Preeti
Kyutai has introduced Moshi, a groundbreaking real-time native multimodal foundation model that surpasses OpenAI’s GPT-4o. Moshi can understand and express emotions, speak with different accents, and handle two audio streams simultaneously. It was fine-tuned through 100,000 synthetic conversations and achieves an impressive end-to-end latency of 200 milliseconds. Kyutai emphasizes responsible AI use by incorporating watermarking to detect AI-generated audio.
Moshi is powered by a 7-billion-parameter multimodal language model that processes speech input and output with a two-channel I/O system. It was trained on synthetic data and can be fine-tuned with minimal audio. The deployment of Moshi showcases its efficiency, supporting various backends and benefiting from optimizations in inference code.
Kyutai plans to release a technical report and open model versions, including the 7B model and the audio codec. Future iterations like Moshi 1.1, 1.2, and 2.0 will refine the model based on user feedback. Moshi is an open-source model, inviting collaboration and innovation for widespread adoption.
In conclusion, Moshi represents the potential of small teams to achieve remarkable advancements in AI technology. It offers new opportunities for research assistance, language learning, and more, with on-device deployment and flexibility. The open-source nature of Moshi encourages collaboration and ensures accessibility to all users.
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Source link: https://www.marktechpost.com/2024/07/03/kyutai-open-sources-moshi-a-real-time-native-multimodal-foundation-ai-model-that-can-listen-and-speak/?amp
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