All models/ Meta / Audiobox TTA · AudioCaps research baseline Meta / Audiobox TTA-RAG
Audiobox TTA · AudioCaps research baseline Text-to-sound model trained for the TTA-RAG paper’s AudioCaps comparison, without retrieved audio conditioning.
restricted Closed weights Sound effects generation
Context window Not collected Awaiting source review
Maximum output Not collected Awaiting source review
Input billing Not collected Meta · research reference
Output billing Not collected No inferred price
Specifications Access & pricing Caching Evaluation Sources & lifecycle
Specifications Provider-documented facts. “Not collected” means we have not verified that field yet, not that the feature is unsupported.
Context / input limit Not collected
Maximum output Not collected
Reasoning Not collected
Tool calling Not collected
Structured output Not collected
Streaming Not collected
Batch processing Not collected
Fine-tuning Not collected
Knowledge cutoff Not collected
Parameters Not collected
Languages Not collected Context caching Not collected No model-specific inference service or discounted prompt-cache contract is established for these research weights.
Access & pricing Prices belong to an endpoint and billing tier. A provider listing does not guarantee access for every account or region.
API model ID Not collectedDescriptive catalog identity, not a verified selectable API ID.
Access Author-hosted paper and generated audio examples; original checkpoints and a production inference API are not supplied.
Regions Research deployment only; public availability and residency are not established Endpoint pricing has not been collected. Check the provider for applicable rates.
Data & enterprise terms The cited research release does not supply a current production processing/retention agreement. Research access or an expired demonstration is not a production API entitlement.
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Caching Caching can reuse earlier input processing. Availability, lifetime and billing depend on the endpoint. A cache hit still generates a new response.
01 / INITIAL INPUT Process & retain The system processes eligible input and retains reusable state.
02 / LATER REQUEST Check available state Reuse depends on matching input, session state and the cache policy.
03 / CACHE HIT Reuse & generate Reusing state can reduce repeated work. Any charges follow the billing rules below.
Meta · research reference Not verified No published service cache policy for this research record.
How it works Not collected No published service cache policy for this research record.
Minimum reusable input Not collected No published service cache policy for this research record.
Lifetime & refresh Not collected No published service cache policy for this research record.
How to enable it Not collected No published service cache policy for this research record.
Usage fields Not collected No published service cache policy for this research record.
Billing rules Not collected No published service cache policy for this research record.
Limits & data boundaries Not collected No published service cache policy for this research record.
No verified cache rate for this endpoint. An empty rate is not a zero charge.
What the measurements say
AudioCaps test FAD Meta research authors · provider-reported 2.54FAD Table 2 baseline row 4; trained on the AudioCaps training split without retrieval. Not the earlier Audiobox Sound paper’s best-of-32 FAD.
Evaluation source (opens in a new tab) Latency and throughput depend on the host, request size, reasoning settings and load. There is no measured speed or global usage figure in this record.
The record behind the model Why it is included The Meta/University of Texas at Dallas authors publish the TTA-RAG paper and a linked audio-sample site. The reviewed artifacts distribute example audio, not the original baseline or retrieval-adapted checkpoints, and supply no callable API. Public CLAP, Encodec and T5 components are not the trained TTA-RAG system.
Classification evidence (opens in a new tab) Lifecycle dates Sources reviewed Model notes Nine current/historical Muse identities plus closed-weight research models, trained variants and named pipeline components, including 2026 Text-Audiobox research. API availability is explicitly separated from research references. Regional/private models, historical successors and hosted partner routes remain under review.
Research reference only. No public API selector or model-specific price has been verified. Current Muse tariffs, token limits and processing policies do not apply to this record.
The training examples’ approximate ten-second duration is not a documented output limit. A numeric sample rate, full parameter count and API quota are not inferred from base-model familiarity.
Research retrieval context is audio, not a priced chat prompt or a standard LLM text context window.
AudioCaps in-domain, few-shot and zero-shot results use different evaluations. Higher CLAP similarity does not guarantee lower FAD or KL divergence.
This paper explicitly trains its own TTA baseline. It is not silently merged with the earlier Audiobox Sound checkpoint trained on a different dataset.
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