Local self-hosted
$0
one-time
The official code and model weights are released under the MIT License. Users provide their own compute, storage and deployment infrastructure.
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OpenAI Whisper
OpenAI Whisper is an open-source multilingual speech recognition model for transcription, speech translation, language identification and timestamped audio processing.
OpenAI Whisper Decision Snapshot
A quick way to understand who this product may suit, where its limits matter, and which facts are most relevant before you compare alternatives.
Platforms
API, Command line, Linux, macOS, Python, PyTorch
TechZella assessment
8.1/10
Overview
OpenAI Whisper is an open-source automatic speech recognition model released in September 2022. It converts audio into text, identifies spoken languages, and translates supported non-English speech into English.
The official implementation uses a Transformer encoder-decoder architecture. It processes audio in 30-second windows and supports local inference through Python code or a command-line interface.
Whisper is available in multiple model sizes, including tiny, base, small, medium, large and turbo. Larger models generally require more memory and provide different speed and accuracy trade-offs.
OpenAI Whisper Pros & Cons
Pros
- Open-source MIT licensing supports local and offline deployment.
- Supports multilingual transcription and translation into English.
- Offers multiple model sizes for hardware and latency trade-offs.
- Provides command-line and Python interfaces.
- Supports subtitle formats and timestamped transcription.
- Hosted API pricing is straightforward at $0.006 per minute.
Cons
- Local installation requires Python, PyTorch and FFmpeg.
- Large models may require substantial GPU memory.
- Recognition quality varies by language and audio conditions.
- The whisper-1 API does not support realtime streaming.
- Speaker diarization is not a built-in official feature.
- OpenAI now recommends newer transcription models for some new general-purpose API use cases.
OpenAI Whisper Pricing & Plans
Whisper API
$0.006 per minute
usage-based
OpenAI's hosted whisper-1 model supports audio transcription and translation through API endpoints.
Check current pricing →Pricing may change. Verify current plans on the vendor's website.
OpenAI Whisper Competitors & Alternatives
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Direct competitors
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OpenAI Whisper Features
Speech Recognition
- Multilingual audio transcription
- English-only model variants
- Language identification
- Speech-to-English translation
- Voice activity detection support in the model architecture
Audio Processing
- 30-second sliding-window processing
- FFmpeg-based audio loading
- Multiple audio input formats
- Local command-line inference
- Python inference interface
Output and API
- Plain text output
- JSON output
- SRT subtitle output
- VTT subtitle output
- TSV output
- Segment and word timestamps through whisper-1 API
- Transcription and translation endpoints
Models
- tiny and tiny.en
- base and base.en
- small and small.en
- medium and medium.en
- large
- turbo
Developer Support
- MIT-licensed code and weights
- PyTorch implementation
- Python package installation
- OpenAI hosted API
- OpenAI API client examples
OpenAI Whisper Specifications
| Product type | Open-source automatic speech recognition model |
|---|---|
| Developer | OpenAI |
| Original release | September 2022 |
| License | MIT |
| Local runtime | Python and PyTorch |
| Supported local operating systems | Linux, macOS and Windows |
| Command-line interface | Yes |
| API model | whisper-1 |
| API endpoints | /v1/audio/transcriptions and /v1/audio/translations |
| API pricing | $0.006 per minute |
| API upload limit | 25 MB for whisper-1 transcription uploads |
| API supported formats | mp3, mp4, mpeg, mpga, m4a, wav and webm |
| Integrations | Python, PyTorch, FFmpeg, OpenAI API and OpenAI SDKs |
| Free trial | No separate Whisper trial verified |
| Realtime streaming | Not supported by whisper-1 API |
| Languages | 98 languages listed in OpenAI documentation |
| Model sizes | tiny, base, small, medium, large and turbo |
Our evidence-based assessment
TechZella Score
A proprietary editorial score based on product capabilities, usability, value, performance, support and user sentiment evidence.
8.1/10Moderate confidence
Whisper provides multilingual transcription, translation, language detection, timestamps and several model sizes. The MIT license supports self-hosting, but installation requires Python, PyTorch, FFmpeg and suitable hardware. OpenAI documentation now recommends newer transcription models for some general-purpose API workloads.
Methodology v1.0. The assessment weighs feature coverage (25%), ease of use (15%), value for money (20%), performance (15%), support (10%) and user sentiment (15%), using researched product evidence and verified review evidence where available. It is a TechZella editorial assessment, not a direct user-review average.
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FAQ
Is OpenAI Whisper free?
The local implementation is available under the MIT License and has no software charge. Hosting, compute and storage costs still apply. The hosted Whisper API is billed at $0.006 per minute.
Does Whisper work offline?
Yes. The official repository supports downloading model files and running inference locally without sending audio to OpenAI’s API.
Which operating systems support Whisper?
The documented installation process covers Linux, macOS and Windows. It runs through Python and PyTorch rather than a standalone native desktop application.
Can Whisper translate speech?
Yes. Multilingual models can translate supported non-English speech into English. The turbo model is intended for transcription and isn’t trained for translation.
Does Whisper provide speaker diarization?
The official Whisper implementation provides transcription, language detection, translation and timestamps. Speaker diarization isn’t listed as a built-in feature in the official repository.
Can Whisper process realtime audio?
The local model can be incorporated into streaming applications, but the hosted whisper-1 API doesn’t support streaming. Realtime workloads require an application-level implementation or another OpenAI transcription model.