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no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 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0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 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2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" 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6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 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<label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow 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dir=\"auto\"\u003eFreeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM\u003c/h1\u003e\u003ca id=\"user-content-freeze-omni-a-smart-and-low-latency-speech-to-speech-dialogue-model-with-frozen-llm\" class=\"anchor\" aria-label=\"Permalink: Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM\" href=\"#freeze-omni-a-smart-and-low-latency-speech-to-speech-dialogue-model-with-frozen-llm\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/VITA-MLLM/Freeze-Omni/blob/main/assets/logo.png\"\u003e\u003cimg src=\"/VITA-MLLM/Freeze-Omni/raw/main/assets/logo.png\" width=\"70%\" height=\"70%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003c/p\u003e\u003cdiv align=\"center\" dir=\"auto\"\u003e [\u003ca href=\"https://freeze-omni.github.io\" rel=\"nofollow\"\u003e🍎 Project(Demo) Page\u003c/a\u003e] [\u003ca href=\"https://arxiv.org/abs/2411.00774\" rel=\"nofollow\"\u003e📖 arXiv Paper\u003c/a\u003e] [\u003ca href=\"https://huggingface.co/VITA-MLLM/Freeze-Omni\" rel=\"nofollow\"\u003e🤗 Hugging Face\u003c/a\u003e] [\u003ca href=\"/VITA-MLLM/Freeze-Omni/blob/main/assets/wechat_code.jpg\"\u003e💬 WeChat (微信)\u003c/a\u003e]\u003c/div\u003e\u003cp dir=\"auto\"\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🔥 News\u003c/h2\u003e\u003ca id=\"user-content--news\" class=\"anchor\" aria-label=\"Permalink: 🔥 News\" href=\"#-news\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ccode\u003e2024.11.26\u003c/code\u003e\u003c/strong\u003e 🌟 The inference code, server demo and model weights \u003cstrong\u003ehave been released\u003c/strong\u003e. Long wait!\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ccode\u003e2024.11.4\u003c/code\u003e\u003c/strong\u003e 🌟 We are very proud to launch Freeze-Omni, a speech-to-speech dialogue model\nwith both low-latency and high intelligence! We have submitted the open-source code, yet it is under review internally. We are moving the process forward as quickly as possible, stay tuned!\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContents \u003c/h2\u003e\u003ca id=\"user-content-contents-\" class=\"anchor\" aria-label=\"Permalink: Contents \" href=\"#contents-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#-freeze-omni-overview\"\u003eFreeze-Omni Overview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#-experimental-results\"\u003eExperimental Results\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#-inference\"\u003eInference\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#requirements-and-installation\"\u003eRequirements and Installation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#quick-start\"\u003eQuick Start\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#real-time-interactive-demo\"\u003eReal-Time Interactive Demo\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e👀 Freeze-Omni Overview\u003c/h2\u003e\u003ca id=\"user-content--freeze-omni-overview\" class=\"anchor\" aria-label=\"Permalink: 👀 Freeze-Omni Overview\" href=\"#-freeze-omni-overview\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFreeze-Omni is a speech-to-speech dialogue model, exhibiting the characteristic of being \"\u003cstrong\u003esmart\u003c/strong\u003e\" as it is constructed upon a \"\u003cstrong\u003efrozen\u003c/strong\u003e\" text-modality LLM. This enables it to keep the original intelligence of the LLM backbone, without being affected by the forgetting problem induced by the fine-tuning process for integration of the speech modality. Specifically, Freeze-Omni contains a speech encoder that supports streaming speech input and a speech decoder that generates streaming output speech. \u003cstrong\u003eThree key strategies\u003c/strong\u003e are adopted to implement the speech-to-speech dialogue system:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eChunk-wise Streaming Input\u003c/strong\u003e. Freeze-Omni has a speech encoder supporting chunk-wise streaming input speech features to obtain a fast response to input. A 3-stage training strategy can help it keep strong acoustic robustness.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAR-base Speech Output\u003c/strong\u003e. Freeze-Omni has an AR speech decoder based on a single codebook, which can achieve low-latency speech output in streaming. A prefix tuning method is used so that training on only a small amount of Q\u0026amp;A data can achieve the ability to produce high-quality speech synthesis.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eChunk-level State Prediction\u003c/strong\u003e. Freeze-Omni adds a classification layer after the last layer of the backbone LLM to predict different states. These states will determine whether or not the user interrupts the dialogue to achieve a duplex dialogue for the user and the bot.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/VITA-MLLM/Freeze-Omni/blob/main/assets/overview.png\"\u003e\u003cimg src=\"/VITA-MLLM/Freeze-Omni/raw/main/assets/overview.png\" width=\"88%\" height=\"88%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eBesides we implement a Model as a Server strategy. We first started several models simultaneously and regarded them as a server. Then, when a user's VAD was triggered, the speech would be sent to the server in the form of chunks, and the server would be responsible for scheduling which idle model should respond to the current chunk. Since we separated all the kv-cache and CNN cache of the speech encoder and LLM during the inference process, the server only needs to save the inference cache for each user. In this way, any model in the server could respond to any chunk of any user, and there was no need to specify which model was used as a monitor or a generator.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e📈 Experimental Results\u003c/h2\u003e\u003ca id=\"user-content--experimental-results\" class=\"anchor\" aria-label=\"Permalink: 📈 Experimental Results\" href=\"#-experimental-results\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eEvaluation of speech understanding through ASR tasks, using CER(%) and WER(%).\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/VITA-MLLM/Freeze-Omni/blob/main/assets/asr_res.png\"\u003e\u003cimg src=\"/VITA-MLLM/Freeze-Omni/raw/main/assets/asr_res.png\" width=\"70%\" height=\"70%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eEvaluation of output speech quality on different top-k of AR decoder by using CER(%).\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/VITA-MLLM/Freeze-Omni/blob/main/assets/out_cer.png\"\u003e\u003cimg src=\"/VITA-MLLM/Freeze-Omni/raw/main/assets/out_cer.png\" width=\"50%\" height=\"50%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eEvaluation of spoken question answering on accuracy(%).\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/VITA-MLLM/Freeze-Omni/blob/main/assets/qa.png\"\u003e\u003cimg src=\"/VITA-MLLM/Freeze-Omni/raw/main/assets/qa.png\" width=\"70%\" height=\"70%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eAnalysis of end-to-end latency for different parts.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/VITA-MLLM/Freeze-Omni/blob/main/assets/latency.png\"\u003e\u003cimg src=\"/VITA-MLLM/Freeze-Omni/raw/main/assets/latency.png\" width=\"70%\" height=\"70%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e📐 Inference\u003c/h2\u003e\u003ca id=\"user-content--inference\" class=\"anchor\" aria-label=\"Permalink: 📐 Inference\" href=\"#-inference\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRequirements and Installation\u003c/h3\u003e\u003ca id=\"user-content-requirements-and-installation\" class=\"anchor\" aria-label=\"Permalink: Requirements and Installation\" href=\"#requirements-and-installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEnvironment Requirements\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/VITA-MLLM/Freeze-Omni\ncd Freeze-Omni\nconda create -n freeze-omni python=3.10 -y\nconda activate freeze-omni\npip install --upgrade pip\npip install -r requirements.txt\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003egit clone https://github.com/VITA-MLLM/Freeze-Omni\ncd Freeze-Omni\nconda create -n freeze-omni python=3.10 -y\nconda activate freeze-omni\npip install --upgrade pip\npip install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRequired weights\u003c/strong\u003e:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDownload \u003ca href=\"https://huggingface.co/VITA-MLLM/Freeze-Omni\" rel=\"nofollow\"\u003eFreeze-Omni checkpoint\u003c/a\u003e then move \u003ccode\u003echeckpoints\u003c/code\u003e into the root dir of this repo (\u003ccode\u003eFreeze-Omni/checkpoints\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003eDownload \u003ca href=\"https://huggingface.co/Qwen/Qwen2-7B-Instruct\" rel=\"nofollow\"\u003eQwen2-7B-Instruct checkpoint\u003c/a\u003e then move \u003ccode\u003eQwen2-7B-Instruct\u003c/code\u003e into the root dir of this repo (\u003ccode\u003eFreeze-Omni/Qwen2-7B-Instruct\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuick Start\u003c/h3\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFrom python command\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"export PYTHONPATH=./:$PYTHONPATH\nCUDA_VISIBLE_DEVICES=0 python3 bin/inference.py \\\n --model_path ./checkpoints \\\n --input_wav ./assets/question.wav \\\n --output_wav ./assets/answer.wav \\\n --llm_path ./Qwen2-7B-Instruct \\\n --top_p 0.8 \\\n --top_k 20 \\\n --temperature 0.8\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eexport PYTHONPATH=./:$PYTHONPATH\nCUDA_VISIBLE_DEVICES=0 python3 bin/inference.py \\\n --model_path ./checkpoints \\\n --input_wav ./assets/question.wav \\\n --output_wav ./assets/answer.wav \\\n --llm_path ./Qwen2-7B-Instruct \\\n --top_p 0.8 \\\n --top_k 20 \\\n --temperature 0.8\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFrom script\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"sh scripts/run_inference.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003esh scripts/run_inference.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReal-Time Interactive Demo\u003c/h3\u003e\u003ca id=\"user-content-real-time-interactive-demo\" class=\"anchor\" aria-label=\"Permalink: Real-Time Interactive Demo\" href=\"#real-time-interactive-demo\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo have a good interactive experience, please pay attention to the following three points:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eEnsure a high-speed network connection\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUse high-performance GPUs for deployment\u003c/strong\u003e. In the demo video, we use 1 Nvidia A100 GPU. A800, H800, or H20 will be much better.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMaintain a quiet environment\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFrom python command\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"export PYTHONPATH=./:$PYTHONPATH\nCUDA_VISIBLE_DEVICES=0 python3 bin/server.py \\\n --ip your_server_ip \\\n --port your_server_port \\\n --max_users 3 \\\n --llm_exec_nums 1 \\\n --timeout 180 \\\n --model_path ./checkpoints \\\n --llm_path ./Qwen2-7B-Instruct \\\n --top_p 0.8 \\\n --top_k 20 \\\n --temperature 0.8\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eexport PYTHONPATH=./:$PYTHONPATH\nCUDA_VISIBLE_DEVICES=0 python3 bin/server.py \\\n --ip your_server_ip \\\n --port your_server_port \\\n --max_users 3 \\\n --llm_exec_nums 1 \\\n --timeout 180 \\\n --model_path ./checkpoints \\\n --llm_path ./Qwen2-7B-Instruct \\\n --top_p 0.8 \\\n --top_k 20 \\\n --temperature 0.8\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFrom script\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eChange the \u003cstrong\u003eip\u003c/strong\u003e and \u003cstrong\u003eport\u003c/strong\u003e in \u003ccode\u003escripts/run_demo_server.sh\u003c/code\u003e with yours and run:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"sh scripts/run_demo_server.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003esh scripts/run_demo_server.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e✒️ Citation\u003c/h2\u003e\u003ca id=\"user-content-️-citation\" class=\"anchor\" aria-label=\"Permalink: ✒️ Citation\" href=\"#️-citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you find our work helpful for your research, please consider citing our work.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@article{xiong2024freeze,\n title={Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM},\n author={Xiong Wang and Yangze Li and Chaoyou Fu and Yunhang Shen and Lei Xie and Ke Li and Xing Sun and Long Ma},\n journal={arXiv preprint arXiv:2411.00774},\n year={2024}\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@article\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003exiong2024freeze\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eFreeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eXiong Wang and Yangze Li and Chaoyou Fu and Yunhang Shen and Lei Xie and Ke Li and Xing Sun and Long Ma\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ejournal\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003earXiv preprint arXiv:2411.00774\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2024\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e📣 Statement\u003c/h2\u003e\u003ca id=\"user-content--statement\" class=\"anchor\" aria-label=\"Permalink: 📣 Statement\" href=\"#-statement\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFreeze-Omni is trained on large-scale corpus, and its output has randomness. Any content generated by Freeze-Omni does not represent the views of the model developers. We are not responsible for any problems arising from the use, misuse, and dissemination of Freeze-Omni, including but not limited to public opinion risks and data security issues.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e📜 Related Works\u003c/h2\u003e\u003ca id=\"user-content--related-works\" class=\"anchor\" aria-label=\"Permalink: 📜 Related Works\" href=\"#-related-works\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExplore our related researches:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e[VITA]\u003c/strong\u003e \u003ca href=\"https://github.com/VITA-MLLM/VITA\"\u003eTowards Open-Source Interactive Omni Multimodal LLM\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e[Awesome-MLLM]\u003c/strong\u003e \u003ca href=\"https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models\"\u003eA Survey on Multimodal Large Language Models\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e[MME]\u003c/strong\u003e \u003ca href=\"https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation\"\u003eMME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e[Video-MME]\u003c/strong\u003e \u003ca href=\"https://github.com/BradyFU/Video-MME\"\u003eVideo-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e👍 Acknowledgement\u003c/h2\u003e\u003ca id=\"user-content--acknowledgement\" class=\"anchor\" aria-label=\"Permalink: 👍 Acknowledgement\" href=\"#-acknowledgement\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFreeze-Omni is built with reference to the following outstanding works: \u003ca href=\"https://huggingface.co/Qwen/Qwen2-7B-Instruct\" rel=\"nofollow\"\u003eQwen2-7B-Instruct\u003c/a\u003e, \u003ca href=\"https://github.com/y-ren16/ticodec\"\u003eTiCodec\u003c/a\u003e\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM","anchor":"freeze-omni-a-smart-and-low-latency-speech-to-speech-dialogue-model-with-frozen-llm","htmlText":"Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM"},{"level":2,"text":"🔥 News","anchor":"-news","htmlText":"🔥 News"},{"level":2,"text":"Contents ","anchor":"contents-","htmlText":"Contents "},{"level":2,"text":"👀 Freeze-Omni Overview","anchor":"-freeze-omni-overview","htmlText":"👀 Freeze-Omni Overview"},{"level":2,"text":"📈 Experimental Results","anchor":"-experimental-results","htmlText":"📈 Experimental Results"},{"level":2,"text":"📐 Inference","anchor":"-inference","htmlText":"📐 Inference"},{"level":3,"text":"Requirements and Installation","anchor":"requirements-and-installation","htmlText":"Requirements and Installation"},{"level":3,"text":"Quick Start","anchor":"quick-start","htmlText":"Quick Start"},{"level":3,"text":"Real-Time Interactive Demo","anchor":"real-time-interactive-demo","htmlText":"Real-Time Interactive Demo"},{"level":2,"text":"✒️ Citation","anchor":"️-citation","htmlText":"✒️ Citation"},{"level":2,"text":"📣 Statement","anchor":"-statement","htmlText":"📣 Statement"},{"level":2,"text":"📜 Related Works","anchor":"-related-works","htmlText":"📜 Related Works"},{"level":2,"text":"👍 Acknowledgement","anchor":"-acknowledgement","htmlText":"👍 Acknowledgement"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FVITA-MLLM%2FFreeze-Omni"}},{"displayName":"License.txt","repoName":"Freeze-Omni","refName":"main","path":"License.txt","preferredFileType":"license","tabName":"License","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FVITA-MLLM%2FFreeze-Omni"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> <div 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fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M8 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3ZM1.5 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Zm13 0a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path></svg></button></div></div></div><div class="Box-sc-g0xbh4-0 vIPPs"><div data-hpc="true"><button hidden="" data-testid="focus-next-element-button" data-hotkey="j"></button><button hidden="" data-testid="focus-previous-element-button" data-hotkey="k"></button><h2 class="sr-only prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading" id="folders-and-files">Folders and files</h2><table aria-labelledby="folders-and-files" class="Box-sc-g0xbh4-0 fdROMU"><thead class="Box-sc-g0xbh4-0 jGKpsv"><tr class="Box-sc-g0xbh4-0 jdgHnn"><th colSpan="2" class="Box-sc-g0xbh4-0 bQivRW"><span class="text-bold">Name</span></th><th colSpan="1" class="Box-sc-g0xbh4-0 ldkMIO"><span class="text-bold">Name</span></th><th class="hide-sm"><div title="Last commit message" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit message</span></div></th><th colSpan="1" class="Box-sc-g0xbh4-0 jMbWeI"><div title="Last commit date" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit date</span></div></th></tr></thead><tbody><tr class="Box-sc-g0xbh4-0 gpqjiB"><td colSpan="3" class="bgColor-muted p-1 rounded-top-2"><div class="Box-sc-g0xbh4-0 dzCJzi"><h2 class="sr-only prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">Latest commit</h2><div style="width:120px" class="Skeleton Skeleton--text" data-testid="loading"> </div><div class="d-flex flex-shrink-0 gap-2"><div data-testid="latest-commit-details" class="d-none d-sm-flex flex-items-center"></div><div class="d-flex gap-2"><h2 class="sr-only prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">History</h2><a href="/VITA-MLLM/Freeze-Omni/commits/main/" class="types__StyledButton-sc-ws60qy-0 dPmZyJ d-none d-lg-flex LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" aria-describedby=":Raqj8pab:-loading-announcement"><span data-component="buttonContent" class="Box-sc-g0xbh4-0 gUkoLg"><span data-component="leadingVisual" class="Box-sc-g0xbh4-0 hzSPyu"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span><span data-component="text"><span class="fgColor-default">14 Commits</span></span></span></a><div class="d-sm-none"></div><div class="d-flex d-lg-none"><span role="tooltip" aria-label="14 Commits" id="history-icon-button-tooltip" class="Tooltip__TooltipBase-sc-17tf59c-0 hWlpPn tooltipped-n"><a href="/VITA-MLLM/Freeze-Omni/commits/main/" class="types__StyledButton-sc-ws60qy-0 dPmZyJ LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" aria-describedby=":R1iqj8pab:-loading-announcement history-icon-button-tooltip"><span data-component="buttonContent" class="Box-sc-g0xbh4-0 gUkoLg"><span data-component="leadingVisual" class="Box-sc-g0xbh4-0 hzSPyu"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 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title="assets" aria-label="assets, (Directory)" class="Link--primary" href="/VITA-MLLM/Freeze-Omni/tree/main/assets">assets</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="assets" aria-label="assets, (Directory)" class="Link--primary" href="/VITA-MLLM/Freeze-Omni/tree/main/assets">assets</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-1"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="bin" aria-label="bin, (Directory)" class="Link--primary" href="/VITA-MLLM/Freeze-Omni/tree/main/bin">bin</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="bin" aria-label="bin, (Directory)" class="Link--primary" href="/VITA-MLLM/Freeze-Omni/tree/main/bin">bin</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-2"><td 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width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="models" aria-label="models, (Directory)" class="Link--primary" href="/VITA-MLLM/Freeze-Omni/tree/main/models">models</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-3"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="scripts" aria-label="scripts, (Directory)" class="Link--primary" href="/VITA-MLLM/Freeze-Omni/tree/main/scripts">scripts</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 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0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="web" aria-label="web, (Directory)" class="Link--primary" href="/VITA-MLLM/Freeze-Omni/tree/main/web">web</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="web" aria-label="web, (Directory)" class="Link--primary" href="/VITA-MLLM/Freeze-Omni/tree/main/web">web</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-5"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 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1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM</h1><a id="user-content-freeze-omni-a-smart-and-low-latency-speech-to-speech-dialogue-model-with-frozen-llm" class="anchor" aria-label="Permalink: Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM" href="#freeze-omni-a-smart-and-low-latency-speech-to-speech-dialogue-model-with-frozen-llm"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/VITA-MLLM/Freeze-Omni/blob/main/assets/logo.png"><img src="/VITA-MLLM/Freeze-Omni/raw/main/assets/logo.png" width="70%" height="70%" style="max-width: 100%;"></a> </p> <p dir="auto"></p><div align="center" dir="auto"> [<a href="https://freeze-omni.github.io" rel="nofollow">🍎 Project(Demo) Page</a>] [<a href="https://arxiv.org/abs/2411.00774" rel="nofollow">📖 arXiv Paper</a>] [<a href="https://huggingface.co/VITA-MLLM/Freeze-Omni" rel="nofollow">🤗 Hugging Face</a>] [<a href="/VITA-MLLM/Freeze-Omni/blob/main/assets/wechat_code.jpg">💬 WeChat (微信)</a>]</div><p dir="auto"></p> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🔥 News</h2><a id="user-content--news" class="anchor" aria-label="Permalink: 🔥 News" href="#-news"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong><code>2024.11.26</code></strong> 🌟 The inference code, server demo and model weights <strong>have been released</strong>. Long wait!</li> <li><strong><code>2024.11.4</code></strong> 🌟 We are very proud to launch Freeze-Omni, a speech-to-speech dialogue model with both low-latency and high intelligence! We have submitted the open-source code, yet it is under review internally. We are moving the process forward as quickly as possible, stay tuned!</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contents </h2><a id="user-content-contents-" class="anchor" aria-label="Permalink: Contents " href="#contents-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="#-freeze-omni-overview">Freeze-Omni Overview</a></li> <li><a href="#-experimental-results">Experimental Results</a></li> <li><a href="#-inference">Inference</a> <ul dir="auto"> <li><a href="#requirements-and-installation">Requirements and Installation</a></li> <li><a href="#quick-start">Quick Start</a></li> <li><a href="#real-time-interactive-demo">Real-Time Interactive Demo</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">👀 Freeze-Omni Overview</h2><a id="user-content--freeze-omni-overview" class="anchor" aria-label="Permalink: 👀 Freeze-Omni Overview" href="#-freeze-omni-overview"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Freeze-Omni is a speech-to-speech dialogue model, exhibiting the characteristic of being "<strong>smart</strong>" as it is constructed upon a "<strong>frozen</strong>" text-modality LLM. This enables it to keep the original intelligence of the LLM backbone, without being affected by the forgetting problem induced by the fine-tuning process for integration of the speech modality. Specifically, Freeze-Omni contains a speech encoder that supports streaming speech input and a speech decoder that generates streaming output speech. <strong>Three key strategies</strong> are adopted to implement the speech-to-speech dialogue system:</p> <ul dir="auto"> <li> <p dir="auto"><strong>Chunk-wise Streaming Input</strong>. Freeze-Omni has a speech encoder supporting chunk-wise streaming input speech features to obtain a fast response to input. A 3-stage training strategy can help it keep strong acoustic robustness.</p> </li> <li> <p dir="auto"><strong>AR-base Speech Output</strong>. Freeze-Omni has an AR speech decoder based on a single codebook, which can achieve low-latency speech output in streaming. A prefix tuning method is used so that training on only a small amount of Q&A data can achieve the ability to produce high-quality speech synthesis.</p> </li> <li> <p dir="auto"><strong>Chunk-level State Prediction</strong>. Freeze-Omni adds a classification layer after the last layer of the backbone LLM to predict different states. These states will determine whether or not the user interrupts the dialogue to achieve a duplex dialogue for the user and the bot.</p> </li> </ul> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/VITA-MLLM/Freeze-Omni/blob/main/assets/overview.png"><img src="/VITA-MLLM/Freeze-Omni/raw/main/assets/overview.png" width="88%" height="88%" style="max-width: 100%;"></a> </p> <p dir="auto">Besides we implement a Model as a Server strategy. We first started several models simultaneously and regarded them as a server. Then, when a user's VAD was triggered, the speech would be sent to the server in the form of chunks, and the server would be responsible for scheduling which idle model should respond to the current chunk. Since we separated all the kv-cache and CNN cache of the speech encoder and LLM during the inference process, the server only needs to save the inference cache for each user. In this way, any model in the server could respond to any chunk of any user, and there was no need to specify which model was used as a monitor or a generator.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">📈 Experimental Results</h2><a id="user-content--experimental-results" class="anchor" aria-label="Permalink: 📈 Experimental Results" href="#-experimental-results"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong>Evaluation of speech understanding through ASR tasks, using CER(%) and WER(%).</strong>.</li> </ul> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/VITA-MLLM/Freeze-Omni/blob/main/assets/asr_res.png"><img src="/VITA-MLLM/Freeze-Omni/raw/main/assets/asr_res.png" width="70%" height="70%" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li><strong>Evaluation of output speech quality on different top-k of AR decoder by using CER(%).</strong></li> </ul> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/VITA-MLLM/Freeze-Omni/blob/main/assets/out_cer.png"><img src="/VITA-MLLM/Freeze-Omni/raw/main/assets/out_cer.png" width="50%" height="50%" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li><strong>Evaluation of spoken question answering on accuracy(%).</strong></li> </ul> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/VITA-MLLM/Freeze-Omni/blob/main/assets/qa.png"><img src="/VITA-MLLM/Freeze-Omni/raw/main/assets/qa.png" width="70%" height="70%" style="max-width: 100%;"></a> </p> <ul dir="auto"> <li><strong>Analysis of end-to-end latency for different parts.</strong></li> </ul> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/VITA-MLLM/Freeze-Omni/blob/main/assets/latency.png"><img src="/VITA-MLLM/Freeze-Omni/raw/main/assets/latency.png" width="70%" height="70%" style="max-width: 100%;"></a> </p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">📐 Inference</h2><a id="user-content--inference" class="anchor" aria-label="Permalink: 📐 Inference" href="#-inference"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Requirements and Installation</h3><a id="user-content-requirements-and-installation" class="anchor" aria-label="Permalink: Requirements and Installation" href="#requirements-and-installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Environment Requirements</strong>:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="git clone https://github.com/VITA-MLLM/Freeze-Omni cd Freeze-Omni conda create -n freeze-omni python=3.10 -y conda activate freeze-omni pip install --upgrade pip pip install -r requirements.txt"><pre class="notranslate"><code>git clone https://github.com/VITA-MLLM/Freeze-Omni cd Freeze-Omni conda create -n freeze-omni python=3.10 -y conda activate freeze-omni pip install --upgrade pip pip install -r requirements.txt </code></pre></div> <p dir="auto"><strong>Required weights</strong>:</p> <ul dir="auto"> <li>Download <a href="https://huggingface.co/VITA-MLLM/Freeze-Omni" rel="nofollow">Freeze-Omni checkpoint</a> then move <code>checkpoints</code> into the root dir of this repo (<code>Freeze-Omni/checkpoints</code>)</li> <li>Download <a href="https://huggingface.co/Qwen/Qwen2-7B-Instruct" rel="nofollow">Qwen2-7B-Instruct checkpoint</a> then move <code>Qwen2-7B-Instruct</code> into the root dir of this repo (<code>Freeze-Omni/Qwen2-7B-Instruct</code>)</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Quick Start</h3><a id="user-content-quick-start" class="anchor" aria-label="Permalink: Quick Start" href="#quick-start"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>From python command</strong></p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="export PYTHONPATH=./:$PYTHONPATH CUDA_VISIBLE_DEVICES=0 python3 bin/inference.py \ --model_path ./checkpoints \ --input_wav ./assets/question.wav \ --output_wav ./assets/answer.wav \ --llm_path ./Qwen2-7B-Instruct \ --top_p 0.8 \ --top_k 20 \ --temperature 0.8"><pre class="notranslate"><code>export PYTHONPATH=./:$PYTHONPATH CUDA_VISIBLE_DEVICES=0 python3 bin/inference.py \ --model_path ./checkpoints \ --input_wav ./assets/question.wav \ --output_wav ./assets/answer.wav \ --llm_path ./Qwen2-7B-Instruct \ --top_p 0.8 \ --top_k 20 \ --temperature 0.8 </code></pre></div> <p dir="auto"><strong>From script</strong></p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="sh scripts/run_inference.sh"><pre class="notranslate"><code>sh scripts/run_inference.sh </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Real-Time Interactive Demo</h3><a id="user-content-real-time-interactive-demo" class="anchor" aria-label="Permalink: Real-Time Interactive Demo" href="#real-time-interactive-demo"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To have a good interactive experience, please pay attention to the following three points:</p> <ul dir="auto"> <li><strong>Ensure a high-speed network connection</strong>.</li> <li><strong>Use high-performance GPUs for deployment</strong>. In the demo video, we use 1 Nvidia A100 GPU. A800, H800, or H20 will be much better.</li> <li><strong>Maintain a quiet environment</strong>.</li> </ul> <p dir="auto"><strong>From python command</strong></p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="export PYTHONPATH=./:$PYTHONPATH CUDA_VISIBLE_DEVICES=0 python3 bin/server.py \ --ip your_server_ip \ --port your_server_port \ --max_users 3 \ --llm_exec_nums 1 \ --timeout 180 \ --model_path ./checkpoints \ --llm_path ./Qwen2-7B-Instruct \ --top_p 0.8 \ --top_k 20 \ --temperature 0.8"><pre class="notranslate"><code>export PYTHONPATH=./:$PYTHONPATH CUDA_VISIBLE_DEVICES=0 python3 bin/server.py \ --ip your_server_ip \ --port your_server_port \ --max_users 3 \ --llm_exec_nums 1 \ --timeout 180 \ --model_path ./checkpoints \ --llm_path ./Qwen2-7B-Instruct \ --top_p 0.8 \ --top_k 20 \ --temperature 0.8 </code></pre></div> <p dir="auto"><strong>From script</strong></p> <p dir="auto">Change the <strong>ip</strong> and <strong>port</strong> in <code>scripts/run_demo_server.sh</code> with yours and run:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="sh scripts/run_demo_server.sh"><pre class="notranslate"><code>sh scripts/run_demo_server.sh </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">✒️ Citation</h2><a id="user-content-️-citation" class="anchor" aria-label="Permalink: ✒️ Citation" href="#️-citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you find our work helpful for your research, please consider citing our work.</p> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@article{xiong2024freeze, title={Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM}, author={Xiong Wang and Yangze Li and Chaoyou Fu and Yunhang Shen and Lei Xie and Ke Li and Xing Sun and Long Ma}, journal={arXiv preprint arXiv:2411.00774}, year={2024} }"><pre><span class="pl-k">@article</span>{<span class="pl-en">xiong2024freeze</span>, <span class="pl-s">title</span>=<span class="pl-s"><span class="pl-pds">{</span>Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM<span class="pl-pds">}</span></span>, <span class="pl-s">author</span>=<span class="pl-s"><span class="pl-pds">{</span>Xiong Wang and Yangze Li and Chaoyou Fu and Yunhang Shen and Lei Xie and Ke Li and Xing Sun and Long Ma<span class="pl-pds">}</span></span>, <span class="pl-s">journal</span>=<span class="pl-s"><span class="pl-pds">{</span>arXiv preprint arXiv:2411.00774<span class="pl-pds">}</span></span>, <span class="pl-s">year</span>=<span class="pl-s"><span class="pl-pds">{</span>2024<span class="pl-pds">}</span></span> }</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">📣 Statement</h2><a id="user-content--statement" class="anchor" aria-label="Permalink: 📣 Statement" href="#-statement"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Freeze-Omni is trained on large-scale corpus, and its output has randomness. Any content generated by Freeze-Omni does not represent the views of the model developers. We are not responsible for any problems arising from the use, misuse, and dissemination of Freeze-Omni, including but not limited to public opinion risks and data security issues.</strong></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">📜 Related Works</h2><a id="user-content--related-works" class="anchor" aria-label="Permalink: 📜 Related Works" href="#-related-works"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Explore our related researches:</p> <ul dir="auto"> <li><strong>[VITA]</strong> <a href="https://github.com/VITA-MLLM/VITA">Towards Open-Source Interactive Omni Multimodal LLM</a></li> <li><strong>[Awesome-MLLM]</strong> <a href="https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models">A Survey on Multimodal Large Language Models</a></li> <li><strong>[MME]</strong> <a href="https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation">MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models</a></li> <li><strong>[Video-MME]</strong> <a href="https://github.com/BradyFU/Video-MME">Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">👍 Acknowledgement</h2><a id="user-content--acknowledgement" class="anchor" aria-label="Permalink: 👍 Acknowledgement" href="#-acknowledgement"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Freeze-Omni is built with reference to the following outstanding works: <a href="https://huggingface.co/Qwen/Qwen2-7B-Instruct" rel="nofollow">Qwen2-7B-Instruct</a>, <a href="https://github.com/y-ren16/ticodec">TiCodec</a></p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="+1bYAaJBnlKB9jc8TOFirNigmpauQYW5ksiDDHsG5nqkifdlfixJauSMo0jNv1a8SvOlsXnPPfuPJ3tdjsmynQ==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> ✨✨Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:speech" href="/topics/speech" title="Topic: speech" data-view-component="true" class="topic-tag topic-tag-link"> speech </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:speech-synthesis" href="/topics/speech-synthesis" title="Topic: speech-synthesis" data-view-component="true" class="topic-tag topic-tag-link"> speech-synthesis </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:speech-recognition" href="/topics/speech-recognition" title="Topic: speech-recognition" data-view-component="true" class="topic-tag topic-tag-link"> speech-recognition </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:speech-to-speech" href="/topics/speech-to-speech" title="Topic: speech-to-speech" data-view-component="true" class="topic-tag topic-tag-link"> speech-to-speech </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:large-language-models" href="/topics/large-language-models" title="Topic: large-language-models" data-view-component="true" class="topic-tag topic-tag-link"> large-language-models </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:multimodal-large-language-models" href="/topics/multimodal-large-language-models" title="Topic: multimodal-large-language-models" data-view-component="true" class="topic-tag topic-tag-link"> multimodal-large-language-models </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{"category":"Repository Overview","action":"click","label":"location:sidebar;file:readme"}" href="#readme-ov-file"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-book mr-2"> <path d="M0 1.75A.75.75 0 0 1 .75 1h4.253c1.227 0 2.317.59 3 1.501A3.743 3.743 0 0 1 11.006 1h4.245a.75.75 0 0 1 .75.75v10.5a.75.75 0 0 1-.75.75h-4.507a2.25 2.25 0 0 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1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>