MODEL PROFILE · 模型档案

distil-large-v3.5

distil-whisper · distil-large-v3.5

Distil-Whisper is the knowledge-distilled version of OpenAI's Whisper-Large-v3, described in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. As the newest addition to the Distil-Whisper English family, Distil-Large-v3.5 maintains the high efficiency of its predecessors while delivering better performance. Compared to earlier models, Distil-Large-v3.5 has been trained on over 4× more diverse public data (98k hours) and uses a "patient" teacher with an extended training schedule and aggressive data augmentation (SpecAugment) during distillation. This results in enhanced robustness and accuracy compared to previous Distil-Whisper models, making it suitable as a drop-in replacement. 1. It offers a different balance between accuracy and efficiency, remains **~1.5x faster** than Whisper-Large-v3-Turbo while performing slightly better on short-form transcription and falling ~1% behind on long-form transcription. 2. It works perfectly as a draft model for **speculative decoding** with Whisper-Large-v3. By keeping the encoder frozen during training, we need to load just two extra decoder layers and forward the encoder only once. This achieves ~2x faster inference compared to Whisper-Large-v3 while maintaining identical outputs.

译文排队中,当前展示来源原文。

语音识别license:mit
上下文窗口来源未披露
输入 → 输出音频 → 文本
模型权重待核验
档案更新时间2026/11/2 19:16:37
OVERVIEW

模型介绍

Distil-Whisper is the knowledge-distilled version of OpenAI's Whisper-Large-v3, described in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. As the newest addition to the Distil-Whisper English family, Distil-Large-v3.5 maintains the high efficiency of its predecessors while delivering better performance. Compared to earlier models, Distil-Large-v3.5 has been trained on over 4× more diverse public data (98k hours) and uses a "patient" teacher with an extended training schedule and aggressive data augmentation (SpecAugment) during distillation. This results in enhanced robustness and accuracy compared to previous Distil-Whisper models, making it suitable as a drop-in replacement. 1. It offers a different balance between accuracy and efficiency, remains **~1.5x faster** than Whisper-Large-v3-Turbo while performing slightly better on short-form transcription and falling ~1% behind on long-form transcription. 2. It works perfectly as a draft model for **speculative decoding** with Whisper-Large-v3. By keeping the encoder frozen during training, we need to load just two extra decoder layers and forward the encoder only once. This achieves ~2x faster inference compared to Whisper-Large-v3 while maintaining identical outputs.

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

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预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

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。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

许可证:mit。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。