Merin Mandanna @_SlackOverflow

Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.

@peegeekay @TheKenWeb 👀👀

@pjux I'm happy being the only friend who gets this privilege.

Muse Voice Transcribe is now available as a plugin in LiveKit Agents. Start building: docs.livekit.io/agents/models/…

LiveKit @livekit

a month ago

Muse Voice Transcribe from @AIatMeta @MetaforDevs is now live! You can now use Meta's new streaming STT model in LiveKit Agents, with real-time diarization for 20+ speakers. Try Muse in LiveKit Agents: docs.livekit.io/agents/models/…

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Muse Voice Transcribe was built for real-time voice agents, combining accuracy and low latency with diarization, keyword biasing, endpointing and mixed-language transcription. Great to see it achieve the lowest semantic WER in Pipecat’s open-source benchmark 👏

kwindla @kwindla

a month ago

Pipecat v1.9.0 is out today, with support for @AIatMeta's new Muse Voice Transcribe model. Muse Voice Transcribe is a streaming speech-to-text model. It has the best (lowest) semantic word error rate of any model we've tested. We maintain a public, open source test suite called

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Pipecat v1.9.0 is out today, with support for @AIatMeta's new Muse Voice Transcribe model. Muse Voice Transcribe is a streaming speech-to-text model. It has the best (lowest) semantic word error rate of any model we've tested. We maintain a public, open source test suite called the Pipecat STT benchmark. This benchmark measures how well an STT model performs in a voice agent pipeline. We calculate a "semantic word error rate" across 1,000 speech input fragments. Semantic WER ignores small differences in transcription that don't impact an LLM's understanding of user speech, and in our experience is a better proxy for STT model accuracy than the simpler WER algorithms used in most speech recognition benchmarks. The other critical metric for a voice agent is latency. We measure latency as "time to final segment" of the transcription. You can think of this as how long it takes for the STT model to deliver a complete text transcription after the user finishes speaking. Muse Voice Transcribe's TTFS is very good, though its long tail TTFS is a bit higher than we'd like to see. (The Meta team says they are optimizing for natural, model-driven endpointing, which certainly makes sense.) The P50 TTFS is 392 milliseconds and the P95 is 1,292 milliseconds. We'd like to see a P50 under 300ms and a P95 as close to that as possible! Note, though, that this P50 number is as good as we've seen for any initial release of a new ASR model. The model also supports speaker labels (diarization) and keyword biasing, has built-in endpointing, and can transcribe mixed-language speech. Kudos to the Meta team. We love seeing new models that are strong performers in realtime voice agent pipelines.

@NetflixIndia you know why we're asking, but you're aware today is the 10th, right? It's the 10th of September and it's past 7pm. 🥹

@snegahhh ahahahaha you said it! 🤣

Underrated tweet 💥

Sidu Ponnappa @ponnappa

a month ago

the enterprise will the last bastion against the singularity

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Introducing Muse, the personal agent that understands your goals and works 24/7 to get things done for you.

Meta has released Muse Voice Transcribe, taking the #1 spot for Final Transcript accuracy on AA-WER Streaming with 3.1% WER at 0.16s after end of speech Muse Voice Transcribe is the first streaming Speech to Text model developed by Meta Superintelligence Labs. Meta states that the model was trained on more than 70 languages, with 25 extensively verified, and supports audio inputs exceeding one hour without required post-processing. It processes audio in 80ms chunks and is available through the Meta Model API, Meta AI for Mac and Muse Code. Key takeaways ➤ Final Transcript: Muse Voice Transcribe achieves 3.1% WER at 0.16s after end of speech. It is more accurate and faster than Cartesia Ink-2 (semantic endpoints) at 3.4% and 0.43s, and more accurate but slower than Cartesia Ink-2 (external endpoints) at 4.0% and 0.07s. It is also more accurate, though slightly slower, than ElevenLabs Scribe v2 Realtime at 3.6% and 0.14s. ➤ First Partial Transcript: The model achieves 3.6% WER at 0.13s, just ahead of ElevenLabs Scribe v2 Realtime on accuracy and latency. It is more accurate and faster than Cartesia Ink-2 (semantic endpoints) at 4.9% and 0.17s, and more accurate but slower than Cartesia Ink-2 (external endpoints) at 4.0% and 0.07s. ➤ Price: Muse Voice Transcribe costs $0.18 per hour, or $3 per 1,000 minutes. This is below Cartesia Ink-2 at $4 and less than half the $6.50 charged for ElevenLabs Scribe v2 Realtime and Deepgram Flux. See more details below ⬇️

1/ today we're rolling out muse voice transcribe, our first real-time audio perception model - SOTA in streaming speech-to-text. also handles speaker diarization and endpointing natively in a single model.

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Muse Voice Transcribe is MSL's first real-time audio perception model -- rolling out today. SOTA in streaming speech-to-text, it handles speaker diarization, and endpointing natively in a single model.

@ponnappa Amaze, thank you!

@ponnappa Oh, yum! Looks delightful. Where? 👀

This race weekend is gonna make me weep.

The Race @wearetherace

2 months ago

Ferrari's new race suits for Monza in honour of Michael Schumacher 🤩

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Muse Image is now available on Meta Model API and priced for production volumes at $0.01/image. It’s an agentic image model that reasons before it renders. Each call searches the web to refine the output through iterative passes and evaluates the prompt for precise elements like charts and QR codes. Text-to-image, single-image and multi-image editing, and multi-reference composition all live in one model, so there's no multi-step pipeline to stitch together. Start building → bit.ly/4gsPeOV

Today we're also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally. Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model. Meta is a strong supporter of open source and I'm proud of these releases. Congrats to @alexandr_wang and the MSL team for all your great work on these models.

Meta has released Muse Spark 1.2. It's their third release in four months and scores 54 on the Artificial Analysis Intelligence Index, significantly improving agentic knowledge work capabilities over prior releases and putting Meta next to SpaceXAI in a tie for third place amongst US labs Muse Spark 1.2 (xhigh) lands at 54, up 3 points from Muse Spark 1.1 (51) and 11 points from Muse Spark 1.0 (43, April). It enters effectively tied with GPT-5.5 (xhigh, 55) and Grok 4.5 (high, 54), narrowly behind current frontier models Claude Opus 5 (max, 61), Claude Fable 5 (max w/ fallback, 60), GPT-5.6 Sol (max, 59), and Kimi K3 (max, 57) Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release! Key Takeaways: ➤ Muse Spark 1.2 gets closer to the frontier on agentic knowledge work. At Muse Spark 1.1's launch, we noted agentic knowledge work as its clearest gap; Muse Spark 1.2's gains help to close this. Its GDPval-AA v2 Elo rose 260 points to 1631, #5 among all models we have benchmarked and ahead of Claude Opus 4.8 (max, 1588). Terminal-Bench 2.1 gained 2 points (78% to 80%), and Tau3-Bench Banking rose 2 points (25% to 27%) ➤ Among the most cost-efficient models at its intelligence level. Muse Spark 1.2 costs $0.40 per Intelligence Index task at Meta's unchanged $1.25/$4.25 per 1M token pricing, with only Grok 4.5 (high, $0.37) and GPT-5.6 Sol (medium, $0.39) cheaper in its intelligence cluster - GPT-5.6 Terra (max, $0.51), Kimi K3 (max, $0.86), and GPT-5.5 (xhigh, $1.18) all cost more per task. The cost increase over Muse Spark 1.1 ($0.29 per task) is driven by increased token usage per Intelligence Index task ➤ AA-Omniscience abstention rate increases. The score rose from 18 to 22 as the hallucination rate fell 10 points (38% to 28%) and the attempt rate dropped from 82% to 67%. This heavy abstention (not answering questions when unsure) now drives both the low hallucination rate and a lower accuracy (41% to 38%) ➤ Scientific Reasoning results remain largely unchanged. CritPt notably gained 3 points (15% to 18%), while SciCode fell 2 points (58% to 56%), and Humanity's Last Exam fell 1 point (45% to 44%) Other model details: ➤ Context window: 1M tokens, unchanged from Muse Spark 1.1 ➤ Pricing: unchanged from Muse Spark 1.1: $1.25/$4.25 per 1M input/output tokens, with cache hits discounted to $0.15 per 1M ➤ Availability: Meta's first-party API at launch

Releasing Muse Code in beta today. It's a terminal coding agent that takes on complete software engineering tasks across large repos: planning changes, writing code, validating the results. Powered by Muse Spark 1.2, a coding-focused model update.