{"id":"20c2b647-91d4-4b0a-aeb4-05f2fdcf3c77","title":"Best Local Speech To Text Models","created_at":"2025-10-08T20:41:12Z","saved":true,"shared":true,"branch_id":"00000000-0000-4000-0000-000000000000","profile":{"id":null,"name":null,"subtitle":null,"model":"ki","model_name":"Ki","model_provider":"kagi","model_input_limit":1000000,"recommended":true,"model_info":"\n\n<div class=\"heading\">\n <div class=\"left\">\n \n \n \n <svg class=\"model-icon icon-xs\" data-model=\"ki\">\n <use href=\"#icon-kagi\"/>\n </svg>\n <h3>\n \n <span class=\"model-provider\">Kagi</span>\n \n <span class=\"model-name\">none</span>\n </h3>\n </div>\n\n \n <a href=\"/settings?p=custom_assistant&id=none\">\n <svg class=\"icon-sm edit-icon\" aria-hidden=\"true\">\n <use href=\"#icon-pencil\" />\n </svg>\n </a>\n \n</div>\n\n\n<hr />\n<ul>\n \n <li>\n <h4>\n Cost\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Moderate\">\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-cost --medium\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Quality\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 4 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Speed\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 3 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Privacy\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 5 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li class=\"divider\" aria-hidden=\"true\"></li>\n <li>\n <h4>Last updated</h4>\n <span class=\"date\">\n \n --\n \n </span>\n </li>\n \n</ul>\n","model_provider_name":"Kagi","internet_access":true,"personalizations":false,"shortcut":null,"is_default_profile":false},"tag_ids":[],"read_only":true}

[{"id":"05db1468-74dc-473d-9132-69cfb72e2247","thread_id":"20c2b647-91d4-4b0a-aeb4-05f2fdcf3c77","created_at":"2025-10-08T20:41:12Z","state":"done","prompt":"find the best speech to text local hosted models with translation capabilities","references_html":"<ol data-ref-list>\n \n <li>\n <a href=\"https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825\" >SeamlessM4T vs. Whisper — A Speech-to-Text Benchmark</a>\n <span class=\"__domain-name\">medium.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 20%, transparent 20%)\">20%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" >The Best Open-Source Speech-to-Text (STT) Tools in 2025</a>\n <span class=\"__domain-name\">medium.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 19%, transparent 19%)\">19%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/\" >Introducing speech-to-text, text-to-speech, and more for ... - Meta AI</a>\n <span class=\"__domain-name\">ai.meta.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 18%, transparent 18%)\">18%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/\" >I compared the different open source whisper packages for long ... - Reddit</a>\n <span class=\"__domain-name\">www.reddit.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 7%, transparent 7%)\">7%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://github.com/facebookresearch/seamless_communication\" >facebookresearch/seamless_communication</a>\n <span class=\"__domain-name\">github.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 6%, transparent 6%)\">6%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/\" >SeamlessM4T—Massively Multilingual &amp; Multimodal ...</a>\n <span class=\"__domain-name\">ai.meta.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 6%, transparent 6%)\">6%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://ai.meta.com/research/seamless-communication/\" >Seamless Communication - AI at Meta</a>\n <span class=\"__domain-name\">ai.meta.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 5%, transparent 5%)\">5%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://huggingface.co/openai/whisper-large\" >openai/whisper-large · Hugging Face</a>\n <span class=\"__domain-name\">huggingface.co</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 5%, transparent 5%)\">5%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://ai.meta.com/blog/seamless-m4t/\" >SeamlessM4T—Massively Multilingual &amp; Multimodal Machine ... - Meta AI</a>\n <span class=\"__domain-name\">ai.meta.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 4%, transparent 4%)\">4%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://huggingface.co/openai/whisper-large-v3\" >openai/whisper-large-v3 · Hugging Face</a>\n <span class=\"__domain-name\">huggingface.co</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 4%, transparent 4%)\">4%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://www.knowledgescience.ai/1687822/13995427\" >Episode 102 - Whisper V3 und Distil-Whisper</a>\n <span class=\"__domain-name\">www.knowledgescience.ai</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 2%, transparent 2%)\">2%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://www.heise.de/news/FFmpeg-8-0-integriert-Whisper-Lokale-Audio-Transkription-ohne-Cloud-10521966.html\" >FFmpeg 8.0 integriert Whisper: Lokale... | heise online</a>\n <span class=\"__domain-name\">www.heise.de</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 2%, transparent 2%)\">2%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/nlp/machine_translation/machine_translation.html\" >Machine Translation Models — NVIDIA NeMo Framework User Guide</a>\n <span class=\"__domain-name\">docs.nvidia.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 1%, transparent 1%)\">1%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/speech_ai/index.html\" >Speech AI Models — NVIDIA NeMo Framework User Guide</a>\n <span class=\"__domain-name\">docs.nvidia.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) 1%, transparent 1%)\">1%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/speechlm2/models.html\" >Models — NVIDIA NeMo Framework User Guide</a>\n <span class=\"__domain-name\">docs.nvidia.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) &lt; 1%, transparent &lt; 1%)\">&lt; 1%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://huggingface.co/docs/transformers/de/index\" >Transformers</a>\n <span class=\"__domain-name\">huggingface.co</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) &lt; 1%, transparent &lt; 1%)\">&lt; 1%</span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n</ol>\n","references_md":"[^1]: [SeamlessM4T vs. Whisper — A Speech-to-Text Benchmark](https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825) (20%)\n[^2]: [The Best Open-Source Speech-to-Text (STT) Tools in 2025](https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4) (19%)\n[^3]: [Introducing speech-to-text, text-to-speech, and more for ... - Meta AI](https://ai.meta.com/blog/multilingual-model-speech-recognition/) (18%)\n[^4]: [I compared the different open source whisper packages for long ... - Reddit](https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/) (7%)\n[^5]: [facebookresearch/seamless_communication](https://github.com/facebookresearch/seamless_communication) (6%)\n[^6]: [SeamlessM4T—Massively Multilingual & Multimodal ...](https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/) (6%)\n[^7]: [Seamless Communication - AI at Meta](https://ai.meta.com/research/seamless-communication/) (5%)\n[^8]: [openai/whisper-large · Hugging Face](https://huggingface.co/openai/whisper-large) (5%)\n[^9]: [SeamlessM4T—Massively Multilingual & Multimodal Machine ... - Meta AI](https://ai.meta.com/blog/seamless-m4t/) (4%)\n[^10]: [openai/whisper-large-v3 · Hugging Face](https://huggingface.co/openai/whisper-large-v3) (4%)\n[^11]: [Episode 102 - Whisper V3 und Distil-Whisper](https://www.knowledgescience.ai/1687822/13995427) (2%)\n[^12]: [FFmpeg 8.0 integriert Whisper: Lokale... | heise online](https://www.heise.de/news/FFmpeg-8-0-integriert-Whisper-Lokale-Audio-Transkription-ohne-Cloud-10521966.html) (2%)\n[^13]: [Machine Translation Models — NVIDIA NeMo Framework User Guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/nlp/machine_translation/machine_translation.html) (1%)\n[^14]: [Speech AI Models — NVIDIA NeMo Framework User Guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/speech_ai/index.html) (1%)\n[^15]: [Models — NVIDIA NeMo Framework User Guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/speechlm2/models.html) (< 1%)\n[^16]: [Transformers](https://huggingface.co/docs/transformers/de/index) (< 1%)\n","reply":"<details><summary>Planned research</summary>\n<ul>\n<li>Search for best local/self-hosted speech-to-text models with translation capabilities</li>\n<li>Identify top models that support multilingual transcription and translation</li>\n<li>Compare their features, performance, and language support</li>\n<li>Check for recent models as of 2025</li>\n</ul>\n</details>\n<details><summary>Searched with Kagi<p></p>\n<ul>\n<li>best local self-hosted speech to text models with translation 2025</li>\n<li>open source STT models multilingual translation</li>\n<li>Whisper alternatives local speech translation</li></ul></summary> Sources (15):<br>\n<strong>We Tested 10 Speech-to-Text Models</strong><br>\n<a href=\"https://www.willowtreeapps.com/craft/10-speech-to-text-models-tested\" target=\"_blank\">https://www.willowtreeapps.com/craft/10-speech-to-text-models-tested</a><br>\nBased on our testing, we found assemblyai-universal-2 to be the best model in terms of word error rate, but all the models performed very well.\n\n<p><strong>Best Self-Hosted TTS Models in 2025 - a2e.ai</strong><br>\n<a href=\"https://a2e.ai/best-self-hosted-tts-models-2025/\" target=\"_blank\">https://a2e.ai/best-self-hosted-tts-models-2025/</a><br>\n We break down the best self-hosted TTS models. The demand for high-quality, human-like text-to-speech (TTS) solutions has surged.</p>\n<p><strong>Die besten 298 speech to text voice AI Tools - Toolify</strong><br>\n<a href=\"https://www.toolify.ai/de/category-attribute/speech-to-text-voice\" target=\"_blank\">https://www.toolify.ai/de/category-attribute/speech-to-text-voice</a><br>\nJul 26, 2024 Voice to ChatGPT, PlayAI, CoeFont, Deepgram AI Voice Generator, VoiceBar, Sound of Text, MyVocal.ai, Voice Pen: Speech to Text AI, Text to Voice Generator, Text to Speech Online sind die besten kostenpflichtigen / kostenlosen speech to text voice Tools.speakSync - Voice Translator. KI-Sprachübersetzung für über 70 Sprachen.</p>\n<p><strong>Die besten 17 speech to text for free AI Tools - Toolify</strong><br>\n<a href=\"https://www.toolify.ai/de/category-attribute/speech-to-text-for-free\" target=\"_blank\">https://www.toolify.ai/de/category-attribute/speech-to-text-for-free</a><br>\nMar 19, 2025 Deepgram, TTSynth.com, VideoToWords AI, FreeSubtitles.AI, TranscribeMe, TTSMaker, Azen, VoksPilot, MeetGPT, Mictoo sind die besten kostenpflichtigen / kostenlosen speech to text for free Tools.Alice - Your Personal AI Assistant App. Alice ist eine KI-Assistent-App zum Chatten mit KI-Modellen und zur Automatisierung von Aufgaben.</p>\n<p><strong>Die besten 285 speech-to-text free AI Tools - Toolify</strong><br>\n<a href=\"https://www.toolify.ai/de/category-attribute/speech-to-text-free\" target=\"_blank\">https://www.toolify.ai/de/category-attribute/speech-to-text-free</a><br>\nAug 04, 2024 PlayAI, ttsMP3.com, Text to Speech.im, Luvvoice, GoVoice, Translate My Audio, Texttovoice.online, Voice Remaker, VanillaVoice, voicechanger.im sind die besten kostenpflichtigen / kostenlosen speech-to-text free Tools.Alice - Your Personal AI Assistant App. Alice ist eine KI-Assistent-App zum Chatten mit KI-Modellen und zur Automatisierung von Aufgaben.</p>\n<p><strong>openai/whisper: Robust Speech Recognition via Large- ...</strong><br>\n<a href=\"https://github.com/openai/whisper\" target=\"_blank\">https://github.com/openai/whisper</a><br>\n A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken ...</p>\n<p><strong>facebookresearch / seamless_communication: Foundational Models for ...</strong><br>\n<a href=\"https://github.com/facebookresearch/seamless_communication\" target=\"_blank\">https://github.com/facebookresearch/seamless_communication</a><br>\n We open-source the metadata to SeamlessAlign, the largest open dataset for multimodal translation, totaling 270k+ hours of aligned Speech and Text data. The dataset can be rebuilt by the community based on the SeamlessAlign readme.</p>\n<p><strong>GitHub - CodeTittans/SeamlessM4T: Foundational Models for State-of-the ...</strong><br>\n<a href=\"https://github.com/CodeTittans/SeamlessM4T\" target=\"_blank\">https://github.com/CodeTittans/SeamlessM4T</a><br>\n We open-source the metadata to SeamlessAlign, the largest open dataset for multimodal translation, totaling 270k+ hours of aligned Speech and Text data. The dataset can be rebuilt by the community based on the SeamlessAlign readme.</p>\n<p><strong>The Best Open-Source Speech-to-Text (STT) Tools in 2025</strong><br>\n<a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4</a><br>\n While Google, Amazon, and Microsoft lead in cloud-based STT, developers and researchers increasingly look to open-source alternatives for cost savings, offline use, and privacy. In 2025,...</p>\n<p><strong>Neural Network-based Multilingual Translation Models</strong><br>\n<a href=\"https://publikationen.bibliothek.kit.edu/1000166676\" target=\"_blank\">https://publikationen.bibliothek.kit.edu/1000166676</a><br>\nMar 15, 2025 Repository KITopen. Neural Network-based Multilingual Translation Models.</p>\n<p><strong>Speech to Text - Whisper alternatives? : r/LocalLLaMA</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1d7cjbf/speech_to_text_whisper_alternatives/\" target=\"_blank\">https://www.reddit.com/r/LocalLLaMA/comments/1d7cjbf/speech_to_text_whisper_alternatives/</a><br>\n Whisper's solid for solo speech or clear audio, but it definitely struggles with multiple speakers. I've been using a tool lately that combines ...</p>\n<p><strong>I compared the different open source whisper packages for long ... - Reddit</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/\" target=\"_blank\">https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/</a><br>\n Subreddit to discuss about Llama, the large language model created by Meta AI. Hey everyone! I hope you're having a great day. I recently compared all the open source whisper-based packages that support long-form transcription.</p>\n<p><strong>Web-UI for Whisper, an awesome audio transcription AI. Easy to ... - Reddit</strong><br>\n<a href=\"https://www.reddit.com/r/selfhosted/comments/z6r9x1/webui_for_whisper_an_awesome_audio_transcription/\" target=\"_blank\">https://www.reddit.com/r/selfhosted/comments/z6r9x1/webui_for_whisper_an_awesome_audio_transcription/</a><br>\n A place to share, discuss, discover, assist with, gain assistance for, and critique self-hosted alternatives to our favorite web apps, web services, and online tools.</p>\n<p><strong>The best Whisper Alternatives in 2025 - sally.io</strong><br>\n<a href=\"https://www.sally.io/blog/the-best-whisper-alternatives\" target=\"_blank\">https://www.sally.io/blog/the-best-whisper-alternatives</a><br>\n For local, offline transcription: Use Whisper itself or alternatives like Vosk that run on your own machine and offer full data control. Now, let’s dive into the best Whisper alternatives available today.</p>\n<p><strong>How to Integrate Whisper API into Your Application for Audio...</strong><br>\n<a href=\"https://vomo.ai/de/blog/how-to-integrate-whisper-api-into-your-application-for-audio-transcription\" target=\"_blank\">https://vomo.ai/de/blog/how-to-integrate-whisper-api-into-your-application-for-audio-transcription</a><br>\nAug 09, 2025 By connecting Whisper’s speech recognition capabilities, your app can perform real-time or batch Audio zu Text transcription, unlocking powerful features such as automated note-taking, caption generation, and content analysis. What Is Whisper API and Why Integrate It?Here’s a clear, step-by-step guide for how to use the Whisper API so you can integrate speech-to-text into your workflow with ChatGPT or other tools.</p>\n</details>\n<details><summary>Contemplated</summary> The search results show mentions of Whisper (OpenAI), SeamlessM4T (Facebook/Meta), and some cloud services. I need to get more specific information about the best LOCAL/self-hosted models with translation capabilities. Let me check the detailed content from the Medium article about open-source STT tools and the SeamlessM4T GitHub page, as these seem most relevant. I also need to search more specifically for models that combine STT with translation.<p></p>\n</details>\n<details><summary>Gathered key details from:<p></p>\n<ul>\n<li><a href=\"https://medium.com/%40shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">The Best Open-Source Speech-to-Text (STT) Tools in 2025</a></li></ul></summary>\n\n<h3>Query</h3>\n<p>What are the best open-source speech-to-text models in 2025 that can be self-hosted, particularly those with translation capabilities?</p>\n<h3>Response</h3>\n<h2>1. Whisper (by OpenAI, community maintained)</h2>\n<p><strong>Overview</strong>: A breakthrough multilingual STT model supporting <strong>~100 languages</strong>.</p>\n<p><strong>Strengths</strong>:</p>\n<ul>\n<li>High accuracy even on noisy dat</li>\n<li>Multilingual + translation capabilities</li>\n<li>Widely adopted, with many community forks (Whisper.cpp, Faster-Whisper)</li>\n</ul>\n<p><strong>Limitations</strong>:</p>\n<ul>\n<li>Heavy models can be slow without GPU acceleration</li>\n</ul>\n<p><strong>Best For</strong>: Transcription, captioning, multi-language applications.</p>\n</details>\n<details><summary>Gathered key details from:<p></p>\n<ul>\n<li><a href=\"https://github.com/facebookresearch/seamless_communication\" target=\"_blank\">seamless_communication GitHub Repo</a></li></ul></summary>\n\n<h3>Query</h3>\n<p>What are the features and capabilities of SeamlessM4T, particularly for speech-to-text with translation?</p>\n<h3>Response</h3>\n<p>SeamlessM4T is a foundational, massively multilingual, and multimodal machine translation model that supports around 100 languages. Its capabilities include Speech-to-text translation (S2TT). It also supports Speech-to-speech translation (S2ST), Text-to-speech translation (T2ST), Text-to-text translation (T2TT), and Automatic speech recognition (ASR).</p>\n</details>\n<details><summary>Searched with Kagi<p></p>\n<ul>\n<li>Whisper large v3 multilingual translation features</li>\n<li>SeamlessM4T vs Whisper local hosting comparison</li>\n<li>MMS Meta speech translation model</li></ul></summary> Sources (15):<br>\n<strong>openai/whisper-large-v3</strong><br>\n<a href=\"https://huggingface.co/openai/whisper-large-v3\" target=\"_blank\">https://huggingface.co/openai/whisper-large-v3</a><br>\nThe large-v3 model shows improved performance over a wide variety of languages, showing 10% to 20% reduction of errors compared to Whisper large-v2 . For more ...\n\n<p><strong>huggingface.co/datasets/argmaxinc/whisperkit-evals-multilingual/blob...</strong><br>\n<a href=\"https://huggingface.co/datasets/argmaxinc/whisperkit-evals-multilingual/blob/main/WhisperKit/openai_whisper-large-v3/common_voice_17_0-argmax_subset-400/forced/de/2024-10-03_17%3A15%3A17_GMT-0700.json\" target=\"_blank\">https://huggingface.co/datasets/argmaxinc/whisperkit-evals-multilingual/blob/main/WhisperKit/openai_whisper-large-v3/common_voice_17_0-argmax_subset-400/forced/de/2024-10-03_17%3A15%3A17_GMT-0700.json</a><br>\nOct 28, 2024 / whisperkit-evals-multilingual.whisperkittools 2bad817: Eval openai_whisper-large-v3 on common_voice_17_0-argmax_subset-400. b89b4b4 verified 10 months ago.</p>\n<p><strong>openai/whisper-large · Hugging Face</strong><br>\n<a href=\"https://huggingface.co/openai/whisper-large\" target=\"_blank\">https://huggingface.co/openai/whisper-large</a><br>\n For speech translation, the model predicts transcriptions to a different language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only.</p>\n<p><strong>openai/whisper-large-v3 · Suddenly all my transcriptions are in English</strong><br>\n<a href=\"https://huggingface.co/openai/whisper-large-v3/discussions/71\" target=\"_blank\">https://huggingface.co/openai/whisper-large-v3/discussions/71</a><br>\n Ok after some discussion, it was decided to revert the behavior back to the original Whisper-v3 behavior which is to automatically detect the language followed by transcribing the audio to the detected language.</p>\n<p><strong>chris365312/whisper-large-v3-de-at · Hugging Face</strong><br>\n<a href=\"https://huggingface.co/chris365312/whisper-large-v3-de-at\" target=\"_blank\">https://huggingface.co/chris365312/whisper-large-v3-de-at</a><br>\nJun 14, 2025 Model Card for chris365312/whisper-large-v3-de-at. Ein feingetuntes Whisper-large-v3 Modell für österreichisches Deutsch, trainiert mit LoRA auf dem Common Voice v14 Datensatz.model_id = \"chris365312/whisper-large-v3-de-at\" proc = WhisperProcessor.from_pretrained(model_id) model = WhisperForConditionalGeneration.from_pretrained(model_id, device_map=\"auto\") model.generation_config.forced_decoder_ids = None #. Beispiel: WAV-Datei laden und transkribieren audio, sr = torchaudio.load(\"mein_audio.wav\") if sr !=</p>\n<p><strong>SeamlessM4T vs. Whisper — A Speech-to-Text Benchmark</strong><br>\n<a href=\"https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825\" target=\"_blank\">https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825</a><br>\n Analysis. In this benchmark, Seamless exhibited superior performance compared to Whisper, showcasing faster inferences at a rate of ...</p>\n<p><strong>Experiments on Meta's Seamless (SeamlessM4T v2)</strong><br>\n<a href=\"https://medium.com/@GenerationAI/experiments-on-metas-seamless-seamlessm4t-v2-part-2-2af171dd5106\" target=\"_blank\">https://medium.com/@GenerationAI/experiments-on-metas-seamless-seamlessm4t-v2-part-2-2af171dd5106</a><br>\n Whisper processes data in 30 second chunks to achieve better speech recognition and translation quality, but this is disadvantageous for real-time data streams.</p>\n<p><strong>Whisper-Universelles Sprachmodell für die Spracherkennung</strong><br>\n<a href=\"https://www.aibase.com/de/tool/24774\" target=\"_blank\">https://www.aibase.com/de/tool/24774</a><br>\nAug 01, 2024 LLM Leaderboard. Comparison and ranking the performance of over 100 AI models.Whisper ist ein universelles Sprachmodell zur Spracherkennung. Es wurde mit einer großen Menge an vielfältigen Audiodaten trainiert und ist ein Multi-Task-Modell, das mehrsprachige Spracherkennung, Sprachübersetzung und Spracherkennung durchführen kann.</p>\n<p><strong>Azure Cognitive Services Sprache-Ermöglicht intelligenten Austausch...</strong><br>\n<a href=\"https://www.aibase.com/de/tool/31358\" target=\"_blank\">https://www.aibase.com/de/tool/31358</a><br>\nAug 01, 2024 SeamlessM4T. SeamlessM4T — Ein auf einem multimodalen Modell basierendes Sprachübersetzungsprodukt, das automatische Spracherkennung, Sprachübersetzung, Textübersetzung und Sprachsynthese für fast 100 Sprachen unterstützt.</p>\n<p><strong>Hanwang Sprachassistent-Flaggschiff-Anwendung für intelligente...</strong><br>\n<a href=\"https://www.aibase.com/de/tool/32427\" target=\"_blank\">https://www.aibase.com/de/tool/32427</a><br>\nAug 15, 2024 Whisper — Universelles Sprachmodell für die Spracherkennung.SeamlessM4T — Ein auf einem multimodalen Modell basierendes Sprachübersetzungsprodukt, das automatische Spracherkennung, Sprachübersetzung, Textübersetzung und Sprachsynthese für fast 100 Sprachen unterstützt. Produktivität.</p>\n<p><strong>Introducing speech-to-text, text-to-speech, and more for ...</strong><br>\n<a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/\" target=\"_blank\">https://ai.meta.com/blog/multilingual-model-speech-recognition/</a><br>\n MMS supports speech-to-text and text-to-speech for 1,107 languages and language identification for over 4,000 languages.</p>\n<p><strong>SeamlessM4T—Massively Multilingual &amp; Multimodal ...</strong><br>\n<a href=\"https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/\" target=\"_blank\">https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/</a><br>\n A single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic ...</p>\n<p><strong>SeamlessM4T—Massively Multilingual &amp; Multimodal Machine ... - Meta AI</strong><br>\n<a href=\"https://ai.meta.com/blog/seamless-m4t/\" target=\"_blank\">https://ai.meta.com/blog/seamless-m4t/</a><br>\n To build this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations, dubbed SeamlessAlign.</p>\n<p><strong>Seamless Communication - AI at Meta</strong><br>\n<a href=\"https://ai.meta.com/research/seamless-communication/\" target=\"_blank\">https://ai.meta.com/research/seamless-communication/</a><br>\n In August 2023, we introduced the first version of SeamlessM4T, a foundational multilingual and multitask model that delivered state-of-the-art results for translation and transcription across speech and text.</p>\n<p>**<a href=\"https://www.themoonlight.io/de/review/transcribing-and-translating-fast-and-slow-joint-speech-translation-and-recognition\" target=\"_blank\">https://www.themoonlight.io/de/review/transcribing-and-translating-fast-and-slow-joint-speech-translation-and-recognition</a>and-translating-fast-and-slow-joint-speech-translation-and-recognition)<br>\nJun 15, 2025 The paper titled \"Transcribing and Translating, Fast and Slow: Joint Speech Translation and Recognition\" introduces a cutting-edge model named JSTAR (Joint Speech Translation and Recognition), which aims to enhance the process of simultaneous automatic speech recognition (ASR) and speech translation (ST). This integration allows for high-quality streaming results that are particularly suitable for bilingual conversational contexts, such as those. facilitated by smart glasses.</p>\n</details>\n<details><summary>Contemplated</summary> Good progress! I now have information on Whisper and SeamlessM4T. I also found a reference to Meta's MMS model which supports 1,107 languages. Let me get more detailed information about the Medium article to see what other models are discussed, and check the SeamlessM4T benchmark comparison. I should also search for more recent models and implementations that are optimized for local hosting.<p></p>\n</details>\n<details><summary>Gathered key details from:<p></p>\n<ul>\n<li><a href=\"https://medium.com/%40shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">The Best Open-Source Speech-to-Text (STT) Tools in 2025</a></li></ul></summary>\n\n<h3>Query</h3>\n<p>List all the open-source speech-to-text models discussed in this article with their key features, especially those that can be self-hosted</p>\n<h3>Response</h3>\n<h2>1. Whisper (by OpenAI, community maintained)</h2>\n<p><strong>Overview</strong>: A breakthrough multilingual STT model supporting <strong>~100 languages</strong>.</p>\n<p><strong>Strengths</strong>:</p>\n<ul>\n<li>High accuracy even on noisy dat</li>\n<li>Multilingual + translation capabilities</li>\n<li>Widely adopted, with many community forks (Whisper.cpp, Faster-Whisper)</li>\n</ul>\n<p><strong>Limitations</strong>:</p>\n<ul>\n<li>Heavy models can be slow without GPU acceleration</li>\n</ul>\n<p><strong>Best For</strong>: Transcription, captioning, multi-language applications.</p>\n<hr>\n<h2>2. Whisper.cpp / Faster-Whisper</h2>\n<p><strong>Overview</strong>: Efficient C++ and optimized Python implementations of Whisper.</p>\n<p><strong>Strengths</strong>:</p>\n<ul>\n<li>Faster inference, supports CPU-only and edge devices (Raspberry Pi, Jetson)</li>\n<li>Quantized models reduce size and improve speed</li>\n</ul>\n<p><strong>Limitations</strong>:</p>\n<ul>\n<li>Same core limitations as Whisper (large models = resource heavy)</li>\n</ul>\n<p><strong>Best For</strong>: On-device transcription, real-time STT, lightweight deployments.</p>\n<hr>\n<h2>3. Vosk</h2>\n<p><strong>Overview</strong>: A lightweight STT toolkit based on Kaldi.</p>\n</details>\n<details><summary>Gathered key details from:<p></p>\n<ul>\n<li><a href=\"https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825\" target=\"_blank\">SeamlessM4T vs. Whisper — A Speech-to-Text Benchmark</a></li></ul></summary>\n\n<h3>Query</h3>\n<p>What are the benchmark results comparing SeamlessM4T and Whisper for speech-to-text performance?</p>\n<h3>Response</h3>\n<h2>Outcome of the benchmark</h2>\n<h3>Word Error Rate</h3>\n<p>Press enter or click to view image in full size</p>\n<p>Word Error Rate</p>\n<h3>Character Error Rate</h3>\n<p>Press enter or click to view image in full size</p>\n<h3>Processing Time</h3>\n<p>Press enter or click to view image in full size</p>\n<h2>Analysis</h2>\n<hr>\n<h2>Analysis</h2>\n<p>In this benchmark, Seamless exhibited superior performance compared to Whisper, showcasing faster inferences at a rate of approximately 50% to 60% swifter.</p>\n<p>Seamless demonstrated excellent proficiency in the Common Voice dataset, achieving commendable Word Error Rate (WER) and Character Error Rate (CER) scores.</p>\n<p>However, in environments with higher levels of noise, such as the AMI dataset, Whisper demonstrated a slight edge over Seamless. It’s worth noting that both models would benefit from fine-tuning to further enhance their accuracy when dealing with noisy audio files.</p>\n<p>One noteworthy observation is that Whisper occasionally suffers from what can be termed as “hallucinations”. These instances involve the model generating excessively long text when it’s uncertain about the actual content. An illustrative example in English vividly highlights this:</p>\n<p><strong>Hallucination Example with Whisper:</strong></p>\n<p><em>REFERENCE:</em> “Due to the expense of production it is not being used for this purpose.”</p>\n<p><em>PREDICTION:</em> “I said go go go, do what you want, do what you want, do what you want…” (repeated multiple times)</p>\n<p>The resulting Word Error Rate (WER) for this sample with Whisper reached a substantial 2542, significantly influencing the overall evaluation. In contrast, Seamless achieved a WER of 7.14 for the same audio sample.</p>\n<h3>Limitations</h3>\n<p>While this benchmark provides valuable insights, it’s important to acknowledge its limitations. The assessment encompassed a diverse range of datasets, yet the number of languages evaluated remained limited.</p>\n<p>Furthermore, a more comprehensive evaluation could be conducted with an increased volume of audio files.</p>\n<p>Lastly, filtering out predictions containing hallucinations would change the outcome of the benchmark.</p>\n<h2>Conclusion</h2>\n<hr>\n<h2>Conclusion</h2>\n<p>In this benchmark, Seamless emerged as the front runner, displaying impressive speed and accuracy, particularly in the Common Voice dataset. However, it’s essential to acknowledge Whisper’s strength in handling noisy environments, underscoring its adaptability.</p>\n<p>It’s worth noting that Whisper does encounter occasional challenges, as demonstrated by instances of “hallucinations”. These moments, while infrequent, strongly impact its global performance.</p>\n<p>To wrap up, it is clear this benchmark gave us some solid intel. Further validation on other languages could help get the full scoop on what these models can really achieve.</p>\n<p>#AI #Speech2text #Benchmark</p>\n</details>\n<details><summary>Searched with Kagi<p></p>\n<ul>\n<li>faster-whisper local hosting performance</li>\n<li>whisper.cpp vs faster-whisper 2025</li>\n<li>Meta MMS speech translation self-hosted</li></ul></summary> Sources (15):<br>\n<strong>Running Whisper Large on local GPU : r/LocalLLM</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLM/comments/1izezly/running_whisper_large_on_local_gpu/\" target=\"_blank\">https://www.reddit.com/r/LocalLLM/comments/1izezly/running_whisper_large_on_local_gpu/</a><br>\nThe GPU running the Whisper Large model needs to service atleast 100 users at a time, and it must seem real-time. But we estimate only 4-5 ...\n\n<p><strong>Faster Whisper Server - an OpenAI compatible server with ... - Reddit</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1d1j31r/faster_whisper_server_an_openai_compatible_server/\" target=\"_blank\">https://www.reddit.com/r/LocalLLaMA/comments/1d1j31r/faster_whisper_server_an_openai_compatible_server/</a><br>\n The Whisper model usually consumes all the available compute resources (both CPU and GPU) when processing audio data. Having a better GPU mostly results in faster transcription time rather than lower compute % usage.</p>\n<p><strong>Self-Hosting Whisper on dedicated hardware - Reddit</strong><br>\n<a href=\"https://www.reddit.com/r/homeassistant/comments/1cpabyf/selfhosting_whisper_on_dedicated_hardware_how/\" target=\"_blank\">https://www.reddit.com/r/homeassistant/comments/1cpabyf/selfhosting_whisper_on_dedicated_hardware_how/</a><br>\n I started by setting up the fully local pipeline. I run HA on a Pi 4, so I knew the performance might not be great, but I was pretty shocked just how slow it was - especially the STT (whisper) step.</p>\n<p><strong>whisper self hosted what's the most cost-efficient way</strong><br>\n<a href=\"https://www.reddit.com/r/selfhosted/comments/178twi1/whisper_self_hosted_whats_the_most_costefficient/\" target=\"_blank\">https://www.reddit.com/r/selfhosted/comments/178twi1/whisper_self_hosted_whats_the_most_costefficient/</a><br>\n It can actually be compiled with options to use graphics cards too, to give a bit of a speedup. I highly recommend it, I used it when at a trade show to quickly transcribe a video on a Macbook Air and it did the job superbly in very little time.</p>\n<p><strong>Lokale Sprache-zu-Text-Transkription unter Windows und Linux...</strong><br>\n<a href=\"https://www.speech-to-text.cloud/de/local-speech-to-text-transcription-on-windows-and-linux/\" target=\"_blank\">https://www.speech-to-text.cloud/de/local-speech-to-text-transcription-on-windows-and-linux/</a><br>\nJan 15, 2024 4 Verwendung von Faster-Whisper auf Linux. 5 Faster-Whisper unter Windows verwenden. 6 Arbeiten mit Speech Note.Speech Note local Speech-to-Text Transcription on Linux. Die Anwendung Speech Note ist ein Desktop-Programm für die Spracherkennung und Notizen unter Linux. Es bietet eine einfache Benutzeroberfläche für die Transkription von Audio-Dateien und die Durchführung von Sprachübersetzungen.</p>\n<p><strong>I compared the different open source whisper packages for ...</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/\" target=\"_blank\">https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/</a><br>\n I recently compared all the open source whisper-based packages that support long-form transcription. Long-form transcription is basically transcribing audio ...</p>\n<p><strong>FFmpeg 8.0 integrates Whisper: Local audio... | heise online</strong><br>\n<a href=\"https://www.heise.de/en/news/FFmpeg-8-0-integrates-Whisper-Local-audio-transcription-without-the-cloud-10522091.html\" target=\"_blank\">https://www.heise.de/en/news/FFmpeg-8-0-integrates-Whisper-Local-audio-transcription-without-the-cloud-10522091.html</a><br>\nAug 13, 2025 Aug 13, 2025 at 9:25 pm CEST.The new whisper filter in FFmpeg works locally and does not transfer content to the cloud. The whisper.cpp library is required, then an --enable-whisper activates the feature. By default, the software automatically recognizes the language; Whisper can transcribe audio recordings in over 90 languages. However, if in doubt, a language can be specified; the same applies to the use of a GPU, which is activated by default.</p>\n<p><strong>FFmpeg 8.0 integriert Whisper: Lokale... | heise online</strong><br>\n<a href=\"https://www.heise.de/news/FFmpeg-8-0-integriert-Whisper-Lokale-Audio-Transkription-ohne-Cloud-10521966.html\" target=\"_blank\">https://www.heise.de/news/FFmpeg-8-0-integriert-Whisper-Lokale-Audio-Transkription-ohne-Cloud-10521966.html</a><br>\nAug 13, 2025 FFmpeg 8.0 integriert Whisper: Lokale Audio-Transkription ohne Cloud. Die kommende Version 8.0 von FFmpeg erhält optional Whisper.. FFmpeg, das weitverbreitete Multimedia-Framework, integriert Whisper: Mit der neuen Funktion können Nutzer ihre Audioinhalte direkt innerhalb von FFmpeg automatisch transkribieren. Das auf maschinellem Lernen basierende Spracherkennungssystem stammt von OpenAI.</p>\n<p><strong>Spracherkennung im Eigenbau | c't | heise magazine</strong><br>\n<a href=\"https://www.heise.de/select/ct/2023/14/2305417013270445796\" target=\"_blank\">https://www.heise.de/select/ct/2023/14/2305417013270445796</a><br>\nMay 26, 2023 Die Open-Source-Spracherkennung Whisper transkribiert Sprache aus Audiodateien mit sehr guter Erkennungsquote und versteht sich sogar auf Zeichensetzung.Das erlaubt es Entwicklern, die Sprachmodelle und Algorithmen recht frei in eigene Projekte zu integrieren. Faster-Whisper will schneller und ressourcenschonender transkribieren; das Python-Projekt verwendet CTranslate2 anstelle von Torch für die Berechnung. whisper.cpp setzt die Spracherkennung in der Programmiersprache C++ um.</p>\n<p><strong>Comparision with faster-whisper · Issue #1127 · ggml-org/ ...</strong><br>\n<a href=\"https://github.com/ggml-org/whisper.cpp/issues/1127\" target=\"_blank\">https://github.com/ggml-org/whisper.cpp/issues/1127</a><br>\n For eg. It takes faster-whisper 14seconds with the small.en , whereas with whisper.cpp it's 46seconds. What causes this slowness? Or I am ...</p>\n<p><strong>SeamlessM4T—Massively Multilingual &amp; Multimodal ...</strong><br>\n<a href=\"https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/\" target=\"_blank\">https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/</a><br>\n A single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic ...</p>\n<p><strong>Introducing speech-to-text, text-to-speech, and more for ... - Meta AI</strong><br>\n<a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/\" target=\"_blank\">https://ai.meta.com/blog/multilingual-model-speech-recognition/</a><br>\n MMS supports speech-to-text and text-to-speech for 1,107 languages and language identification for over 4,000 languages.</p>\n<p><strong>SeamlessM4T—Massively Multilingual &amp; Multimodal Machine ... - Meta AI</strong><br>\n<a href=\"https://ai.meta.com/blog/seamless-m4t/\" target=\"_blank\">https://ai.meta.com/blog/seamless-m4t/</a><br>\n To build this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations, dubbed SeamlessAlign.</p>\n<p><strong>Seamless Communication - AI at Meta</strong><br>\n<a href=\"https://ai.meta.com/research/seamless-communication/\" target=\"_blank\">https://ai.meta.com/research/seamless-communication/</a><br>\n SeamlessStreaming is the first massively multilingual model that delivers translations with around two-seconds of latency and nearly the same accuracy as an offline model.</p>\n<p><strong>Hilfe erhalten</strong><br>\n<a href=\"https://www.meta.com/de-de/help/support/\" target=\"_blank\">https://www.meta.com/de-de/help/support/</a><br>\nMar 18, 2022</p>\n</details>\n<details><summary>Contemplated</summary> I'm getting good information on Whisper variants (Faster-Whisper, Whisper.cpp). Now I need to get the complete list from the Medium article and search for more information about other models mentioned, particularly those with strong translation capabilities. I should also look for more recent models like Distil-Whisper and check if there are any newer alternatives.<p></p>\n</details>\n<details><summary>Gathered key details from:<p></p>\n<ul>\n<li><a href=\"https://medium.com/%40shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">The Best Open-Source Speech-to-Text (STT) Tools in 2025</a></li></ul></summary>\n\n<h3>Query</h3>\n<p>What are all the other speech-to-text models discussed after Whisper and Vosk? Include their features, strengths, and limitations.</p>\n<h3>Response</h3>\n<p>There are no other speech-to-text models discussed after Whisper and Vosk in the provided document.</p>\n</details>\n<details><summary>Searched with Kagi<p></p>\n<ul>\n<li>Distil-Whisper local hosting performance 2025</li>\n<li>NVIDIA NeMo speech translation models</li>\n<li>Wav2Vec2 multilingual translation</li></ul></summary> Sources (15):<br>\n<strong>Audio Transcription Effortlessly with Distill Whisper AI</strong><br>\n<a href=\"https://www.digitalocean.com/community/tutorials/distill-whisper\" target=\"_blank\">https://www.digitalocean.com/community/tutorials/distill-whisper</a><br>\nDiscover Distil Whisper, a lightweight version of OpenAI's Whisper model that delivers up to 6x faster audio transcription without ...\n\n<p><strong>I compared the different open source whisper packages for long ... - Reddit</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/\" target=\"_blank\">https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/</a><br>\n Yes whisper large v3 for me is much less accurate than v2 and both v2 and v3 hallucinate a lot, but distilled one improves performance!</p>\n<p><strong>Episode 102 - Whisper V3 und Distil-Whisper</strong><br>\n<a href=\"https://www.knowledgescience.ai/1687822/13995427\" target=\"_blank\">https://www.knowledgescience.ai/1687822/13995427</a><br>\nNov 21, 2023 Send us a text. In der heutigen Sendung sprechen wir über das neue Speech to Text Model von OpenAI Whisper V3. Welche Veränderungen zu einer besseren Performance führten und wie Whisper Modelle mittels Distillation noch verkleinert werden können. Diese Verkleinerung führt zu ganz neuen Anwendungsfällen. Bleiben Sie dran.</p>\n<p><strong>Adapting Whisper for Lightweight and Efficient Automatic ...</strong><br>\n<a href=\"https://arxiv.org/html/2507.14451v1\" target=\"_blank\">https://arxiv.org/html/2507.14451v1</a><br>\n With this motivation, this study aims to develop a lightweight and efficient ASR system for children that is capable of running locally on an ...</p>\n<p><strong>Distil-Whisper is Now Available to the Developer Community on ... - Groq</strong><br>\n<a href=\"https://groq.com/blog/distil-whisper-is-now-available-to-the-developer-community-on-groqcloud-for-faster-and-more-efficient-speech-recognition\" target=\"_blank\">https://groq.com/blog/distil-whisper-is-now-available-to-the-developer-community-on-groqcloud-for-faster-and-more-efficient-speech-recognition</a><br>\n With its compatibility with popular Whisper libraries, Distil-Whisper is an attractive option for commercial applications seeking to improve transcription efficiency without sacrificing quality.</p>\n<p><strong>Speech AI Models — NVIDIA NeMo Framework User Guide</strong><br>\n<a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/speech_ai/index.html\" target=\"_blank\">https://docs.nvidia.com/nemo-framework/user-guide/latest/speech_ai/index.html</a><br>\n NVIDIA NeMo Framework supports the training and customization of Speech AI models, specifically designed to enable voice-based interfaces for conversational AI ...</p>\n<p><strong>Machine Translation Models — NVIDIA NeMo Framework User Guide</strong><br>\n<a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/nlp/machine_translation/machine_translation.html\" target=\"_blank\">https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/nlp/machine_translation/machine_translation.html</a><br>\n For now, the only family of language models supported are transformer language models trained in NeMo. NOTE: The transformer language model needs to be trained using the same tokenizer as the decoder tokenizer in the NMT system.</p>\n<p><strong>Machine Translation Models - NeMo Collections</strong><br>\n<a href=\"https://docs.nvidia.com/nemo-framework/user-guide/25.02/nemotoolkit/nlp/machine_translation/machine_translation.html\" target=\"_blank\">https://docs.nvidia.com/nemo-framework/user-guide/25.02/nemotoolkit/nlp/machine_translation/machine_translation.html</a><br>\n Supervised machine translation models require parallel corpora which comprises many examples of sentences in a source language and their corresponding ...</p>\n<p><strong>Automatic Speech Recognition (ASR) — NVIDIA NeMo Framework User Guide</strong><br>\n<a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/intro.html\" target=\"_blank\">https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/intro.html</a><br>\n Automatic Speech Recognition (ASR) # Automatic Speech Recognition (ASR), also known as Speech To Text (STT), refers to the problem of automatically transcribing spoken language. You can use NeMo to transcribe speech using open-sourced pretrained models in 14+ languages, or train your own ASR models. Transcribe speech with 3 lines of ...</p>\n<p><strong>Models — NVIDIA NeMo Framework User Guide</strong><br>\n<a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/speechlm2/models.html\" target=\"_blank\">https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/speechlm2/models.html</a><br>\n The Duplex Speech-to-Speech (S2S) collection consists of several model architectures designed to enable conversational AI systems with speech understanding and generation capabilities.</p>\n<p><strong>masoudmzb/wav2vec2-xlsr-multilingual-53-fa</strong><br>\n<a href=\"https://huggingface.co/masoudmzb/wav2vec2-xlsr-multilingual-53-fa\" target=\"_blank\">https://huggingface.co/masoudmzb/wav2vec2-xlsr-multilingual-53-fa</a><br>\n This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages.</p>\n<p><strong>Wav2Vec2-BERT</strong><br>\n<a href=\"https://huggingface.co/docs/transformers/model_doc/wav2vec2-bert\" target=\"_blank\">https://huggingface.co/docs/transformers/model_doc/wav2vec2-bert</a><br>\n The Wav2Vec2-BERT model was proposed in Seamless: Multilingual Expressive and Streaming Speech Translation by the Seamless Communication team from Meta AI.</p>\n<p><strong>Wav2Vec2-BERT - Hugging Face</strong><br>\n<a href=\"https://huggingface.co/docs/transformers/v4.49.0/en/model_doc/wav2vec2-bert\" target=\"_blank\">https://huggingface.co/docs/transformers/v4.49.0/en/model_doc/wav2vec2-bert</a><br>\n The Wav2Vec2-BERT model was proposed in Seamless: Multilingual Expressive and Streaming Speech Translation by the Seamless Communication team from Meta AI. This model was pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages.</p>\n<p><strong>Transformers</strong><br>\n<a href=\"https://huggingface.co/docs/transformers/de/index\" target=\"_blank\">https://huggingface.co/docs/transformers/de/index</a><br>\nJul 17, 2019 MarianMT Machine translation models trained using OPUS data by Jörg Tiedemann. The Marian Framework is being developed by the Microsoft Translator Team.Wav2Vec2 (from Facebook AI) released with the paper wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.</p>\n<p><strong>Crossing language identification: Multilingual ASR framework based on ...</strong><br>\n<a href=\"https://www.sciencedirect.com/science/article/pii/S2666827023000427\" target=\"_blank\">https://www.sciencedirect.com/science/article/pii/S2666827023000427</a><br>\n They compared different strategies for pre-training, fine-tuning, and decoding multilingual speech data and analyzed the factors affecting multilingual ASR systems’ performance.</p>\n</details>\n<details><summary>Contemplated</summary> Good, I found information on Distil-Whisper. Now I need to gather more specific details on the key models I've identified: Whisper (and its variants), SeamlessM4T, and MMS. I should also search for any other recent models and compare their translation capabilities specifically. Let me get more details on these models' actual translation features.<p></p>\n</details>\n<details><summary>Searched with Kagi<p></p>\n<ul>\n<li>SeamlessM4T v2 local deployment github</li>\n<li>Meta MMS model translation capabilities download</li>\n<li>Whisper large v3 turbo translation performance</li></ul></summary> Sources (15):<br>\n<strong>facebookresearch/seamless_communication</strong><br>\n<a href=\"https://github.com/facebookresearch/seamless_communication\" target=\"_blank\">https://github.com/facebookresearch/seamless_communication</a><br>\nWe are releasing SeamlessM4T v2, an updated version with our novel UnitY2 architecture. This new model improves over SeamlessM4T v1 in quality as well as ...\n\n<p><strong>SeamlessM4T - GitHub</strong><br>\n<a href=\"https://github.com/facebookresearch/seamless_communication/blob/main/docs/m4t/README.md\" target=\"_blank\">https://github.com/facebookresearch/seamless_communication/blob/main/docs/m4t/README.md</a><br>\n The v2 version of SeamlessM4T is a multitask adaptation of our novel UnitY2 architecture. Unity2 with its hierarchical character-to-unit upsampling and non-autoregressive text-to-unit decoding considerably improves over SeamlessM4T v1 in quality and inference speed.</p>\n<p><strong>Abhi-vish/SeamlessM4t-Translator</strong><br>\n<a href=\"https://github.com/Abhi-vish/SeamlessM4t-Translator\" target=\"_blank\">https://github.com/Abhi-vish/SeamlessM4t-Translator</a><br>\n These instructions will help you set up and use the Seamless M4T Translator on your local system. Prerequisites. Before getting started, make sure you have ...</p>\n<p><strong>Smart-Transformers/README_te.md at main...</strong><br>\n<a href=\"https://github.com/smartibase/Smart-Transformers/blob/main/README_te.md\" target=\"_blank\">https://github.com/smartibase/Smart-Transformers/blob/main/README_te.md</a><br>\nJan 29, 2025 SeamlessM4Tv2 (from Meta AI) released with the paper Seamless: Multilingual Expressive and Streaming Speech Translation by the Seamless Communication team. SegFormer (from NVIDIA) released with the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.</p>\n<p><strong>Smart-Transformers/README_ja.md at main...</strong><br>\n<a href=\"https://github.com/smartibase/Smart-Transformers/blob/main/README_ja.md\" target=\"_blank\">https://github.com/smartibase/Smart-Transformers/blob/main/README_ja.md</a><br>\nJan 29, 2025 SeamlessM4Tv2 (from Meta AI) released with the paper Seamless: Multilingual Expressive and Streaming Speech Translation by the Seamless Communication team. SegFormer (NVIDIA から) Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo から公開された研究論文: SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers.</p>\n<p><strong>Seamless Communication - AI at Meta</strong><br>\n<a href=\"https://ai.meta.com/research/seamless-communication/\" target=\"_blank\">https://ai.meta.com/research/seamless-communication/</a><br>\n A model that can deliver speech and text translations with around two seconds of latency. SeamlessM4T v2. A foundational multilingual and multitask model that ...</p>\n<p><strong>SeamlessM4T—Massively Multilingual &amp; Multimodal ...</strong><br>\n<a href=\"https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/\" target=\"_blank\">https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/</a><br>\n A single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic ...</p>\n<p><strong>Introducing speech-to-text, text-to-speech, and more for ... - Meta AI</strong><br>\n<a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/\" target=\"_blank\">https://ai.meta.com/blog/multilingual-model-speech-recognition/</a><br>\n Meta is focused on multilinguality in general: For text, the NLLB project scaled multilingual translation to 200 languages, and the Massively Multilingual Speech project scales speech technology to many more languages.</p>\n<p><strong>SeamlessM4T—Massively Multilingual &amp; Multimodal Machine ... - Meta AI</strong><br>\n<a href=\"https://ai.meta.com/blog/seamless-m4t/\" target=\"_blank\">https://ai.meta.com/blog/seamless-m4t/</a><br>\n To build this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations, dubbed SeamlessAlign.</p>\n<p><strong>facebook/mms-tts · Hugging Face</strong><br>\n<a href=\"https://huggingface.co/facebook/mms-tts\" target=\"_blank\">https://huggingface.co/facebook/mms-tts</a><br>\n This repository contains a collection of text-to-speech (TTS) models, offering support for over 1000 languages. These models are part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages.</p>\n<p><strong>openai/whisper-large-v3-turbo</strong><br>\n<a href=\"https://huggingface.co/openai/whisper-large-v3-turbo\" target=\"_blank\">https://huggingface.co/openai/whisper-large-v3-turbo</a><br>\n Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper Robust Speech Recognition via Large- ...</p>\n<p><strong>README.md · RachidAR/Whisper-v3-large-turbo at main</strong><br>\n<a href=\"https://huggingface.co/RachidAR/Whisper-v3-large-turbo/blob/main/README.md\" target=\"_blank\">https://huggingface.co/RachidAR/Whisper-v3-large-turbo/blob/main/README.md</a><br>\n Whisper's performance varies widely depending on the language. The figure below shows a performance breakdown of large-v3 and large-v2 models by language, using WERs (word error rates) or CER (character error rates, shown in Italic) evaluated on the Common Voice 15 and Fleurs datasets.</p>\n<p><strong>qualcomm/Whisper-Large-V3-Turbo · Hugging Face</strong><br>\n<a href=\"https://huggingface.co/qualcomm/Whisper-Large-V3-Turbo\" target=\"_blank\">https://huggingface.co/qualcomm/Whisper-Large-V3-Turbo</a><br>\n This repository provides scripts to run Whisper-Large-V3-Turbo on Qualcomm® devices. More details on model performance across various devices, can be found here.</p>\n<p><strong>README.md · qualcomm/Whisper-Large-V3-Turbo at main</strong><br>\n<a href=\"https://huggingface.co/qualcomm/Whisper-Large-V3-Turbo/blob/main/README.md\" target=\"_blank\">https://huggingface.co/qualcomm/Whisper-Large-V3-Turbo/blob/main/README.md</a><br>\n This repository provides scripts to run Whisper-Large-V3-Turbo on Qualcomm® devices. More details on model performance across various devices, can be found here.</p>\n<p><strong>openai/whisper-large-v3 · Hugging Face</strong><br>\n<a href=\"https://huggingface.co/openai/whisper-large-v3\" target=\"_blank\">https://huggingface.co/openai/whisper-large-v3</a><br>\n The large-v3 model shows improved performance over a wide variety of languages, showing 10% to 20% reduction of errors compared to Whisper large-v2 . For more details on the different checkpoints available, refer to the section Model details.</p>\n</details>\n<details><summary>Gathered key details from:<p></p>\n<ul>\n<li><a href=\"https://www.digitalocean.com/community/tutorials/distill-whisper\" target=\"_blank\">Audio Transcription Effortlessly with Distill Whisper AI | DigitalOcean</a></li></ul></summary>\n\n<h3>Query</h3>\n<p>What are the details about Distil-Whisper's performance, speed improvements, and how it compares to the original Whisper model?</p>\n<h3>Response</h3>\n<ul>\n<li><strong>Model Size Reduction</strong>: Distil Whisper is 49% smaller than the original Whisper model while maintaining critical functionality.</li>\n<li><strong>Performance Boost</strong>: Achieves up to <strong>6x speed improvements</strong> in inference time compared to the original Whisper model, making it ideal for real-time applications and large-scale transcription tasks.</li>\n<li><strong>Accuracy Retention</strong>: Maintains performance within 1% Word Error Rate (WER) of the original Whisper model on out-of-distribution audio datasets.</li>\n<li><strong>Technical Innovations</strong>: Implements layer-based compression, pseudo-labeling, and Kullback-Leibler divergence techniques to effectively transfer knowledge from the teacher model.</li>\n<li><strong>Enhanced Robustness</strong>: Shows 1.3x fewer instances of repeated word duplications and 2.1% reduction in insertion error rate compared to the original model, resulting in better handling of noisy audio.</li>\n<li><strong>Training Data</strong>: Trained on 22,000+ hours of pseudo-labeled audio data spanning 10 domains and 18,000+ speakers for comprehensive coverage.</li>\n<li><strong>Commercial License</strong>: Available under a commercial license, making it suitable for business applications and production environments.</li>\n</ul>\n<hr>\n<p><a href=\"https://github.com/openai/whisper\" target=\"_blank\">Whisper</a>, boasts a remarkable enhancement - being 6 times faster, 49% smaller in size, and achieving a performance level within 1% word error rate (WER) on out-of-distribution evaluation sets.</p>\n<p>To achieve this, it’s worth noticing in particular that the training objective was optimized to involve minimizing both the KL divergence between the distilled model and the Whisper model, and the <a href=\"https://www.digitalocean.com/community/tutorials/loss-functions-in-python#4-cross-entropy-loss-function-in-python\" target=\"_blank\">cross-entropy</a> loss computed on pseudo-labeled audio data.</p>\n<hr>\n<p>Faster Inference: Achieving an inference speed six times faster, while maintaining performance within 1% <a href=\"https://en.wikipedia.org/wiki/Word%5Ferror%5Frate\" target=\"_blank\">Word Error Rate</a> (WER) of Whisper on out-of-distribution audio.</p>\n<p><img alt=\"image\" src=\"https://doimages.nyc3.cdn.digitaloceanspaces.com/010AI-ML/content/images/2023/11/Screenshot-2023-11-12-at-10.03.59-PM.png\"></p>\n<p>Robustness to noise and hallucinations: The plot shows that as the noise becomes more intensive, the WER of the Distil-Whisper degrades less severely compared to other models, which are trained on the <a href=\"https://www.openslr.org/12\" target=\"_blank\">LibriSpeech</a> corpus. Quantified by 1.3 times fewer instances of repeated 5-gram word duplicates and a 2.1% reduction in insertion error rate (IER) compared to Whisper. This suggests that the extent of hallucination is reduced in Distil-Whisper compared to the original Whisper model. The average deletion error rate (DER) remains comparable for both large-v2 and distil-large-v2, with performance differing by ~ 0.3% DER.</p>\n<hr>\n<p>**1. What makes Distil Whisper different from the original Whisper model?**Distil Whisper is a distilled version of the original Whisper, meaning it’s trained to retain most of the performance while being smaller and faster. Unlike the heavy Whisper model that can be computationally expensive, Distil Whisper is optimized for speed and efficiency, allowing near real-time transcription without needing <a href=\"https://www.digitalocean.com/products/gradient/gpu-droplets?utm%5Fsource=google&amp;utm%5Fmedium=cpc&amp;utm%5Fcampaign=search%5Fbrand%5Fgpu%5Fin%5Fen&amp;utm%5Fadgroup=digitalocean&amp;utm%5Fterm=digitalocean%20gpu&amp;utm%5Fcreative=724359337372&amp;utm%5Flocation=9198376&amp;utm%5Fmatchtype=p&amp;utm%5Fdevice=c&amp;gad%5Fsource=1&amp;gad%5Fcampaignid=21990077663&amp;gbraid=0AAAAADw9jcuKwaE7XfraFYt9cfXUQeaY5&amp;gclid=Cj0KCQjwn8XFBhCxARIsAMyH8BuDbVcJp8D%5FB5zvaraztRkpw6cP7VEZc0efhLLZtpmxsKTyDUmXn38aAvJ-EALw%5FwcB\" target=\"_blank\">high-end GPUs</a>.</p>\n<p>**2. Does Distil Whisper compromise accuracy for speed?**Not significantly. While there is a slight reduction in accuracy compared to the full Whisper model, Distil Whisper is designed to strike a balance—providing much faster transcription with accuracy levels that remain competitive and usable across different applications.</p>\n</details>\n<details><summary>Response planned</summary> I have gathered comprehensive information about local speech-to-text models with translation capabilities. Key findings:<p></p>\n<ol>\n<li>\n<p><strong>Whisper family</strong> (citations 6, 16, 18, 20, 33, 70, 74): The most popular open-source STT model with ~100 language support and built-in translation capabilities. Multiple variants exist:</p>\n<ul>\n<li>Whisper Large v3: 10-20% error reduction vs v2</li>\n<li>Whisper Large v3 Turbo: Latest optimized version</li>\n<li>Faster-Whisper (citation 34): Optimized Python implementation with faster inference</li>\n<li>Whisper.cpp (citation 34, 44, 46): C++ implementation for edge devices</li>\n<li>Distil-Whisper (citations 50, 75-78): 6x faster, 49% smaller, maintains 1% WER of original</li>\n</ul>\n</li>\n<li>\n<p><strong>SeamlessM4T/v2</strong> (citations 7, 8, 17, 23, 29-31, 37, 38, 64): Meta's foundational multimodal translation model supporting ~100 languages. Key capabilities: S2TT, S2ST, T2ST, T2TT, ASR. Benchmark shows 50-60% faster inference than Whisper and better performance on Common Voice dataset, though Whisper performs better in noisy environments. Has hallucination issues less than Whisper.</p>\n</li>\n<li>\n<p><strong>Meta MMS</strong> (citation 28, 68, 69): Supports 1,107 languages for STT/TTS and 4,000+ for language identification. Primarily focused on massively multilingual support.</p>\n</li>\n<li>\n<p><strong>Other models</strong>: Vosk (citation 35) - lightweight, Kaldi-based; NVIDIA NeMo (citations 54, 57) - enterprise solutions; Wav2Vec2-BERT (citations 60, 61) - used in Seamless project, 4.5M hours training data, 143 languages.</p>\n</li>\n</ol>\n<p>Translation capabilities specifically mentioned for Whisper and SeamlessM4T families. SeamlessM4T appears superior for translation tasks based on benchmark (citation 37).</p>\n<ol>\n<li>\n<p>Introduction: Briefly state that several excellent local/self-hosted STT models exist with translation capabilities as of 2025.</p>\n</li>\n<li>\n<p>Top models with translation capabilities (in order of recommendation):<br>\na. SeamlessM4T/v2 - Best for translation-focused tasks<br>\nb. Whisper family - Most popular and versatile<br>\nc. Distil-Whisper - Best for speed/efficiency<br>\nd. Meta MMS - Best for language coverage</p>\n</li>\n<li>\n<p>For each model, provide:</p>\n<ul>\n<li>Key features and capabilities</li>\n<li>Translation support specifics</li>\n<li>Performance characteristics</li>\n<li>Hardware requirements</li>\n<li>Best use cases</li>\n</ul>\n</li>\n<li>\n<p>Comparison insights based on benchmarks</p>\n</li>\n<li>\n<p>Implementation recommendations (variants like Faster-Whisper, Whisper.cpp for different use cases)</p>\n</li>\n</ol>\n<p>Use a structured format with clear sections for each model. Start with a summary table comparing the top models across key dimensions (languages supported, translation capability, speed, model size). Then provide detailed descriptions for each model. Use bullet points for features and capabilities. Include performance benchmarks where available. End with practical recommendations based on different use cases (real-time vs batch, resource constraints, translation quality priority).</p>\n</details><h2>Best Local Speech-to-Text Models with Translation Capabilities (2025)</h2>\n<p>Here are the top self-hosted STT models that offer translation capabilities, ranked by their strengths:</p>\n<h3>Quick Comparison</h3>\n<table>\n<thead>\n<tr>\n<th>Model</th>\n<th>Languages</th>\n<th>Translation Support</th>\n<th>Speed</th>\n<th>Best For</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td><strong>SeamlessM4T v2</strong></td>\n<td>~100</td>\n<td>Excellent (S2TT, S2ST)</td>\n<td>50-60% faster than Whisper</td>\n<td>Translation-focused tasks</td>\n</tr>\n<tr>\n<td><strong>Whisper Large v3</strong></td>\n<td>~100</td>\n<td>Good (multilingual + translation)</td>\n<td>Moderate (GPU recommended)</td>\n<td>General transcription &amp; translation</td>\n</tr>\n<tr>\n<td><strong>Distil-Whisper</strong></td>\n<td>~100</td>\n<td>Good (inherits Whisper capabilities)</td>\n<td>6x faster than Whisper</td>\n<td>Real-time applications</td>\n</tr>\n<tr>\n<td><strong>Meta MMS</strong></td>\n<td>1,107+</td>\n<td>Limited</td>\n<td>Varies</td>\n<td>Massive language coverage</td>\n</tr>\n</tbody>\n</table>\n<hr>\n<h2>1. SeamlessM4T v2 (Meta) ⭐ Best for Translation</h2>\n<p><strong>Key Features:</strong><sup class=\"trigger\"><a href=\"https://github.com/facebookresearch/seamless_communication\" target=\"_blank\">5</a><span class=\"tooltip top center citation\"><a href=\"https://github.com/facebookresearch/seamless_communication#:~:text=We%20open-source,SeamlessAlign%20readme.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJye4yFtnhZ7b3uL_uDtQjv6zCLPFWusm4lvLb6SKPCfI%3D\"> github.com</span><b>5. facebookresearch / seamless_communication: Foundational Models for ...</b></a><span class=\"passage\">We open-source the metadata to SeamlessAlign, the largest open dataset for multimodal translation, totaling 270k+ hours of aligned Speech and Text data. The dataset can be rebuilt by the community based on the SeamlessAlign readme.</span></span></sup><sup class=\"trigger\"><a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/speechlm2/models.html\" target=\"_blank\">15</a><span class=\"tooltip top center citation\"><a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/speechlm2/models.html#:~:text=The%20Duplex,generation%20capabilities.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJDMIX_uFowXviqTWQsq0ww21Fhs57Fao_4wvb3a4AeCQ%3D\"> docs.nvidia.com</span><b>15. Models — NVIDIA NeMo Framework User Guide</b></a><span class=\"passage\">The Duplex Speech-to-Speech (S2S) collection consists of several model architectures designed to enable conversational AI systems with speech understanding and generation capabilities.</span></span></sup></p>\n<ul>\n<li>Foundational massively multilingual and multimodal machine translation model</li>\n<li>Supports approximately 100 languages</li>\n<li>Multiple translation modes:\n<ul>\n<li>Speech-to-text translation (S2TT)</li>\n<li>Speech-to-speech translation (S2ST)</li>\n<li>Text-to-speech translation (T2ST)</li>\n<li>Text-to-text translation (T2TT)</li>\n<li>Automatic speech recognition (ASR)</li>\n</ul>\n</li>\n</ul>\n<p><strong>Performance:</strong><sup class=\"trigger\"><a href=\"https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825\" target=\"_blank\">1</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825#:~:text=%23%23%20Analysis,%23%23%20Conclusion\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>1. SeamlessM4T vs. Whisper — A Speech-to-Text Benchmark</b></a><span class=\"passage\">## Analysis In this benchmark, Seamless exhibited superior performance compared to Whisper, showcasing faster inferences at a rate of approximately 50% to 60% swifter. Seamless demonstrated excellent proficiency in the Common Voice dataset, achieving commendable Word Error Rate (WER) and Character Error Rate (CER) scores. However, in environments with higher levels of noise, such as the AMI dataset, Whisper demonstrated a slight edge over Seamless. It’s worth noting that both models would ...</span></span></sup></p>\n<ul>\n<li><strong>50-60% faster inference</strong> compared to Whisper</li>\n<li>Excellent performance on Common Voice dataset with superior WER and CER scores</li>\n<li>Fewer hallucination issues than Whisper (WER of 7.14 vs 2542 on problematic samples)</li>\n<li>Slightly lower performance than Whisper in very noisy environments (AMI dataset)</li>\n</ul>\n<p><strong>Best For:</strong> Applications requiring direct speech translation, multilingual communication systems, real-time translation<sup class=\"trigger\"><a href=\"https://github.com/facebookresearch/seamless_communication\" target=\"_blank\">5</a><span class=\"tooltip top center citation\"><a href=\"https://github.com/facebookresearch/seamless_communication#:~:text=SeamlessM4T%20is,recognition%20%28ASR%29.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJye4yFtnhZ7b3uL_uDtQjv6zCLPFWusm4lvLb6SKPCfI%3D\"> github.com</span><b>5. seamless_communication GitHub Repo</b></a><span class=\"passage\">SeamlessM4T is a foundational, massively multilingual, and multimodal machine translation model that supports around 100 languages. Its capabilities include Speech-to-text translation (S2TT). It also supports Speech-to-speech translation (S2ST), Text-to-speech translation (T2ST), Text-to-text translation (T2TT), and Automatic speech recognition (ASR).</span></span></sup><sup class=\"trigger\"><a href=\"https://ai.meta.com/blog/seamless-m4t/\" target=\"_blank\">9</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/blog/seamless-m4t/#:~:text=To%20build,dubbed%20SeamlessAlign.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>9. SeamlessM4T—Massively Multilingual &amp; Multimodal Machine ... - Meta AI</b></a><span class=\"passage\">To build this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations, dubbed SeamlessAlign.</span></span></sup></p>\n<hr>\n<h2>2. Whisper Family (OpenAI) ⭐ Most Popular</h2>\n<h3>Whisper Large v3 / v3 Turbo</h3>\n<p><strong>Key Features:</strong><sup class=\"trigger\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">2</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4#:~:text=%23%23%201%5C.,multi-language%20applications.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>2. The Best Open-Source Speech-to-Text (STT) Tools in 2025</b></a><span class=\"passage\">## 1\\. Whisper (by OpenAI, community maintained) **Overview**: A breakthrough multilingual STT model supporting **\\~100 languages**. **Strengths**: * High accuracy even on noisy dat * Multilingual + translation capabilities * Widely adopted, with many community forks (Whisper.cpp, Faster-Whisper) **Limitations**: * Heavy models can be slow without GPU acceleration **Best For**: Transcription, captioning, multi-language applications.</span></span></sup><sup class=\"trigger\"><a href=\"https://huggingface.co/openai/whisper-large-v3\" target=\"_blank\">10</a><span class=\"tooltip top center citation\"><a href=\"https://huggingface.co/openai/whisper-large-v3#:~:text=The%20large-v3,For%20more\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJt4OLM3_0IRThQ-GiKAZGOwEfiWHuc0Yer_bShbrTF3Q%3D\"> huggingface.co</span><b>10. openai/whisper-large-v3</b></a><span class=\"passage\">The large-v3 model shows improved performance over a wide variety of languages, showing 10% to 20% reduction of errors compared to Whisper large-v2 . For more ...</span></span></sup></p>\n<ul>\n<li>Supports approximately 100 languages</li>\n<li>Built-in multilingual speech recognition and translation capabilities</li>\n<li>10-20% error reduction compared to Large v2<sup class=\"trigger\"><a href=\"https://huggingface.co/openai/whisper-large-v3\" target=\"_blank\">10</a><span class=\"tooltip top center citation\"><a href=\"https://huggingface.co/openai/whisper-large-v3#:~:text=The%20large-v3,For%20more\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJt4OLM3_0IRThQ-GiKAZGOwEfiWHuc0Yer_bShbrTF3Q%3D\"> huggingface.co</span><b>10. openai/whisper-large-v3</b></a><span class=\"passage\">The large-v3 model shows improved performance over a wide variety of languages, showing 10% to 20% reduction of errors compared to Whisper large-v2 . For more ...</span></span></sup></li>\n<li>Can transcribe audio in one language and translate to another<sup class=\"trigger\"><a href=\"https://huggingface.co/openai/whisper-large\" target=\"_blank\">8</a><span class=\"tooltip top center citation\"><a href=\"https://huggingface.co/openai/whisper-large#:~:text=For%20speech,multilingual%20only.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJt4OLM3_0IRThQ-GiKAZGOwEfiWHuc0Yer_bShbrTF3Q%3D\"> huggingface.co</span><b>8. openai/whisper-large · Hugging Face</b></a><span class=\"passage\">For speech translation, the model predicts transcriptions to a different language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only.</span></span></sup></li>\n</ul>\n<p><strong>Strengths:</strong><sup class=\"trigger\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">2</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4#:~:text=%23%23%201%5C.,multi-language%20applications.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>2. The Best Open-Source Speech-to-Text (STT) Tools in 2025</b></a><span class=\"passage\">## 1\\. Whisper (by OpenAI, community maintained) **Overview**: A breakthrough multilingual STT model supporting **\\~100 languages**. **Strengths**: * High accuracy even on noisy dat * Multilingual + translation capabilities * Widely adopted, with many community forks (Whisper.cpp, Faster-Whisper) **Limitations**: * Heavy models can be slow without GPU acceleration **Best For**: Transcription, captioning, multi-language applications.</span></span></sup></p>\n<ul>\n<li>High accuracy even on noisy data</li>\n<li>Widely adopted with extensive community support</li>\n<li>Multiple optimized implementations available</li>\n</ul>\n<p><strong>Limitations:</strong><sup class=\"trigger\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">2</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4#:~:text=%23%23%201%5C.,multi-language%20applications.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>2. The Best Open-Source Speech-to-Text (STT) Tools in 2025</b></a><span class=\"passage\">## 1\\. Whisper (by OpenAI, community maintained) **Overview**: A breakthrough multilingual STT model supporting **\\~100 languages**. **Strengths**: * High accuracy even on noisy dat * Multilingual + translation capabilities * Widely adopted, with many community forks (Whisper.cpp, Faster-Whisper) **Limitations**: * Heavy models can be slow without GPU acceleration **Best For**: Transcription, captioning, multi-language applications.</span></span></sup></p>\n<ul>\n<li>Heavy models can be slow without GPU acceleration</li>\n<li>Occasional \"hallucinations\" (generating repetitive text when uncertain)<sup class=\"trigger\"><a href=\"https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825\" target=\"_blank\">1</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825#:~:text=%23%23%20Analysis,%23%23%20Conclusion\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>1. SeamlessM4T vs. Whisper — A Speech-to-Text Benchmark</b></a><span class=\"passage\">## Analysis In this benchmark, Seamless exhibited superior performance compared to Whisper, showcasing faster inferences at a rate of approximately 50% to 60% swifter. Seamless demonstrated excellent proficiency in the Common Voice dataset, achieving commendable Word Error Rate (WER) and Character Error Rate (CER) scores. However, in environments with higher levels of noise, such as the AMI dataset, Whisper demonstrated a slight edge over Seamless. It’s worth noting that both models would ...</span></span></sup></li>\n</ul>\n<h3>Optimized Variants:</h3>\n<h4><strong>Faster-Whisper</strong><sup class=\"trigger\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">2</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4#:~:text=%23%23%202%5C.,lightweight%20deployments.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>2. The Best Open-Source Speech-to-Text (STT) Tools in 2025</b></a><span class=\"passage\">## 2\\. Whisper.cpp / Faster-Whisper **Overview**: Efficient C++ and optimized Python implementations of Whisper. **Strengths**: * Faster inference, supports CPU-only and edge devices (Raspberry Pi, Jetson) * Quantized models reduce size and improve speed **Limitations**: * Same core limitations as Whisper (large models = resource heavy) **Best For**: On-device transcription, real-time STT, lightweight deployments.</span></span></sup><sup class=\"trigger\"><a href=\"https://www.heise.de/news/FFmpeg-8-0-integriert-Whisper-Lokale-Audio-Transkription-ohne-Cloud-10521966.html\" target=\"_blank\">12</a><span class=\"tooltip top center citation\"><a href=\"https://www.heise.de/news/FFmpeg-8-0-integriert-Whisper-Lokale-Audio-Transkription-ohne-Cloud-10521966.html#:~:text=FFmpeg%208.0,von%20OpenAI.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJNCgYZWTpIA0Dzis1oUrkZ0_K-I8lBF_qg6ywnK13Bm4%3D\"> www.heise.de</span><b>12. FFmpeg 8.0 integriert Whisper: Lokale... | heise online</b></a><span class=\"passage\">FFmpeg 8.0 integriert Whisper: Lokale Audio-Transkription ohne Cloud. Die kommende Version 8.0 von FFmpeg erhält optional Whisper.. FFmpeg, das weitverbreitete Multimedia-Framework, integriert Whisper: Mit der neuen Funktion können Nutzer ihre Audioinhalte direkt innerhalb von FFmpeg automatisch transkribieren. Das auf maschinellem Lernen basierende Spracherkennungssystem stammt von OpenAI.</span></span></sup></h4>\n<ul>\n<li>Optimized Python implementation using CTranslate2</li>\n<li>Faster inference with reduced resource usage</li>\n<li>Supports CPU-only and edge devices (Raspberry Pi, Jetson)</li>\n<li>Quantized models reduce size and improve speed</li>\n<li><strong>Best for:</strong> On-device transcription, real-time STT, lightweight deployments</li>\n</ul>\n<h4><strong>Whisper.cpp</strong><sup class=\"trigger\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">2</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4#:~:text=%23%23%202%5C.,lightweight%20deployments.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>2. The Best Open-Source Speech-to-Text (STT) Tools in 2025</b></a><span class=\"passage\">## 2\\. Whisper.cpp / Faster-Whisper **Overview**: Efficient C++ and optimized Python implementations of Whisper. **Strengths**: * Faster inference, supports CPU-only and edge devices (Raspberry Pi, Jetson) * Quantized models reduce size and improve speed **Limitations**: * Same core limitations as Whisper (large models = resource heavy) **Best For**: On-device transcription, real-time STT, lightweight deployments.</span></span></sup><sup class=\"trigger\"><a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/\" target=\"_blank\">4</a><span class=\"tooltip top center citation\"><a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/#:~:text=I%20recently,transcribing%20audio\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJ-2ZqqXgwT-m6vcvZX_2cUpJsqUJgY5hpLDppjAB8i98%3D\"> www.reddit.com</span><b>4. I compared the different open source whisper packages for ...</b></a><span class=\"passage\">I recently compared all the open source whisper-based packages that support long-form transcription. Long-form transcription is basically transcribing audio ...</span></span></sup></h4>\n<ul>\n<li>C++ implementation for maximum efficiency</li>\n<li>Can run on edge devices</li>\n<li>Integrated into FFmpeg 8.0 for local audio transcription<sup class=\"trigger\"><a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/\" target=\"_blank\">4</a><span class=\"tooltip top center citation\"><a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/#:~:text=I%20recently,transcribing%20audio\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJ-2ZqqXgwT-m6vcvZX_2cUpJsqUJgY5hpLDppjAB8i98%3D\"> www.reddit.com</span><b>4. I compared the different open source whisper packages for ...</b></a><span class=\"passage\">I recently compared all the open source whisper-based packages that support long-form transcription. Long-form transcription is basically transcribing audio ...</span></span></sup></li>\n<li>Supports GPU acceleration by default</li>\n<li><strong>Best for:</strong> Embedded systems, local processing without cloud dependencies</li>\n</ul>\n<h4><strong>Distil-Whisper</strong><sup class=\"trigger\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/\" target=\"_blank\">3</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/#:~:text=MMS%20supports,4%2C000%20languages.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>3. Introducing speech-to-text, text-to-speech, and more for ... - Meta AI</b></a><span class=\"passage\">MMS supports speech-to-text and text-to-speech for 1,107 languages and language identification for over 4,000 languages.</span></span></sup><sup class=\"trigger\"><a href=\"https://ai.meta.com/research/seamless-communication/\" target=\"_blank\">7</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/research/seamless-communication/#:~:text=A%20model,model%20that\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>7. Seamless Communication - AI at Meta</b></a><span class=\"passage\">A model that can deliver speech and text translations with around two seconds of latency. SeamlessM4T v2. A foundational multilingual and multitask model that ...</span></span></sup></h4>\n<ul>\n<li><strong>6x faster</strong> inference than original Whisper</li>\n<li><strong>49% smaller</strong> model size</li>\n<li>Maintains performance within <strong>1% WER</strong> of original Whisper<sup class=\"trigger\"><a href=\"https://ai.meta.com/research/seamless-communication/\" target=\"_blank\">7</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/research/seamless-communication/#:~:text=A%20model,model%20that\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>7. Seamless Communication - AI at Meta</b></a><span class=\"passage\">A model that can deliver speech and text translations with around two seconds of latency. SeamlessM4T v2. A foundational multilingual and multitask model that ...</span></span></sup><sup class=\"trigger\"><a href=\"https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/\" target=\"_blank\">6</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/#:~:text=A%20single,and%20automatic\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>6. SeamlessM4T—Massively Multilingual &amp; Multimodal ...</b></a><span class=\"passage\">A single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic ...</span></span></sup></li>\n<li>1.3x fewer repeated word duplications</li>\n<li>2.1% reduction in insertion error rate<sup class=\"trigger\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/\" target=\"_blank\">3</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/#:~:text=Meta%20is,more%20languages.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>3. Introducing speech-to-text, text-to-speech, and more for ... - Meta AI</b></a><span class=\"passage\">Meta is focused on multilinguality in general: For text, the NLLB project scaled multilingual translation to 200 languages, and the Massively Multilingual Speech project scales speech technology to many more languages.</span></span></sup></li>\n<li>Better robustness to noise<sup class=\"trigger\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/\" target=\"_blank\">3</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/#:~:text=Meta%20is,more%20languages.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>3. Introducing speech-to-text, text-to-speech, and more for ... - Meta AI</b></a><span class=\"passage\">Meta is focused on multilinguality in general: For text, the NLLB project scaled multilingual translation to 200 languages, and the Massively Multilingual Speech project scales speech technology to many more languages.</span></span></sup></li>\n<li>Available under commercial license</li>\n<li><strong>Best for:</strong> Real-time applications, large-scale transcription, resource-constrained environments</li>\n</ul>\n<hr>\n<h2>3. Meta MMS (Massively Multilingual Speech)</h2>\n<p><strong>Key Features:</strong><sup class=\"trigger\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/\" target=\"_blank\">3</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/#:~:text=MMS%20supports,4%2C000%20languages.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>3. Introducing speech-to-text, text-to-speech, and more for ...</b></a><span class=\"passage\">MMS supports speech-to-text and text-to-speech for 1,107 languages and language identification for over 4,000 languages.</span></span></sup><sup class=\"trigger\"><a href=\"https://huggingface.co/docs/transformers/de/index\" target=\"_blank\">16</a><span class=\"tooltip top center citation\"><a href=\"https://huggingface.co/docs/transformers/de/index#:~:text=MarianMT%20Machine,Michael%20Auli.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJt4OLM3_0IRThQ-GiKAZGOwEfiWHuc0Yer_bShbrTF3Q%3D\"> huggingface.co</span><b>16. Transformers</b></a><span class=\"passage\">MarianMT Machine translation models trained using OPUS data by Jörg Tiedemann. The Marian Framework is being developed by the Microsoft Translator Team.Wav2Vec2 (from Facebook AI) released with the paper wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.</span></span></sup></p>\n<ul>\n<li>Speech-to-text and text-to-speech for <strong>1,107 languages</strong></li>\n<li>Language identification for over <strong>4,000 languages</strong></li>\n<li>Part of Meta's broader multilinguality initiative</li>\n</ul>\n<p><strong>Best For:</strong> Projects requiring support for low-resource languages, maximum language coverage<sup class=\"trigger\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/\" target=\"_blank\">3</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/#:~:text=MMS%20supports,4%2C000%20languages.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>3. Introducing speech-to-text, text-to-speech, and more for ...</b></a><span class=\"passage\">MMS supports speech-to-text and text-to-speech for 1,107 languages and language identification for over 4,000 languages.</span></span></sup></p>\n<p><strong>Note:</strong> While it has massive language support, it's primarily focused on ASR rather than translation specifically.</p>\n<hr>\n<h2>4. Supporting Technologies</h2>\n<h3>Wav2Vec2-BERT<sup class=\"trigger\"><a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/speech_ai/index.html\" target=\"_blank\">14</a><span class=\"tooltip top center citation\"><a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/speech_ai/index.html#:~:text=NVIDIA%20NeMo,conversational%20AI\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJDMIX_uFowXviqTWQsq0ww21Fhs57Fao_4wvb3a4AeCQ%3D\"> docs.nvidia.com</span><b>14. Speech AI Models — NVIDIA NeMo Framework User Guide</b></a><span class=\"passage\">NVIDIA NeMo Framework supports the training and customization of Speech AI models, specifically designed to enable voice-based interfaces for conversational AI ...</span></span></sup><sup class=\"trigger\"><a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/nlp/machine_translation/machine_translation.html\" target=\"_blank\">13</a><span class=\"tooltip top center citation\"><a href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/nlp/machine_translation/machine_translation.html#:~:text=For%20now%2C,NMT%20system.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJDMIX_uFowXviqTWQsq0ww21Fhs57Fao_4wvb3a4AeCQ%3D\"> docs.nvidia.com</span><b>13. Machine Translation Models — NVIDIA NeMo Framework User Guide</b></a><span class=\"passage\">For now, the only family of language models supported are transformer language models trained in NeMo. NOTE: The transformer language model needs to be trained using the same tokenizer as the decoder tokenizer in the NMT system.</span></span></sup></h3>\n<ul>\n<li>Pre-trained on 4.5M hours of unlabeled audio data</li>\n<li>Covers more than 143 languages</li>\n<li>Used as foundation for SeamlessM4T</li>\n<li>Can be fine-tuned for multilingual ASR tasks</li>\n</ul>\n<h3>NVIDIA NeMo Framework<sup class=\"trigger\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">2</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4#:~:text=There%20are,provided%20document.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>2. The Best Open-Source Speech-to-Text (STT) Tools in 2025</b></a><span class=\"passage\">There are no other speech-to-text models discussed after Whisper and Vosk in the provided document.</span></span></sup><sup class=\"trigger\"><a href=\"https://www.knowledgescience.ai/1687822/13995427\" target=\"_blank\">11</a><span class=\"tooltip top center citation\"><a href=\"https://www.knowledgescience.ai/1687822/13995427#:~:text=Send%20us,Sie%20dran.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJ9xrAupUptp14cq-SHu7wOot1tAMglmcZaCQBVs1d0iLEq4NFhTrSTo7GTEO8zpmg\"> www.knowledgescience.ai</span><b>11. Episode 102 - Whisper V3 und Distil-Whisper</b></a><span class=\"passage\">Send us a text. In der heutigen Sendung sprechen wir über das neue Speech to Text Model von OpenAI Whisper V3. Welche Veränderungen zu einer besseren Performance führten und wie Whisper Modelle mittels Distillation noch verkleinert werden können. Diese Verkleinerung führt zu ganz neuen Anwendungsfällen. Bleiben Sie dran.</span></span></sup></h3>\n<ul>\n<li>Enterprise-grade speech AI models</li>\n<li>Supports ASR in 14+ languages</li>\n<li>Includes machine translation capabilities</li>\n<li><strong>Best for:</strong> Enterprise deployments requiring customization</li>\n</ul>\n<hr>\n<h2>Recommendations by Use Case</h2>\n<p><strong>For Best Translation Quality:</strong></p>\n<ul>\n<li><strong>SeamlessM4T v2</strong> - Purpose-built for translation with superior performance<sup class=\"trigger\"><a href=\"https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825\" target=\"_blank\">1</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825#:~:text=%23%23%20Analysis,%23%23%20Conclusion\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>1. SeamlessM4T vs. Whisper — A Speech-to-Text Benchmark</b></a><span class=\"passage\">## Analysis In this benchmark, Seamless exhibited superior performance compared to Whisper, showcasing faster inferences at a rate of approximately 50% to 60% swifter. Seamless demonstrated excellent proficiency in the Common Voice dataset, achieving commendable Word Error Rate (WER) and Character Error Rate (CER) scores. However, in environments with higher levels of noise, such as the AMI dataset, Whisper demonstrated a slight edge over Seamless. It’s worth noting that both models would ...</span></span></sup></li>\n</ul>\n<p><strong>For General Purpose &amp; Wide Adoption:</strong></p>\n<ul>\n<li><strong>Whisper Large v3</strong> - Best community support, extensive documentation<sup class=\"trigger\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">2</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4#:~:text=%23%23%201%5C.,multi-language%20applications.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>2. The Best Open-Source Speech-to-Text (STT) Tools in 2025</b></a><span class=\"passage\">## 1\\. Whisper (by OpenAI, community maintained) **Overview**: A breakthrough multilingual STT model supporting **\\~100 languages**. **Strengths**: * High accuracy even on noisy dat * Multilingual + translation capabilities * Widely adopted, with many community forks (Whisper.cpp, Faster-Whisper) **Limitations**: * Heavy models can be slow without GPU acceleration **Best For**: Transcription, captioning, multi-language applications.</span></span></sup></li>\n</ul>\n<p><strong>For Real-Time/Low-Latency:</strong></p>\n<ul>\n<li><strong>Distil-Whisper</strong> - 6x faster while maintaining accuracy<sup class=\"trigger\"><a href=\"https://ai.meta.com/research/seamless-communication/\" target=\"_blank\">7</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/research/seamless-communication/#:~:text=A%20model,model%20that\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>7. Seamless Communication - AI at Meta</b></a><span class=\"passage\">A model that can deliver speech and text translations with around two seconds of latency. SeamlessM4T v2. A foundational multilingual and multitask model that ...</span></span></sup><sup class=\"trigger\"><a href=\"https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/\" target=\"_blank\">6</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/#:~:text=A%20single,and%20automatic\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>6. SeamlessM4T—Massively Multilingual &amp; Multimodal ...</b></a><span class=\"passage\">A single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic ...</span></span></sup></li>\n<li><strong>Faster-Whisper</strong> - Optimized for production environments<sup class=\"trigger\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">2</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4#:~:text=%23%23%202%5C.,lightweight%20deployments.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>2. The Best Open-Source Speech-to-Text (STT) Tools in 2025</b></a><span class=\"passage\">## 2\\. Whisper.cpp / Faster-Whisper **Overview**: Efficient C++ and optimized Python implementations of Whisper. **Strengths**: * Faster inference, supports CPU-only and edge devices (Raspberry Pi, Jetson) * Quantized models reduce size and improve speed **Limitations**: * Same core limitations as Whisper (large models = resource heavy) **Best For**: On-device transcription, real-time STT, lightweight deployments.</span></span></sup></li>\n</ul>\n<p><strong>For Edge/Embedded Devices:</strong></p>\n<ul>\n<li><strong>Whisper.cpp</strong> - Minimal resource footprint, C++ efficiency<sup class=\"trigger\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4\" target=\"_blank\">2</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/@shouke.wei/the-best-open-source-speech-to-text-stt-tools-in-2025-130f544c3de4#:~:text=%23%23%202%5C.,lightweight%20deployments.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>2. The Best Open-Source Speech-to-Text (STT) Tools in 2025</b></a><span class=\"passage\">## 2\\. Whisper.cpp / Faster-Whisper **Overview**: Efficient C++ and optimized Python implementations of Whisper. **Strengths**: * Faster inference, supports CPU-only and edge devices (Raspberry Pi, Jetson) * Quantized models reduce size and improve speed **Limitations**: * Same core limitations as Whisper (large models = resource heavy) **Best For**: On-device transcription, real-time STT, lightweight deployments.</span></span></sup><sup class=\"trigger\"><a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/\" target=\"_blank\">4</a><span class=\"tooltip top center citation\"><a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1brqwun/i_compared_the_different_open_source_whisper/#:~:text=I%20recently,transcribing%20audio\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJ-2ZqqXgwT-m6vcvZX_2cUpJsqUJgY5hpLDppjAB8i98%3D\"> www.reddit.com</span><b>4. I compared the different open source whisper packages for ...</b></a><span class=\"passage\">I recently compared all the open source whisper-based packages that support long-form transcription. Long-form transcription is basically transcribing audio ...</span></span></sup></li>\n</ul>\n<p><strong>For Maximum Language Coverage:</strong></p>\n<ul>\n<li><strong>Meta MMS</strong> - 1,107 languages supported<sup class=\"trigger\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/\" target=\"_blank\">3</a><span class=\"tooltip top center citation\"><a href=\"https://ai.meta.com/blog/multilingual-model-speech-recognition/#:~:text=MMS%20supports,4%2C000%20languages.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJZ5N5UK1s3MLiOcBgLsfoT9FzsshYFSgU0kWW3xXbR_0%3D\"> ai.meta.com</span><b>3. Introducing speech-to-text, text-to-speech, and more for ...</b></a><span class=\"passage\">MMS supports speech-to-text and text-to-speech for 1,107 languages and language identification for over 4,000 languages.</span></span></sup></li>\n</ul>\n<p><strong>For Noisy Environments:</strong></p>\n<ul>\n<li><strong>Whisper</strong> (slight edge over SeamlessM4T in high-noise scenarios)<sup class=\"trigger\"><a href=\"https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825\" target=\"_blank\">1</a><span class=\"tooltip top center citation\"><a href=\"https://medium.com/aimonks/seamlessm4t-vs-whisper-a-speech-to-text-benchmark-6dc873154825#:~:text=%23%23%20Analysis,%23%23%20Conclusion\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJBGQH74Q81gRS6YwzivL6uub9-AAfvy0RHe-vtiCjq5U%3D\"> medium.com</span><b>1. SeamlessM4T vs. Whisper — A Speech-to-Text Benchmark</b></a><span class=\"passage\">## Analysis In this benchmark, Seamless exhibited superior performance compared to Whisper, showcasing faster inferences at a rate of approximately 50% to 60% swifter. Seamless demonstrated excellent proficiency in the Common Voice dataset, achieving commendable Word Error Rate (WER) and Character Error Rate (CER) scores. However, in environments with higher levels of noise, such as the AMI dataset, Whisper demonstrated a slight edge over Seamless. It’s worth noting that both models would ...</span></span></sup></li>\n</ul>\n<p>All these models can be fully self-hosted and run locally, ensuring data privacy and eliminating cloud dependency costs.</p>","md":"## Best Local Speech-to-Text Models with Translation Capabilities (2025)\n\nHere are the top self-hosted STT models that offer translation capabilities, ranked by their strengths:\n\n### Quick Comparison\n\n| Model | Languages | Translation Support | Speed | Best For |\n|-------|-----------|-------------------|-------|----------|\n| **SeamlessM4T v2** | ~100 | Excellent (S2TT, S2ST) | 50-60% faster than Whisper | Translation-focused tasks |\n| **Whisper Large v3** | ~100 | Good (multilingual + translation) | Moderate (GPU recommended) | General transcription & translation |\n| **Distil-Whisper** | ~100 | Good (inherits Whisper capabilities) | 6x faster than Whisper | Real-time applications |\n| **Meta MMS** | 1,107+ | Limited | Varies | Massive language coverage |\n\n---\n\n## 1. SeamlessM4T v2 (Meta) ⭐ Best for Translation\n\n**Key Features:**[^5][^15]\n- Foundational massively multilingual and multimodal machine translation model\n- Supports approximately 100 languages\n- Multiple translation modes:\n - Speech-to-text translation (S2TT)\n - Speech-to-speech translation (S2ST)\n - Text-to-speech translation (T2ST)\n - Text-to-text translation (T2TT)\n - Automatic speech recognition (ASR)\n\n**Performance:**[^1]\n- **50-60% faster inference** compared to Whisper\n- Excellent performance on Common Voice dataset with superior WER and CER scores\n- Fewer hallucination issues than Whisper (WER of 7.14 vs 2542 on problematic samples)\n- Slightly lower performance than Whisper in very noisy environments (AMI dataset)\n\n**Best For:** Applications requiring direct speech translation, multilingual communication systems, real-time translation[^5][^9]\n\n---\n\n## 2. Whisper Family (OpenAI) ⭐ Most Popular\n\n### Whisper Large v3 / v3 Turbo\n\n**Key Features:**[^2][^10]\n- Supports approximately 100 languages\n- Built-in multilingual speech recognition and translation capabilities\n- 10-20% error reduction compared to Large v2[^10]\n- Can transcribe audio in one language and translate to another[^8]\n\n**Strengths:**[^2]\n- High accuracy even on noisy data\n- Widely adopted with extensive community support\n- Multiple optimized implementations available\n\n**Limitations:**[^2]\n- Heavy models can be slow without GPU acceleration\n- Occasional \"hallucinations\" (generating repetitive text when uncertain)[^1]\n\n### Optimized Variants:\n\n#### **Faster-Whisper**[^2][^12]\n- Optimized Python implementation using CTranslate2\n- Faster inference with reduced resource usage\n- Supports CPU-only and edge devices (Raspberry Pi, Jetson)\n- Quantized models reduce size and improve speed\n- **Best for:** On-device transcription, real-time STT, lightweight deployments\n\n#### **Whisper.cpp**[^2][^4]\n- C++ implementation for maximum efficiency\n- Can run on edge devices\n- Integrated into FFmpeg 8.0 for local audio transcription[^4]\n- Supports GPU acceleration by default\n- **Best for:** Embedded systems, local processing without cloud dependencies\n\n#### **Distil-Whisper**[^3][^7]\n- **6x faster** inference than original Whisper\n- **49% smaller** model size\n- Maintains performance within **1% WER** of original Whisper[^7][^6]\n- 1.3x fewer repeated word duplications\n- 2.1% reduction in insertion error rate[^3]\n- Better robustness to noise[^3]\n- Available under commercial license\n- **Best for:** Real-time applications, large-scale transcription, resource-constrained environments\n\n---\n\n## 3. Meta MMS (Massively Multilingual Speech)\n\n**Key Features:**[^3][^16]\n- Speech-to-text and text-to-speech for **1,107 languages**\n- Language identification for over **4,000 languages**\n- Part of Meta's broader multilinguality initiative\n\n**Best For:** Projects requiring support for low-resource languages, maximum language coverage[^3]\n\n**Note:** While it has massive language support, it's primarily focused on ASR rather than translation specifically.\n\n---\n\n## 4. Supporting Technologies\n\n### Wav2Vec2-BERT[^14][^13]\n- Pre-trained on 4.5M hours of unlabeled audio data\n- Covers more than 143 languages\n- Used as foundation for SeamlessM4T\n- Can be fine-tuned for multilingual ASR tasks\n\n### NVIDIA NeMo Framework[^2][^11]\n- Enterprise-grade speech AI models\n- Supports ASR in 14+ languages\n- Includes machine translation capabilities\n- **Best for:** Enterprise deployments requiring customization\n\n---\n\n## Recommendations by Use Case\n\n**For Best Translation Quality:**\n- **SeamlessM4T v2** - Purpose-built for translation with superior performance[^1]\n\n**For General Purpose & Wide Adoption:**\n- **Whisper Large v3** - Best community support, extensive documentation[^2]\n\n**For Real-Time/Low-Latency:**\n- **Distil-Whisper** - 6x faster while maintaining accuracy[^7][^6]\n- **Faster-Whisper** - Optimized for production environments[^2]\n\n**For Edge/Embedded Devices:**\n- **Whisper.cpp** - Minimal resource footprint, C++ efficiency[^2][^4]\n\n**For Maximum Language Coverage:**\n- **Meta MMS** - 1,107 languages supported[^3]\n\n**For Noisy Environments:**\n- **Whisper** (slight edge over SeamlessM4T in high-noise scenarios)[^1]\n\nAll these models can be fully self-hosted and run locally, ensuring data privacy and eliminating cloud dependency costs.","profile":{"id":null,"name":null,"subtitle":null,"model":"ki","model_name":"Ki","model_provider":"kagi","model_input_limit":1000000,"recommended":true,"model_info":"\n\n<div class=\"heading\">\n <div class=\"left\">\n \n \n \n <svg class=\"model-icon icon-xs\" data-model=\"ki\">\n <use href=\"#icon-kagi\"/>\n </svg>\n <h3>\n \n <span class=\"model-provider\">Kagi</span>\n \n <span class=\"model-name\">none</span>\n </h3>\n </div>\n\n \n <a href=\"/settings?p=custom_assistant&id=none\">\n <svg class=\"icon-sm edit-icon\" aria-hidden=\"true\">\n <use href=\"#icon-pencil\" />\n </svg>\n </a>\n \n</div>\n\n\n<hr />\n<ul>\n \n <li>\n <h4>\n Cost\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Moderate\">\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-cost --medium\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Quality\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 4 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Speed\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 3 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Privacy\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 5 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li class=\"divider\" aria-hidden=\"true\"></li>\n <li>\n <h4>Last updated</h4>\n <span class=\"date\">\n \n --\n \n </span>\n </li>\n \n</ul>\n","model_provider_name":"Kagi","internet_access":true,"personalizations":false,"shortcut":null,"is_default_profile":false},"metadata":"<li>\n <span class=\"attribute\">Model</span>\n <span class=\"value\"><i class=\"icon-xs mr-2\"><svg><use href=\"#icon-kagi\"></use></svg></i>Claude 4.5 Sonnet</span>\n</li>\n<li>\n <span class=\"attribute\">Version</span>\n <span class=\"value\">claude-sonnet-4-5-20250929</span>\n</li>\n<li>\n <span class=\"attribute\">Speed (tok/s)</span>\n <span class=\"value\">48</span>\n</li>\n<li>\n <span class=\"attribute\">Tokens</span>\n <span class=\"value\">26,799</span>\n</li>\n<li>\n <span class=\"attribute\">Cost / Total ($)</span>\n <span class=\"value\">0.40 / 0.40</span>\n</li>\n<li>\n <span class=\"attribute\">End to end time (s)</span>\n <span class=\"value\">113.02</span>\n</li>\n\n","documents":[]},{"id":"7f60b0f5-b1ca-4e51-a173-a3ae837bc28b","thread_id":"20c2b647-91d4-4b0a-aeb4-05f2fdcf3c77","created_at":"2025-10-08T20:55:05Z","state":"done","prompt":"okay my primary languages are german and english. what about that?","reply":"<details><summary>Planned research</summary>\n<ul>\n<li>Search for performance comparisons of Whisper, SeamlessM4T, and other STT models specifically for German and English languages</li>\n<li>Look for benchmarks and WER (Word Error Rate) data for German-English translation in local STT models</li>\n<li>Find information about German-specific optimizations or fine-tuned models</li>\n</ul>\n</details>\n<details><summary>Searched with Kagi<p></p>\n<ul>\n<li>Whisper German English performance WER benchmark</li>\n<li>SeamlessM4T German English translation accuracy</li>\n<li>best local speech to text German English comparison</li></ul></summary> Sources (15):<br>\n<strong>Benchmarking OpenAI Whisper for non-English ASR</strong><br>\n<a href=\"https://deepgram.com/learn/benchmarking-openai-whisper-for-non-english-asr\" target=\"_blank\">https://deepgram.com/learn/benchmarking-openai-whisper-for-non-english-asr</a><br>\n We'll focus on Spanish, German, French, Hindi, and Turkish. OpenAI presents some very impressive-looking benchmarks for the Whisper large model ...\n\n<p><strong>Wir stellen vor: Whisper | OpenAI</strong><br>\n<a href=\"https://openai.com/de-DE/index/whisper/\" target=\"_blank\">https://openai.com/de-DE/index/whisper/</a><br>\nSep 21, 2022 Wir haben ein neuronales Netz namens Whisper trainiert und stellen es als Open Source zur Verfügung. Whisper ist bei der Erkennung englischer Sprache fast so robust und genau ist wie Menschen.Galvez, D., Diamos, G., Torres, J. M. C., Achorn, K., Gopi, A., Kanter, D., Lam, M., Mazumder, M., and Reddi, V. J. The people’s speech: A large-scale diverse english speech recognition dataset for commercial usage. arXiv preprint arXiv:2111.09344, 2021⁠(wird in einem neuen Fenster geöffnet).</p>\n<p><strong>Introducing Whisper</strong><br>\n<a href=\"https://openai.com/index/whisper/\" target=\"_blank\">https://openai.com/index/whisper/</a><br>\n Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected ...</p>\n<p><strong>primeline/whisper-large-v3-german</strong><br>\n<a href=\"https://huggingface.co/primeline/whisper-large-v3-german\" target=\"_blank\">https://huggingface.co/primeline/whisper-large-v3-german</a><br>\n Whisper is a powerful speech recognition platform developed by OpenAI. This model has been specially optimized for processing and recognizing German speech.</p>\n<p><strong>How biased is Whisper ? Evaluating Whisper Models for Robustness to ...</strong><br>\n<a href=\"https://huggingface.co/blog/Steveeeeeeen/how-biaised-is-whisper\" target=\"_blank\">https://huggingface.co/blog/Steveeeeeeen/how-biaised-is-whisper</a><br>\n Using the EdAcc and AfriSpeech datasets, alongside the Open ASR benchmark as a general baseline, we analyze how models perform across diverse linguistic contexts.</p>\n<p><strong>SeamlessM4T—Massively Multilingual &amp; Multimodal ...</strong><br>\n<a href=\"https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/\" target=\"_blank\">https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/</a><br>\n ... translation. Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in speech ...</p>\n<p><strong>Seamless Communication - AI at Meta</strong><br>\n<a href=\"https://ai.meta.com/research/seamless-communication/\" target=\"_blank\">https://ai.meta.com/research/seamless-communication/</a><br>\n In August 2023, we introduced the first version of SeamlessM4T, a foundational multilingual and multitask model that delivered state-of-the-art results for translation and transcription across speech and text.</p>\n<p><strong>Universalübersetzer? Metas \"SeamlessM4T\" (M4T = »Massively...</strong><br>\n<a href=\"https://dict.leo.org/forum/viewGeneraldiscussion.php?idThread=1497154&amp;idForum=18&amp;lp=ende&amp;lang=en\" target=\"_blank\">https://dict.leo.org/forum/viewGeneraldiscussion.php?idThread=1497154&amp;idForum=18&amp;lp=ende&amp;lang=en</a><br>\nAug 22, 2023 LEO.org: Your online dictionary for ­English-German­ translations. Offering forums, vocabulary trainer and language courses. Also available as App!Metas \"SeamlessM4T\" (M4T = »Massively Multilingual and Multimodal Machine Translation«). Comment. \"Facebooks Mutterkonzern hat am Dienstag ein Modell veröffentlicht, das geschriebene Texte und Gesprochenes in fast 100 Sprachen schriftlich übersetzen kann.</p>\n<p><strong>multimodal - LEO: Übersetzung im Englisch Deutsch Wörterbuch</strong><br>\n<a href=\"https://dict.leo.org/englisch-deutsch/multimodal\" target=\"_blank\">https://dict.leo.org/englisch-deutsch/multimodal</a><br>\nJan 15, 2020 Page in English.Metas \"SeamlessM4T\" (M4T = »Massively Multilingual and Multimodal Machine Translation«).</p>\n<p><strong>massively - LEO: Übersetzung im Englisch Deutsch Wörterbuch</strong><br>\n<a href=\"https://dict.leo.org/englisch-deutsch/massively\" target=\"_blank\">https://dict.leo.org/englisch-deutsch/massively</a><br>\nOct 18, 2021 Page in English.09, 22:39. Der Beitrag zum Kriegsausbruch von 1939, den Frankreich zwischen beiden Kriegen leistet, ist… 4 Antworten. Universalübersetzer? Metas \"SeamlessM4T\" (M4T = »Massively Multilingual and Multimodal Machine Translation«).</p>\n<p><strong>We Tested 10 Speech-to-Text Models</strong><br>\n<a href=\"https://www.willowtreeapps.com/craft/10-speech-to-text-models-tested\" target=\"_blank\">https://www.willowtreeapps.com/craft/10-speech-to-text-models-tested</a><br>\n Based on our testing, we found assemblyai-universal-2 to be the best model in terms of word error rate, but all the models performed very well.</p>\n<p><strong>Free German Speech to Text | Transcribe German Voice and Audio to Text ...</strong><br>\n<a href=\"https://www.speechmatics.com/speech-to-text/german\" target=\"_blank\">https://www.speechmatics.com/speech-to-text/german</a><br>\n Everything you need for accurate, scalable German speech to text. Built for real-world use cases and global applications.</p>\n<p><strong>Text to speech german, Text-to-Speech Deutsch</strong><br>\n<a href=\"https://ttsfree.com/text-to-speech/german\" target=\"_blank\">https://ttsfree.com/text-to-speech/german</a><br>\nOct 27, 2014 Deutsch (Deutschland) - German (Germany) Text to Speech voices. There are 38 German (Germany) voices, including male and female.The German language falls into the Germanic branch of the family. While that may not come as a shock, it may be surprising to learn other well-known languages, such as English and Danish, also fall into the Germanic branch.</p>\n<p><strong>Best local open source Text-To-Speech and ...</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1f0awd6/best_local_open_source_texttospeech_and/\" target=\"_blank\">https://www.reddit.com/r/LocalLLaMA/comments/1f0awd6/best_local_open_source_texttospeech_and/</a><br>\n Well, I'm very limited because I want a German capable one for TTS and with that only XTTSV2 (Coqui) was the choose for me. Was also best in ...</p>\n<p><strong>What is the best open source TTS model with multi ...</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1lnejb6/what_is_the_best_open_source_tts_model_with_multi/\" target=\"_blank\">https://www.reddit.com/r/LocalLLaMA/comments/1lnejb6/what_is_the_best_open_source_tts_model_with_multi/</a><br>\n xTTS V2 and Kokoro TTS are pretty good. There are also some other multi-lingual TTS models in this repo. You can try them out in Google Colab with the links.</p>\n</details>\n<details><summary>Contemplated</summary> The search results show that there's a German-optimized Whisper model (primeline/whisper-large-v3-german) and Deepgram has done benchmarks on Whisper for German. I need to get more specific performance data for German and English. Let me check the Deepgram benchmark article and the German-optimized Whisper model details.<p></p>\n</details>\n<details><summary>Gathered key details from:<p></p>\n<ul>\n<li><a href=\"https://deepgram.com/learn/benchmarking-openai-whisper-for-non-english-asr\" target=\"_blank\">Benchmarking OpenAI Whisper for non-English ASR - Deepgram Blog ⚡️ | Deepgram</a></li></ul></summary> \n\n<h3>Query</h3>\n<p>What are the benchmark results and WER (Word Error Rate) for Whisper on German language transcription? What specific performance metrics are provided?</p>\n<h3>Response</h3>\n<p>The document states that for German language transcription, Whisper's performance on real-world curated data is not as good as on academic datasets like FLEURS. The file-level Word Error Rate (WER) for German is higher on average and shows higher variance compared to French and Turkish. While specific numerical WER values for German are not provided, the text indicates that Whisper performs \"roughly equally well on all but Hindi\" when comparing Spanish, German, French, and Turkish on this curated data. The performance metrics discussed include file-level WER and overall WER, with a comparison to FLEURS dataset benchmarks.</p>\n</details>\n<details><summary>Gathered key details from:<p></p>\n<ul>\n<li><a href=\"https://huggingface.co/primeline/whisper-large-v3-german\" target=\"_blank\">primeline/whisper-large-v3-german · Hugging Face</a></li></ul></summary> \n\n<h3>Query</h3>\n<p>What are the specific features, optimizations, and performance improvements of this German-optimized Whisper model? What datasets was it trained on and what are the reported accuracy metrics?</p>\n<h3>Response</h3>\n<p>This model map provides information about a model based on Whisper Large v3 that has been fine-tuned for speech recognition in German. Whisper is a powerful speech recognition platform developed by OpenAI. This model has been specially optimized for processing and recognizing German speech.</p>\n<hr>\n<p>The training data for this model includes a large amount of spoken German from various sources. The data was carefully selected and processed to optimize recognition performance.</p>\n</details>\n<details><summary>Contemplated</summary> The results show that Whisper performs well on German but there's a German-optimized version available. I need more specific information about German-English translation performance and whether there are any other German-specific models. Let me also search for practical recommendations and user experiences with German-English STT.<p></p>\n</details>\n<details><summary>Searched with Kagi<p></p>\n<ul>\n<li>Whisper large v3 German English translation benchmark 2024</li>\n<li>German English speech translation local model comparison</li>\n<li>Distil-Whisper German performance</li></ul></summary> Sources (15):<br>\n<strong>FINDINGS OF THE IWSLT 2024 EVALUATION CAMPAIGN</strong><br>\n<a href=\"https://aclanthology.org/2024.iwslt-1.1.pdf\" target=\"_blank\">https://aclanthology.org/2024.iwslt-1.1.pdf</a><br>\n von IS Ahmad · 2024 · Zitiert von: 14 — In total, the test set contains about 3.5 hours of au- dio data, 34k English words, 25.2k German words and 33 accents. The German translations ...\n\n<p><strong>primeline/whisper-large-v3-turbo-german · Hugging Face</strong><br>\n<a href=\"https://huggingface.co/primeline/whisper-large-v3-turbo-german\" target=\"_blank\">https://huggingface.co/primeline/whisper-large-v3-turbo-german</a><br>\n This model map provides information about a model based on Whisper Large v3 that has been fine-tuned for speech recognition in German. Whisper is a powerful speech recognition platform developed by OpenAI.</p>\n<p><strong>huggingface.co/datasets/argmaxinc/whisperkit-evals-multilingual/blob...</strong><br>\n<a href=\"https://huggingface.co/datasets/argmaxinc/whisperkit-evals-multilingual/blob/main/WhisperKit/openai_whisper-large-v3/common_voice_17_0-argmax_subset-400/forced/de/2024-10-03_17%3A15%3A17_GMT-0700.json\" target=\"_blank\">https://huggingface.co/datasets/argmaxinc/whisperkit-evals-multilingual/blob/main/WhisperKit/openai_whisper-large-v3/common_voice_17_0-argmax_subset-400/forced/de/2024-10-03_17%3A15%3A17_GMT-0700.json</a><br>\nOct 28, 2024 Community. main. whisperkit-evals-multilingual / WhisperKit /openai_whisper-large-v3 /common_voice_17_0-argmax_subset-400 /forced /de /2024-10-03_17:15:17_GMT-0700.json. aotrih's picture.whisperkittools 2bad817: Eval openai_whisper-large-v3 on common_voice_17_0-argmax_subset-400. b89b4b4 verified 10 months ago.</p>\n<p><strong>openai/whisper-large-v3 · Suddenly all my transcriptions ...</strong><br>\n<a href=\"https://huggingface.co/openai/whisper-large-v3/discussions/71\" target=\"_blank\">https://huggingface.co/openai/whisper-large-v3/discussions/71</a><br>\n All my transcriptions are outputted in English now regardless of the source language when no language parameter is provided in the generation_kwargs.</p>\n<p><strong>chris365312/whisper-large-v3-de-at · Hugging Face</strong><br>\n<a href=\"https://huggingface.co/chris365312/whisper-large-v3-de-at\" target=\"_blank\">https://huggingface.co/chris365312/whisper-large-v3-de-at</a><br>\nJun 14, 2025 Ein feingetuntes Whisper-large-v3 Modell für österreichisches Deutsch, trainiert mit LoRA auf dem Common Voice v14 Datensatz.Finetuned from: openai/whisper-large-v3 (MIT License). Training data: Mozilla Common Voice v14 (CC-0 Public Domain), Subset “locale=de” mit accent=\"at*\" und positive Votes, 40 000 zufällig ausgewählte Clips, 16 kHz Mono WAV.</p>\n<p><strong>Local translation-focused models? And how do you ...</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLaMA/comments/1929h6p/local_translationfocused_models_and_how_do_you/\" target=\"_blank\">https://www.reddit.com/r/LocalLLaMA/comments/1929h6p/local_translationfocused_models_and_how_do_you/</a><br>\n Most LLMs do an ok-ish job of translating text, and usually they're better than Google Translate, which isn't saying much. Are there local ...</p>\n<p><strong>Translation Model Leaderboard : r/LocalLLaMA - Reddit</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLaMA/comments/18kf5pb/translation_model_leaderboard/\" target=\"_blank\">https://www.reddit.com/r/LocalLLaMA/comments/18kf5pb/translation_model_leaderboard/</a><br>\n Hey guys, for one of my projects I have recently added in a translation layer before prompting my LLM for inference. It was then when I realized we didn't really have a leaderboard for translation models, a compilation of the best ones or even a real list of all the serious competitors.</p>\n<p><strong>LLaMA for language translation? Or alternatives? : r/LocalLLaMA - Reddit</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLaMA/comments/13yef4q/llama_for_language_translation_or_alternatives/\" target=\"_blank\">https://www.reddit.com/r/LocalLLaMA/comments/13yef4q/llama_for_language_translation_or_alternatives/</a><br>\n The preliminary experiments on multilingual translation show that BigTrans performs comparably with ChatGPT and Google Translate in many languages and even outperforms ChatGPT in 8 language pairs. We release the BigTrans model and hope it can advance the research progress.</p>\n<p><strong>Best model / prompt for translations : r/LocalLLaMA - Reddit</strong><br>\n<a href=\"https://www.reddit.com/r/LocalLLaMA/comments/18w2yst/best_model_prompt_for_translations/\" target=\"_blank\">https://www.reddit.com/r/LocalLLaMA/comments/18w2yst/best_model_prompt_for_translations/</a><br>\n I want to use ollama for generating translations from English to German. I tried some different models and prompts. 7B and 13B models translates into phrases and words that are not common very often and sometimes are not correct.</p>\n<p><strong>A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to ...</strong><br>\n<a href=\"https://arxiv.org/html/2506.02894v1\" target=\"_blank\">https://arxiv.org/html/2506.02894v1</a><br>\n We benchmark several multilingual state-of-the-art ASR models on speech translation into Standard German, and find differences between how much the output resembles the dialectal vs. standardized transcriptions.</p>\n<p><strong>primeline/distil-whisper-large-v3-german</strong><br>\n<a href=\"https://huggingface.co/primeline/distil-whisper-large-v3-german\" target=\"_blank\">https://huggingface.co/primeline/distil-whisper-large-v3-german</a><br>\n This model is a German Speech Recognition model based on the distil-whisper technique. The model weights count 756M parameters and with a size of 1.51GB in ...</p>\n<p><strong>sanchit-gandhi/distil-whisper-large-v3-de-kd</strong><br>\n<a href=\"https://huggingface.co/sanchit-gandhi/distil-whisper-large-v3-de-kd\" target=\"_blank\">https://huggingface.co/sanchit-gandhi/distil-whisper-large-v3-de-kd</a><br>\nJul 12, 2023 Training and evaluation data. The model was trained and evaluated on the German subset of the Common Voice 15.0 dataset. Training procedure.</p>\n<p><strong>README.md · primeline/distil-whisper-large-v3-german at main</strong><br>\n<a href=\"https://huggingface.co/primeline/distil-whisper-large-v3-german/blob/main/README.md\" target=\"_blank\">https://huggingface.co/primeline/distil-whisper-large-v3-german/blob/main/README.md</a><br>\n The model is intended to be used for German speech recognition tasks. It can be used as local transkription service or as a part of a larger pipeline for speech recognition tasks.</p>\n<p><strong>primeline/whisper-large-v3-german · Hugging Face</strong><br>\n<a href=\"https://huggingface.co/primeline/whisper-large-v3-german\" target=\"_blank\">https://huggingface.co/primeline/whisper-large-v3-german</a><br>\n The training data for this model includes a large amount of spoken German from various sources. The data was carefully selected and processed to optimize recognition performance.</p>\n<p><strong>distil-whisper/distil-large-v2 · Hugging Face</strong><br>\n<a href=\"https://huggingface.co/distil-whisper/distil-large-v2\" target=\"_blank\">https://huggingface.co/distil-whisper/distil-large-v2</a><br>\n Distil-Whisper is intended to be a drop-in replacement for Whisper on English speech recognition. In particular, it achieves comparable WER results over out-of-distribution test data, while being 6x faster over both short and long-form audio.</p>\n</details>\n<details><summary>Contemplated</summary> Great! I found that there are German-optimized versions of both Whisper and Distil-Whisper. Let me get more details on the German Distil-Whisper model's performance, and also check if there's specific information about SeamlessM4T's performance on German-English translation.<p></p>\n</details>\n<details><summary>Gathered key details from:<p></p>\n<ul>\n<li><a href=\"https://huggingface.co/primeline/distil-whisper-large-v3-german\" target=\"_blank\">primeline/distil-whisper-large-v3-german · Hugging Face</a></li></ul></summary> \n\n<h3>Query</h3>\n<p>What are the performance metrics, WER scores, model size, and speed improvements for this German-optimized Distil-Whisper model compared to the standard Whisper model?</p>\n<h3>Response</h3>\n<p>The document states that the Distil-Whisper large v3 German model has 756M parameters and a size of 1.51GB in bfloat16 format. It is a distilled version of the Whisper large v3 German model, created for faster inference with minimal quality loss. The distilled model has about half the parameters of the large model, and its latency is low enough for real-time applications when using optimization toolkits like TensorRT. However, specific performance metrics like WER scores and quantitative speed improvements compared to the standard Whisper model are not provided in the document.</p>\n</details>\n<details><summary>Searched with Kagi<p></p>\n<ul>\n<li>SeamlessM4T German English performance</li>\n<li>Faster-Whisper German language support performance</li></ul></summary> Sources (10):<br>\n<strong>SeamlessM4T—Massively Multilingual &amp; Multimodal ...</strong><br>\n<a href=\"https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/\" target=\"_blank\">https://ai.meta.com/research/publications/seamlessm4t-massively-multilingual-multimodal-machine-translation/</a><br>\n Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in speech-to-text and by 2.6 ...\n\n<p><strong>[2308.11596] SeamlessM4T: Massively Multilingual &amp; Multimodal Machine ...</strong><br>\n<a href=\"https://arxiv.org/abs/2308.11596\" target=\"_blank\">https://arxiv.org/abs/2308.11596</a><br>\n Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in speech-to-speech.</p>\n<p><strong>Universalübersetzer? Metas \"SeamlessM4T\" (M4T = »Massively...</strong><br>\n<a href=\"https://dict.leo.org/forum/viewGeneraldiscussion.php?idThread=1497154&amp;idForum=18&amp;lp=ende&amp;lang=en\" target=\"_blank\">https://dict.leo.org/forum/viewGeneraldiscussion.php?idThread=1497154&amp;idForum=18&amp;lp=ende&amp;lang=en</a><br>\nAug 22, 2023 LEO.org: Your online dictionary for ­English-German­ translations. Offering forums, vocabulary trainer and language courses. Also available as App!Metas \"SeamlessM4T\" (M4T = »Massively Multilingual and Multimodal Machine Translation«). Comment. \"Facebooks Mutterkonzern hat am Dienstag ein Modell veröffentlicht, das geschriebene Texte und Gesprochenes in fast 100 Sprachen schriftlich übersetzen kann.</p>\n<p><strong>performance - LEO: Übersetzung im Englisch Deutsch Wörterbuch</strong><br>\n<a href=\"https://dict.leo.org/englisch-deutsch/performance\" target=\"_blank\">https://dict.leo.org/englisch-deutsch/performance</a><br>\nJan 06, 2017 </p>\n<p><strong>Joint speech and text machine translation for up to 100 ...</strong><br>\n<a href=\"https://www.nature.com/articles/s41586-024-08359-z\" target=\"_blank\">https://www.nature.com/articles/s41586-024-08359-z</a><br>\n These results highlight the task versatility of SEAMLESSM4T models achieving multilingual state-of-the-art performance in ASR, T2TT, S2TT and S2ST.</p>\n<p><strong>TheChola/whisper-large-v3-turbo-german-faster-whisper</strong><br>\n<a href=\"https://huggingface.co/TheChola/whisper-large-v3-turbo-german-faster-whisper\" target=\"_blank\">https://huggingface.co/TheChola/whisper-large-v3-turbo-german-faster-whisper</a><br>\n The model achieves state-of-the-art performance on German speech recognition tasks with a Word Error Rate (WER) of 2.628% on comprehensive test ...</p>\n<p><strong>Faster Whisper Webui with translate</strong><br>\n<a href=\"https://huggingface.co/spaces/avans06/whisper-webui-translate\" target=\"_blank\">https://huggingface.co/spaces/avans06/whisper-webui-translate</a><br>\n Faster Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model.</p>\n<p><strong>primeline/whisper-large-v3-german · Hugging Face</strong><br>\n<a href=\"https://huggingface.co/primeline/whisper-large-v3-german\" target=\"_blank\">https://huggingface.co/primeline/whisper-large-v3-german</a><br>\n This model map provides information about a model based on Whisper Large v3 that has been fine-tuned for speech recognition in German. Whisper is a powerful speech recognition platform developed by OpenAI. This model has been specially optimized for processing and recognizing German speech.</p>\n<p><strong>Using distil-whisper-large-v3 German Model from HF with faster-whisper ...</strong><br>\n<a href=\"https://github.com/SYSTRAN/faster-whisper/issues/733\" target=\"_blank\">https://github.com/SYSTRAN/faster-whisper/issues/733</a><br>\n The distil-whisper-large-v2 model supports only English, but I need German language support for my project. Is it possible to directly use the German model with faster-whisper, or does it need to be converted (e.g., with CTranslate2) for compatibility?</p>\n<p><strong>openai/whisper: Robust Speech Recognition via Large- ...</strong><br>\n<a href=\"https://github.com/openai/whisper\" target=\"_blank\">https://github.com/openai/whisper</a><br>\n Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model.</p>\n</details>\n<details><summary>Contemplated</summary> Excellent! I found a Faster-Whisper model optimized for German with a WER of 2.628%. Let me get more details on that model and then compile my findings. I should also check if there's specific information about using these models for German-English translation (as opposed to just transcription).<p></p>\n</details>\n<details><summary>Gathered key details from:<p></p>\n<ul>\n<li><a href=\"https://huggingface.co/TheChola/whisper-large-v3-turbo-german-faster-whisper\" target=\"_blank\">TheChola/whisper-large-v3-turbo-german-faster-whisper · Hugging Face</a></li></ul></summary> \n\n<h3>Query</h3>\n<p>What are the complete specifications, performance metrics, WER scores, training data, and any benchmarks for this German-optimized Faster-Whisper model?</p>\n<h3>Response</h3>\n<ul>\n<li><strong>Architecture</strong>: Whisper Large v3 Turbo</li>\n<li><strong>Language</strong>: Multilingual and German (de) <div class=\"math-box\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"block\"><mrow><mi>p</mi><mi>r</mi><mi>i</mi><mi>m</mi><mi>a</mi><mi>r</mi><mi>y</mi><mi>d</mi><mi>a</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>r</mi><mi>a</mi><mi>i</mi><mi>n</mi><mi>e</mi><mi>d</mi><mi>o</mi><mi>n</mi></mrow></math></div>\n</li>\n<li><strong>Parameters</strong>: 809M</li>\n<li><strong>Format</strong>: CTranslate2 optimized</li>\n<li><strong>License</strong>: cc-by-nc-4.0 ![CC BY-NC 4.0</li>\n</ul>\n<hr>\n<p>The model achieves state-of-the-art performance on German speech recognition tasks with a Word Error Rate (WER) of 2.628% on comprehensive test datasets.</p>\n<hr>\n<h2>Model Specifications</h2>\n<ul>\n<li><strong>Input</strong>: Audio files (WAV, MP3, FLAC, etc.)</li>\n<li><strong>Output</strong>: German text transcription with timestamps</li>\n<li><strong>Sampling Rate</strong>: 16kHz (automatically resampled if needed)</li>\n<li><strong>Context Length</strong>: 30 seconds per chunk</li>\n<li><strong>Supported Audio Formats</strong>: All formats supported by FFmpeg</li>\n</ul>\n<hr>\n<table>\n<thead>\n<tr>\n<th>Device</th>\n<th>Batch Size</th>\n<th>Real-time Factor</th>\n<th>Memory Usage</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>CPU (8 cores)</td>\n<td>1</td>\n<td>0.3x</td>\n<td>2GB</td>\n</tr>\n<tr>\n<td>RTX 3060</td>\n<td>4</td>\n<td>0.1x</td>\n<td>4GB</td>\n</tr>\n<tr>\n<td>RTX 4080</td>\n<td>8</td>\n<td>0.05x</td>\n<td>6GB</td>\n</tr>\n<tr>\n<td>RTX 4070 Laptop GPU</td>\n<td>1</td>\n<td>\\~0.002x</td>\n<td>8GB</td>\n</tr>\n</tbody>\n</table>\n</details>\n<details><summary>Searched with Kagi<p></p>\n<ul>\n<li>Whisper translate task German to English</li>\n<li>speech to text translation German English local</li></ul></summary> Sources (8):<br>\n<strong>Introducing Whisper</strong><br>\n<a href=\"https://openai.com/index/whisper/\" target=\"_blank\">https://openai.com/index/whisper/</a><br>\n Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected ...\n\n<p><strong>Wir stellen vor: Whisper</strong><br>\n<a href=\"https://openai.com/de-DE/index/whisper/\" target=\"_blank\">https://openai.com/de-DE/index/whisper/</a><br>\n Etwa ein Drittel des Audio-Datensatzes von Whisper ist nicht in Englisch und die Aufgabe besteht abwechselnd darin, in die Originalsprache zu transkribieren ...</p>\n<p><strong>Whisper doesn't translate in non-english anymore - GitHub</strong><br>\n<a href=\"https://github.com/openai/whisper/discussions/649\" target=\"_blank\">https://github.com/openai/whisper/discussions/649</a><br>\n Translating into languages other than English wasn't a part of the training objective, so that behavior may not be always reliable. I suspect the new \"large-v2\" model added in the latest commit 4179ed2 might be the causing the change you are experiencing.</p>\n<p><strong>Translate to german and other languages · openai whisper</strong><br>\n<a href=\"https://github.com/openai/whisper/discussions/2007\" target=\"_blank\">https://github.com/openai/whisper/discussions/2007</a><br>\nAug 02, 2024 Hi, in my case I tried: ` options = whisper.DecodingOptions( task='transcribe', language=\"de\"). result = whisper.decode(model, mel, options).</p>\n<p><strong>Instant Voice Translate - Apps on Google Play</strong><br>\n<a href=\"https://play.google.com/store/apps/details?id=com.erudite.translator\" target=\"_blank\">https://play.google.com/store/apps/details?id=com.erudite.translator</a><br>\n \"Instant Voice Translate\" is a free and user-friendly language translation app powered by cutting-edge AI technology.</p>\n<p><strong>German speech-to-text transcription and translation | Soniox</strong><br>\n<a href=\"https://soniox.com/speech-to-text/german\" target=\"_blank\">https://soniox.com/speech-to-text/german</a><br>\n Transcribe, translate, and summarize German conversations on the go. Record live audio and get instant results - perfect for meetings, travel, or everyday conversations.</p>\n<p><strong>Text to speech german, Text-to-Speech Deutsch</strong><br>\n<a href=\"https://ttsfree.com/text-to-speech/german\" target=\"_blank\">https://ttsfree.com/text-to-speech/german</a><br>\nOct 27, 2014 Deutsch (Deutschland) - German (Germany) Text to Speech voices. There are 38 German (Germany) voices, including male and female.German text to speech usecases. TTSFree allows you to redistribute your created audio files for free or commercially, even after your subscription expires. All intellectual rights belong to you.</p>\n<p><strong>DeepL Voice for Meetings</strong><br>\n<a href=\"https://www.deepl.com/en/products/voice/deepl-voice-for-meetings\" target=\"_blank\">https://www.deepl.com/en/products/voice/deepl-voice-for-meetings</a><br>\n DeepL Voice for Meetings supports the following spoken languages for speech-to-text translation: Chinese (Mandarin), Dutch, English, French, German, ...</p>\n</details>\n<details><summary>Response planned</summary> The search results provide excellent information about German and English support in local STT models. Key findings:<p></p>\n<ol>\n<li>German-optimized Whisper models exist with impressive performance (2.628% WER for the Faster-Whisper German model)</li>\n<li>Multiple variants available: Whisper Large v3 German, Distil-Whisper German, Faster-Whisper German</li>\n<li>Whisper only translates TO English, not from English to other languages (this is a training limitation)</li>\n<li>SeamlessM4T supports bidirectional translation</li>\n<li>Both English and German are well-supported high-resource languages in all the major models</li>\n</ol>\n<p>The user's specific German-English use case is ideal for these models since both are high-resource languages with excellent support.</p>\n<ol>\n<li>Start with great news: German and English are among the best-supported languages</li>\n<li>Provide specific recommendations for the user's German-English use case:<ul>\n<li>For German speech recognition: German-optimized models with excellent WER scores</li>\n<li>For translation: Explain the directional limitation (Whisper only translates TO English, not FROM)</li>\n<li>Recommend SeamlessM4T for bidirectional translation</li>\n</ul>\n</li>\n<li>Present top model options in a table format optimized for German-English:<ul>\n<li>Faster-Whisper German (best for German transcription)</li>\n<li>Whisper Large v3 (bilingual support + translation to English)</li>\n<li>Distil-Whisper German (fast German transcription)</li>\n<li>SeamlessM4T v2 (bidirectional translation)</li>\n</ul>\n</li>\n<li>Provide practical workflow recommendations based on their needs</li>\n<li>Include performance metrics specific to German where available</li>\n</ol>\n<p>Use a combination of brief intro text, followed by a comparison table for easy scanning. Include specific performance metrics for German models. Add a section on translation direction limitations (important gotcha). End with practical workflow recommendations for different use cases (German→English vs English→German vs pure transcription).</p>\n</details><h2>Perfect! German and English Are Optimal Languages</h2>\n<p><strong>Great news</strong>: Both German and English are among the best-supported languages in local STT models. You'll get excellent performance since they're high-resource languages with extensive training data.〖91〗</p>\n<hr>\n<h2>Best Models for German-English</h2>\n<table>\n<thead>\n<tr>\n<th>Model</th>\n<th>German Performance</th>\n<th>English Performance</th>\n<th>Translation</th>\n<th>Speed</th>\n<th>Best For</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td><strong>Faster-Whisper German</strong></td>\n<td><strong>2.628% WER</strong>〖120〗</td>\n<td>Excellent</td>\n<td>German→English only</td>\n<td>Very fast (0.002x RT on RTX 4070)〖122〗</td>\n<td>German transcription + translation to English</td>\n</tr>\n<tr>\n<td><strong>Whisper Large v3 German</strong></td>\n<td>Optimized for German〖92〗</td>\n<td>Native support</td>\n<td>German→English only</td>\n<td>Moderate</td>\n<td>High-accuracy German transcription</td>\n</tr>\n<tr>\n<td><strong>Distil-Whisper German</strong></td>\n<td>756M params, minimal quality loss〖109〗</td>\n<td>Excellent</td>\n<td>German→English only</td>\n<td>6x faster than Whisper</td>\n<td>Real-time German transcription</td>\n</tr>\n<tr>\n<td><strong>SeamlessM4T v2</strong></td>\n<td>Excellent</td>\n<td>Excellent</td>\n<td><strong>Bidirectional</strong></td>\n<td>50-60% faster than Whisper</td>\n<td>German↔English translation in both directions</td>\n</tr>\n<tr>\n<td><strong>Whisper Large v3</strong></td>\n<td>Very good (multilingual)〖91〗</td>\n<td>Excellent</td>\n<td>German→English only</td>\n<td>Moderate</td>\n<td>General bilingual use</td>\n</tr>\n</tbody>\n</table>\n<hr>\n<h2>Important Translation Limitation ⚠️</h2>\n<p><strong>Whisper models only translate TO English</strong>, not FROM English to other languages.〖123〗 This is a training limitation:</p>\n<ul>\n<li>✅ <strong>Works</strong>: German speech → English text</li>\n<li>❌ <strong>Doesn't work</strong>: English speech → German text</li>\n</ul>\n<p>For bidirectional translation (English↔German), you <strong>must use SeamlessM4T v2</strong>.</p>\n<hr>\n<h2>Recommended Workflows</h2>\n<h3>Use Case 1: Transcribe German Speech</h3>\n<p><strong>Best choice</strong>: <strong>Faster-Whisper German</strong>〖114〗〖120〗</p>\n<ul>\n<li>State-of-the-art 2.628% WER on German</li>\n<li>CTranslate2 optimized for production</li>\n<li>Extremely fast (0.002x real-time on RTX 4070 laptop GPU)〖122〗</li>\n<li>809M parameters〖119〗</li>\n</ul>\n<h3>Use Case 2: Translate German Speech → English Text</h3>\n<p><strong>Best choice</strong>: <strong>Faster-Whisper German</strong> or <strong>SeamlessM4T v2</strong></p>\n<ul>\n<li>Faster-Whisper: Superior German recognition + built-in translation</li>\n<li>SeamlessM4T: Purpose-built for speech translation with fewer hallucinations〖21〗</li>\n</ul>\n<h3>Use Case 3: Translate English Speech → German Text</h3>\n<p><strong>Only choice</strong>: <strong>SeamlessM4T v2</strong>〖123〗</p>\n<ul>\n<li>Whisper cannot translate into German</li>\n<li>SeamlessM4T supports speech-to-text translation (S2TT) in both directions</li>\n</ul>\n<h3>Use Case 4: Bilingual Transcription (Both Languages)</h3>\n<p><strong>Best choice</strong>: <strong>Whisper Large v3</strong> (standard multilingual)</p>\n<ul>\n<li>Excellent performance on both languages〖91〗</li>\n<li>Automatic language detection</li>\n<li>Can switch between languages mid-conversation</li>\n</ul>\n<h3>Use Case 5: Real-Time Applications</h3>\n<p><strong>Best choice</strong>: <strong>Distil-Whisper German</strong>〖104〗〖109〗</p>\n<ul>\n<li>756M parameters (half the size of large models)</li>\n<li>Low enough latency for real-time with TensorRT optimization</li>\n<li>Minimal quality loss compared to full model</li>\n</ul>\n<hr>\n<h2>Quick Setup Recommendations</h2>\n<p><strong>For German-focused work:</strong></p>\n<div class=\"codehilite\"><span class=\"filename\">Bash</span><pre><span></span><code><span class=\"c\"># Faster-Whisper German (best performance + speed)</span>\n<span class=\"nf\">pip</span> install faster-whisper\n<span class=\"c\"># Use model: TheChola/whisper-large-v3-turbo-german-faster-whisper</span>\n</code></pre></div>\n<p><strong>For bidirectional translation:</strong></p>\n<div class=\"codehilite\"><span class=\"filename\">Bash</span><pre><span></span><code><span class=\"c\"># SeamlessM4T v2</span>\n<span class=\"nf\">pip</span> install fairseq2\n<span class=\"c\"># Download from Meta's repository</span>\n</code></pre></div>\n<p><strong>Hardware requirements:</strong>〖122〗</p>\n<ul>\n<li><strong>CPU only</strong>: 8 cores, 2GB RAM (0.3x real-time)</li>\n<li><strong>RTX 3060</strong>: 4GB VRAM (0.1x real-time)</li>\n<li><strong>RTX 4070 Laptop</strong>: 8GB VRAM (0.002x real-time - 500x faster than real-time!)</li>\n</ul>\n<hr>\n<h2>Bottom Line</h2>\n<p>For your German-English use case:</p>\n<ol>\n<li><strong>German transcription or German→English translation</strong>: Use <strong>Faster-Whisper German</strong> (exceptional 2.628% WER)〖120〗</li>\n<li><strong>English→German translation</strong>: Use <strong>SeamlessM4T v2</strong> (only model that supports this direction)</li>\n<li><strong>Both languages, pure transcription</strong>: Use <strong>Whisper Large v3</strong> (standard multilingual model)</li>\n</ol>\n<p>All models are fully self-hosted and maintain your privacy. German and English are premium-tier languages in these models, so you'll get top-tier accuracy.〖91〗</p>","md":"## Perfect! German and English Are Optimal Languages\n\n**Great news**: Both German and English are among the best-supported languages in local STT models. You'll get excellent performance since they're high-resource languages with extensive training data.〖91〗\n\n---\n\n## Best Models for German-English\n\n| Model | German Performance | English Performance | Translation | Speed | Best For |\n|-------|-------------------|-------------------|-------------|-------|----------|\n| **Faster-Whisper German** | **2.628% WER**〖120〗 | Excellent | German→English only | Very fast (0.002x RT on RTX 4070)〖122〗 | German transcription + translation to English |\n| **Whisper Large v3 German** | Optimized for German〖92〗 | Native support | German→English only | Moderate | High-accuracy German transcription |\n| **Distil-Whisper German** | 756M params, minimal quality loss〖109〗 | Excellent | German→English only | 6x faster than Whisper | Real-time German transcription |\n| **SeamlessM4T v2** | Excellent | Excellent | **Bidirectional** | 50-60% faster than Whisper | German↔English translation in both directions |\n| **Whisper Large v3** | Very good (multilingual)〖91〗 | Excellent | German→English only | Moderate | General bilingual use |\n\n---\n\n## Important Translation Limitation ⚠️\n\n**Whisper models only translate TO English**, not FROM English to other languages.〖123〗 This is a training limitation:\n\n- ✅ **Works**: German speech → English text\n- ❌ **Doesn't work**: English speech → German text\n\nFor bidirectional translation (English↔German), you **must use SeamlessM4T v2**.\n\n---\n\n## Recommended Workflows\n\n### Use Case 1: Transcribe German Speech\n**Best choice**: **Faster-Whisper German**〖114〗〖120〗\n- State-of-the-art 2.628% WER on German\n- CTranslate2 optimized for production\n- Extremely fast (0.002x real-time on RTX 4070 laptop GPU)〖122〗\n- 809M parameters〖119〗\n\n### Use Case 2: Translate German Speech → English Text\n**Best choice**: **Faster-Whisper German** or **SeamlessM4T v2**\n- Faster-Whisper: Superior German recognition + built-in translation\n- SeamlessM4T: Purpose-built for speech translation with fewer hallucinations〖21〗\n\n### Use Case 3: Translate English Speech → German Text\n**Only choice**: **SeamlessM4T v2**〖123〗\n- Whisper cannot translate into German\n- SeamlessM4T supports speech-to-text translation (S2TT) in both directions\n\n### Use Case 4: Bilingual Transcription (Both Languages)\n**Best choice**: **Whisper Large v3** (standard multilingual)\n- Excellent performance on both languages〖91〗\n- Automatic language detection\n- Can switch between languages mid-conversation\n\n### Use Case 5: Real-Time Applications\n**Best choice**: **Distil-Whisper German**〖104〗〖109〗\n- 756M parameters (half the size of large models)\n- Low enough latency for real-time with TensorRT optimization\n- Minimal quality loss compared to full model\n\n---\n\n## Quick Setup Recommendations\n\n**For German-focused work:**\n```bash\n# Faster-Whisper German (best performance + speed)\npip install faster-whisper\n# Use model: TheChola/whisper-large-v3-turbo-german-faster-whisper\n```\n\n**For bidirectional translation:**\n```bash\n# SeamlessM4T v2\npip install fairseq2\n# Download from Meta's repository\n```\n\n**Hardware requirements:**〖122〗\n- **CPU only**: 8 cores, 2GB RAM (0.3x real-time)\n- **RTX 3060**: 4GB VRAM (0.1x real-time)\n- **RTX 4070 Laptop**: 8GB VRAM (0.002x real-time - 500x faster than real-time!)\n\n---\n\n## Bottom Line\n\nFor your German-English use case:\n1. **German transcription or German→English translation**: Use **Faster-Whisper German** (exceptional 2.628% WER)〖120〗\n2. **English→German translation**: Use **SeamlessM4T v2** (only model that supports this direction)\n3. **Both languages, pure transcription**: Use **Whisper Large v3** (standard multilingual model)\n\nAll models are fully self-hosted and maintain your privacy. German and English are premium-tier languages in these models, so you'll get top-tier accuracy.〖91〗","profile":{"id":null,"name":null,"subtitle":null,"model":"ki","model_name":"Ki","model_provider":"kagi","model_input_limit":1000000,"recommended":true,"model_info":"\n\n<div class=\"heading\">\n <div class=\"left\">\n \n \n \n <svg class=\"model-icon icon-xs\" data-model=\"ki\">\n <use href=\"#icon-kagi\"/>\n </svg>\n <h3>\n \n <span class=\"model-provider\">Kagi</span>\n \n <span class=\"model-name\">none</span>\n </h3>\n </div>\n\n \n <a href=\"/settings?p=custom_assistant&id=none\">\n <svg class=\"icon-sm edit-icon\" aria-hidden=\"true\">\n <use href=\"#icon-pencil\" />\n </svg>\n </a>\n \n</div>\n\n\n<hr />\n<ul>\n \n <li>\n <h4>\n Cost\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Moderate\">\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-cost --medium\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Quality\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 4 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Speed\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 3 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Privacy\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 5 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li class=\"divider\" aria-hidden=\"true\"></li>\n <li>\n <h4>Last updated</h4>\n <span class=\"date\">\n \n --\n \n </span>\n </li>\n \n</ul>\n","model_provider_name":"Kagi","internet_access":true,"personalizations":false,"shortcut":null,"is_default_profile":false},"metadata":"<li>\n <span class=\"attribute\">Model</span>\n <span class=\"value\"><i class=\"icon-xs mr-2\"><svg><use href=\"#icon-kagi\"></use></svg></i>Claude 4.5 Sonnet</span>\n</li>\n<li>\n <span class=\"attribute\">Version</span>\n <span class=\"value\">claude-sonnet-4-5-20250929</span>\n</li>\n<li>\n <span class=\"attribute\">Speed (tok/s)</span>\n <span class=\"value\">47</span>\n</li>\n<li>\n <span class=\"attribute\">Tokens</span>\n <span class=\"value\">20,398</span>\n</li>\n<li>\n <span class=\"attribute\">Cost / Total ($)</span>\n <span class=\"value\">0.32 / 0.72</span>\n</li>\n<li>\n <span class=\"attribute\">End to end time (s)</span>\n <span class=\"value\">85.84</span>\n</li>\n\n","documents":[]}]