How to Transcribe Audio to Text: 5 Methods Compared (2026)

The best way to transcribe audio to text depends on three things: how clean the recording is, how sensitive the content is, and which language people are speaking. For clear English, Word's built-in tool or a cloud AI service is fastest. For private files, a local model keeps audio on your machine. For legal or published work where errors are costly, a human transcriber is still the safest choice.

This guide compares the five main methods on cost, speed, privacy and language support. It also covers the part most guides skip: what to do when the audio has accents, crosstalk or a language that mainstream tools handle badly.

Manual typing and Word's built-in transcription

Typing it yourself is free but slow, and Word's Transcribe feature is the lowest-effort AI option if you already pay for Microsoft 365.

Typing it yourself

Manual transcription runs at roughly 4x to 6x real time. One guide puts an hour of audio at 3 to 6 hours for a skilled worker and 4 to 8 hours for a beginner. That is acceptable for a five-minute voice note. It is not acceptable for a two-hour interview.

Typing does have one use: reviewing. Even with AI, you will end up listening and correcting. Budget for that.

Word's Transcribe

Word lets you upload a recording or record directly, then returns text you can edit. According to Microsoft's documentation, it accepts .wav, .mp4, .m4a and .mp3 files from the file picker.

The limit to know about is volume. Microsoft 365 subscribers get 300 minutes of uploaded audio per month, while users with a Microsoft Copilot license get up to 30,000 minutes per month. Five hours sounds like plenty until you have a week of interviews.

Word works well when you want a rough text draft inside a document you are already writing. It is less suited to team workflows where you need speaker-separated notes, a summary, and a record you can search later.

Cloud AI transcription and meeting assistants

Cloud AI services upload your audio, return a transcript in minutes, and often add speaker labels and summaries. The category is wide, so it helps to separate its parts.

Speech-to-text services for developers

Services such as Amazon Transcribe support over 100 languages and offer custom vocabularies, PII redaction and speaker diarization. These are building blocks. You or your engineers wire them into an app. Most team leads do not want to do that for a weekly meeting.

Meeting assistants and upload tools

These sit on top of speech recognition and package it as a product: record or upload, get a transcript, get a summary. Some join your call as a visible bot. Others record from your browser or take an uploaded file. If the bot question matters to you, we cover it in Meeting Transcription Without Bot.

What to check before choosing one:

• Language support in practice. "Supports 100 languages" and "works well in Burmese" are different claims. Test with a real recording.
• Where the audio goes and how long it stays. Look for a retention policy and a deletion option.
• Pricing model. Subscriptions suit daily use. Pay-as-you-go suits occasional use. For a comparison of two popular subscription tools, see Otter vs Fireflies.
• What you get besides text. Speaker labels, decisions and action items save more time than a perfect transcript that nobody reads.

Local models: Whisper and Audacity

Running a model on your own computer means the audio never leaves it. That is the strongest privacy option, and it costs you convenience.

OpenAI Whisper is released under an MIT open-source license, so you can run it locally. The same source notes that it has no out-of-the-box user interface, no built-in editor and no native speaker diarization. Out of the box it is a tool for technical users, and the output is a block of text without speaker names.

If you do not want to use a command line, Audacity has an official local Whisper plugin. It transcribes on-device and exports label tracks as text or subtitle files, with no cloud upload.

A local setup makes sense when:

• The recording is confidential and policy forbids uploading it.
• You have a lot of audio and want no per-minute fees.
• You are comfortable fixing speaker labels and punctuation by hand.

It makes less sense if you need summaries, shared access for a team, or quick turnaround on a modest laptop.

Human transcription when accuracy is non-negotiable

Human transcribers cost more and take longer, but they remain the right call for legal proceedings, published quotes and anything where one wrong word creates a problem.

Based on one comparison, human-verified services typically cost between $0.79 and $2.00 or more per audio minute. GoTranscript starts at $1.02 per minute and Rev's human service is $1.99 per minute. Turnaround ranges from 12 hours to 5 days.

Do the math on a one-hour recording. At $1.02 per minute it is about $61. At $1.99 it is about $119. For a single court-bound interview that is reasonable. For a weekly team meeting it is not.

A practical middle path: let AI produce the first draft, then pay a human to check only the sections that matter.

Which way to transcribe audio to text fits your job

Match the method to the constraint that matters most. Here is how the five compare.

Two quick rules:

1. If the audio is sensitive and you can tolerate rough output, go local.
2. If you need the meeting turned into decisions and action items, use a tool built for that rather than a raw transcript. Our post on action items from meetings explains why the transcript alone is rarely the goal.

Why accents and smaller languages break transcription

AI transcription is least reliable where it has the least training data, and that is often where our readers work. Most guides test on clear English. Real meetings in Yangon, Lagos or Bangkok look different.

What goes wrong

• Accents and regional pronunciation. A model trained mostly on one accent will guess wrong more often on another.
• Smaller languages. Burmese, Hausa and Yoruba have far less published audio than English. A tool can list them as "supported" and still produce unusable text.
• Code-switching. Many teams switch between two languages mid-sentence. A model that expects one language per file can garble the other. We explain this in Why Burmese-English Code-Switching Breaks Transcription Tools.
• Crosstalk. When two people talk at once, the recording is a single mixed signal. Diarization, the step that assigns lines to speakers, struggles here.
• Missing vocabulary. Names, product terms and local place names are often misspelled because the model has never seen them.

What helps

Choose a tool tested on your language, not just one that lists it. Set the language manually when you can. Where a service offers custom vocabulary, as Amazon Transcribe does, load your names and terms. For Burmese specifically, follow the steps in How to Transcribe Burmese Audio Accurately. For a broader look at mixed-language meetings, read Multilingual Meeting Transcription: What Actually Works.

A workflow to fix audio before you transcribe

Most accuracy problems are decided before you press the button. Spend ten minutes on the file and you will spend far less on corrections.

1. Check the recording. Listen to the first minute and a loud section. If you cannot understand a word, the model probably cannot either.
2. Reduce noise where you can. Trim long silences and obvious hiss. Audacity's noise reduction is enough for most office recordings. Do not over-process, because heavy filtering can distort voices.
3. Use a common format. .wav, .mp3, .m4a and .mp4 are accepted by most tools, including Word.
4. Set the language. If the audio mixes two languages, use a tool that handles that explicitly.
5. Prepare a short term list. Write down names, acronyms and product terms. Use custom vocabulary if the tool supports it, and otherwise do a find-and-replace afterward.
6. Run the transcription. For long files, split by topic or by speaker turn only if the tool struggles with length.
7. Review against the audio. Scan for names, numbers and dates. Those are the errors that cause real damage.
8. Export in the right format. Common formats are DOCX for editing, SRT or VTT for video subtitles, TXT for plain archives and JSON for software integrations.

For recordings of calls, see How to Summarize a Zoom Meeting for what to do after the text exists.

Where Loka Note fits

Loka Note is a cloud tool, so it is not the answer if your policy requires audio to stay on your device. It fits the case where you want a finished meeting record instead of a raw transcript, and where the language is one that mainstream tools handle poorly.

You can record in the browser with no bot joining the call, or upload an existing audio or video file. It produces a transcript, a summary with decisions and action items, and an Ask Loka chat across your past meetings. It is built Burmese-first, handles mixed Burmese-English speech, and also supports English, Thai, Vietnamese, Chinese, Yoruba and Hausa.

Pricing has no feature tiers. Top-ups start at $1.99 for 60 minutes and run to $39.99 for 3,000 minutes, valid for 6 months. An Unlimited plan is $19.99 per month. Recordings are kept for 6 months and can be deleted at any time, and customer audio and transcripts are never used to train AI models. Details are on our security page.

Try it on one real recording in your own language: start at app.lokanote.com/signup