ESPnet vs TranscribeMe
ESPnet
espnet.github.ioTranscribeMe
transcribeme.comSide by side
| Figure | ESPnet | TranscribeMe |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Web | — | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| iOS | — | Yes |
| Android | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 2 | 10 |
| Spec score · Audio Transcription Software | 8.9 (#6) | 8.0 (#26) |
| Integrations | The installation guide lists W&B and TensorBoard among the training dependencies. | The company says its API has been integrated with IPSOS and Medallia, and its terms mention SFTP and dedicated Dropbox folders. |
| Licence | The repository and website identify the software as Apache License 2.0. | The applications are identified as the exclusive property of TranscribeMe and its licensors, with customers receiving limited non-exclusive access rights. |
| What it does | ESPnet is an end-to-end speech-processing toolkit for recognition, synthesis, translation, enhancement, diarization, spoken-language understanding and related tasks. | TranscribeMe converts audio and video into text using automated speech recognition combined with human transcriptionists. |
Plans
TranscribeMe
- Automated transcription$0.07, paid once
- Automated Transcription$0.07, paid once
- Data annotation$0.10, paid once
- Translation$0.11, paid once
- Human-edited transcription$0.79, paid once
- Human Edited Transcription$0.79, paid once
- Human Edited with Extra Review$1.25, paid once
- AI training datasets$2, paid once
- Verbatim Transcripts$2, paid once
- Enterprise Custom ProjectPriced on request
In their own words
ESPnet
ESPnet is an open-source toolkit for speech processing, including recognition, text-to-speech, translation, enhancement, speaker diarization, and spoken language understanding. It provides complete recipes for a range of tasks, plus tutorials, notebooks, and demos for existing models. Users can install ESPnet and espnet-model-zoo with pip to run inference on existing models; published models can also run in CPU or GPU inference containers without checking out the repository. Installation supports editable pip setup and optional dependency groups for tasks such as ASR, TTS, and speech enhancement. Training requires a separate extra that includes Lightning, TensorBoard, W&B, torch_optimizer, matplotlib, and Dask. Some task-specific packages must also be installed separately when a recipe needs them. ESPnet 1 is no longer supported, while ESPnet2 users can skip installing Kaldi. The toolkit is released under Apache License 2.0 and permits commercial use. It offers API access and lists Linux, macOS, Windows, and self-hosted platforms; WAV is the listed export format.
TranscribeMe
TranscribeMe has no write-up on Specifiction yet; its figures are above.