OpenAI Whisper Large V3 · Any Language
Voice to Text
Upload an audio file and get an accurate transcript instantly
Drag & drop an audio file or click to browse
MP3, WAV, OGG, FLAC, M4A, WebM · max 10 MB
Upload an audio file and get an accurate transcript instantly
Drag & drop an audio file or click to browse
MP3, WAV, OGG, FLAC, M4A, WebM · max 10 MB
Our Voice to Text tool uses OpenAI's Whisper Large V3, one of the most capable open-source automatic speech recognition (ASR) models available. Whisper was trained by OpenAI on 680,000 hours of multilingual audio data collected from the internet, making it exceptionally robust to accents, background noise, technical jargon, and informal speech — conditions where earlier ASR systems struggled significantly.
Whisper uses an encoder-decoder Transformer architecture. The audio is first converted to a log-Mel spectrogram (a frequency representation of sound over time), which the encoder processes to extract acoustic features. The decoder then autoregressively generates the transcript token by token, using attention to focus on the relevant audio segments for each word.
When you upload a file, the audio bytes are sent directly to the HuggingFace Inference API running Whisper Large V3. The model transcribes the audio and returns the text. No audio data is stored after processing — the file is sent, transcribed, and the response returned to your browser.
The tool accepts the most common audio formats: MP3 (audio/mpeg), WAV, OGG Vorbis, FLAC, M4A, and WebM — covering virtually all audio files you might encounter. The maximum file size is 10 MB, which corresponds to approximately 8–10 minutes of speech at typical MP3 bitrates (128 kbps).
Whisper Large V3 supports transcription in 99 languages without requiring you to specify the language — it automatically detects the spoken language from the audio content. Supported languages include all major European languages, Arabic, Chinese (Mandarin), Japanese, Korean, Hindi, Turkish, and many more. Detection works reliably when at least 30 seconds of speech are present; very short clips may be misidentified.
For best results, use clear recordings with minimal background noise and one primary speaker. Whisper handles multiple speakers but does not distinguish between them (speaker diarization). Background music, overlapping speech, and very heavy accents can reduce accuracy, though Whisper is more resilient to these conditions than most competing models.
Whisper Large V3 achieves state-of-the-art word error rates across most benchmark languages, often matching or approaching human-level accuracy for clear speech in well-supported languages. English achieves the lowest error rates given its dominance in training data. Languages with less internet audio representation — including many African and indigenous languages — may have higher error rates despite being technically supported.
The model performs punctuation and capitalization automatically, producing ready-to-use text without requiring manual formatting. Technical vocabulary, proper nouns, and domain-specific terms are generally handled well if they appear in the training data.
Privacy is maintained by design: your audio file is transmitted to the API for processing and the transcript is returned to your browser. No audio files are stored on our servers after processing. For highly sensitive recordings, consider the privacy policies of all services in the processing chain.