-BA-VesnaNeural (f), bs-BA-GoranNeural (m)
ca-ES (Catalan)	ca-ES-EnricNeural (m), ca-ES-JoanaNeural (f)
cs-CZ (Czech)	cs-CZ-AntoninNeural (m), cs-CZ-VlastaNeural (f)
cy-GB (Welsh)	cy-GB-AledNeural (m), cy-GB-NiaNeural (f)
da-DK (Danish)	da-DK-ChristelNeural (f), da-DK-JeppeNeural (m)
de-AT (German)	de-AT-IngridNeural (f), de-AT-JonasNeural (m)
de-CH (German)	de-CH-JanNeural (m), de-CH-LeniNeural (f)
de-DE (German)	de-DE-SeraphinaMultilingualNeural (f), de-DE-FlorianMultilingualNeural (m), de-DE-AmalaNeural (f), de-DE-ConradNeural (m), de-DE-KatjaNeural (f), de-DE-KillianNeural (m)
el-GR (Greek)	el-GR-AthinaNeural (f), el-GR-NestorasNeural (m)
en-AU (English)	en-AU-WilliamMultilingualNeural (m), en-AU-NatashaNeural (f)
en-CA (English)	en-CA-ClaraNeural (f), en-CA-LiamNeural (m)
en-GB (English)	en-GB-LibbyNeural (f), en-GB-MaisieNeural (f), en-GB-RyanNeural (m), en-GB-SoniaNeural (f), en-GB-ThomasNeural (m)
en-HK (English)	en-HK-YanNeural (f), en-HK-SamNeural (m)
en-IE (English)	en-IE-ConnorNeural (m), en-IE-EmilyNeural (f)
en-IN (English)	en-IN-NeerjaExpressiveNeural (f), en-IN-NeerjaNeural (f), en-IN-PrabhatNeural (m)
en-KE (English)	en-KE-AsiliaNeural (f), en-KE-ChilembaNeural (m)
en-NG (English)	en-NG-AbeoNeural (m), en-NG-EzinneNeural (f)
en-NZ (English)	en-NZ-MitchellNeural (m), en-NZ-MollyNeural (f)
en-PH (English)	en-PH-JamesNeural (m), en-PH-RosaNeural (f)
en-SG (English)	en-SG-LunaNeural (f), en-SG-WayneNeural (m)
en-TZ (English)	en-TZ-ElimuNeural (m), en-TZ-ImaniNeural (f)
en-US (English)	en-US-AvaNeural (f), en-US-AndrewNeural (m), en-US-EmmaNeural (f), en-US-BrianNeural (m), en-US-AnaNeural (f), en-US-AndrewMultilingualNeural (m), en-US-AriaNeural (f), en-US-AvaMultilingualNeural (f), en-US-BrianMultilingualNeural (m), en-US-ChristopherNeural (m), en-US-EmmaMultilingualNeural (f), en-US-EricNeural (m), en-US-GuyNeural (m), en-US-JennyNeural (f), en-US-MichelleNeural (f), en-US-RogerNeural (m), en-US-SteffanNeural (m)
en-ZA (English)	en-ZA-LeahNeural (f), en-ZA-LukeNeural (m)
es-AR (Spanish)	es-AR-ElenaNeural (f), es-AR-TomasNeural (m)
es-BO (Spanish)	es-BO-MarceloNeural (m), es-BO-SofiaNeural (f)
es-CL (Spanish)	es-CL-CatalinaNeural (f), es-CL-LorenzoNeural (m)
es-CO (Spanish)	es-CO-GonzaloNeural (m), es-CO-SalomeNeural (f)
es-CR (Spanish)	es-CR-JuanNeural (m), es-CR-MariaNeural (f)
es-CU (Spanish)	es-CU-BelkysNeural (f), es-CU-ManuelNeural (m)
es-DO (Spanish)	es-DO-EmilioNeural (m), es-DO-RamonaNeural (f)
es-EC (Spanish)	es-EC-AndreaNeural (f), es-EC-LuisNeural (m)
es-ES (Spanish)	es-ES-XimenaNeural (f), es-ES-AlvaroNeural (m), es-ES-ElviraNeural (f)
es-GQ (Spanish)	es-GQ-JavierNeural (m), es-GQ-TeresaNeural (f)
es-GT (Spanish)	es-GT-AndresNeural (m), es-GT-MartaNeural (f)
es-HN (Spanish)	es-HN-CarlosNeural (m), es-HN-KarlaNeural (f)
es-MX (Spanish)	es-MX-DaliaNeural (f), es-MX-JorgeNeural (m)
es-NI (Spanish)	es-NI-FedericoNeural (m), es-NI-YolandaNeural (f)
es-PA (Spanish)	es-PA-MargaritaNeural (f), es-PA-RobertoNeural (m)
es-PE (Spanish)	es-PE-AlexNeural (m), es-PE-CamilaNeural (f)
es-PR (Spanish)	es-PR-KarinaNeural (f), es-PR-VictorNeural (m)
es-PY (Spanish)	es-PY-MarioNeural (m), es-PY-TaniaNeural (f)
es-SV (Spanish)	es-SV-LorenaNeural (f), es-SV-RodrigoNeural (m)
es-US (Spanish)	es-US-AlonsoNeural (m), es-US-PalomaNeural (f)
es-UY (Spanish)	es-UY-MateoNeural (m), es-UY-ValentinaNeural (f)
es-VE (Spanish)	es-VE-PaolaNeural (f), es-VE-SebastianNeural (m)
et-EE (Estonian)	et-EE-AnuNeural (f), et-EE-KertNeural (m)
fa-IR (Persian)	fa-IR-DilaraNeural (f), fa-IR-FaridNeural (m)
fi-FI (Finnish)	fi-FI-HarriNeural (m), fi-FI-NooraNeural (f)
fil-PH (Filipino)	fil-PH-AngeloNeural (m), fil-PH-BlessicaNeural (f)
fr-BE (French)	fr-BE-CharlineNeural (f), fr-BE-GerardNeural (m)
fr-CA (French)	fr-CA-ThierryNeural (m), fr-CA-AntoineNeural (m), fr-CA-JeanNeural (m), fr-CA-SylvieNeural (f)
fr-CH (French)	fr-CH-ArianeNeural (f), fr-CH-FabriceNeural (m)
fr-FR (French)	fr-FR-VivienneMultilingualNeural (f), fr-FR-RemyMultilingualNeural (m), fr-FR-DeniseNeural (f), fr-FR-EloiseNeural (f), fr-FR-HenriNeural (m)
ga-IE (Irish)	ga-IE-ColmNeural (m), ga-IE-OrlaNeural (f)
gl-ES (Galician)	gl-ES-RoiNeural (m), gl-ES-SabelaNeural (f)
gu-IN (Gujarati)	gu-IN-DhwaniNeural (f), gu-IN-NiranjanNeural (m)
he-IL (Hebrew)	he-IL-AvriNeural (m), he-IL-HilaNeural (f)
hi-IN (Hindi)	hi-IN-MadhurNeural (m), hi-IN-SwaraNeural (f)
hr-HR (Croatian)	hr-HR-GabrijelaNeural (f), hr-HR-SreckoNeural (m)
hu-HU (Hungarian)	hu-HU-NoemiNeural (f), hu-HU-TamasNeural (m)
id-ID (Indonesian)	id-ID-ArdiNeural (m), id-ID-GadisNeural (f)
is-IS (Icelandic)	is-IS-GudrunNeural (f), is-IS-GunnarNeural (m)
it-IT (Italian)	it-IT-GiuseppeMultilingualNeural (m), it-IT-DiegoNeural (m), it-IT-ElsaNeural (f), it-IT-IsabellaNeural (f)
iu-Cans-CA (Inuktitut)	iu-Cans-CA-SiqiniqNeural (f), iu-Cans-CA-TaqqiqNeural (m)
iu-Latn-CA (Inuktitut)	iu-Latn-CA-SiqiniqNeural (f), iu-Latn-CA-TaqqiqNeural (m)
ja-JP (Japanese)	ja-JP-KeitaNeural (m), ja-JP-NanamiNeural (f)
jv-ID (Javanese)	jv-ID-DimasNeural (m), jv-ID-SitiNeural (f)
ka-GE (Georgian)	ka-GE-EkaNeural (f), ka-GE-GiorgiNeural (m)
kk-KZ (Kazakh)	kk-KZ-AigulNeural (f), kk-KZ-DauletNeural (m)
km-KH (Khmer)	km-KH-PisethNeural (m), km-KH-SreymomNeural (f)
kn-IN (Kannada)	kn-IN-GaganNeural (m), kn-IN-SapnaNeural (f)
ko-KR (Korean)	ko-KR-HyunsuMultilingualNeural (m), ko-KR-InJoonNeural (m), ko-KR-SunHiNeural (f)
lo-LA (Lao)	lo-LA-ChanthavongNeural (m), lo-LA-KeomanyNeural (f)
lt-LT (Lithuanian)	lt-LT-LeonasNeural (m), lt-LT-OnaNeural (f)
lv-LV (Latvian)	lv-LV-EveritaNeural (f), lv-LV-NilsNeural (m)
mk-MK (Macedonian)	mk-MK-AleksandarNeural (m), mk-MK-MarijaNeural (f)
ml-IN (Malayalam)	ml-IN-MidhunNeural (m), ml-IN-SobhanaNeural (f)
mn-MN (Mongolian)	mn-MN-BataaNeural (m), mn-MN-YesuiNeural (f)
mr-IN (Marathi)	mr-IN-AarohiNeural (f), mr-IN-ManoharNeural (m)
ms-MY (Malay)	ms-MY-OsmanNeural (m), ms-MY-YasminNeural (f)
mt-MT (Maltese)	mt-MT-GraceNeural (f), mt-MT-JosephNeural (m)
my-MM (Burmese)	my-MM-NilarNeural (f), my-MM-ThihaNeural (m)
nb-NO (Norwegian Bokmål)	nb-NO-FinnNeural (m), nb-NO-PernilleNeural (f)
ne-NP (Nepali)	ne-NP-HemkalaNeural (f), ne-NP-SagarNeural (m)
nl-BE (Dutch)	nl-BE-ArnaudNeural (m), nl-BE-DenaNeural (f)
nl-NL (Dutch)	nl-NL-ColetteNeural (f), nl-NL-FennaNeural (f), nl-NL-MaartenNeural (m)
pl-PL (Polish)	pl-PL-MarekNeural (m), pl-PL-ZofiaNeural (f)
ps-AF (Pashto)	ps-AF-GulNawazNeural (m), ps-AF-LatifaNeural (f)
pt-BR (Portuguese)	pt-BR-ThalitaMultilingualNeural (f), pt-BR-AntonioNeural (m), pt-BR-FranciscaNeural (f)
pt-PT (Portuguese)	pt-PT-DuarteNeural (m), pt-PT-RaquelNeural (f)
ro-RO (Romanian)	ro-RO-AlinaNeural (f), ro-RO-EmilNeural (m)
ru-RU (Russian)	ru-RU-DmitryNeural (m), ru-RU-SvetlanaNeural (f)
si-LK (Sinhala)	si-LK-SameeraNeural (m), si-LK-ThiliniNeural (f)
sk-SK (Slovak)	sk-SK-LukasNeural (m), sk-SK-ViktoriaNeural (f)
sl-SI (Slovenian)	sl-SI-PetraNeural (f), sl-SI-RokNeural (m)
so-SO (Somali)	so-SO-MuuseNeural (m), so-SO-UbaxNeural (f)
sq-AL (Albanian)	sq-AL-AnilaNeural (f), sq-AL-IlirNeural (m)
sr-RS (Serbian)	sr-RS-NicholasNeural (m), sr-RS-SophieNeural (f)
su-ID (Sundanese)	su-ID-JajangNeural (m), su-ID-TutiNeural (f)
sv-SE (Swedish)	sv-SE-MattiasNeural (m), sv-SE-SofieNeural (f)
sw-KE (Swahili)	sw-KE-RafikiNeural (m), sw-KE-ZuriNeural (f)
sw-TZ (Swahili)	sw-TZ-DaudiNeural (m), sw-TZ-RehemaNeural (f)
ta-IN (Tamil)	ta-IN-PallaviNeural (f), ta-IN-ValluvarNeural (m)
ta-LK (Tamil)	ta-LK-KumarNeural (m), ta-LK-SaranyaNeural (f)
ta-MY (Tamil)	ta-MY-KaniNeural (f), ta-MY-SuryaNeural (m)
ta-SG (Tamil)	ta-SG-AnbuNeural (m), ta-SG-VenbaNeural (f)
te-IN (Telugu)	te-IN-MohanNeural (m), te-IN-ShrutiNeural (f)
th-TH (Thai)	th-TH-NiwatNeural (m), th-TH-PremwadeeNeural (f)
tr-TR (Turkish)	tr-TR-EmelNeural (f), tr-TR-AhmetNeural (m)
uk-UA (Ukrainian)	uk-UA-OstapNeural (m), uk-UA-PolinaNeural (f)
ur-IN (Urdu)	ur-IN-GulNeural (f), ur-IN-SalmanNeural (m)
ur-PK (Urdu)	ur-PK-AsadNeural (m), ur-PK-UzmaNeural (f)
uz-UZ (Uzbek)	uz-UZ-MadinaNeural (f), uz-UZ-SardorNeural (m)
vi-VN (Vietnamese)	vi-VN-HoaiMyNeural (f), vi-VN-NamMinhNeural (m)
zh-CN (Chinese)	zh-CN-XiaoxiaoNeural (f), zh-CN-XiaoyiNeural (f), zh-CN-YunjianNeural (m), zh-CN-YunxiNeural (m), zh-CN-YunxiaNeural (m), zh-CN-YunyangNeural (m)
zh-CN-liaoning (Chinese)	zh-CN-liaoning-XiaobeiNeural (f)
zh-CN-shaanxi (Chinese)	zh-CN-shaanxi-XiaoniNeural (f)
zh-HK (Chinese)	zh-HK-HiuGaaiNeural (f), zh-HK-HiuMaanNeural (f), zh-HK-WanLungNeural (m)
zh-TW (Chinese)	zh-TW-HsiaoChenNeural (f), zh-TW-YunJheNeural (m), zh-TW-HsiaoYuNeural (f)
zu-ZA (Zulu)	zu-ZA-ThandoNeural (f), zu-ZA-ThembaNeural (m)

Tips

• One file per chapter: turn on "Also save one file per chapter" for players without chapter support, or to share single chapters.
• Many small files into one book: put them in a ZIP (they are read in natural name order: 1, 2, 10) and turn on "Merge into one audiobook".
• Scanned books and handwriting: AI OCR reads any language without settings. OCR language (for example tur, deu+eng, chi_sim) is used only by the classic OCR fallback.
• Technical documents: code blocks are skipped by default; turn on "Read code blocks" to hear them. Tables are read row by row; turn off "Read tables" to skip them.

Limitations

• Files up to 300 MB; up to 200 documents per run; up to 2,000 OCR pages per document.
• Speech is generated by online neural voices (the Microsoft Edge read-aloud voices, through the open-source edge-tts library). The service sometimes stops answering for one voice; the Actor then tries the voice again, then moves to another voice of the same language (voicesUsed).
• Chapter detection depends on the document: a PDF without bookmarks and without "Chapter" lines becomes one long chapter.
• DRM-protected e-books (Kindle KFX, Adobe ADEPT) and password-protected files cannot be read.
• Only convert documents you have the right to use. Audio made from someone else's book is for your own listening unless you hold the rights.

FAQ

Are there extra fees? No: you pay only the per-minute price above (and OCR pages, if any). There is no separate API key or subscription.

Which player shows the chapters? M4B: Apple Books, BookPlayer, Smart AudioBook Player, VLC and most audiobook apps. MP3 chapters (ID3 CHAP) are shown by VLC, Pocket Casts, Overcast, foobar2000 and many podcast players; others simply play the file from start to end.

Can I read a web page? Yes. Put its URL in "Document or web page URLs"; the main article is extracted (like a browser's reader view).

My audiobook has no chapters. The document had no headings, bookmarks or "Chapter" lines. Split it into files and use a ZIP with "Merge into one audiobook", or add headings.

How long does a run take? About 2 to 4 minutes per hour of audio, plus a little for OCR and large Office files.

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• Site SEO Crawler: Broken Links & On-Page Audit: site-wide crawl for broken links, redirects and on-page SEO issues.
• Website Tech Stack Detector: Bulk Lead Filters: technologies used by websites, with filters for lead lists.
• Domain Availability & Expiry Checker (RDAP): domain availability, registrar and expiry dates from RDAP.
• Status Page SLA Monitor: Uptime & Incidents: SaaS vendor uptime, incidents and a monthly SLA table from public status pages.

Company data and compliance

• Company Contact Finder: Emails, Phones & Socials: contact details published on company websites.
• UK New Companies Feed: Companies House Daily: newly incorporated UK companies with sector filters.
• EU VAT Number Validator: Bulk VIES Checker: bulk VAT checks with name, address and consultation number.
• LEI Corporate Tree: GLEIF Parents & Subsidiaries: LEI lookup with parents, subsidiaries and a KYC summary.
• UK Company Distress Watch: Insolvency Alerts: administrations, liquidations and winding-up petitions from The Gazette.
• IBAN Validator: Bulk Check & Bank Lookup: IBAN format and checksum, plus bank name and BIC where open data exists.
• Sanctions Screening: OFAC, EU, UN & UK Lists: names screened against official sanctions lists, with match scores.
• OSHA & EPA Violation Monitor: Fines by Company: US OSHA and EPA enforcement cases and penalties by company.
• Email List Precheck: Syntax, MX & Disposable Check: cheap email list hygiene before a real verification.
• UK Company Officer Changes: Director & PSC Alerts: director, owner, address and status changes for a watchlist of UK companies.
• FDA Warning Letters Monitor: By Company & Subject: FDA warning letters by company and subject, with cited regulations.
• Supplier Risk Report: Sanctions, VAT & Insolvency: one risk row per supplier from eight official registers, with changes since last run.
• Brand Impersonation Domain Monitor: Look-alikes: look-alike domains and certificates that imitate your brand, with a risk score.

Media and AI

• Audio & Video Transcriber: Podcasts, SRT, Speakers: audio, video, podcasts and social links to text, SRT and VTT.
• Podcast Mention Alerts: Brand Mentions by Second: where your brand is said in new podcast episodes, with the exact second.
• News Brand Mentions Monitor: New Articles Only: new news articles about your brand, people or topics.
• YouTube Channel Monitor: New Videos & Growth: new videos and view growth of the channels you watch.

Market signals

• US WARN Layoff Notices: 12 States Daily Feed: layoff and plant closure notices from official state sources.
• US Product Recalls Monitor: FDA & CPSC Feed: FDA and CPSC recalls in one feed, with severity.
• EU Tenders Feed: TED Public Procurement Alerts: EU public tenders from TED with CPV, country and value filters.
• Liquor License Openings: New Bars & Stores (US): new US liquor licences from official state open data.
• Clinical Trials Change Monitor: Status & Updates: status, enrollment and results changes on ClinicalTrials.gov studies.
• Career Site Jobs Monitor: ATS Jobs & New Roles: open roles from company career sites, with new and closed roles since the last run.
• App Reviews Monitor: App Store & Google Play: app reviews from both stores, only new ones in monitor mode.
• US Federal Contracts Feed: SAM.gov & USAspending: SAM.gov contract opportunities with the buyer's award history.
• Grants.gov Opportunities: US Federal Grants Feed: US federal grant opportunities by keyword, eligibility and agency.
• Federal Register Monitor: US Rules & Notices Feed: new US rules, proposed rules and notices by agency or topic.
• B2B Sales Trigger Feed: Hiring + Tech Leads: companies hiring for a role and using (or not using) a technology, with contacts.
• Local Business Leads: OpenStreetMap, No Website: local businesses by city and category, including those with no website.
• Solana Token Pair Monitor: New & Trending Pairs: new and trending Solana pairs with liquidity, volume and safety flags.