Audio & Video Transcriber - Speech to Text, SRT & Timestamps
Pricing
from $24.00 / 1,000 audio minute transcribeds
Transcribe audio and video to text with Whisper inside the Actor - no API key. Any direct media URL. Returns text, timestamped segments, SRT/VTT subtitles and optional RAG chunks. Built for long recordings: repetition loops and silence hallucinations are repaired. Billed per minute of speech.
All notable changes to this Actor. Newest first. Numbers are Apify build numbers within version 0.1.
"Common uses" rewritten to name the jobs people actually search for.
Measured the same day: Store search indexes README text and a rare
multi-word phrase there can reach the top 5 — detected speech appears only
in this README and ranks #1. Single common words never rank, whatever
they are in.
Added as ordinary prose, not a keyword list: webinar transcription, lecture transcription, speech to text RAG, speech timestamps, and transcribe MP3 alongside the other supported containers. Every phrase describes something the Actor already does.
Running as a controlled experiment against phrases deliberately left
untouched — tools/readme_experiment.py, baseline recorded before deploy.
The example input now demonstrates every output. With the previous
prefill (outputFormats: ["text"], chunkForRag: false) a first run left
the Subtitles and RAG chunks views on the Output tab completely
empty — two of four tabs, on the screen a user reaches immediately after
being charged. Nothing was broken: those fields are correctly conditional on
input. It simply looked broken, which is the same problem the ?clean=true
defect caused in 0.1.7 wearing different clothes.
The prefill now requests text, srt and vtt and enables RAG chunking, so
the demo run — and the daily platform health check, which runs the same
input — populates all four views.
API behaviour is unchanged. prefill drives only the pre-populated form
and the health check. What a caller who omits a field receives is governed by
default, which is deliberately untouched: outputFormats still defaults to
["text"] and chunkForRag to false. No integration changes shape and no
billing changes — charging is per minute of detected speech either way.
Guarded by test_prefill_exercises_every_dataset_view, confirmed to fail on
the previous configuration.
The run's Output tab in Apify Console could not display results. It
showed "Cannot load data from dataset. Reason: Expected property clean to
be of type boolean but received type string" — from this Actor's first
build onwards.
The output schema passed ?clean=true in its dataset template. The Console
parses that query string into a typed options object, where clean is a
boolean, so the string "true" failed validation. Runs always succeeded and
the data was always correct; only the display was broken — on the first screen
a user sees after their first run.
Dataset templates now use only the documented ?view= parameter, and the
Actor exposes an output per view: full dataset, Transcript text, Subtitles
and RAG chunks. A regression test refuses any other query parameter.
Three more dataset views — Transcript, Subtitles (SRT/VTT) and RAG chunks — beside the existing Overview.
Apify lets a published task choose which dataset view renders on its public landing page, and those pages are indexed by Google and readable by AI agents as markdown. One generic view meant every task page would have shown the same table; each use case can now show the output that actually matters to it.
A step-by-step "How to use it", an FAQ, and a link to the author's other Actor. Apify's README guidance names all three: an FAQ covering legality, troubleshooting and cost is one of the recommended sections, and Actor recommendation "won't recommend a completely different Actor from the same creator" — so cross-linking has to be done by hand.
The FAQ answers, in the buyer's own terms, the questions that otherwise have to be discovered by running the Actor: what a run actually costs, what happens when one file in a batch fails, why a transcript can be shorter than the recording, why there is no speaker labelling, and why social-platform links are not supported.
The dataset schema now describes all 20 output fields, with types, enums and per-field descriptions, alongside the table view it already had.
Apify's hosted MCP server infers a structured output schema for every Actor
tool so an AI agent knows what it will get back before it calls. Until now
that inference had only the raw rows to work from. Field-level descriptions
also feed the "congruency" category of the Actor quality score, which ranks
both Store search and the agent-facing search-actors tool.
Guarded by a test asserting that every field the Actor pushes is documented, so the schema cannot drift away from the code.
The example input now lists two files on independent hosts. Apify runs
every Actor daily against its input-schema prefill and expects a
Succeeded run with a non-empty dataset. Three consecutive failures flag
the Actor under maintenance; continued failures eventually deprecate it.
The example previously pointed at a single archive.org file, so an outage at one host — not any fault in the Actor — could have flagged or deprecated it. A second file on unrelated infrastructure is now included, and because a failed item is isolated rather than fatal, one host being unreachable no longer fails the run.
The second file is a FLAC, so the example also shows that the Actor handles more than MP3.
