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  1. 14. Juni 2024 · Our audio-visual Whisper-Flamingo outperforms audio-only Whisper on English speech recognition and En-X translation for 6 languages in noisy conditions. Moreover, Whisper-Flamingo is a versatile model and conducts all of these tasks using one set of parameters, while prior methods are trained separately on each language.

  2. 8. Juni 2024 · Whisper WebGPU by a Hugging Face Engineer (nickname ‘Xenova’) is a groundbreaking technology that leverages OpenAI’s Whisper model to bring real-time, in-browser speech recognition to fruition. This remarkable development is a monumental shift in interaction with AI-driven web applications.

    • Asif Razzaq
  3. 11. Juni 2024 · We, at CAMB.AI, are super stoked to announce the open source release of MARS5, a new speech emulation model that is able to replicate even extremely tough prosody like sports commentary, anime, movies with just a few seconds of audio reference. Check out our release: https://github.com/Camb-ai/MARS5-TTS. Watch our demo here:

  4. 8. Juni 2024 · Use Whisper to transcribe voice notes accurately & fast for free. Utilize ChatGPT to create concise, structured notes from the transcriptions effortlessly. Save your summarized notes in a note-taking app like Notion or in an all-in-one tool like AudioPen.

    • Dibakar Ghosh
    • Contributor
  5. 10. Juni 2024 · You can input a URL or upload a file to have Whisper Web create a transcription in a matter of seconds. Launched last week, the tool has been added to the open-source AI platform Hugging Face and...

  6. 19. Juni 2024 · In some cases, Whisper incorrectly detects the language, and instead of transcribing what they said, it translates the entire transcription into the language it detected incorrectly. It obviously understands what they said bec...

  7. 14. Juni 2024 · Our audio-visual Whisper-Flamingo outperforms audio-only Whisper on English speech recognition and En-X translation for 6 languages in noisy conditions. Moreover, Whisper-Flamingo is a versatile model and conducts all of these tasks using one set of parameters, while prior methods are trained separately on each language.