Nvidia 发布全能自适应 Nemotron 3.5 音频转录和翻译模型
Nvidia Corp. 今天的发布是多语种录音抄写和翻译的领跑板获奖公开人工智能模型，同时还有微调 Nemotron 3.5 Trancistic的开放数据管道。
传统的转录AI模型遵循一个工作流程，该工作流程检测语音，转录，识别和标签不同的发音，然后回答有关转录的问题。 Transis用一个单模式的通行证来做这一切，能够消化和回答大约100小时的音频问题，使用40种语言，最多可以给8个发言者贴上标签。
它是一个开放重量300亿参数的专家混合模型，有40亿活性参数。 这意味着它可以适应合理小的硬件，尽管更强大的硬件让它更快地转录。
对于它的大小，Transis的出击比它的重量要高得多，它可以根据压缩情况从边缘到数据中心的任何地方运行。 该模型运行在一个单一的Nvidia图形处理单元上，从一个Jetson AGX Thor，一个边缘计算机机器人大脑，或者DGX Spark，一个桌面AI超级计算机，一直到一个布莱克威尔数据中心GPU. Nvidia 称 a Blackwell Server Edition GPU，配有NVFP4精度和四分之一型号压缩在三分钟以内处理100小时音频。
据该公司称，Transis 达到是将文字正确化的高精度基准测试，有30.8%的出错率出箱出错率，在仔细的提示下可以降低到20.8%. 对专业工作进行了精细调整，例如，关于Eka Care India医学演讲的基准测试，该模型打出11.8%的字误率，大幅降低。
用户可以指导抄录分析或用提示来提问。 同一个检查站可以将录音转换成不同的输出，并有单独的提示。
例如，用户可以指示该模型使用“将录音和给每个发言者贴上标签”，或“将录音译成英文”，或“在本次会议上作出了哪些决定？” 或某种组合。 用户还可以为模型提供重要的术语或对话框来帮助其在转录前更好地理解对话，提高准确性。
为了构建模型，Nvidia将Granary v1扩展为Granary v2，该公司庞大的开源公共多语言语音AI数据集。 由Nvidia NeMo Curator所建，v2 扩展在上一代Granary上占有显著地位。 该公开名单涵盖48种语言的463,062小时和16个源氏体，高于Granary v1的25种语言。 这些数字说明了数据集的释放情况，而不是Transis的全部训练混合物或其取样权重。
Granary v2为每张录音以干净的逐字记录风格生成了3个记录誊本，可以从Transis中快速请求。 一者提供一切，包括“ums”和“ahs”。 另一种说法则将完整的措辞简化为数字，如将数字拼写为符号。 第三取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取取。
所有这一切让开发者利用管道数据将不同语言的抄录修改为可读格式和微调模型输出。
Translax作为Hugging Face demo提供，开发者可以下载一个检查点，并适应微调的域名专业知识。
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Nvidia releases all-in-one adaptable Nemotron 3.5 audio transcription and translation model
Nvidia Corp. today released a leaderboard-winning open artificial intelligence model for multilingual audio transcription and translation, along with an open data pipeline for fine-tuning Nemotron 3.5 Transcribe.
Traditional transcription AI models follow a workflow that detects speech, transcribes it, identifies and labels different speakers, then answers questions about the transcription. Transcribe does all of this in a single model pass and is capable of digesting and answering questions about 100 hours of audio, in 40 languages and can label up to eight speakers.
It’s an open-weight, 30-billion parameter mixture-of-experts model with 4 billion active parameters. That means it can fit on reasonably small hardware, though more powerful hardware lets it transcribe faster.
For its size, Transcribe punches way above its weight and it can run anywhere from the edge to the data center depending on compression. The model runs on a single Nvidia graphics processing unit, from a Jetson AGX Thor, an edge computer robot brain, or DGX Spark, a desktop AI supercomputer, all the way to a Blackwell data-center GPU. Nvidia said a Blackwell Server Edition GPU with NVFP4 precision and one-quarter model compression processes 100 hours of audio in under three minutes.
According to the company, Transcribe hits a high accuracy benchmark for getting words right, with 30.8% error rates out of the box, which can be reduced to 20.8% with careful prompting. Fine-tuned for specialized work, for example, on the Eka Care Indian medical speech benchmark, the model hit 11.8% word error rate, a substantial reduction.
Users can guide transcription analysis or ask questions with prompts. The same checkpoint can turn a recording into different outputs with separate prompts.
For example, a user could instruct the model with, “Transcribe the recording and label each speaker,” or “Translate this audio into English,” or “What decisions were made in this meeting?” or some combination. Users can also provide the model with important jargon or dialogue to help it better understand the conversation before transcribing, improving accuracy.
To build the model, Nvidia extended Granary v1 into Granary v2, the company’s massive open-source public multilingual speech AI dataset. Built with Nvidia NeMo Curator, v2 expands significantly on the previous generation of Granary. The public manifest covers 463,062 hours across 48 languages and 16 source corpora, up from 25 languages in Granary v1. These figures describe the dataset release, not Transcribe’s full training mixture or its sampling weights.
Granary v2 generates three transcripts for each recording in clean verbatim styles that can be requested from Transcribe with a prompt. One provides everything, including “ums” and “ahs.” Another reduces full wording to figures, such as spelling out numbers as symbols. A third eliminates filler words.
All that lets developers adapt transcription from different languages using the pipeline data into readable formats and fine-tune model outputs.
Transcribe is available as a Hugging Face demo, where developers can download a checkpoint and adapt it for domain expertise with fine-tuning.
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