Transcribe audio and video files to text with AI. Free, accurate, multilingual.
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MP3, WAV, M4A, MP4, WEBM — Max 25 MB, 5 min
Speech recognition by NVIDIA Parakeet TDT 0.6B v3 (CC BY 4.0) for 25 European languages and OpenAI Whisper (MIT) for all others.
KlipTools Speech to Text converts spoken audio into written text with two open speech recognition models, and picks the right one for your language. Recordings in 25 European languages — including English, Spanish, French, Portuguese, German and Italian — are transcribed by NVIDIA Parakeet TDT 0.6B v3. Every other language, from Japanese and Chinese to Arabic, Hindi and Turkish, goes to OpenAI Whisper. Both cope with accents, ordinary microphones and everyday background noise far better than older speech recognition systems.
Whether you need to transcribe a podcast episode, convert a recorded lecture into study notes, create subtitles for a video, or turn a meeting recording into actionable minutes, this tool handles it. Upload your audio or video file (up to 5 minutes and 25 MB), choose a language or let the AI auto-detect it, and receive a complete text transcript in seconds.
No account is needed. Every visitor gets a free daily allowance (shared by all KlipTools tools; it resets at midnight UTC), and the line above the button shows what you have left. After that, transcription costs 2,000 credits per minute of audio (about 3 cents). Your file is sent to our server, transcribed, and deleted right away; we keep no copy of your recording. Between them, the two models cover over 90 languages and deliver strong results even with imperfect audio quality, making this a practical choice for professionals, students, content creators, journalists, and researchers alike.
No speech recognition model is best at everything, so this tool uses two. NVIDIA Parakeet TDT 0.6B v3 is a 600-million-parameter model built for 25 European languages. It pairs a FastConformer encoder with a Token-and-Duration Transducer (TDT) decoder, which predicts each word together with how long it lasts and can therefore skip ahead through the audio instead of stepping through it frame by frame. That design is why it is fast, and it writes punctuation and capitalization as it goes.
OpenAI Whisper is a transformer encoder-decoder. The original release was trained with weak supervision on 680,000 hours of multilingual audio paired with transcripts collected from the internet, which taught it to handle real-world recordings with background music, varying microphone quality and regional accents. It turns the audio into a log-mel spectrogram and generates text one token at a time, which is what lets it cover more than 90 languages and recognise which language is being spoken.
When you leave the language on Auto-detect, Whisper listens first and identifies the language. If it is one of Parakeet's 25, Parakeet transcribes the file; otherwise Whisper does. Either way this tool only transcribes: the text always comes back in the language that was spoken, not translated into English.
Parakeet covers Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swedish and Ukrainian. Whisper handles the rest of its 90+ languages, including Japanese, Korean, Chinese (Mandarin), Arabic, Hindi, Turkish, Indonesian, Vietnamese, Thai and Hebrew.
We measured the difference on our own server with the FLEURS test set, which is read speech. Across English, French, Spanish and Portuguese, Parakeet averaged a word error rate of 5.3% against 7.3% for the Whisper model we used before — roughly 28% fewer word errors — and ran several times faster. On Brazilian Portuguese alone the figures were 4.9% and 6.6%. Read speech is a best case: spontaneous conversation, music or noise will raise the error rate of any model.
For the languages Whisper handles, accuracy follows how much of each language it saw in training: widely spoken languages do best, and less-represented ones are generally still usable for most practical purposes. The auto-detect feature correctly identifies the spoken language in the vast majority of cases, though manually selecting the language can improve results when you know the source language in advance.
Accuracy also depends heavily on audio quality. Professional studio recordings and clear phone calls produce near-perfect transcriptions, while recordings with heavy background noise, multiple overlapping speakers, or very low volume may contain more errors. Even in challenging conditions, both models typically outperform older speech recognition technologies.
Two. NVIDIA Parakeet TDT 0.6B v3 transcribes the 25 European languages it supports, including English, Spanish, French, Portuguese, German and Italian. OpenAI Whisper transcribes every other language, such as Japanese, Chinese, Korean, Arabic, Hindi and Turkish. On auto-detect, Whisper identifies the language first and the file then goes to whichever model handles it. In our own tests on read speech, Parakeet made roughly 28% fewer word errors than the Whisper model we used before in English, French, Spanish and Portuguese, and ran several times faster; Whisper remains the broader model, covering more than 90 languages.
Over 90 languages. Twenty-five European languages are transcribed by NVIDIA Parakeet: Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swedish and Ukrainian. All others, including Chinese, Japanese, Korean, Arabic, Hindi, Turkish, Norwegian, Thai, Vietnamese, Indonesian, Malay, Filipino and Hebrew, are transcribed by OpenAI Whisper.
Accuracy depends on audio quality, language, and speaking clarity. On clear read speech we measured an average word error rate of about 5% with Parakeet across English, French, Spanish and Portuguese, and 4.9% for Brazilian Portuguese. The other European languages Parakeet covers, and the major languages Whisper handles, also transcribe well. Results degrade with heavy background noise, overlapping speakers, or very quiet audio, as they do for any speech recognition system.
The tool accepts most common audio formats including MP3, WAV, M4A, OGG, FLAC, and AAC. For video files, it supports MP4, WEBM, MKV, MOV, and AVI -- the audio track is automatically extracted for transcription. If your file is in an unsupported format, convert it to MP3 first using any standard audio converter.
Up to 25 MB and up to five minutes of audio, whichever you reach first. The length is the limit that actually bites, and it is not about storage: this tool transcribes while you wait, inside a single request, and the proxy in front of it gives up after about a hundred seconds. A twenty-minute upload could never finish, however small you made the file — so shrinking the bitrate to fit more audio in does not help, and we no longer suggest it. For anything longer, use the Subtitle Generator: it queues the work, shows real progress, takes up to an hour, and hands back SRT and VTT as well as plain text.
Both models learned from large amounts of real-world recordings, and Whisper in particular was trained on internet audio full of background noise, music and other interference. They handle moderate background noise well, often transcribing speech accurately even in noisy environments like cafes, cars, or outdoor settings. However, very loud or persistent noise that obscures speech will reduce accuracy. For best results, try to use recordings where the speaker's voice is clearly audible above the background.
The tool transcribes all audible speech in the recording, but neither model performs speaker diarization, meaning it does not label or separate different speakers. The output will be a continuous transcript of everything spoken. If you need speaker identification, you would need to manually add speaker labels after transcription or use a dedicated diarization tool alongside this transcription.
The transcript shown on the page is plain text, so it pastes cleanly into documents, emails or notes. The same transcription is also offered as timed .srt and .vtt subtitle files, with each line placed where it is spoken, so you get timestamps without a second step. For videos longer than five minutes, use the Subtitle Generator, which does the same work as a queued job.
Yes. Your audio file is uploaded to our server only for processing and is immediately deleted after transcription is complete. Files are never stored permanently, logged, or shared with third parties. The transcription happens in memory and the audio data is discarded as soon as the text result is returned to your browser. We do not retain any copy of your recordings.
This is an upload-based transcription tool. You upload a pre-recorded audio or video file and receive the transcript after processing. It does not support live microphone streaming or real-time transcription. For most use cases -- meeting notes, lecture transcripts, podcast episodes, interview recordings -- upload-based transcription is the practical choice because it allows the AI to analyze the full audio context for higher accuracy.
Both models were trained on large, varied collections of recorded speech, so they have encountered a wide variety of regional accents, speech patterns, and dialects. British, Australian, Indian, and South African English accents are generally handled well, as are regional variations in Spanish, French, Portuguese, and other languages. Very strong accents or heavy dialect will still produce more errors than standard speech, so review the transcript before relying on it.
While both models produce impressively accurate transcriptions, this tool should not be used as the sole transcription method for medical or legal documents where absolute accuracy is required. These fields use highly specialized terminology and any transcription errors could have serious consequences. Use the tool to create a first draft, then have a qualified professional review and correct the output before relying on it for clinical, legal, or regulatory purposes.
Yes. Both models automatically add punctuation including periods, commas, question marks, and exclamation points. They also handle capitalization at the beginning of sentences and for proper nouns. This is a significant advantage over older speech recognition systems that output unpunctuated text, as it means the transcript is immediately readable without extensive manual editing.
The transcript is displayed in a text area on the page. You can copy the entire text to your clipboard using the "Copy Text" button, then paste it into any text editor, word processor, or note-taking application where you can make corrections, add formatting, insert speaker labels, or restructure the content as needed.
Yes. The tool is fully responsive and works on smartphones and tablets running modern browsers. You can upload audio files from your phone's storage, record a voice memo and upload it directly, or select files from cloud storage apps. The interface adapts to smaller screens while maintaining full functionality.
Yes. When the transcription finishes you can download it as an .srt or .vtt subtitle file as well as copy the plain text. Lines are kept to two rows of at most 42 characters, the layout most players and platforms expect. For videos longer than five minutes, the Subtitle Generator takes files of up to an hour.
Absolutely. There are no restrictions on how you use the transcribed text. You can use it for business documents, commercial content, client deliverables, published articles, YouTube captions, podcast show notes, or any other purpose. The text output belongs to you and can be used, modified, and distributed without limitation.
It depends on the length of the recording and how busy the server is. In the 25 European languages Parakeet handles, the model runs several times faster than real time, so a five-minute recording (the longest this tool accepts) usually takes about a minute or less, plus the upload. Other languages use Whisper and take somewhat longer. For longer recordings, use the Subtitle Generator, which queues the work and shows its progress.
The tool works in all modern browsers including Google Chrome, Mozilla Firefox, Microsoft Edge, Safari, Opera, and Brave. Both desktop and mobile versions of these browsers are supported. We recommend using the latest version of your preferred browser for the best experience. Internet Explorer is not supported.
No. The transcription requires server-side processing with the Parakeet and Whisper models, so an internet connection is necessary. Your audio file is uploaded to the server, processed by the AI, and the text result is sent back to your browser. If you need offline speech recognition, both models are openly available and can be installed on your own computer, which requires Python and sufficient hardware resources.