{ "data": { "a": { "slug": "granite-speech-5-0-2b", "name": "Granite Speech 5.0 2B", "vendor": "IBM", "vendorUrl": "https://www.ibm.com/granite", "kind": "weights", "category": "speech-to-text", "summary": "Granite Speech 5.0 2B is IBM's open-weight speech model of 2.3B parameters for transcription in six languages and speech translation to and from English, with keyword lists. Apache 2.0, with IBM's own GGUF builds from about 2 GB.", "url": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b", "markdownUrl": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-speech-5-0-2b.json", "dns": "granite-speech-5-0-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ibm-granite/granite-speech-5.0-2b" }, { "registry": "huggingface", "name": "ibm-granite/granite-speech-5.0-2b-GGUF" }, { "registry": "ollama", "name": "ibm/granite5-speech" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The safetensors and GGUF repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs little: 3.1 GB at Q8_0 with its audio projector, which IBM recommends, or 5.0 GB in BF16. IBM also runs a trial endpoint on Hugging Face that takes a Hugging Face token (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b", "capabilities": [ "speech.stt", "speech.batch", "speech.languages", "speech.translation", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "speech-translation", "multilingual", "keyword-biasing", "small", "apache-2.0", "safetensors", "gguf", "signed", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 19, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 55, "reach": 95, "risk": 70, "upkeep": 92 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small, well-kept speech model with a clean licence: six languages, translation, keyword lists, IBM's own GGUF and an Ollama entry, a signed repository and training data listed corpus by corpus. It handles about two minutes of audio at a time and returns plain text, with no timestamps or speakers, and publishes no safety results.", "bestFor": "Agents that need to transcribe or translate short recordings in the six supported languages on modest hardware", "strengths": [ "Apache 2.0, ungated, with a signed repository", "Runs in llama.cpp, Ollama, vLLM and Transformers, each documented", "IBM's own GGUF builds, about 3 GB at the precision it recommends", "Training data listed with hours and sources", "Keyword lists of up to 2,000 terms", "Updated in the last week, and both questions on the repository answered" ], "weaknesses": [ "About two minutes of audio per request, so long recordings must be split", "Plain text out: no timestamps, segments or speaker labels", "Six languages", "Builds below Q8_0 lose accuracy on longer audio, by IBM's own account", "No safety results", "No cut-off for the training data" ], "agentNotes": [ "Split recordings into segments of up to about two minutes of 16 kHz mono audio before sending them", "Use the card's prompt for the task word for word, such as `transcribe the speech with proper punctuation and capitalization.`", "Use Q8_0. IBM says lower precisions lose accuracy on audio over about a minute", "To spell names and jargon right, add a keyword list to the prompt, up to 2,000 terms", "With vLLM, serve with `--max-model-len 2048`, as the card does", "An unknown prompt falls back to transcription, so check that the output is the task you asked for" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 55, "reach": 95, "risk": 70, "upkeep": 92 }, "provenanceScore": 88, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "brew install llama.cpp\nllama-cli -st -hf ibm-granite/granite-speech-5.0-2b-GGUF:Q8_0 --audio audio.wav -p \"transcribe the speech with proper punctuation and capitalization.\"\n# or: ollama run ibm/granite5-speech:2b, or vllm serve ibm-granite/granite-speech-5.0-2b --max-model-len 2048", "http": "# vLLM and llama-server both serve an OpenAI-compatible chat API. Send 16 kHz mono audio of up to about\n# two minutes as base64 in the message, with one of the card's task prompts as the text" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/granite-speech-5-0-2b" }, "sameCompany": [ "granite-embedding-small-english-r2", "granite-guardian", "ibm-watson-stt", "granite-switch", "granite-4-2-30b", "granite-4-2-3b", "granite-4-2-8b" ], "weights": { "trainedBy": "IBM", "builtOn": "An intermediate checkpoint of granite-4.0-1b-base, per the card", "params": "2.3B", "goodFor": "Transcribing and translating short recordings in six languages on a laptop, with a keyword list for names and jargon.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "Transformers" ], "builds": [ { "name": "granite-speech-5.0-2b-GGUF Q4_K_M", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b-GGUF/blob/main/granite-speech-5.0-2b-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.3, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "1.14 GB with the 1.16 GB audio projector. IBM says precisions below Q8_0 lose accuracy on audio over about a minute" }, { "name": "granite-speech-5.0-2b-GGUF Q8_0", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b-GGUF/blob/main/granite-speech-5.0-2b-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 3.12, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ], "note": "1.96 GB with the 1.16 GB audio projector. The build IBM recommends, and the one in Ollama's library" }, { "name": "granite-speech-5.0-2b (BF16 safetensors)", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b/tree/main", "who": "publisher", "precision": "bf16", "sizeGB": 5.04, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "Transformers" ], "note": "Two shards" } ], "run": "llama-cli -st -hf ibm-granite/granite-speech-5.0-2b-GGUF:Q8_0 --audio audio.wav -p \"transcribe the speech with proper punctuation and capitalization.\"", "runBuild": "granite-speech-5.0-2b-GGUF Q8_0", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "IBM (Granite)", "family": "Granite Speech", "url": "https://www.anchorterminal.com/open-weights/#ibm", "others": [ { "name": "granite-speech-5.0-470m-turboctc", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-470m-turboctc", "params": "470M, per the name" }, { "name": "granite-speech-4.1-2b", "url": "https://huggingface.co/ibm-granite/granite-speech-4.1-2b", "params": "2B, per the name" } ], "olderCount": 64 }, "area": "voice", "provenance": { "legalEntity": "International Business Machines Corporation (per IBM's naming guidance, as read for granite-guardian)", "domain": "ibm.com", "domainRegistered": "1986-03-19", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b/commits/main", "securityTxt": "valid", "checked": "2026-11-10", "notes": [ "IBM's legal entity, domain and security.txt are carried from our Granite 4.2 listings.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 28 commits from 7 October to 6 November 2026.", "The card gives 20 October 2026 as the release date." ], "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b.json" }, "answer": "Granite Speech 5.0 2B scores 61 (C) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in every scored category.", "b": { "slug": "underdog-bark-3", "name": "Underdog Bark 3.0", "vendor": "Conway Research", "vendorUrl": "https://conway.tech", "kind": "weights", "category": "speech-to-text", "summary": "Underdog Bark 3.0 is Conway Research's open-weight English speech recognition model, a post-trained and compressed Parakeet TDT 0.6B v3 in one 163 MB file, run with Conway's loader on Transformers, MLX or Core ML, under CC BY 4.0.", "url": "https://www.anchorterminal.com/tools/underdog-bark-3", "markdownUrl": "https://www.anchorterminal.com/tools/underdog-bark-3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/underdog-bark-3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/underdog-bark-3.json", "dns": "underdog-bark-3.grade.anchorterminal.com", "repo": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0", "license": "CC-BY-4.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ConwayResearch/Underdog-Bark-3.0" } ], "auth": "none", "authNotes": "The files download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). The card's optional line about `hf auth login` applies only if access ever needs it.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under CC BY 4.0, which asks for attribution of NVIDIA, Moondream and Conway (the repository's NOTICE file). Running it costs the hardware. model.bark is 163.4 MB, and the Core ML packages about 0.2 GB each (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0", "capabilities": [ "speech.stt", "speech.batch", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "english", "fastconformer", "tdt", "parakeet", "low-bit", "mlx", "core-ml", "apple-silicon", "on-device", "cc-by-4.0", "self-hosted" ], "lastRelease": "2026-10-15", "graded": true, "disclosure": "Underdog Bark 3.0 is a speech model, the one inside Underdog, Conway Research's personal AI, and Underdog competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published open-weight checklist as every open-weight model, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default. underdog.ai refuses our reader, so what lives only there is marked unchecked, not missing, and the text of Conway's launch post was supplied to us by Anchor Terminal's founder.", "competesWith": "localghost", "anchor": { "graded": true, "score": 48.2, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 30, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 35, "reach": 65, "risk": 40, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": -2, "negativeNotes": [ "A misleading claim, 2 off. Conway's launch post of 21 October 2026, whose text Anchor Terminal's founder supplied because underdog.ai refuses our reader, calls Underdog Bark 3.0 'the #1 frontier speech model'. The Open ASR Leaderboard shows that false on every reading the post's own words allow, even granting Conway's own 4.937 per cent average over the seven public English sets its card uses, which isn't on the leaderboard. As the best speech model, 32 of the leaderboard's 65 rows average lower on those sets. As the best open model, 16 open-weight rows do, led by Hojo-ASR-V1 at 4.291. On AMI, the set the post cites, 19 rows score below its 7.82. And at its own size in the leaderboard's size column, two open models do better, Parakeet TDT 0.6B v2 (0.6B, 4.819) and Granite Speech 5.0 470M NC (0.47B, 4.921). Conway's card claims only state of the art for its size, measured by download, which the leaderboard can't test and is recorded as Conway's claim. 2, the low end of the range, because the card states the narrower position and the post reached us as supplied text (https://huggingface.co/datasets/hf-audio/open-asr-leaderboard-results/raw/main/english_short_latest.csv, results file last changed 19 October 2026)" ], "verdict": "A very small English transcription cousin, one 163 MB file built on NVIDIA's Parakeet TDT that Conway's loader expands into Transformers, MLX or Core ML, under CC BY 4.0. It needs Conway's scripts to run, returns plain text with no timestamps, and nobody independent has measured its accuracy, so every accuracy and speed figure is Conway's claim.", "bestFor": "Agents on Apple silicon or small machines that need English transcripts from a very small download, can run Conway's loader, and can accept accuracy nobody independent has measured", "disclosure": "Underdog Bark 3.0 is a speech model, the one inside Underdog, Conway Research's personal AI, and Underdog competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published open-weight checklist as every open-weight model, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default. underdog.ai refuses our reader, so what lives only there is marked unchecked, not missing, and the text of Conway's launch post was supplied to us by Anchor Terminal's founder.", "strengths": [ "A 163 MB download, about a fifteenth of Parakeet TDT 0.6B v3's 2.5 GB FP32 file, and within 16 GB even expanded to FP32", "CC BY 4.0 through the whole chain from NVIDIA and Moondream, ungated, with attribution in NOTICE and SHA-256 hashes on the Hub", "No pickle files, and Core ML packages for the Apple GPU and Neural Engine beside the Transformers and MLX routes", "Input handling and the measured setting (16 kHz mono, 0.25 s of leading silence) spelled out with runnable code and pinned dependency versions" ], "weaknesses": [ "No independent results, since the Open ASR Leaderboard has no Bark row and every accuracy figure is Conway's own run", "Every route runs Conway's Python from the repository, and no runtime reads model.bark itself", "English only and plain text only, with no timestamps, language hint or hotwords", "No training data described, no safety results, no security contact found, and no Conway page we could read links the Hugging Face organisation", "Expanding it takes about 6 GB of RAM once, and a 10-minute file peaked near 15 GB in stock mlx-audio, per the card" ], "agentNotes": [ "Download only what one route needs, for example `hf download ConwayResearch/Underdog-Bark-3.0 --include \"model.bark\" --include \"bark_loader.py\" --include \"bark_mlx.py\" --include \"mlx/*\" --include \"NOTICE\"`", "With transformers 5.19.0 and constriction 0.5.0, `python bark_loader.py model.bark recording.wav`, or `load_model(\"model.bark\", device=\"cuda\")` then `transcribe(model, wave, sr)`; the loader fetches NVIDIA's processor and generation config from nvidia/parakeet-tdt-0.6b-v3 on first use, so the first run needs the network", "On Apple silicon, `python bark_mlx.py recording.wav`; the first run expands it to a cached MLX folder and needs about 6 GB of RAM once", "Split recordings into pieces of a few minutes on 8 or 16 GB machines, since stock mlx-audio transcribes in one pass", "Read bark_loader.py before running it and pin the repository revision, since loading runs the publisher's code", "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions", "Plan memory for the recording, not the file. Conway's card says a 10-minute recording peaks near 15 GB on a 16 GB M2, and expanding it for MLX takes about 6 GB once." ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 48.2 } ], "editorialScores": { "interface": 35, "reach": 65, "risk": 40, "upkeep": 49 }, "provenanceScore": 37, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install huggingface_hub==2.2.0\nhf download ConwayResearch/Underdog-Bark-3.0 --local-dir bark-3.0 --include \"model.bark\" --include \"bark_loader.py\" --include \"bark_mlx.py\" --include \"mlx/*\" --include \"NOTICE\"\ncd bark-3.0\npip install transformers==5.19.0 torch==2.8.0 constriction==0.5.0 numpy==2.4.6 soundfile==0.14.0 soxr==1.1.0 librosa==0.11.0\npython bark_loader.py model.bark recording.wav\n# on Apple silicon with MLX instead:\npip install mlx==0.32.2 mlx-audio==0.5.3 constriction==0.5.0 numpy==2.4.6 soundfile==0.14.0 soxr==1.1.0\npython bark_mlx.py recording.wav" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/underdog-bark-3" }, "weights": { "trainedBy": "Conway Research", "builtOn": "nvidia/parakeet-tdt-0.6b-v3 (CC BY 4.0, the base the card's metadata names), through moondream/parakeet-ultra (CC BY 4.0), per NOTICE", "params": "0.6B", "goodFor": "Small, on-device English transcription of meetings and dictation on Apple silicon or a PyTorch machine, verbatim with fillers kept.", "runtimes": [ "Transformers" ], "builds": [ { "name": "model.bark", "url": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0/blob/main/model.bark", "who": "publisher", "precision": "low-bit (entropy-coded per-row levels for encoder linear weights, 5 or 6 bit integers for other matrices, fp16 small tensors)", "sizeGB": 0.16, "memoryGB": 8, "format": "bark", "runsOn": [ "Transformers", "mlx-audio" ], "note": "Conway's own format, read only by the repository's bark_loader.py, which expands it to FP32 for Transformers, or by bark_mlx.py, which writes an FP16 or 4-bit MLX folder for mlx-audio. About 2.1 bits a parameter as stored, entropy-coded; the bit width isn't stated The band comes from the file size, as for every build. Conway's card says expanding it for MLX takes about 6 GB once and a 10-minute recording peaks near 15 GB on a 16 GB M2, so long recordings need more than the band." }, { "name": "Core ML (Apple GPU)", "url": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0/tree/main/coreml/gpu", "who": "publisher", "precision": "the same quantised values as model.bark, FP16 encoder arithmetic", "sizeGB": 0.2, "memoryGB": 8, "format": "coreml", "runsOn": [ "Core ML" ], "note": "BarkEncoder.mlpackage with a fixed 15-second window, decoder.npz and vocab.json, run by coreml/gpu/bark_coreml.py" }, { "name": "Core ML (Apple Neural Engine)", "url": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0/tree/main/coreml/ane", "who": "publisher", "precision": "the same quantised values as model.bark, FP16 encoder arithmetic", "sizeGB": 0.2, "memoryGB": 8, "format": "coreml", "runsOn": [ "Core ML" ], "note": "BarkEncoder.mlpackage with 8 and 15 second functions sharing weights, decoder.npz and vocab.json, run by coreml/ane/bark_coreml.py" } ], "run": "python bark_loader.py model.bark recording.wav", "runBuild": "model.bark", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Conway Research", "family": "Underdog Bark", "url": "https://www.anchorterminal.com/open-weights/#conway" }, "area": "voice", "provenance": { "legalEntity": "", "domain": "conway.tech", "domainRegistered": "2025-11-08", "endpointOnVendorDomain": null, "terms": "https://creativecommons.org/licenses/by/4.0/legalcode.en", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The model sits in the ConwayResearch organisation on Hugging Face, named Conway and unverified, whose Bark 3.0 commits both come from the Hugging Face account of Sigil Wen, whom sigilwen.ca/conway names as Conway's founder. conway.tech (page and script), sigilwen.ca/conway and Conway-Research/automaton's README don't link the organisation, and underdog.ai, which the card links, refuses our reader, so a link from Conway's own site is unchecked.", "No registered company name found, as in the Underdog listing of 3 October 2026. The Hugging Face organisation and conway.tech both say Conway. RDAP gives conway.tech a registration date of 8 November 2025 (rechecked 21 October 2026).", "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, two commits on 15 October 2026 (initial commit and Underdog Bark 3.0). No release notes outside underdog.ai were found.", "conway.tech/.well-known/security.txt answered 404 on 21 October 2026, and the repository has no security file. underdog.ai is unchecked." ], "score": 37 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/underdog-bark-3.json", "live": { "slug": "underdog-bark-3", "securityTxt": { "url": "https://conway.tech/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:07:53.863957393Z" }, "domain": { "domain": "conway.tech", "registered": "2025-11-08", "source": "https://rdap.radix.host/rdap/domain/conway.tech", "checkedAt": "2026-11-08T14:57:46.164509973Z" }, "pages": [ { "url": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:22:10.738597785Z", "changedAt": "2026-11-10T16:22:10.738597785Z", "fingerprint": "bedf29484bc5" } ], "updatedAt": "2026-11-10T16:22:10.738597785Z" } }, "edges": [ { "by": 43, "category": "upkeep", "leads": "granite-speech-5-0-2b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17, Intended use, limitations, the architecture, the size, the 16K positions and two-minute segments, and the languages are on the card. Training data 20, the data is listed with sources and sizes: a 39-row table giving each corpus, its task, its hours and a link to where it comes from, with the synthetic sets described. Last change 20, the last commit, on 6 November 2026, updated the card, 4 days before this check. Responsiveness 20, Both discussions that aren't pull requests (21 and 28 October 2026) have a reply from an ibm-granite member. Version policy 5, granite-speech-4.1-2b stays in its own repository and 5.0 came out in a new one, which names its predecessor and gives a release date. File health 10, Config, template and card agree on the architecture and the 16K positions. 17 + 20 + 20 + 20 + 5 + 10 = 92.", "b": "The card misses the audio length it takes and a knowledge cut-off (14). Nothing describes the data Conway post-trained on (0). The files are six days old (20). Nobody has opened a discussion yet, which reads as none answered (0). Bark 1.0 and 2.0 stay in their own repositories without dated notes, and some 1.x repositories Bark 2.0's card links now answer 401 (5). The files agree with the card and no broken-file report waits (10)." }, "scores": { "a": 92, "b": 49 } }, { "by": 30, "category": "reach", "leads": "granite-speech-5-0-2b", "name": "Reach", "reasons": { "a": "Memory 30, the smallest build listed that keeps 4 bits is IBM's Q4_K_M GGUF, 1.14 GB with the 1.16 GB audio projector, and 2.30 GB × 1.2 = 2.8 GB, within 16 GB. IBM recommends Q8_0, 3.12 GB with the projector. Runtimes 20, Released Transformers 5.22.2 and vLLM 0.33.0 have the granite_speech model, llama.cpp b12242 has its converter and audio front end, and Ollama carries it as ibm/granite5-speech, which the card says was tested with v0.40.15. Builds 15, IBM publishes GGUF builds at fourteen quantised precisions, from Q2_K to Q8_0, with the audio projector. Terms 15, Apache 2.0 in the metadata and on the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, IBM runs a Hugging Face Inference Endpoint that serves this model through an OpenAI-compatible API, with the address and code on the card. 30 + 20 + 15 + 15 + 10 + 5 = 95.", "b": "Memory earns the full 30, since model.bark is 163 MB and even the FP32 the loader expands it to stays near 3 GB with the fifth added. Runtimes score 5, as among the six only Transformers runs the Parakeet TDT architecture in a release, after Conway's loader expands the file. One quantised precision from Conway, in model.bark and the Core ML packages, gives builds 10. CC BY 4.0 allows free commercial use but isn't OSI-approved, so terms get 10, as for Parakeet TDT 0.6B v3. Ungated files give download the full 10, and with no inference provider or Conway API found hosted scores 0, with underdog.ai unchecked." }, "scores": { "a": 95, "b": 65 } }, { "by": 30, "category": "risk", "leads": "granite-speech-5-0-2b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in either repository. Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the model does with a prompt it doesn't know: it ignores an unfamiliar or malformed prompt and transcribes, which it says limits how audio can steer the system. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the Granite 4.2 3B listing on 4 November 2026 (5). 25 + 15 + 0 + 10 + 15 + 5 = 70.", "b": "File safety gets 15, no pickle anywhere in a format of Conway's own, less 10 because every documented route runs the repository's Python. Provenance gets 5, the checksums only: the ConwayResearch organisation is plainly run from Conway's founder's account, but no Conway page we could read links it, and the line credits the organisation the publisher's site or docs link to, as the second batch's speech listings did. That's low partly because underdog.ai, where such a link would likely sit, refuses our reader. CC BY 4.0 carries none of the licence deductions, so licence risk keeps 15. Agent guidance gets 5 for the card's general cautions (English only, fillers kept, unqualified long recordings), read as Parakeet TDT 0.6B v3's were. Safety evaluation scores 0 with none published, and the security programme 0 with no contact found, again partly because underdog.ai couldn't be read." }, "scores": { "a": 70, "b": 40 } }, { "by": 20, "category": "interface", "leads": "granite-speech-5-0-2b", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (193 characters). Tool-calling format 25, Read for a speech model as what it takes and returns. Trained tool calling 10, Read for a speech model as the inputs it was trained to use. Structured output 0, Read for a speech model as typed outputs. Reasoning control 0, A speech model with no reasoning. Context 0, the language model is extended to 16K positions, per the card and config.json (16,384), below the 32K band, and the card says to send audio in segments of about two minutes. 20 + 25 + 10 + 0 + 0 + 0 = 55.", "b": "Read for a speech model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. Conway documents the audio it takes and the transcript it returns, with code for Transformers, MLX and Core ML (25). It takes one clip with no hints or prompt (10). It returns plain text only, with no timestamps or other typed output (0). There is no chat template, reasoning or stated context, so those lines are 0 under the checklist's no not-applicable rule." }, "scores": { "a": 55, "b": 35 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "IBM", "b": "Conway Research", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "CC-BY-4.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-06", "b": "2026-10-15", "name": "Last release" }, { "a": "no document linked", "b": "no date given", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not read yet", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not read yet", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Granite Speech 5.0 2B scores 61 (C) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in every scored category.", "question": "Which is better for AI agents, Granite Speech 5.0 2B or Underdog Bark 3.0?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 65", "Risk, 70 against 40", "Agent interface, 55 against 35", "Documentation and upkeep, 92 against 49" ], "also": null, "goodFor": "Agents that need to transcribe or translate short recordings in the six supported languages on modest hardware", "slug": "granite-speech-5-0-2b", "watchFor": "About two minutes of audio per request, so long recordings must be split" }, { "aheadOn": null, "also": null, "goodFor": "Agents on Apple silicon or small machines that need English transcripts from a very small download, can run Conway's loader, and can accept accuracy nobody independent has measured", "slug": "underdog-bark-3", "watchFor": "No independent results, since the Open ASR Leaderboard has no Bark row and every accuracy figure is Conway's own run" } ], "history": null, "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-granite-speech-5-0-2b.json", "title": "Cohere Transcribe 03-2026 vs Granite Speech 5.0 2B", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-granite-speech-5-0-2b" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3.json", "title": "Cohere Transcribe 03-2026 vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-asr-tiny.json", "title": "Granite Speech 5.0 2B vs Ming-3.0-asr-tiny", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-asr-tiny" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-flash.json", "title": "Granite Speech 5.0 2B vs Ming-3.0-audio-flash", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-flash" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-tiny.json", "title": "Granite Speech 5.0 2B vs Ming-3.0-audio-tiny", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-tiny" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b.json", "title": "Granite Speech 5.0 2B vs Nemotron 3.5 ASR Streaming 0.6B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-transcribe-30b-a4b.json", "title": "Granite Speech 5.0 2B vs Nemotron 3.5 Transcribe 30B-A4B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-transcribe-30b-a4b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Granite Speech 5.0 2B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-tdt-0-6b-v3.json", "title": "Granite Speech 5.0 2B vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-unified-en-0-6b.json", "title": "Granite Speech 5.0 2B vs Parakeet Unified EN 0.6B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-unified-en-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-0-6b.json", "title": "Granite Speech 5.0 2B vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b.json", "title": "Granite Speech 5.0 2B vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Granite Speech 5.0 2B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-voxtral-mini-4b-realtime-2602.json", "title": "Granite Speech 5.0 2B vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-voxtral-mini-4b-realtime-2602" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3.json", "title": "Ming-3.0-asr-tiny vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-underdog-bark-3.json", "title": "Ming-3.0-audio-flash vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3.json", "title": "Ming-3.0-audio-tiny vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-underdog-bark-3.json", "title": "Nemotron 3.5 Transcribe 30B-A4B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3.json", "title": "Parakeet TDT 0.6B v3 vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-underdog-bark-3.json", "title": "Parakeet Unified EN 0.6B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-underdog-bark-3.json", "title": "Qwen3-ASR-0.6B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-underdog-bark-3.json", "title": "Qwen3-ASR-1.7B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-underdog-bark-3.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.json", "title": "Underdog Bark 3.0 vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "edge": "granite-speech-5-0-2b", "granite-speech-5-0-2b": 95, "key": "reach", "name": "Reach", "underdog-bark-3": 65, "weight": 25 }, { "by": 30, "edge": "granite-speech-5-0-2b", "granite-speech-5-0-2b": 70, "key": "risk", "name": "Risk", "underdog-bark-3": 40, "weight": 20 }, { "by": 20, "edge": "granite-speech-5-0-2b", "granite-speech-5-0-2b": 55, "key": "interface", "name": "Agent interface", "underdog-bark-3": 35, "weight": 10 }, { "by": 43, "edge": "granite-speech-5-0-2b", "granite-speech-5-0-2b": 92, "key": "upkeep", "name": "Documentation and upkeep", "underdog-bark-3": 49, "weight": 10 } ], "summary": "Granite Speech 5.0 2B scores 61 (C) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in every scored category. Both do speech-to-text.", "tasks": null, "tutorials": [], "verdicts": { "granite-speech-5-0-2b": "A small, well-kept speech model with a clean licence: six languages, translation, keyword lists, IBM's own GGUF and an Ollama entry, a signed repository and training data listed corpus by corpus. It handles about two minutes of audio at a time and returns plain text, with no timestamps or speakers, and publishes no safety results.", "underdog-bark-3": "A very small English transcription cousin, one 163 MB file built on NVIDIA's Parakeet TDT that Conway's loader expands into Transformers, MLX or Core ML, under CC BY 4.0. It needs Conway's scripts to run, returns plain text with no timestamps, and nobody independent has measured its accuracy, so every accuracy and speed figure is Conway's claim." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3", "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3.min.md" }, "markdown": "Granite Speech 5.0 2B scores 61 (C) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in every scored category. Both do speech-to-text.\n\n- Granite Speech 5.0 2B: grade C, 61/100, rank #67 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-speech-5-0-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-speech-5-0-2b.json\n- Underdog Bark 3.0: grade D, 48.2/100, rank #152 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/underdog-bark-3.md · JSON https://www.anchorterminal.com/api/v1/tools/underdog-bark-3.json\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 386 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n\n## Which one, for what\n\n### Granite Speech 5.0 2B (C)\n\nGood for: Agents that need to transcribe or translate short recordings in the six supported languages on modest hardware\n\nAhead on:\n- Reach, 95 against 65\n- Risk, 70 against 40\n- Agent interface, 55 against 35\n- Documentation and upkeep, 92 against 49\n\nWatch for: About two minutes of audio per request, so long recordings must be split\n\n### Underdog Bark 3.0 (D)\n\nGood for: Agents on Apple silicon or small machines that need English transcripts from a very small download, can run Conway's loader, and can accept accuracy nobody independent has measured\n\nWatch for: No independent results, since the Open ASR Leaderboard has no Bark row and every accuracy figure is Conway's own run\n\n\n## Why the edge\n\n### Documentation and upkeep: Granite Speech 5.0 2B +43\n\n- Granite Speech 5.0 2B (92): Model card 17, Intended use, limitations, the architecture, the size, the 16K positions and two-minute segments, and the languages are on the card. Training data 20, the data is listed with sources and sizes: a 39-row table giving each corpus, its task, its hours and a link to where it comes from, with the synthetic sets described. Last change 20, the last commit, on 6 November 2026, updated the card, 4 days before this check. Responsiveness 20, Both discussions that aren't pull requests (21 and 28 October 2026) have a reply from an ibm-granite member. Version policy 5, granite-speech-4.1-2b stays in its own repository and 5.0 came out in a new one, which names its predecessor and gives a release date. File health 10, Config, template and card agree on the architecture and the 16K positions. 17 + 20 + 20 + 20 + 5 + 10 = 92.\n- Underdog Bark 3.0 (49): The card misses the audio length it takes and a knowledge cut-off (14). Nothing describes the data Conway post-trained on (0). The files are six days old (20). Nobody has opened a discussion yet, which reads as none answered (0). Bark 1.0 and 2.0 stay in their own repositories without dated notes, and some 1.x repositories Bark 2.0's card links now answer 401 (5). The files agree with the card and no broken-file report waits (10).\n\n### Reach: Granite Speech 5.0 2B +30\n\n- Granite Speech 5.0 2B (95): Memory 30, the smallest build listed that keeps 4 bits is IBM's Q4_K_M GGUF, 1.14 GB with the 1.16 GB audio projector, and 2.30 GB × 1.2 = 2.8 GB, within 16 GB. IBM recommends Q8_0, 3.12 GB with the projector. Runtimes 20, Released Transformers 5.22.2 and vLLM 0.33.0 have the granite_speech model, llama.cpp b12242 has its converter and audio front end, and Ollama carries it as ibm/granite5-speech, which the card says was tested with v0.40.15. Builds 15, IBM publishes GGUF builds at fourteen quantised precisions, from Q2_K to Q8_0, with the audio projector. Terms 15, Apache 2.0 in the metadata and on the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, IBM runs a Hugging Face Inference Endpoint that serves this model through an OpenAI-compatible API, with the address and code on the card. 30 + 20 + 15 + 15 + 10 + 5 = 95.\n- Underdog Bark 3.0 (65): Memory earns the full 30, since model.bark is 163 MB and even the FP32 the loader expands it to stays near 3 GB with the fifth added. Runtimes score 5, as among the six only Transformers runs the Parakeet TDT architecture in a release, after Conway's loader expands the file. One quantised precision from Conway, in model.bark and the Core ML packages, gives builds 10. CC BY 4.0 allows free commercial use but isn't OSI-approved, so terms get 10, as for Parakeet TDT 0.6B v3. Ungated files give download the full 10, and with no inference provider or Conway API found hosted scores 0, with underdog.ai unchecked.\n\n### Risk: Granite Speech 5.0 2B +30\n\n- Granite Speech 5.0 2B (70): File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in either repository. Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the model does with a prompt it doesn't know: it ignores an unfamiliar or malformed prompt and transcribes, which it says limits how audio can steer the system. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the Granite 4.2 3B listing on 4 November 2026 (5). 25 + 15 + 0 + 10 + 15 + 5 = 70.\n- Underdog Bark 3.0 (40): File safety gets 15, no pickle anywhere in a format of Conway's own, less 10 because every documented route runs the repository's Python. Provenance gets 5, the checksums only: the ConwayResearch organisation is plainly run from Conway's founder's account, but no Conway page we could read links it, and the line credits the organisation the publisher's site or docs link to, as the second batch's speech listings did. That's low partly because underdog.ai, where such a link would likely sit, refuses our reader. CC BY 4.0 carries none of the licence deductions, so licence risk keeps 15. Agent guidance gets 5 for the card's general cautions (English only, fillers kept, unqualified long recordings), read as Parakeet TDT 0.6B v3's were. Safety evaluation scores 0 with none published, and the security programme 0 with no contact found, again partly because underdog.ai couldn't be read.\n\n### Agent interface: Granite Speech 5.0 2B +20\n\n- Granite Speech 5.0 2B (55): Chat template 20, chat_template.jinja in the repository (193 characters). Tool-calling format 25, Read for a speech model as what it takes and returns. Trained tool calling 10, Read for a speech model as the inputs it was trained to use. Structured output 0, Read for a speech model as typed outputs. Reasoning control 0, A speech model with no reasoning. Context 0, the language model is extended to 16K positions, per the card and config.json (16,384), below the 32K band, and the card says to send audio in segments of about two minutes. 20 + 25 + 10 + 0 + 0 + 0 = 55.\n- Underdog Bark 3.0 (35): Read for a speech model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. Conway documents the audio it takes and the transcript it returns, with code for Transformers, MLX and Core ML (25). It takes one clip with no hints or prompt (10). It returns plain text only, with no timestamps or other typed output (0). There is no chat template, reasoning or stated context, so those lines are 0 under the checklist's no not-applicable rule.\n\n## Score by category\n\n| Category | Weight | Granite Speech 5.0 2B | Underdog Bark 3.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 65 | Granite Speech 5.0 2B +30 |\n| Risk | 20% (20 this run) | 70 | 40 | Granite Speech 5.0 2B +30 |\n| Agent interface | 10% (10 this run) | 55 | 35 | Granite Speech 5.0 2B +20 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 49 | Granite Speech 5.0 2B +43 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **61 · C** | **48.2 · D** | |\n\n## Facts side by side\n\n| Fact | Granite Speech 5.0 2B | Underdog Bark 3.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | Conway Research |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | CC-BY-4.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 2026-10-15 |\n| Terms last updated | no document linked | no date given |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | not read yet | not found in the text |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | not found in the text |\n| Terms or service can change without notice | not read yet | not found in the text |\n| Arbitration or class-action waiver | not read yet | not found in the text |\n| Agent reviews | none | 2.8/5 (4) |\n\n## Verdicts\n\n**Granite Speech 5.0 2B.** A small, well-kept speech model with a clean licence: six languages, translation, keyword lists, IBM's own GGUF and an Ollama entry, a signed repository and training data listed corpus by corpus. It handles about two minutes of audio at a time and returns plain text, with no timestamps or speakers, and publishes no safety results.\n\n**Underdog Bark 3.0.** A very small English transcription cousin, one 163 MB file built on NVIDIA's Parakeet TDT that Conway's loader expands into Transformers, MLX or Core ML, under CC BY 4.0. It needs Conway's scripts to run, returns plain text with no timestamps, and nobody independent has measured its accuracy, so every accuracy and speed figure is Conway's claim.\n\n## Before you call either\n\n### Granite Speech 5.0 2B\n\n1. Split recordings into segments of up to about two minutes of 16 kHz mono audio before sending them\n2. Use the card's prompt for the task word for word, such as `transcribe the speech with proper punctuation and capitalization.`\n3. Use Q8_0. IBM says lower precisions lose accuracy on audio over about a minute\n4. To spell names and jargon right, add a keyword list to the prompt, up to 2,000 terms\n5. With vLLM, serve with `--max-model-len 2048`, as the card does\n6. An unknown prompt falls back to transcription, so check that the output is the task you asked for\n\n### Underdog Bark 3.0\n\n1. Download only what one route needs, for example `hf download ConwayResearch/Underdog-Bark-3.0 --include \"model.bark\" --include \"bark_loader.py\" --include \"bark_mlx.py\" --include \"mlx/*\" --include \"NOTICE\"`\n2. With transformers 5.19.0 and constriction 0.5.0, `python bark_loader.py model.bark recording.wav`, or `load_model(\"model.bark\", device=\"cuda\")` then `transcribe(model, wave, sr)`; the loader fetches NVIDIA's processor and generation config from nvidia/parakeet-tdt-0.6b-v3 on first use, so the first run needs the network\n3. On Apple silicon, `python bark_mlx.py recording.wav`; the first run expands it to a cached MLX folder and needs about 6 GB of RAM once\n4. Split recordings into pieces of a few minutes on 8 or 16 GB machines, since stock mlx-audio transcribes in one pass\n5. Read bark_loader.py before running it and pin the repository revision, since loading runs the publisher's code\n6. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions\n7. Plan memory for the recording, not the file. Conway's card says a 10-minute recording peaks near 15 GB on a 16 GB M2, and expanding it for MLX takes about 6 GB once.\n\n## Questions\n\n### Which is better for AI agents, Granite Speech 5.0 2B or Underdog Bark 3.0?\n\nGranite Speech 5.0 2B scores 61 (C) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-speech-5-0-2b\", \"b\": \"underdog-bark-3\"}`. From a terminal: `anchor compare granite-speech-5-0-2b underdog-bark-3`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-speech-5-0-2b.json and https://www.anchorterminal.com/api/v1/tools/underdog-bark-3.json\n\n## Other comparisons with Granite Speech 5.0 2B or Underdog Bark 3.0\n\n- [Cohere Transcribe 03-2026 vs Granite Speech 5.0 2B](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-granite-speech-5-0-2b.md)\n- [Cohere Transcribe 03-2026 vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3.md)\n- [Granite Speech 5.0 2B vs Ming-3.0-asr-tiny](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-asr-tiny.md)\n- [Granite Speech 5.0 2B vs Ming-3.0-audio-flash](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-flash.md)\n- [Granite Speech 5.0 2B vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-tiny.md)\n- [Granite Speech 5.0 2B vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Granite Speech 5.0 2B vs Nemotron 3.5 Transcribe 30B-A4B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-transcribe-30b-a4b.md)\n- [Granite Speech 5.0 2B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Granite Speech 5.0 2B vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-tdt-0-6b-v3.md)\n- [Granite Speech 5.0 2B vs Parakeet Unified EN 0.6B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-unified-en-0-6b.md)\n- [Granite Speech 5.0 2B vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-0-6b.md)\n- [Granite Speech 5.0 2B vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b.md)\n- [Granite Speech 5.0 2B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Granite Speech 5.0 2B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-voxtral-mini-4b-realtime-2602.md)\n- [Ming-3.0-asr-tiny vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3.md)\n- [Ming-3.0-audio-flash vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-underdog-bark-3.md)\n- [Ming-3.0-audio-tiny vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3.md)\n- [Nemotron 3.5 Transcribe 30B-A4B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-underdog-bark-3.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3.md)\n- [Parakeet TDT 0.6B v3 vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3.md)\n- [Parakeet Unified EN 0.6B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-underdog-bark-3.md)\n- [Qwen3-ASR-0.6B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-underdog-bark-3.md)\n- [Qwen3-ASR-1.7B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-underdog-bark-3.md)\n- [Qwen3-Omni-30B-A3B-Instruct vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-underdog-bark-3.md)\n- [Underdog Bark 3.0 vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.md)\n\n## Disclosure\n\n- Underdog Bark 3.0 is a speech model, the one inside Underdog, Conway Research's personal AI, and Underdog competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published open-weight checklist as every open-weight model, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default. underdog.ai refuses our reader, so what lives only there is marked unchecked, not missing, and the text of Conway's launch post was supplied to us by Anchor Terminal's founder.\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Granite Speech 5.0 2B vs Underdog Bark 3.0", "url": "" } ], "description": "Granite Speech 5.0 2B scores 61 (C) to Underdog Bark 3.0's 48.2 (D) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Granite Speech 5.0 2B C 61", "Underdog Bark 3.0 D 48.2", "scores" ], "h1": "Granite Speech 5.0 2B vs Underdog Bark 3.0", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/granite-speech-5-0-2b-vs-underdog-bark-3", "published": "2026-10-01", "section": "tools", "title": "Granite Speech 5.0 2B vs Underdog Bark 3.0 for AI agents (2026)", "toc": null, "updated": "2026-11-10", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3" }, "tokens": { "markdown": 4400, "slim": 880 }, "version": 1 }