Whisper vs XTrans
Whisper
github.comXTrans
ldc.upenn.eduSide by side
| Figure | Whisper | XTrans |
|---|---|---|
| Price from | $0.01/mo | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | — |
| Plans published | 3 | 1 |
| Spec score · AI transcription tools | 9.5 (#1) | 7.5 (#35) |
| Spec score · Transcription Software | 8.6 (#16) | 7.3 (#39) |
| Export formats | txt,vtt,srt,tsv,json,jsonl | Tab-delimited format (TDF) |
| Intended users | OpenAI describes the open-sourced models and inference code as a foundation for building applications and further research on robust speech processing. | LDC describes XTrans as designed for high-quality, high-volume transcription tasks involving real-world data. |
| Language support | Whisper supports multilingual speech recognition and translation into English, with the repository tokenizer listing available languages. | XTrans supports bidirectional text input for languages including Arabic, Farsi, Urdu, and Hebrew, and is Unicode compliant. |
| Purpose | Whisper is a general-purpose speech recognition model for multilingual transcription, speech translation, and language identification. | XTrans is a multi-platform, multilingual, multi-channel tool for manual transcription and annotation of audio recordings. |
| Timestamp support | Yes | Yes |
Plans
Whisper
- Self-hosted WhisperFree
- Whisper (open-source code and model weights)Free
- Whisper API$0.01 a month
In their own words
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.