# Transkriptor vs Whisper (OpenAI)

Transkriptor records 628,671 monthly visits with -3.6% month-over-month growth and Free Trial, Paid pricing. Whisper (OpenAI) records 0 monthly visits with — growth and Free pricing.

| Metric | [Transkriptor](https://openaitools.app/tool/transkriptor-com) | [Whisper (OpenAI)](https://openaitools.app/tool/whisper-github) |
| :--- | :--- | :--- |
| Website URL | https://transkriptor.com | https://github.com/openai/whisper |
| Categories | AI Transcription, AI Speech-to-Text, Audio To Text AI, AI Meeting Assistant | AI Speech Recognition, AI Speech-to-Text, AI Transcriber, AI Translate |
| Pricing Model | Free Trial, Paid | Free |
| Monthly Visits | 628,671 | 0 |
| MoM Growth Rate | -3.6% | — |
| Primary Market | Brazil | Global |
| Direct Visits | 296,253 | 0 |
| Review Score | 4.1 | 4.8 |
| Saved Count | 6 | 3 |
| Supported Platforms | Website | Open Source |

## Transkriptor

Transkriptor's core job is converting speech into text quickly and accurately. You can upload a file, record from a browser extension that captures screen, camera or microphone, or let the meeting bot join an online call. The service advertises up to 99% accuracy across more than 100 languages, and output is editable, timestamped and exportable in formats including TXT, DOCX and subtitle files such as SRT and VTT.

Beyond raw transcripts, Transkriptor layers analysis on top. It produces summaries and action items, identifies and labels each speaker through diarization, runs call sentiment analysis that classifies segments as positive, neutral or negative, and extracts key topics. An 'Ask Transkriptor' feature searches every transcript in your library to surface decisions, quotes and answers with cited sources, effectively turning meetings into an internal knowledge base.

The platform is positioned for teams as much as individuals, with note-taking, folders and workspaces for organizing material, plus a long list of integrations spanning Zoom, Microsoft Teams, Google Meet, Google Calendar, Outlook, CRMs, Airtable, ClickUp, Slack, Discord and cloud storage, along with an API and MCP support for developers. Transkriptor states that its platform meets SOC 2, GDPR, ISO 27001 and SSL standards. It reports 85M+ minutes of audio processed and 5M+ app downloads, offers a free Chrome extension, and provides a free tier that covers a limited daily transcription allowance with paid plans for higher volume.

**Pricing & Plans:** Free tier with a daily transcription allowance plus a free trial, and paid subscription plans for higher volume; check the site for current limits and prices.

- **Accurate multi-language transcription:** Convert audio and video to text in 100+ languages with up to 99% advertised accuracy, then edit and export in TXT, DOCX, SRT or VTT.
- **Speaker diarization:** Automatically identify and label who spoke when, making meeting and interview transcripts easy to follow and cite.
- **Summaries and action items:** Generate concise summaries, key topics and to-do items so you skip reading the full transcript.
- **Ask Transkriptor knowledge base:** Search across every meeting transcript in your library for decisions, quotes and answers with cited sources.
- **Meetings, integrations and API:** Record live calls, sync with Zoom, Teams, Meet, CRMs and storage tools, or automate workflows via the API and MCP.

**Strengths:** Strong language coverage with high advertised accuracy; Goes beyond transcription with summaries, speaker labels and sentiment analysis; Wide integration list including meeting platforms, CRMs and cloud storage; Free Chrome extension and a free daily transcription allowance for light use

**Limitations:** Accuracy still depends heavily on audio quality, background noise and accents; Long meetings and heavy volume require paid plans with credit limits to manage; The breadth of features and integrations can take time to learn and configure

[Profile (Markdown)](https://openaitools.app/tool/transkriptor-com.md)

## Whisper (OpenAI)

Whisper was introduced by OpenAI as a general-purpose speech recognition model trained on a very large, diverse dataset of web audio. Instead of stacking separate acoustic, language and punctuation modules, it treats every speech task as a token prediction problem: transcription, translation, language identification and voice activity detection all share one Transformer encoder-decoder and are distinguished by special task tokens. That single-model design is why a straightforward `pip install` is enough to get multilingual transcription running on a laptop, and why Whisper has become a de facto baseline for open speech-to-text work.

The repository ships six model sizes — tiny, base, small, medium, large and a faster turbo variant — trading accuracy for memory and speed. Each size also has an English-only `.en` counterpart that tends to score better on English audio, while the multilingual checkpoints are the ones to use when translating non-English speech into English. Practical VRAM needs range from roughly 1 GB for tiny up to about 10 GB for large, so the smaller checkpoints run comfortably on consumer hardware or even CPU, while large models are usually paired with a GPU.

Because Whisper is a model and a Python package rather than a hosted product, there is no dashboard, account or quota. You install it, point the command line at audio files, and write your own integration on top. It supports many audio formats through ffmpeg, and its per-language accuracy varies widely — strong for high-resource languages, weaker for low-resource ones — which is worth knowing before adopting it for production transcription.

**Pricing & Plans:** Free and open source. The code and weights are published on GitHub under the MIT license with no usage fees or per-minute charges, though you pay for whatever compute you run it on and any OpenAI API usage if you pair it with their hosted services.

- **Multitask single model:** One Transformer handles transcription, speech translation into English, language identification and voice activity detection, selected through special task tokens.
- **Multiple model sizes:** Choose from tiny, base, small, medium, large and turbo checkpoints, with English-only .en variants, to balance accuracy against VRAM and speed.
- **Broad language coverage:** Multilingual checkpoints transcribe and translate dozens of languages, with automatic language detection on mixed-language audio.
- **Command-line and Python API:** Install via pip with openai-whisper and transcribe files from the terminal or from your own Python code, with ffmpeg handling audio decoding.
- **Permissive MIT license:** Freely embed, modify and redistribute the code and weights in commercial and personal projects without licensing fees.

**Strengths:** Completely free and open source under a permissive MIT license; Strong multilingual accuracy across many languages with automatic detection; Runs locally, so audio never has to leave your hardware; Several model sizes let you match accuracy to available compute

**Limitations:** No hosted UI, account or support — you must install and operate it yourself; Large models need substantial VRAM and a GPU for reasonable speed; Accuracy drops noticeably on low-resource languages and noisy audio; Setup requires Python, PyTorch and ffmpeg, which can be fiddly for non-developers

[Profile (Markdown)](https://openaitools.app/tool/whisper-github.md)

## Frequently Asked Questions

### How do Transkriptor and Whisper (OpenAI) compare in monthly traffic?

Transkriptor has 628,671 monthly visits, while Whisper (OpenAI) has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Transkriptor has a month-over-month growth rate of -3.6%; Whisper (OpenAI) has —.

### How does pricing compare?

Transkriptor: Free Trial, Paid. Free tier with a daily transcription allowance plus a free trial, and paid subscription plans for higher volume; check the site for current limits and prices. Whisper (OpenAI): Free. Free and open source. The code and weights are published on GitHub under the MIT license with no usage fees or per-minute charges, though you pay for whatever compute you run it on and any OpenAI API usage if you pair it with their hosted services.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [TurboScribe](https://openaitools.app/tool/turboscribe-ai) | 27,661,771 | +2.1% | Freemium | Offers faster processing for very large audio files without strict file size limits, but lacks live meeting bot features. | [.md](https://openaitools.app/tool/turboscribe-ai.md) |
| 2 | [Otter AI](https://openaitools.app/tool/otter-ai) | 7,360,981 | +9.6% | Freemium | Stronger focus on live meeting recording and team collaboration, but less emphasis on file translation. | [.md](https://openaitools.app/tool/otter-ai.md) |
| 3 | [Happy Scribe](https://openaitools.app/tool/happyscribe-com) | 4,088,330 | +8% | Freemium, Free Trial, Paid | AI transcription and meeting notes in 150+ languages | [.md](https://openaitools.app/tool/happyscribe-com.md) |
| 4 | [Notta](https://openaitools.app/tool/notta) | 2,585,354 | +2% | Freemium, Paid, Contact for Pricing | Excels at real-time cross-device meeting recording and collaborative note-taking, but offers fewer deep document translation options. | [.md](https://openaitools.app/tool/notta.md) |
| 5 | [Transcribe Audio to Text](https://openaitools.app/tool/transcribe-audio-to-text) | 1,631,811 | +27.3% | Freemium | Turn audio, video and YouTube links into accurate text in seconds | [.md](https://openaitools.app/tool/transcribe-audio-to-text.md) |
| 6 | [UniScribe](https://openaitools.app/tool/uniscribe) | 1,486,239 | +0.7% | Freemium | Generates visual mind maps alongside standard text transcripts, but has a smaller feature set for heavy enterprise users. | [.md](https://openaitools.app/tool/uniscribe.md) |
| 7 | [Maestra AI](https://openaitools.app/tool/maestra-ai) | 1,340,377 | +0.7% | Free Trial, Subscription, Pay As You Go, Custom Enterprise | Stronger built-in tools for video dubbing and multi-language subtitle editing, but less focused on general text summaries. | [.md](https://openaitools.app/tool/maestra-ai.md) |
| 8 | [Video Transcriber AI](https://openaitools.app/tool/video-transcriber-ai) | 1,320,064 | +11% | Freemium | Turn any video or audio into text in seconds | [.md](https://openaitools.app/tool/video-transcriber-ai.md) |
| 9 | [Transcript LOL](https://openaitools.app/tool/transcript-lol) | 916,431 | -21% | Paid | Clean interface for turning audio and video into structured summaries, but lacks advanced team collaboration permissions. | [.md](https://openaitools.app/tool/transcript-lol.md) |
| 10 | [Deepgram Voice AI](https://openaitools.app/tool/deepgram-voice-ai) | 863,852 | +31.8% | Free Trial | Real-time speech-to-text, text-to-speech and voice agents in one API platform | [.md](https://openaitools.app/tool/deepgram-voice-ai.md) |

---
Source: https://openaitools.app/compare/transkriptor-com-vs-whisper-github

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)