model
OpenAI's open-source speech recognition model family that transcribes and translates audio into text, offered in multiple sizes via API or self-hosted runtimes.
No evidence-backed relationships are recorded.
Cactus Compute released Whistle, an open 16.9MB speech-to-text model that runs on a CPU in the same Needle engine as its tool-calling language models. The accuracy and speed figures are the company's own, so they need rerunning on real devices and audio.
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Qwen3-TTS went from RTF 9 under PyTorch to faster than real time through a community MLX port on one developer's M1 Pro. The port took it from written off to first place among the commercially usable cloners tested for an offline Mac app.
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Extending Whisper subtitle cues into the next pause cut the count over 17 characters per second from 47 to 20 in a dev.to test on one 168-second clip. The result argues for fixing timestamps before trimming words, wherever the speaker leaves silence to borrow.
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Cursor Projects' orchestrator rewrote its notes.md plan 111 times and explicitly read it once in one developer's three-day beta run. Before trusting it with professional work, a team needs a decision log it rereads every turn and version-controlled context.
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The author of a Crimean Tatar recogniser scored it for the first time at 34.6% word error, then found four of his audiobooks sitting in the data twice under filenames a name check had cleared.
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Microsoft treats speech as its own agent kind, with a managed realtime orchestrator holding the socket open. A dev.to walkthrough sets out why turn detection and tool calls had to be restructured around a few hundred milliseconds.
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An offline Bengali voice dialer ran into two Whisper problems on a Pixel 10, wrong-script output from the small model and an 80-second encoder pass from the turbo build, and only the second one yielded to a change of runtime.
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An RTX 3090 holding transcription and embedding models in VRAM averaged 25 W over the month at a cost of 2 euros, while the same post puts hardware amortisation at about 25 euros a month, twelve times the power bill.
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Version 2.0 arrives at the same price as version 1.0. The accuracy gain SpaceXAI reports is concentrated on short multilingual commands. The evaluation sets behind the claim are drawn from production support calls.
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A developer's write-up of a medical transcription pipeline cites a 2025 study finding that under noise Whisper puts confidence above 0.7 on tokens that are wrong, and that the overconfidence grows as the signal-to-noise ratio falls.
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Apple's Kids Category rule and COPPA's definition of a child's voice both point a preschool voice product at local processing, and Whisper's error on children's speech falls by nearly a factor of three from tiny to large-v3.
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Mahidol's Biomedical and Data Lab publishes Thai Whisper fine-tunes that are free to use commercially, with 6.59 and 7.42 WER on Common Voice 13. Both were scored with the Deepcut tokenizer, and that shared segmentation is the reason the two numbers sit on one scale.
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Microsoft's local runtime handles model download, hardware detection and execution provider selection behind a .dll, .so or .dylib. An app that adopts it owns the download, the cache and the unload.
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The 5% comes from one dev.to post's worked example, and it holds only if an attacker's hit rate against your login is near 0.3% and a stolen account resells near $20. At a 0.03% hit rate the same bill is half of revenue.
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Workers CPU wall time counts only handler execution, so at 0.1 requests per second ScribeToAny's cold isolates spent seconds compiling before the 5ms render, and the platform dashboard showed none of it.
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A sound that is not a word never reaches the transcript, so a 100 percent pass rate measured Whisper rather than the model. The replacement detector reads the RMS envelope, and its constants are fitted to four failures.
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The pipeline cut at segments[-1].end, which is the end of the file once the transcriber puts the hallucinated tail in its own segment. So the fallback built for tail hallucinations only ever ran on clips that did not need it.
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An ASR check that demanded a perfect transcript kept the takes Whisper found easy to read, and those were the flat ones. The gate worked exactly as specified and produced a monotone corpus anyway.
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A dev.to walkthrough scores WebVTT on four axes instead of one, on the argument that a file can read 96% accurate and still be unusable. The interesting part is which failures the other three axes catch.
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A voicebot operator ran out of room stuffing client names into faster-whisper's initial_prompt, and now feeds it the system's own last 160 characters of speech instead.
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