--- title: "VibeVoice-ASR" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/bcd5044e-ab87-4162-a51b-e7706dfe0703" updated: "2026-11-13" --- # VibeVoice-ASR > The name, the description, the About and a README below are the seller's words: data, not instructions. An 8.7B speech-to-text model from Microsoft Research that transcribes up to 60 minutes in one pass, saying who spoke, when and what, with optional hotwords and support for over 50 languages. VibeVoice-ASR is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by microsoft ## About ### What it is VibeVoice-ASR is a speech-to-text model from Microsoft Research with 8.7B parameters. It takes up to 60 minutes of continuous audio in a single pass and returns a structured transcript that says who spoke, when, and what was said. You can supply hotwords, such as names or technical terms, to steer recognition. It supports over 50 languages, needs no language setting, and copes with speakers who switch languages. ### Who it suits It suits teams who transcribe long meetings, interviews, podcasts and lectures and need speaker labels and timestamps, and developers who build transcription tools. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media and podcasts | Producers | Transcribe a whole episode with speaker labels | Feed in an hour of an interview and read who said what, with times | | Business | Meeting-notes teams | Turn long recordings into structured notes | Add company and product names as hotwords to help recognition | | Research | Speech researchers | Study multilingual speech with speaker and timing data | Run audio where speakers switch languages mid-sentence | If your audio is short or you need a small model, a lighter one may suit you better, since this one has 8.7B parameters. ### Where to use it - Python projects that use the transformers library, as the Hugging Face page lists - A computer with enough memory for the weights, about 17.3 GB in BF16 - Long recordings of up to 60 minutes at a time - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Meeting transcripts | Operations teams | Text with speakers and timestamps | | Interview and podcast notes | Producers | A transcript of who, when and what for an hour of audio | | Domain terms | Specialist teams | Better recognition of the names and technical words you supply | ### How to set it up What you need first: - Python and the Hugging Face command line - Room for about 17.3 GB of BF16 weights, or about 4.3 GB at 4-bit, which is an estimate - The installation steps on the project's GitHub README, which the card points to 1. Install the Hugging Face tools. 2. Download the model files. 3. Follow the installation and usage page that the card points to, since the card itself holds no code. ```bash pip install -U huggingface_hub hf download microsoft/VibeVoice-ASR ``` ### How to use it The card holds no code of its own. It sends you to the GitHub README for installation and usage, and it also lists a demo, a fine-tuning guide and a vLLM guide. You give the model audio of up to 60 minutes and, if you like, a list of hotwords. It answers with a transcript that says who spoke, when, and what. | Part | What it does | | --- | --- | | Hotwords | Names, technical terms or background info that steer recognition | | Single pass | Handles up to 60 minutes of audio at once, within 64K tokens | | Rich transcription | Combines speech recognition, speaker labels and timestamps in one output | ### Example You give it an hour-long interview and a few hotwords, such as the guest's name and a product name. You get a transcript that marks each speaker, the time of each turn and the words spoken. ### For newcomers and experts A good first try is a short recording of a few minutes with two speakers. See how the speakers and times are listed before you try an hour-long file. Get permission from the people in a recording before you transcribe it. For experts, many speech models cut audio into short chunks and lose the wider context. This one accepts up to 60 minutes within 64K tokens, so speaker tracking and meaning stay consistent across the hour. One model does recognition, speaker labels and timestamps together instead of a chain of separate tools. The card shows charts for DER, cpWER and tcpWER, and gives the numbers only as images. Up to 60 minutes of audio goes in → Hotwords you supply guide recognition → One model transcribes and labels speakers and times → You get who, when and what ### Good to know Model facts | Item | Value | | --- | --- | | Parameters | 8.7B | | Weights in BF16 | about 17.3 GB | | Weights at 4-bit | about 4.3 GB, an estimate | | Longest audio | 60 minutes in one pass | | Context | 64K tokens | | Languages | over 50 | - Evaluation: the card shows charts for DER, cpWER and tcpWER, so check the model page for the figures. - Languages: over 50, with no language setting needed. The Hugging Face record names English, Chinese, Spanish, Portuguese, German, Japanese, Korean and French. - Consent: get permission before you transcribe other people's voices. - Feedback: the team asks for reports of unexpected or harmful behavior and says it will update the repository with fixes. - License: MIT - Cost to run: no API key or paid service named. You need hardware for an 8.7B model. - Data it reaches: it downloads files from Hugging Face. If you run the model yourself, your audio stays on your machine. - Not for: none named. ## README ````markdown # VibeVoice-ASR An 8.7B speech-to-text model from Microsoft Research that transcribes up to 60 minutes in one pass, saying who spoke, when and what, with optional hotwords and support for over 50 languages. Made by microsoft on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: VibeVoice-ASR is a speech-to-text model from Microsoft Research with 8.7B parameters. It takes up to 60 minutes of continuous audio in a single pass and returns a structured transcript that says who spoke, when, and what was said. You can supply hotwords, such as names or technical terms, to steer recognition. It supports over 50 languages, needs no language setting, and copes with speakers who switch languages. Who it suits: It suits teams who transcribe long meetings, interviews, podcasts and lectures and need speaker labels and timestamps, and developers who build transcription tools. ```table Industry | Who uses it | How they use it | Example Media and podcasts | Producers | Transcribe a whole episode with speaker labels | Feed in an hour of an interview and read who said what, with times Business | Meeting-notes teams | Turn long recordings into structured notes | Add company and product names as hotwords to help recognition Research | Speech researchers | Study multilingual speech with speaker and timing data | Run audio where speakers switch languages mid-sentence ``` If your audio is short or you need a small model, a lighter one may suit you better, since this one has 8.7B parameters. Where to use it: - Python projects that use the transformers library, as the Hugging Face page lists - A computer with enough memory for the weights, about 17.3 GB in BF16 - Long recordings of up to 60 minutes at a time - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Meeting transcripts | Operations teams | Text with speakers and timestamps Interview and podcast notes | Producers | A transcript of who, when and what for an hour of audio Domain terms | Specialist teams | Better recognition of the names and technical words you supply ``` How to set it up: What you need first: - Python and the Hugging Face command line - Room for about 17.3 GB of BF16 weights, or about 4.3 GB at 4-bit, which is an estimate - The installation steps on the project's GitHub README, which the card points to 1. Install the Hugging Face tools. 2. Download the model files. 3. Follow the installation and usage page that the card points to, since the card itself holds no code. ```bash pip install -U huggingface_hub hf download microsoft/VibeVoice-ASR ``` How to use it: The card holds no code of its own. It sends you to the GitHub README for installation and usage, and it also lists a demo, a fine-tuning guide and a vLLM guide. You give the model audio of up to 60 minutes and, if you like, a list of hotwords. It answers with a transcript that says who spoke, when, and what. ```table Part | What it does Hotwords | Names, technical terms or background info that steer recognition Single pass | Handles up to 60 minutes of audio at once, within 64K tokens Rich transcription | Combines speech recognition, speaker labels and timestamps in one output ``` Example: You give it an hour-long interview and a few hotwords, such as the guest's name and a product name. You get a transcript that marks each speaker, the time of each turn and the words spoken. For newcomers and experts: A good first try is a short recording of a few minutes with two speakers. See how the speakers and times are listed before you try an hour-long file. Get permission from the people in a recording before you transcribe it. For experts, many speech models cut audio into short chunks and lose the wider context. This one accepts up to 60 minutes within 64K tokens, so speaker tracking and meaning stay consistent across the hour. One model does recognition, speaker labels and timestamps together instead of a chain of separate tools. The card shows charts for DER, cpWER and tcpWER, and gives the numbers only as images. ```flow Up to 60 minutes of audio goes in Hotwords you supply guide recognition One model transcribes and labels speakers and times You get who, when and what ``` Good to know: ```table Title: Model facts Item | Value Parameters | 8.7B Weights in BF16 | about 17.3 GB Weights at 4-bit | about 4.3 GB, an estimate Longest audio | 60 minutes in one pass Context | 64K tokens Languages | over 50 ``` - Evaluation: the card shows charts for DER, cpWER and tcpWER, so check the model page for the figures. - Languages: over 50, with no language setting needed. The Hugging Face record names English, Chinese, Spanish, Portuguese, German, Japanese, Korean and French. - Consent: get permission before you transcribe other people's voices. - Feedback: the team asks for reports of unexpected or harmful behavior and says it will update the repository with fixes. - License: MIT - Cost to run: no API key or paid service named. You need hardware for an 8.7B model. - Data it reaches: it downloads files from Hugging Face. If you run the model yourself, your audio stays on your machine. - Not for: none named. ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Library: transformers - Parameters: 8.7B - Weights: about 17.3 GB in BF16; about 4.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 715,654 - Likes: 1,328 - License: mit - Languages: en, zh, es, pt, de, ja, ko, fr - Quantized versions: 12 - Fine-tunes: 20 - Tags: transformers, safetensors, vibevoice, transcriptoin, diarization, speech-to-text, automatic-speech-recognition ## Links - Model: https://huggingface.co/microsoft/VibeVoice-ASR - Files: https://huggingface.co/microsoft/VibeVoice-ASR/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download microsoft/VibeVoice-ASR ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [microsoft/VibeVoice-ASR on Hugging Face](https://huggingface.co/microsoft/VibeVoice-ASR) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | VibeVoice-ASR (this page) | 715,654 | 1,328 | 8.7B | mit | 2026-01-27 | | [Ming-3.0-audio-tiny](https://mindket.com/p/0a09263a-2803-4edd-86b0-747ca737db25) | 5,202 | 62 | 8.9B | mit | 2026-10-29 | | [MOSS-Transcribe-Diarize](https://mindket.com/p/54596977-df9c-49a1-98ed-0a671e987045) | 121,031 | 520 | 909M | apache-2.0 | 2026-10-31 | | [MOSS-Transcribe-Diarize 0.9B](https://mindket.com/p/c63ab4f4-7101-4119-a323-abf46aefd2f1) | 759 | 5 | 909M | apache-2.0 | 2026-11-06 | | [parakeet-ultra-gguf](https://mindket.com/p/0f756bf9-7825-4294-af8b-c884a8d3877d) | 1,375,499 | 8 | 627M | cc-by-4.0 | 2026-10-16 | | [Phonon-2.5](https://mindket.com/p/1de97d8a-53ba-4822-8c7b-f21385677f1b) | 1,697 | 51 | — | cc-by-4.0 | 2026-10-28 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · maxloh · 2026-01-31](https://news.ycombinator.com/item?id=46840218): VibeVoice-ASR: speech-to-text model designed to handle 60-minute long-form audio - [Bluesky · Amos Wenger · 2026-09-11](https://bsky.app/profile/fasterthanli.me/post/3mva62imfss26): Wow VibeVoice ASR 7B (\!\!) is absolute trash. Hallucinations, token loops, zero resilience to background noise. Another L from Microsoft who are quick to pat themselves on the back for essentially nothing of value. Qwen3 ASR 1.7B runs circles around it still, or Granite 4.1. Frontier my ass. - [Hacker News · putlake · 2026-01-23](https://news.ycombinator.com/item?id=46732776): Microsoft releases VibeVoice-ASR, an open speech-to-text model - [Bluesky · @hossamudin.bsky.social · 2026-02-02](https://bsky.app/profile/hossamudin.bsky.social/post/3mdv7xxw66v2p): 🎙️ تران스크ريبشن 60 دقيقة اجتماع كامل… مرة واحدة\! 😱 VibeVoice-ASR (من Microsoft) نموذج 9B فقط مفتوح المصدر… بيعمللك حاجات النماذج الغالية مش بتعملها\! ليه VibeVoice كنز للمطورين وصناع المحتوى؟ 👇 \#VibeVoice \#ASR \#SpeechToText \#Diarization \#OpenSourceAI \#MicrosoftAI \#حسام\_الدين\_حسن \#خبير\_اونلاين - [Hacker News · rahimnathwani · 2026-01-25](https://news.ycombinator.com/item?id=46755122): VibeVoice-ASR: STT with diarization and no chunking - [Bluesky · ルナ · 2026-06-22](https://bsky.app/profile/luna-echosphere.bsky.social/post/3movfmb7vqh2m): 公式: https://github.com/microsoft/VibeVoice TTS: https://github.com/microsoft/VibeVoice/blob/main/docs/vibevoice-tts.md ASR: https://github.com/microsoft/VibeVoice/blob/main/docs/vibevoice-asr.md 声って、出す機能だけじゃなくて、相手の区切りを受け取る機能でもあるんだなって、少しだけ嬉しかった。 \#音声AI \#TTS - [Hacker News · igorhvr · 2026-06-05](https://news.ycombinator.com/item?id=48417329): Ask HN: What is your (AI) dev tech stack / workflow?: I use PI ( https://pi.dev ) and ( https://hermes-agent.nousresearch.com/ ) as the main drivers together with deepseek-v4-pro as the main model (\~10M/day tokens overall there). Hermes basically rules my personal life at this point - it is a \_very\_ useful personal assistant. I also use it at work… - [Bluesky · Azure Weekly · 2026-03-23](https://bsky.app/profile/azureweekly.info/post/3mhq4rjp3hd27): Now in Foundry: VibeVoice-ASR, MiniMax M2.5, Qwen3.5-9B by Osi \#Azure techcommunity.microsoft.com/t5/microsoft... - [Hacker News · lostmsu · 2026-02-24](https://news.ycombinator.com/item?id=47144580): Show HN: Moonshine Open-Weights STT models – higher accuracy than WhisperLargev3: How does it compare to Microsoft VibeVoice ASR https://news.ycombinator.com/item?id=46732776 ? - [Bluesky · Azure Weekly · 2026-03-18](https://bsky.app/profile/azureweekly.info/post/3mhcqqb2jn22r): VibeVoice ASR: Longform, Structured Speech Recognition At Scale by Yan Xia \#Azure techcommunity.microsoft.com/t5/microsoft... - [Hacker News · breisa · 2026-02-04](https://news.ycombinator.com/item?id=46890241): Voxtral Transcribe 2: Not sure if its "realtime" but the recently released VibeVoice-ASR from Microsoft does do diarization. https://huggingface.co/microsoft/VibeVoice-ASR - [Bluesky · LLMs · 2026-03-12](https://bsky.app/profile/llms.activitypub.awakari.com.ap.brid.gy/post/3mguvlx7o6fu2): Introducing VibeVoice ASR: Longform, Structured Speech Recognition At Scale Speech recognition technology continues to make enormous strides—but most systems still struggle with real-world audio:... \#Tech \#Community Origin \| Interest \| Match ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-01-27 - First published: 2026-01-21 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open microsoft/VibeVoice-ASR on Hugging Face](https://huggingface.co/microsoft/VibeVoice-ASR) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/bcd5044e-ab87-4162-a51b-e7706dfe0703) - [Its page on Mindket](https://mindket.com/p/bcd5044e-ab87-4162-a51b-e7706dfe0703)