NVIDIA拥有发布 Nemotron 3.5 Trancis，一种开放权重语音LLM为转录而建。 一个型号处理40种语言，录音时间长达1小时。 文本指令选择每个请求的输出样式。 选项包括逐字记录、书面形式、演讲者属性、背景和翻译产出。 对于开发者来说，它可以替换单独的ASR，对齐化和对齐工具。
TL；数据
- 大小：共34B，约4.3B活性参数；65 536个上下文；音频最多1小时。
- 运行在：1x H100 80 GB在BF16. FP8(约35 GB)和NVFP4(约21 GB)检查站；NVFP4运行在RTX 5090上。 GGUF文件的目标是NeMo-Speech.cpp.
- 性能：英语和多语种开放ASR Leaderboards上的最佳开放权重模型；顶级商业API仍然领先。
- 最佳：4.71%在9套公开的英语开放ASR中表示WER(下一个开放型号：4.94%).
- 最糟糕的是：34.95%的cpCER在Ali Meeting 普通话的远野会议上。
- 底线：最好的事：1个开放型号，覆盖文字记录，扬声器标签，时间戳和翻译。 最糟的是：只下线，普通话会议准确性差。
什么是尼莫特龙3.5转写？
尼莫特龙 3.5 Transis是一个来自NVIDIA的语音增强语言模型(SALM)，将音频和指令转化为文本。 一个音频编码器输入嵌入到LLM解码器中，这使得转录可以迅速。 NVIDIA的技术研究报告认为没有一份正确的记录。 数字，填充器和域名术语取决于谁读取输出。 NVIDIA将模型定位为抄写专家，而非通用助理。
建筑如何运作？
它有三个部分：
- 平行专家编码器：可装火车的0.6B变形器ASR编码器运行在冷冻的尼莫特龙3 Diarization模型旁。 潜水器追踪到8个喇叭协会的演讲活动每80分钟增加一次。 LLM因此看到谁在何时说话，没有架构变化。
- LLM骨干：尼莫特龙3.5闪电30B-A3B，一种混合的曼巴-Transformer MoE. 它有52层：23 Mamba-2,23 MoE，只有6个关注层。 教育部每层路线为128名专家中的6名加上1名共享专家。 很少的注意层保持 KV 缓存小于小时音频。
- 添加：用于投机解码的共享多键预测层草稿。 一个50M参数的CTC适配器将完成的记录稿与音频对齐，用于文字时间戳。
型号名称称 30B-A4B，但检查站持有约34B参数。 额外重量来自1.3B MTP模块和音频组件。
在256个H100GPU上使用1个受监督的微调级培训，共28,500步。 随后是短暂的RL阶段：400个普惠制O更新，奖励1减WER. 该模型看到了大约1.72M独特的音频小时，或者说6.5M小时的重复。
基准测试的得分如何？
以下所有数字均由NVIDIA在技术报告中自行报告。
- 英语开放ASR(9套公开版):4.71%为WER。 接下来的公开赛模式Qwen3-ASR-1.7B，得分4.94%. 7个商业系统仍然领先，由OtterAI语音V3.1控制，占3.88%.
- 多语言开放ASR(7种语言):4.62%为WER，而Qwen3-ASR-1.7B为6.49%。ElevenLabs Scribe v2达到3.56%。
- 长式ASR:10.98%为WER,Parakeet-TDT-0.6B-v3为13.37%。
- 多语种，全会话：15.80%为CpWER,16.83%为DiCoW v3.3与DiariZen。 NVIDIA 称是这里第一个击败级联系统的SALM.
- 背景 ASR：一个实体在背景ASR-Bench上列出削减命名实体WER，从12.21%到2.20%.
你怎么部署尼莫特龙3.5转写器？
BF16,FP8,NVFP4和GGUF中的重量级舰只。 一个DFlash2草案模型是可选的。 OpenMDW-1.1许可允许商业使用。
运行时段包括VLLM 0.32.0，带有NeMo Speech插件，变形器和SGLang-Omni 02.0. vLLM曝光OpenAI兼容API.
NVIDIA型号的主要速度来自它的Nemotron Translatis NIM. 与NVFP4和DFlash2,1 RTX PRO 6000 Blackwell Server Edition转录了每秒2,238秒的音频。 它在0.13秒内返回30秒的剪辑。 有个捕捉点：那些服务优化还没有开源。 NVIDIA预计发布日vLLM路径大约慢2x. 在BF16的1H100与MTP2,NVIDIA估计约410RTFx.
对于微调，NVIDIA测试了17项任务。 单机GB300每架用0.5至2.3GPU小时左右。
它与其他开放的ASR模型有什么比较？
*竞争对手在目前9集领导板上的得分来自NVIDIA的报告，引用了Hugging Face领导板从2026年10月起的成绩。 领导牌套装自竞争者发布自己的牌后有所改变，因此2个WER列不能互换。
关键外卖
- 一个开放模式处理40种语文的文字记录、扬声器、时间戳和翻译。
- 4.71%的英语WER是最佳的开放权重分数；商业API仍然领先。
- 约4.3B活性参数；BF16适合1H100,NVFP4适合RTX 5090.
- 一个实体列出削减命名实体WER从12.21%到2.20%.
- 只下线；普通话会议和密码交换仍然是薄弱环节。
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NVIDIA has released Nemotron 3.5 Transcribe, an open-weights speech LLM built for transcription. One model handles 40 languages and recordings up to 1 hour. A text instruction picks the output style for each request. Options include verbatim, written form, speaker-attributed, contextual and translated output. For developers, it can replace separate ASR, diarization and alignment tools.
TL;DR
- Size: 34B total and about 4.3B active parameters; 65,536-token context; audio up to 1 hour.
- Runs on: 1x H100 80 GB in BF16. FP8 (about 35 GB) and NVFP4 (about 21 GB) checkpoints exist; NVFP4 runs on an RTX 5090. GGUF files target NeMo-Speech.cpp.
- Performance: Best open-weights model on the English and multilingual Open ASR Leaderboards; top commercial APIs still lead.
- Best: 4.71% mean WER on the 9 public English Open ASR sets (next open model: 4.94%).
- Worst: 34.95% cpCER on AliMeeting Mandarin far-field meetings.
- Bottom line: Best thing: 1 open model covers transcripts, speaker labels, timestamps and translation. Worst thing: offline only, with weak Mandarin meeting accuracy.
What is Nemotron 3.5 Transcribe?
Nemotron 3.5 Transcribe is a speech-augmented language model (SALM) from NVIDIA that turns audio and instructions into text. An audio encoder feeds embeddings into an LLM decoder, which makes transcription promptable. NVIDIA’s technical research report argues there is no single correct transcript. Numbers, fillers and domain terms depend on who reads the output. NVIDIA positions the model as a transcription specialist, not a general-purpose assistant.
How does the architecture work?
It has three parts:
- Parallel Expert Encoder: a trainable 0.6B Transformer ASR encoder runs beside a frozen Nemotron 3 Diarization model. The diarizer tracks up to 8 speakers. Its speaker activity is added to the ASR features every 80 ms. The LLM therefore sees who spoke when, without architecture changes.
- LLM backbone: Nemotron 3.5 Lightning 30B-A3B, a hybrid Mamba-Transformer MoE. It has 52 layers: 23 Mamba-2, 23 MoE and only 6 attention layers. Each MoE layer routes to 6 of 128 experts plus 1 shared expert. Few attention layers keep the KV cache small for hour-long audio.
- Add-ons: a shared multi-token prediction (MTP) layer drafts tokens for speculative decoding. A 50M-parameter CTC adapter aligns the finished transcript to audio for word timestamps.
The model name says 30B-A4B, but the checkpoint holds about 34B parameters. The extra weight comes from the 1.3B MTP module and the audio components.
Training used 1 supervised fine-tuning stage on 256 H100 GPUs for 28,500 steps. A short RL stage followed: 400 GSPO updates with a reward of 1 minus WER. The model saw about 1.72M unique hours of audio, or 6.5M hours with repeats.
How does it score on benchmarks?
All numbers below are self-reported by NVIDIA in the technical report.
- English Open ASR (9 public sets): 4.71% mean WER. The next open model, Qwen3-ASR-1.7B, scores 4.94%. Seven commercial systems still lead, topped by OtterAI speech-v3.1 at 3.88%.
- Multilingual Open ASR (7 languages): 4.62% mean WER, versus 6.49% for Qwen3-ASR-1.7B. ElevenLabs Scribe v2 reaches 3.56%.
- Long-form ASR: 10.98% mean WER, versus 13.37% for Parakeet-TDT-0.6B-v3.
- Multi-speaker, full sessions: 15.80% mean cpWER, versus 16.83% for DiCoW v3.3 with DiariZen. NVIDIA says it is the first SALM to beat cascaded systems here.
- Contextual ASR: an entity list cuts named-entity WER on ContextASR-Bench from 12.21% to 2.20%.
How do you deploy Nemotron 3.5 Transcribe?
Weights ship in BF16, FP8, NVFP4 and GGUF. A DFlash2 draft model is optional. The OpenMDW-1.1 license allows commercial use.
Runtimes include vLLM 0.32.0 with the NeMo Speech plugin, Transformers and SGLang-Omni 0.2.0. vLLM exposes an OpenAI-compatible API.
NVIDIA model’s main speed comes from its Nemotron Transcribe NIM. With NVFP4 and DFlash2, 1 RTX PRO 6000 Blackwell Server Edition transcribes 2,238 seconds of audio per second. It returns a 30-second clip in 0.13 seconds. There is a catch: those serving optimizations are not open source yet. NVIDIA expects the release-day vLLM path to be roughly 2x slower. On 1 H100 in BF16 with MTP2, NVIDIA estimates about 410 RTFx.
For fine-tuning, NVIDIA tested 17 tasks. Each took about 0.5 to 2.3 GPU-hours on a single GB300.
How does it compare with other open ASR models?
*Competitor scores on the current 9-set leaderboard come from NVIDIA’s report, citing Hugging Face leaderboard results from October 2026. The leaderboard set changed since the competitors published their own cards, so the 2 WER rows are not interchangeable.
Key Takeaways
- One open model handles transcripts, speakers, timestamps and translation across 40 languages.
- 4.71% English WER is the best open-weights score; commercial APIs still lead.
- About 4.3B active parameters; BF16 fits 1 H100, NVFP4 fits an RTX 5090.
- An entity list cuts named-entity WER from 12.21% to 2.20%.
- Offline only; Mandarin meetings and code-switching remain weak spots.
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Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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