Free Hindi Transcription
Transcribe Hindi audio and video to text with AI. Fast, accurate, and free.
How It Works
- Go to the Free.ai Transcriber
- Upload your Hindi audio or video file
- Our AI automatically detects Hindi and transcribes it
- Download your transcript as text or SRT subtitles
Hindi Transcription Features
- ✓Powered by faster-whisper (MIT licensed)
- ✓Automatic Hindi language detection
- ✓Supports MP3, WAV, MP4, M4A, FLAC, and more
- ✓Timestamps and subtitle export (SRT)
- ✓No file size limits on paid plans
- ✓Private and secure -- files are deleted after processing
Language Details
| Language | Hindi |
| ISO Code | hi |
| AI Model | faster-whisper |
| Price | Free |
More Languages
View All LanguagesFAQ
Whisper large-v3-turbo handles Hindi solidly — 7-15% word error rate on benchmark audio. Expect occasional substitutions on named entities, numbers, and dense technical vocabulary; the bulk of the transcript will be correct. (Tier B, 7-15% word error rate on benchmark sets - we publish honest WER tiers rather than marketing claims.)
Yes - Hindi transcription draws from your daily free token pool first. Audio costs about 50 tokens per minute, so the anonymous daily pool covers a few hours of audio per day. Signed-in accounts get a larger 30,000-token daily pool. Past that, transcription is pay-as-you-go, with token top-ups from $1.
Hindi audio frequently code-mixes with English (Hinglish) in urban speech. Whisper handles the mix and transcribes English words in Latin script and Hindi words in Devanagari within the same transcript. Rural speech with heavy regional vocabulary may dip into tier-C accuracy.
MP3, WAV, M4A, FLAC, OGG, OPUS, and WEBM are accepted directly. For video (MP4, MOV, MKV) we extract the audio track server-side before sending it to Whisper - you do not need to convert anything yourself. Same pipeline regardless of source language, including Hindi.
Anonymous uploads cap at roughly 500 MB per file. Signed-in accounts go up to 2 GB. Duration is not a hard limit - long files are chunked automatically (30-second windows with overlap) and stitched back into a single transcript with continuous timestamps. Multi-hour Hindi recordings (podcasts, full lectures, meetings) work fine.
Yes - speaker diarization is on by default for every Hindi transcript. The output is segmented as Speaker 1 / Speaker 2 / Speaker 3 with timestamps, so interviews, panel discussions, and multi-party meetings come back labeled. Diarization runs on a separate model and works the same across all languages we support.
Yes - paste the URL into /transcribe/youtube/ for YouTube or /transcribe/podcast/ for podcast feeds (Apple, Spotify, RSS). We download the audio, run it through Whisper with language=hi, and return the transcript with timestamps and speaker labels. Typical Hindi content: WhatsApp voice notes, YouTube vlogs, and short-form video are the most common Hindi workloads - paste a URL into /transcribe/youtube/ or upload the audio directly.
Whisper costs about 50 tokens per minute of audio, so a one-hour recording is ~3,000 tokens. Most users never spend anything - the free daily pool of 30,000 tokens covers short clips, voice notes, and one-off podcasts. Beyond it, transcription is pay-as-you-go, with token top-ups from $1.
Yes - both segment-level (every ~10-30 seconds) and word-level timestamps are available. Word-level is the default for VTT/SRT subtitle export so the captions sync line-by-line. On the API set timestamps="word" in the request body. Hindi transcripts are returned in Devanagari script (UTF-8).
Yes. POST audio (multipart/form-data, field name "file") to /v1/transcribe/ with language=hi - or omit the language parameter to let Whisper auto-detect. Returns JSON with the transcript, segments, timestamps, and speaker labels. Full reference and SDK snippets at /api/.
Yes - once transcription finishes, click Translate or paste the text into /translate/. Hindi pairs with every other language we support (200+). For meeting minutes pipe the transcript through /summarize/; for dubbing send it to /voice/tts/ to render audio in the target language.
Whisper is trained on hundreds of thousands of hours of real-world audio, so it tolerates background noise and phone-quality recordings on Hindi. For best results, supply clean audio (headset mic, no music bed) — at this tier noise compounds the baseline error rate. If a transcript comes back unusable, email contact@free.ai with the file - we will refund the tokens and look at whether a different engine handles your audio better.