モデル情報

SAME-L

Stability AI · SAME-L

Latent representations are at the heart of the majority of modern generative models. In the audio domain they are typically produced by a neural-audio-codec autoencoder. In this work we introduce SAME (Semantically Aligned Music autoEncoder), a transformer-based autoencoder for stereo music and general audio that reaches a 4096x temporal compression ratio (roughly twice the current standard) while maintaining excellent reconstruction quality and strong downstream generative performance. We achieve this by combining a set of semantic regularisation approaches with phase-aware reconstruction losses. The architecture also delivers substantial computational cost benefits, through both its high compression ratio and its reliance on well-optimised transformer primitives. Two variants (a large SAME-L and a CPU-deployable SAME-S) are released in open-weights form. This model can be used with: 1. the `stable-audio-3` inference and fine-tuning library 2. the `stable-audio-tools` research library Our dataset consists of ~19,500 hours of licensed production audio from AudioSparx which includes a 66/25/9% mix of music, sound effects, and instrument stems.

翻訳待ちのため原文を表示しています。

license:other
コンテキスト長非公開
入力 → 出力待核验 → 待核验
モデルウェイト未確認
情報更新日時2026/11/2 19:16:43
OVERVIEW

モデル紹介

Latent representations are at the heart of the majority of modern generative models. In the audio domain they are typically produced by a neural-audio-codec autoencoder. In this work we introduce SAME (Semantically Aligned Music autoEncoder), a transformer-based autoencoder for stereo music and general audio that reaches a 4096x temporal compression ratio (roughly twice the current standard) while maintaining excellent reconstruction quality and strong downstream generative performance. We achieve this by combining a set of semantic regularisation approaches with phase-aware reconstruction losses. The architecture also delivers substantial computational cost benefits, through both its high compression ratio and its reliance on well-optimised transformer primitives. Two variants (a large SAME-L and a CPU-deployable SAME-S) are released in open-weights form. This model can be used with: 1. the `stable-audio-3` inference and fine-tuning library 2. the `stable-audio-tools` research library Our dataset consists of ~19,500 hours of licensed production audio from AudioSparx which includes a 66/25/9% mix of music, sound effects, and instrument stems.

主な特徴

特点摘要待依据来源补齐,不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

PRICING & COST

出典別料金と費用分析

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出典・チャネル入力出力課金単位収集日時
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月額の基本トークン費用を試算

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試算はキャッシュ割引、段階料金、マルチモーダル追加料金、検索、税金、追加推論出力を含みません。

INDEPENDENT BENCHMARKS

公開評価と実行設定

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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よくある質問

オープンウェイトなら無料で商用利用できますか?

必ずしもそうではありません。バージョン別のライセンス、商用条件、利用方針を確認してください。自前運用には計算資源と保守費用も必要です。

これはリアルタイムの請求料金ですか?

収集日時付きの料金スナップショットです。最新のバージョン、地域、割引、課金方式は各チャネルで確認してください。

出典と追跡記録

许可证:other。模型介绍与第三方评测均以原来源的具体版本和方法为准。