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Open nowPosted 8 hours ago

音频生成算法实习生（Audio Diffusion）

shengshu/index99 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now音频生成算法实习生（Audio Diffusion）shengshu/index · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

12.4% of postings close within 7 days. Measured by our own scanner across the market. shengshu/index postings stay open a median of 4 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.4%7 days
  4. 20.0%14 days
  5. 35.5%30 days
  6. 58.6%60 days
This job: posted 8 hours ago

shengshu/index median: 4 days open

The posting

我们希望寻找一位有音频生成模型研发经验的伙伴，参与音频生成模型的设计、训练与优化，提升生成质量、可控性和推理效率。 岗位职责： 1、研发基于 Diffusion / Flow Matching 的音频生成模型，参与模型架构设计、训练目标设计和训练策略优化。 2、探索文本等条件驱动的音频生成，提升生成内容与条件的一致性，以及音频的自然度、清晰度和时间结构。 3、参与音频数据的清洗、筛选、标注及训练集构建，通过数据与模型的迭代改善生成效果。 4、建立音频生成的评估与分析方法，结合客观指标和主观听测定位问题，并推动改进。 5、跟踪音频生成领域的前沿研究，将有价值的方法转化为可验证的实验和模型能力。

职位要求 1、有基于 Diffusion 的音频生成项目经验，并实际参与过核心模型的训练与优化。 在语音、音乐、音效等至少一个方向有深入实践。 2、理解 Diffusion 的训练目标、噪声调度、采样方法及条件控制机制；了解 Flow Matching 等相关方法。 3、熟悉音频生成中的表示与建模方式，例如 waveform、spectrogram、audio VAE 或 neural audio codec，理解相关设计对音质、序列长度和计算成本的影响。 4、熟练使用 PyTorch，具备扎实的模型实现、训练调试和实验分析能力，能够独立完成方案实现与效果验证。 5、能结合听感、评估指标与消融实验分析模型问题，并清晰说明自己的技术选择、实验结论和实际贡献。

加分项 1、有较大规模音频模型预训练、分布式训练或高质量音频数据建设经验。 2、有少步生成、模型蒸馏、实时或流式音频生成经验。 3、有音频编辑、局部重生成、细粒度条件控制或音视频联合建模经验。 4、有相关论文、开源项目或可展示的音频生成作品。

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