vermeer-XL-CA
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VibeVoice-1.5B
MicrosoftVibeVoice is a novel framework designed for generating expressive, long-form, multi-speaker conversational audio, such as podcasts, from text. It addresses significant challenges in traditional Text-to-Speech (TTS) systems, particularly in scalability, speaker consistency, and natural turn-taking. A core innovation of VibeVoice is its use of continuous speech tokenizers (Acoustic and Semantic) operating at an ultra-low frame rate of 7.5 Hz. These tokenizers efficiently preserve audio fidelity while significantly boosting computational efficiency for processing long sequences. VibeVoice employs a next-token diffusion framework, leveraging a Large Language Model (LLM) to understand textual context and dialogue flow, and a diffusion head to generate high-fidelity acoustic details. The model can synthesize speech up to **90 minutes** long with up to **4 distinct speakers**, surpassing the typical 1-2 speaker limits of many prior models.
Input Text→ Output Audio
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View details ↗VibeVoice-AcousticTokenizer
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗VibeVoice-ASR
Microsoft**VibeVoice-ASR** is a unified speech-to-text model designed to handle **60-minute long-form audio** in a single pass, generating structured transcriptions containing **Who (Speaker), When (Timestamps), and What (Content)**, with support for **Customized Hotwords** and over **50 languages**. ➡️ **Code:** microsoft/VibeVoice ➡️ **Demo:** VibeVoice-ASR-Demo ➡️ **Report:** VibeVoice-ASR Technical Report ➡️ **Finetuning:** Finetuning ➡️ **vLLM:** vLLM-VibeVoice-ASR - **🕒 60-minute Single-Pass Processing**: Unlike conventional ASR models that slice audio into short chunks (often losing global context), VibeVoice ASR accepts up to **60 minutes** of continuous audio input within 64K token length. This ensures consistent speaker tracking and semantic coherence across the entire hour.
Input Audio→ Output Text
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View details ↗VibeVoice-ASR-BitNet
Microsoft**VibeVoice-ASR-BitNet** is a compressed variant of VibeVoice-ASR optimized for **real-time inference on edge CPUs** — no GPU required. Through heterogeneous quantization, the model is compressed from 4.62 GB to **1.58 GB** while achieving **1.6–2.3× faster** inference than Whisper.cpp with real-time capability (RTF ➡️ **Report:** VibeVoice-ASR-BitNet Technical Report ➡️ **Base Model:** microsoft/VibeVoice-ASR This project was conducted by members of Microsoft Research. If you have suggestions, questions, or observe unexpected behavior, please contact us at VibeVoice@microsoft.com.
Input Audio→ Output Text
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View details ↗VibeVoice-ASR-HF
Microsoft**VibeVoice-ASR** is a unified speech-to-text model designed to handle **60-minute long-form audio** in a single pass, generating structured transcriptions containing **Who (Speaker), When (Timestamps), and What (Content)**, with support for **Customized Hotwords** and over **50 languages**. ➡️ **Demo:** VibeVoice-ASR-Demo ➡️ **Report:** VibeVoice-ASR Technical Report - **🕒 60-minute Single-Pass Processing**: Unlike conventional ASR models that slice audio into short chunks (often losing global context), VibeVoice ASR accepts up to **60 minutes** of continuous audio input within 64K token length. This ensures consistent speaker tracking and semantic coherence across the entire hour.
Input Audio · Text→ Output Text
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View details ↗VibeVoice-ASR-Streaming-1.5B
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗VibeVoice-ASR-Streaming-7B
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗VibeVoice-Realtime-0.5B
MicrosoftVibeVoice-Realtime is a **lightweight real‑time** text-to-speech model supporting **streaming text input** and **robust long-form speech generation**. It can be used to build realtime TTS services, narrate live data streams, and let different LLMs start speaking from their very first tokens (plug in your preferred model) long before a full answer is generated. It produces initial audible speech in **~300 ms** (hardware dependent). Although the model is primarily built for English, we found that it still exhibits a certain level of multilingual capability—and even performs reasonably well in some languages. We provide nine additional languages (German, French, Italian, Japanese, Korean, Dutch, Polish, Portuguese, and Spanish) for users to explore and share feedback. The model uses an interleaved, windowed design: it incrementally encodes incoming text chunks while, in parallel, continuing diffusion-based acoustic latent generation from prior context. Unlike the full multi-speaker long-form variants, this streaming model removes the semantic tokenizer and relies solely on an efficient acoustic tokenizer operating at an ultra-low frame rate (7.5 Hz).
Input Text→ Output Audio
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View details ↗VidTok
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗vidtwin
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗villa-x
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗VITRA-VLA-3B
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗vq-diffusion-ithq
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wavecoder-ds-6.7b
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wavecoder-pro-6.7b
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wavecoder-ultra-6.7b
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wavlm-base
MicrosoftThe base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. The model was pre-trained on 960h of Librispeech.
Input Text→ Output Embedding
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View details ↗wavlm-base-plus
MicrosoftThe base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli
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View details ↗wavlm-base-plus-sd
MicrosoftThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei
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View details ↗wavlm-base-plus-sv
MicrosoftThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli
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View details ↗wavlm-base-sd
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wavlm-base-sv
MicrosoftThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. The model was pre-trained on 960h of Librispeech. Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei
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View details ↗wavlm-large
MicrosoftThe large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli
Input Text→ Output Text
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