モデル情報

musicgen-stereo-medium

facebook · musicgen-stereo-medium

We further release a set of stereophonic capable models. Those were fine tuned for 200k updates starting from the mono models. The training data is otherwise identical and capabilities and limitations are shared with the base modes. The stereo models work by getting 2 streams of tokens from the EnCodec model, and interleaving those using the delay pattern. Stereophonic sound, also known as stereo, is a technique used to reproduce sound with depth and direction. It uses two separate audio channels played through speakers (or headphones), which creates the impression of sound coming from multiple directions. MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio.

翻訳待ちのため原文を表示しています。

音声合成license:cc-by-nc-4.0
コンテキスト長非公開
入力 → 出力テキスト → 音声
モデルウェイト未確認
情報更新日時2026/11/2 19:15:50
OVERVIEW

モデル紹介

We further release a set of stereophonic capable models. Those were fine tuned for 200k updates starting from the mono models. The training data is otherwise identical and capabilities and limitations are shared with the base modes. The stereo models work by getting 2 streams of tokens from the EnCodec model, and interleaving those using the delay pattern. Stereophonic sound, also known as stereo, is a technique used to reproduce sound with depth and direction. It uses two separate audio channels played through speakers (or headphones), which creates the impression of sound coming from multiple directions. MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio.

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:cc-by-nc-4.0。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。