Export WhatsApp voice messages as searchable text | ThreadRecap
A WhatsApp conversation that mixes dozens of voice notes with hundreds of text messages is, in practice, two separate documents: one you can search, one you cannot. The text portion responds to Ctrl+F or WhatsApp's own search bar. The voice notes sit behind a play button, opaque to any query. For a journalist chasing a quote, a lawyer building a timeline, or a researcher coding themes across interviews, that opacity is a real obstacle. Transcribing those audio files and indexing the resulting text alongside the original messages turns a partially searchable record into a fully searchable one.
Why voice notes are unsearchable until you transcribe them
WhatsApp stores voice messages as audio files, not text. The app's search function indexes message text, contact names, and dates. It does not scan audio content.
WhatsApp introduced a native transcription feature that displays an inline text rendering of a voice note, but it has two significant constraints. First, it supports only four languages: English, Spanish, Portuguese, and Russian. Second, the inline text is not indexed by WhatsApp's own search, so running a keyword query still will not surface a voice note that contains that word.
The result is a gap between what was said and what is findable. In a long group chat or a months-long source relationship, that gap compounds quickly. A single active WhatsApp thread can accumulate hundreds of voice notes over the course of an investigation or a legal dispute, and none of them are reachable by keyword until they have been transcribed and indexed outside the app.
Full-text search across transcripts: timestamps, sender, and free text
ThreadRecap processes a WhatsApp export, transcribes every voice note using OpenAI Whisper, and stores the resulting text alongside the message metadata already present in the export: sender name or number, date, and time.
The practical outcome is a unified search index. You type a word or phrase, and the results show you every message, whether originally text or audio, that contains that string. Each result displays:
Sender label: who sent the message
Timestamp: the exact date and time from the export
Transcript excerpt: the surrounding context, not just the matching line
Message type indicator: so you know whether the source was typed text or a transcribed voice note
This structure matters because the interesting information in a WhatsApp conversation is rarely confined to one message type. A source may confirm a fact in a voice note and then share a document in the next message. Being able to search across both in a single query, rather than switching between a text search and a manual audio review, is the core efficiency gain.
Citations: linking back to the original voice clip and timestamp
A transcript is useful for search. A transcript with a citation back to its source is useful for evidence.
ThreadRecap links every transcribed segment to its original position in the export. That means when you find a passage in search results, you can navigate directly to the message in the full conversation view, see the surrounding context, and play the source audio clip to verify the transcript against the original recording.
This citation chain matters in three ways:
Verification: Leading APIs operate below a 5% word error rate in conversational English, meaning roughly 95 out of 100 words are correct on clear audio. For the remaining margin, the link to the source clip lets you check the original rather than trusting the text alone.
Dispute resolution: If an opposing party challenges a quote, you can point to the exact message position, timestamp, and audio file rather than relying on a standalone document.
Attribution in published work: Journalists quoting from a voice note can note the date, time, and sender of the original message, giving editors and fact-checkers a precise reference.
WhatsApp is widely used for source communication, particularly in regions where it is the dominant messaging platform and where sources are more comfortable with it than with email or phone. Voice notes are common in these exchanges: a source who would not type out a sensitive statement may record it instead.
The challenge for journalists is that a voice note received through WhatsApp is not, by itself, a usable quote. It needs to be transcribed, attributed, and verified before it can appear in a story or be shared with an editor.
A practical workflow using ThreadRecap:
Export the relevant chat using WhatsApp's built-in export function (Settings, Chats, Export Chat, include media). The export produces a ZIP file containing a text file and the attached media, including voice note audio.
Upload the export to ThreadRecap. The tool processes the text file and transcribes the voice notes. Photos, videos, and documents in the export never leave your device; only the chat text and voice audio are processed.
Search by keyword or date to locate the relevant voice note. The result shows sender, timestamp, and transcript.
Play the source clip to verify the transcript before quoting.
Export the structured output for your notes file or to share with an editor.
One legal consideration worth noting: WhatsApp conversations with sources are generally consented to in the sense that both parties are participating in the exchange. However, if you are recording a conversation separately, or if the voice note was sent in a context where the sender did not expect it to be transcribed and stored, consent and data protection rules in your jurisdiction may apply. States like California, Florida, and Illinois require all-party consent for recorded conversations. If you are working across borders, check the rules for the jurisdiction where the source is located as well as your own.
Workflow for lawyers
In legal and dispute contexts, WhatsApp conversations are increasingly relevant as evidence. Voice notes within those conversations present a specific challenge: they are part of the record, but they are not text-searchable, and they cannot be cited with the same precision as a typed message.
ThreadRecap's evidence-ready output addresses this directly. The structured export includes:
A full transcript of each voice note, attributed to sender and timestamped
A citation reference linking back to the original message position in the export
The original audio file reference, so the transcript can be verified against the source
For legal use, the workflow typically looks like this:
Obtain the WhatsApp export from the relevant device, following your jurisdiction's requirements for evidence preservation. The export should include media.
Upload to ThreadRecap and run the transcription. The resulting output can be used to build a searchable chronological record of the conversation.
Use the timeline view to establish sequence: who said what, and when. See the related guide on building a WhatsApp voice notes timeline for how to structure this for disclosure or court preparation.
Generate the evidence report, which includes sender attribution, timestamps, and transcript text with source citations.
Verify contested passages by playing the original audio clip against the transcript before submitting any document.
Several practical cautions apply. California Senate Bill 574, introduced in 2026, proposes specific duties on attorneys who use generative AI tools, including restrictions on how AI-generated output may be used in decision-making. Even where no specific rule exists, attorneys should treat AI-generated transcripts as a starting point for review rather than a final record. Hybrid review, where a human checks AI output against the source audio for key passages, is the appropriate standard for evidence that will be challenged.
On consent: if the voice notes were recorded in a multi-party call or in a jurisdiction with all-party consent requirements, the admissibility of the recording itself is a separate question from the quality of the transcript. Consult qualified legal counsel for the specific jurisdiction and facts.
Workflow for researchers
Qualitative researchers using WhatsApp for interviews or community observation face a data management problem that is partly structural. Participants in qualitative studies increasingly communicate by voice note rather than text, particularly in mobile-first research contexts. The result is a dataset that is partly coded as text and partly locked in audio files.
Transcription is the prerequisite for qualitative coding. You cannot apply a code to a segment you cannot read. ThreadRecap's output provides the structured text that coding requires, with sender and timestamp metadata already attached.
A research workflow:
Conduct or collect WhatsApp interviews in the normal way. Inform participants how their data will be stored and processed, in line with your ethics approval and applicable data protection rules. Spain's data protection authority (AEPD) published guidance on GDPR compliance when using AI-powered transcription tools, and similar guidance is emerging in other jurisdictions.
Export the relevant chats and upload to ThreadRecap. Voice notes are transcribed automatically.
Search the full transcript corpus to identify recurring terms, phrases, or themes before beginning formal coding.
Export the structured output to your qualitative analysis software. Each segment carries a sender label and timestamp, which maps to the speaker and time codes that most coding tools expect.
Maintain the citation link between coded segments and source audio. If a co-coder or supervisor questions a coding decision, you can play the original clip rather than relying solely on the transcript text.
The accuracy floor matters here too. At below 5% word error rate in conversational English, Whisper-based transcription is suitable for thematic analysis, where the unit of meaning is a phrase or sentence rather than an individual word. For phonetic or discourse analysis, where exact wording is the object of study, human review of the full transcript against the source audio is advisable.
Privacy and data handling
The export-and-upload workflow means you hold the file before anything is processed. When you upload to ThreadRecap, photos, videos, and documents attached to the chat are never transmitted. Only chat text and voice note audio are processed. That data is stored encrypted in your ThreadRecap account, and you can delete it at any time from the dashboard.
For journalists working with sensitive sources, lawyers handling privileged communications, and researchers operating under ethics board oversight, this control over the data lifecycle is a practical requirement, not a feature preference.
Getting started
The starting point is the same for all three use cases: export the WhatsApp chat with media, upload the ZIP to ThreadRecap, and let the transcription run. The searchable, timestamped, citation-linked output is available as soon as processing is complete.
If you have not yet exported a WhatsApp chat with voice notes included, the whatsapp-voice-to-text feature page walks through the export steps for both iOS and Android before you upload.
