Recently, FunASR has launched a powerful multilingual offline transcription software package, offering users an efficient and accurate speech-to-text solution.
The core advantage of this software package lies in its offline file transcription capabilities. It can easily handle audio or video files lasting several hours and generate transcribed text with punctuation. This feature is undoubtedly a boon for professionals who need to process large volumes of audio materials.
FunASR's multilingual support is also impressive. Currently, the software package supports multiple languages including Chinese, English, Japanese, Cantonese, and Korean, demonstrating outstanding speech recognition capabilities. Notably, it also provides word-level timestamps, allowing users to precisely locate specific content within the audio.
To meet users' personalized needs, FunASR has introduced a custom hotword feature. Users can define specific terms or proper nouns, and the software will optimize recognition results accordingly, significantly enhancing the accuracy and practicality of transcription.
From a technical perspective, FunASR integrates several advanced models, including voice activity detection, speech recognition, and punctuation insertion. This comprehensive speech recognition process ensures high-quality transcription results. Additionally, the software supports parallel processing of multiple transcription requests, greatly enhancing work efficiency.
For developers, FunASR offers a rich set of client libraries, covering various programming languages such as HTML, Python, C++, Java, and C#. This diversity facilitates secondary development and system integration.
In practical applications, FunASR performs exceptionally well. It can handle hundreds of concurrent requests simultaneously, suitable for various scenarios such as meeting minutes and interview transcription. The software also supports initial time normalization (ITN), further improving transcription accuracy.
To simplify the deployment process, FunASR provides Docker installation and startup instructions. Users can pull the Docker image and start the server with just a few simple commands, easily experiencing the efficient offline transcription function.
Project address: https://github.com/modelscope/FunASR/blob/main/runtime/docs/SDK_advanced_guide_offline.md
