IBM Watson Speech to Text vs Whisper
IBM Watson Speech to Text
ibm.com
Whisper
github.comSide by side
| Figure | IBM Watson Speech to Text | Whisper |
|---|---|---|
| Price from | $0.02/mo | $0.01/mo |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 5 | 3 |
| Spec score · AI transcription tools | 9.0 (#6) | 9.5 (#1) |
| Spec score · Transcription Software | 8.7 (#11) | 8.6 (#16) |
| Spec score · Speech Recognition Software | 8.7 (#11) | 8.6 (#13) |
| Export formats | JSON | txt,vtt,srt,tsv,json,jsonl |
| Intended users | IBM identifies customer self-service, agent assistance, and call analytics as use cases, and describes Plus as intended for small businesses and development or testing by larger enterprises. | OpenAI describes the open-sourced models and inference code as a foundation for building applications and further research on robust speech processing. |
| Timestamp support | Yes | Yes |
| What it does | The service uses speech recognition APIs to transcribe spoken audio in multiple languages and formats. | Whisper is a general-purpose speech-recognition model that supports multilingual transcription, speech translation and language identification. |
Plans
IBM Watson Speech to Text
- LitePriced on request
- Plus$0.02 a month
- PlusPriced on request
- LiteFree
- PremiumPriced on request
Whisper
- Self-hosted WhisperFree
- Whisper (open-source code and model weights)Free
- Whisper API$0.01 a month
In their own words
IBM Watson Speech to Text
IBM Watson Speech to Text has no write-up on Specifiction yet; its figures are above.
Whisper
Whisper is an open-source speech recognition model for transcription, speech translation, language identification, and voice activity detection. Its documented command-line and Python interfaces process audio files, and it can translate speech from multiple languages into English. The repository lists six model sizes—tiny, base, small, medium, large, and turbo—with different speed and memory tradeoffs; stated VRAM needs range from about 1 GB to 10 GB, depending on size. Installation is documented through pip and requires the ffmpeg command-line tool. Rust may be needed if a prebuilt tiktoken wheel is unavailable. The code and model weights are released under the MIT License, with local installation using Python and ffmpeg. Whisper is listed for Linux, macOS, Windows, and self-hosted deployment. OpenAI says it was trained on 680,000 hours of multilingual and multitask supervised data collected from the web. Performance varies widely by language, and it does not outperform models specialized for LibriSpeech. Turbo is not trained for translation; multilingual models are recommended for translating non-English speech into English.