Use OpenAI Whisper without coding or command line. Simple desktop interface with all Whisper models. Works offline or with OpenAI API.
All OpenAI Whisper features through simple interface
Access tiny, base, small, medium, and large models. Choose based on accuracy needs and hardware capabilities. Built-in model downloader.
Run Whisper offline with whisper.cpp or use OpenAI API for cloud processing. Switch between modes based on privacy needs.
Simple desktop application. No Python installation, no command line, no configuration files. Download and start transcribing.
Full multilingual support built into Whisper. Automatic language detection or manual selection. No additional language packs needed.
NVIDIA CUDA support for 10x faster transcription. Automatically configured. No manual CUDA toolkit installation required.
Transcribe audio files or use microphone for real-time dictation. Supports all major audio formats including MP3, WAV, M4A.
OpenAI Whisper is an automatic speech recognition system released by OpenAI in September 2022. Trained on 680,000 hours of multilingual and multitask supervised data collected from the web, Whisper demonstrates robust performance across languages, accents, and audio conditions.
The model is open-source and available in five sizes (tiny, base, small, medium, large), allowing users to balance accuracy requirements with computational resources. Whisper handles 99+ languages and demonstrates strong performance on code-switching, technical terminology, and noisy audio.
Fastest processing with minimal resource requirements. Runs on CPU without GPU. Suitable for quick transcription where perfect accuracy is not critical. Approximately 90-93% accuracy on clear English audio.
Balanced speed and accuracy. Runs efficiently on most hardware. Good choice for general-purpose transcription. Approximately 93-95% accuracy.
Recommended minimum for professional use. Significant accuracy improvement over base. Works well on modern CPUs or entry-level GPUs. Approximately 95-97% accuracy.
High accuracy for professional applications. Benefits from GPU acceleration. Handles difficult audio and technical vocabulary well. Approximately 97-98% accuracy.
Maximum accuracy approaching human transcription quality. Requires GPU for practical performance. Best for critical applications where accuracy matters most. Approximately 98-99% accuracy.
OpenAI provides Whisper as a cloud API. Upload audio files up to 25MB. Pricing at $0.006 per minute ($0.36 per hour of audio). Fast processing on OpenAI's infrastructure. No local hardware requirements.
API advantages: consistent performance, no setup required, always latest model version. Limitations: requires internet, audio uploaded to OpenAI servers, ongoing costs for high-volume use.
Run Whisper locally using OpenAI's Python implementation or whisper.cpp (C++ port). Requires initial setup: Python installation, model downloads, CUDA configuration for GPU acceleration.
Local advantages: complete privacy, no per-use costs, works offline. Limitations: setup complexity, hardware requirements, maintenance burden.
Desktop applications like StarWhisper provide simple interface for local Whisper. Pre-configured with all dependencies. One-click installer. Offers both local processing and optional API access through single interface.
Journalists and researchers use Whisper for audio to text transcription of recorded interviews. Multilingual support handles interviews conducted in various languages. High accuracy reduces editing time compared to older speech recognition systems.
Convert recorded meetings and calls to searchable text. Extract action items, decisions, and discussion points. Particularly valuable for remote teams with members across time zones.
Podcasters transcribe episodes for show notes and blog content. YouTube creators generate captions and transcripts. Improves content discoverability through search engines.
Transcribe qualitative research interviews, focus groups, and oral histories. Text format enables analysis with qualitative coding software. Multilingual capability valuable for international research.
Generate captions for video content. Create transcripts of lectures and presentations. Makes audio content accessible to deaf and hard-of-hearing audiences.
Whisper handles 99+ languages including English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, Hindi, and many others. Automatic language detection identifies spoken language without manual selection.
Training on diverse web data makes Whisper resilient to background noise, music, and acoustic variations. Performs reasonably well on phone recordings, outdoor audio, and other challenging conditions.
Generates word-level or segment-level timestamps. Enables synchronization between audio and transcript. Useful for video captioning and navigating long recordings.
Outputs plain text, SRT captions, VTT format, or JSON with detailed metadata. Accommodates various downstream use cases from simple transcripts to professional video production.
Create OpenAI API account. Generate API key. Send audio files via API requests. Suitable for developers integrating transcription into applications or users comfortable with API services.
Install Python 3.8+. Install Whisper via pip. Download desired models. Configure CUDA for GPU acceleration. Requires technical knowledge and troubleshooting skills.
Download desktop software with Whisper pre-configured. Click install, choose model size, start transcribing. No technical setup required. Includes both local processing and optional API access.
While highly capable, Whisper has limitations. Performance degrades on very poor audio quality, heavy accents, or domain-specific jargon. Hallucinations can occur where model generates plausible but incorrect text when audio is unclear.
Processing speed depends on model size and hardware. Large model on CPU processes significantly slower than real-time. GPU acceleration essential for interactive use with larger models.
For production applications requiring 99.5%+ accuracy, consider hybrid approach: Whisper for initial transcription, human review for quality assurance.