使用 🤗 Transformers 優化 Bark 語音生成模型
Original: Optimizing Bark using 🤗 Transformers
Bark is an innovative text-to-audio model developed by the team at Suno. It can generate not only high-quality, multilingual speech, but…
Bark 是 Suno 推出基於 Transformer 的文字轉語音（TTS）與音訊生成模型。由於其包含多個子模型，推理時極耗資源。本文詳細說明如何透過 Hugging Face Transformers 整合的優化技術，包括啟用半精度（fp16）、智慧 CPU 卸載（CPU Offloading）、PyTorch 2.0 的 SDPA（縮放點積注意力）以及 `torch.compile`，在不犧牲音質的前提下，將 VRAM 佔用降低 50% 以上，並顯著提升生成速度。
Bark is an innovative text-to-audio model developed by the team at Suno. It can generate not only high-quality, multilingual speech, but also background music, ambient sound effects, and even non-verbal human sounds such as laughter and sighs. However, Bark's architecture is quite complex — it is composed of four separate models chained in sequence: a Text Encoder, a Coarse Acoustics model, a Fine Acoustics model, and an EnCodec decoder. As a result, Bark consumes a large amount of GPU memory (VRAM) and runs slowly by default.
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