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Model infoModelsSnapshot: 15 Oct 2026

Meta / Audiobox TTA-RAG

Audiobox TTA-RAG · text retrieval / AudioSet / 3

Sound-generation research model conditioned on text and retrieved audio examples.

restrictedClosed weightsSound effects generation
Official documentation (opens in a new tab)Reviewed 13 Oct 2026
9 of 21 specification fields collected
Context windowNot collectedAwaiting source review
Maximum outputNot collectedAwaiting source review
Input billingNot collectedMeta · research reference
Output billingNot collectedNo inferred price

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
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Reasoning
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Tool calling
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Structured output
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Streaming
Not collected
Batch processing
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Fine-tuning
Not collected
Knowledge cutoff
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Release date
2024-11-07Source for Release date (opens in a new tab)

Research disclosure date, not general API or weight availability.

Parameters
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Architecture
Audiobox flow-matching generator with Encodec features, T5-base text encoder and text-conditioned retrieval-audio encoderSource for Architecture (opens in a new tab)
Languages
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Context caching
Not collected

No model-specific inference service or discounted prompt-cache contract is established for these research weights.

Published training data
AudioCaps official training split: more than 45,000 captioned audio examples, approximately ten seconds eachSource for Published training data (opens in a new tab)
retrievalEncoder
Three transformer layers, 16 heads, 1024-dimensional hidden state, 4096-dimensional feed-forward layerSource for retrievalEncoder (opens in a new tab)
trainingRetrieval
Text-to-audio retrieval from AudioSet; 3 examplesSource for trainingRetrieval (opens in a new tab)
retrievalFeatures
CLAP embeddings for retrieval; ranked Encodec audio features for generator conditioningSource for retrievalFeatures (opens in a new tab)
evaluationRetrieval
Text-to-audio retrieval from AudioSet for the zero-shot/few-shot studiesSource for evaluationRetrieval (opens in a new tab)

Access & pricing

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Meta · research reference

Provider source (opens in a new tab)
API model ID
Not collected

Descriptive 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 INPUTProcess & retain

The system processes eligible input and retains reusable state.

02 / LATER REQUESTCheck available state

Reuse depends on matching input, session state and the cache policy.

03 / CACHE HITReuse & generate

Reusing state can reduce repeated work. Any charges follow the billing rules below.

Meta · research reference

Not verified

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How it works
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Minimum reusable input
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Lifetime & refresh
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No published service cache policy for this research record.

How to enable it
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Usage fields
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No published service cache policy for this research record.

Billing rules
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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

Zero-shot CLAP alignment

Meta research authors · provider-reported
21.19% CLAP similarity

Table 1; 5546 AudioSet-derived examples with events absent from AudioCaps. 3 retrieved examples, Text-to-audio / AudioSet during training, text-to-audio / AudioSet at inference. CLAP is a similarity score, not accuracy.

Evaluation source (opens in a new tab)

Zero-shot FAD

Meta research authors · provider-reported
8.68FAD

Table 1; 5546 AudioSet-derived examples with events absent from AudioCaps. 3 retrieved examples, Text-to-audio / AudioSet during training, text-to-audio / AudioSet at inference. CLAP is a similarity score, not accuracy.

Evaluation source (opens in a new tab)

Few-shot CLAP alignment

Meta research authors · provider-reported
24.79% CLAP similarity

Table 1; 601 AudioSet-derived examples with events appearing fewer than three times in AudioCaps. 3 retrieved examples, Text-to-audio / AudioSet during training, text-to-audio / AudioSet at inference. CLAP is a similarity score, not accuracy.

Evaluation source (opens in a new tab)

Few-shot FAD

Meta research authors · provider-reported
2.85FAD

Table 1; 601 AudioSet-derived examples with events appearing fewer than three times in AudioCaps. 3 retrieved examples, Text-to-audio / AudioSet during training, text-to-audio / AudioSet at inference. CLAP is a similarity score, not accuracy.

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

TTA-RAG paper first disclosed7 Nov 2024Source (opens in a new tab)
Revised paper6 Jun 2025Source (opens in a new tab)

Sources reviewed