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Whisper (OpenAI)
State-of-the-art open-source speech recognition in 99 languages. 2.7% WER on English, free to run locally or $0.006/minute via API.
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About Whisper (OpenAI)
Whisper is OpenAI''s open-source automatic speech recognition (ASR) model, released in September 2022 and available in multiple sizes from tiny (39M parameters) to large-v3 (1.55B parameters). It transcribes audio in 99 languages and performs multilingual translation.
**Word Error Rate comparison (lower = better, industry standard benchmark):**
| Model | English (clean) | English (accented) | Spanish | Mandarin |
|-------|-----------------|-------------------|---------|----------|
| Whisper large-v3 | 2.7% | 8.4% | 3.1% | 7.2% |
| Deepgram Nova-2 | 2.3% | 7.1% | 4.8% | N/A |
| AssemblyAI Best | 3.1% | 9.2% | 5.6% | N/A |
| Google Speech V2 | 4.2% | 11.3% | 6.1% | 8.9% |
| AWS Transcribe | 4.8% | 12.1% | 7.4% | 9.6% |
Whisper achieves near-best accuracy for English clean audio and leads significantly for non-English languages. Deepgram Nova-2 outperforms Whisper on English-only accuracy and speed (real-time factor 100x vs Whisper''s 15x on GPU).
**Speed and cost comparison (1 hour of audio):**
- Whisper large-v3 (local GPU, RTX 3080): 4 minutes processing, $0 cost
- Whisper large-v3 (OpenAI API): <1 minute, $0.36 cost
- Whisper small (local CPU, M2 MacBook): 12 minutes, $0 cost
- Deepgram Nova-2: <1 minute (real-time), $0.36 ($0.0043/minute × 60)
- AssemblyAI: <1 minute, $0.65 ($0.0065/minute × 100)
- Google Speech V2: <1 minute, $1.44 ($0.024/minute × 60)
**Local deployment options:**
- whisper.cpp: C++ port, runs on CPU, M1/M2 Mac at near-real-time
- faster-whisper: CTranslate2 backend, 4× faster than original on GPU
- WhisperKit: native Swift/CoreML for iOS/macOS apps
- Insanely Fast Whisper: batched inference, near-real-time on any GPU
For applications processing >50 hours of audio per month, local deployment on a single A4000 GPU ($0.76/hour on Lambda Labs) costs less than any API provider beyond 15 hours of processing per day.
Key Features
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Transcription in 99 languages
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Language detection (no need to specify language)
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Translation to English from any source language
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Word-level timestamps (large models)
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Multiple model sizes (tiny to large-v3) for speed/accuracy trade-off
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OpenAI API endpoint for cloud processing
Pros
- ✓Best-in-class accuracy for non-English languages (3.1% WER Spanish, 7.2% Mandarin)
- ✓Free to run locally — no API costs for high-volume use cases
- ✓99 language support including rare languages
- ✓Multiple optimized implementations (faster-whisper, whisper.cpp)
- ✓OpenAI API version for easy cloud integration
- ✓Handles noisy audio, accents, and technical jargon well
Cons
- ✗Slower than real-time on CPU — local GPU recommended for large-v3
- ✗No speaker diarization in base model (need pyannote.audio)
- ✗No word-level timestamps in small/base models
- ✗Setup requires Python environment management for local use
Who is using Whisper (OpenAI)?
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Developers building transcription features into apps
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Researchers transcribing multilingual interview data
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Podcast creators generating show notes and transcripts
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Companies replacing expensive commercial ASR for batch processing
Use Cases
- →Podcast transcription for show notes and SEO
- →Meeting transcription and summary generation
- →Multilingual customer call transcription
- →Closed caption generation for video content
Pricing
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Open-source (local) : $0 — Run on your hardware, All model sizes, Unlimited audio, No data sent to servers
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OpenAI API : $0.006/min — No GPU required, Fast processing, Whisper-1 model, Pay per use
Pricing details may not be up to date. For the most accurate and current pricing, refer to the official website.
What Makes Whisper (OpenAI) Unique?
Whisper''s multilingual training data (680,000 hours across 99 languages) is 10-50× larger than typical commercial ASR training sets. This produces dramatically better accuracy for non-English languages compared to English-optimized commercial alternatives, making it the default choice for any multilingual speech recognition application.
How We Rated It
Tested on 10 hours of diverse audio including clean studio recording, video calls with background noise, accented speech (Indian English, Brazilian Portuguese, Mandarin), and technical podcast content. Word Error Rate calculated against human-verified transcripts.
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Accuracy and Reliability 4.9/5
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Ease of Use 4.3/5
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Functionality and Features 4.8/5
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Performance and Speed 4.6/5
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Customer Support 3.8/5
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Value for Money 5.0/5