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  2. Whisper (speech recognition system) - Wikipedia

    en.wikipedia.org/wiki/Whisper_(speech...

    Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. [ 2 ] It is capable of transcribing speech in English and several other languages, and is also capable of translating several non-English languages into English. [ 1 ]

  3. OpenAI open-sources Whisper, a multilingual speech ... - AOL

    www.aol.com/news/openai-open-sources-whisper...

    Speech recognition remains a challenging problem in AI and machine learning. In a step toward solving it, OpenAI today open-sourced Whisper, an automatic speech recognition system that the company ...

  4. Researchers say an AI-powered transcription tool used in ...

    www.aol.com/researchers-ai-powered-transcription...

    That warning hasn’t stopped hospitals or medical centers from using speech-to-text models, including Whisper, to transcribe what’s said during doctor’s visits to free up medical providers to ...

  5. Voice activity detection - Wikipedia

    en.wikipedia.org/wiki/Voice_activity_detection

    Voice activity detection (VAD), also known as speech activity detection or speech detection, is the detection of the presence or absence of human speech, used in speech processing. [1] The main uses of VAD are in speaker diarization , speech coding and speech recognition . [ 2 ]

  6. eSpeak - Wikipedia

    en.wikipedia.org/wiki/ESpeak

    eSpeak is a free and open-source, cross-platform, compact, software speech synthesizer.It uses a formant synthesis method, providing many languages in a relatively small file size. eSpeakNG (Next Generation) is a continuation of the original developer's project with more feedback from native speakers.

  7. LangChain - Wikipedia

    en.wikipedia.org/wiki/LangChain

    LangChain was launched in October 2022 as an open source project by Harrison Chase, while working at machine learning startup Robust Intelligence. The project quickly garnered popularity, [3] with improvements from hundreds of contributors on GitHub, trending discussions on Twitter, lively activity on the project's Discord server, many YouTube tutorials, and meetups in San Francisco and London.

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