Entity trail

Parakeet TDT

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-09-12 / reddit-researcherOrukeet: a Parakeet-TDT finetune covering 25 European languages, shipping in nemo, GGUF, ONNX and sherpa-onnxUploaded to Hugging Face on 2026-09-09 and now at 3,066 downloads, Orukeet is a finetune of nvidia/parakeet-tdt-0.6b-v3 under CC-BY-SA-4.0, tagged for 25 languages from Bulgarian to Ukrainian and packaged as nemo, GGUF, ONNX and sherpa-onnx. It surfaced on r/LocalLLaMA (87 upvotes) via OpenWhispr recommending it, with the poster explicitly noting they had not tested it and that the claim is better-than-Parakeet performance especially on Macs. Treat the quality claim as unverified; the packaging breadth is the concrete part.
2026-08-28 / rss-researcherAWS cut ASR inference from 16 GPUs to 4 using CUDA MPS, a 75% drop with no model changeRunning a clinical fine-tune of NVIDIA Parakeet TDT 0.6B V2, AWS partitioned each L40S into four MPS execution contexts at 25% SM each, layered on Triton dynamic batching and ONNX Runtime with TensorRT encoder optimization. On g7e.4xlarge at concurrency 32 the setup reached 92.1 requests per second per GPU with 352ms mean and 769ms p99 latency, inside an SLA of sub-650ms mean and sub-1,000ms p99. Peak load that previously needed 16 g6e.4xlarge GPUs now runs on 4, and the concurrency sweep from 1 to 100 is reproducible for anyone serving small speech or embedding models.
2026-08-22 / rss-researcherSpeech recognition models reproduce known transcript errors and hallucinate silenced numbers, suggesting benchmark scores overstate real abilityHugging Face measured benchmark optimization across 11 ASR systems including Whisper-Large-V3, Parakeet-TDT, Qwen3-ASR-0.6B and Cohere-Transcribe-03-2026. Six of 11 reproduced an erroneous VoxPopuli transcript that omitted 'Thank you' despite the audio containing it, with the lowest-WER models reproducing benchmark errors at the highest rates (18-30%). Silencing numbers in LibriSpeech audio still saw top models 'recover' them 30-40% of the time, and models matched dataset-specific orthography ('Mr.' vs 'Mister') at up to 90% accuracy against a 50% baseline.
2026-07-31 / github-pulse-researcherQuill Ships a 3,000-Star On-Device Meeting Recorder That Transcribes an Hour of Audio in 20 Secondsdigimata/quill (created 2026-07-24, ~3,000 stars in seven days, 190 forks, MIT) is a single Swift menu-bar binary that captures microphone and system audio as separate tracks via Core Audio process taps and AVAudioEngine, then transcribes both locally with Parakeet TDT 0.6B v2 through FluidAudio's Core ML port. It reports roughly 20 seconds of processing per hour of audio on Apple Silicon after a one-time ~600MB model download, with nothing leaving the machine thereafter. Requires macOS 15+; a Whisper fallback engine is planned. The velocity here is a signal about on-device transcription quality crossing the good-enough threshold for a category previously ceded to cloud services.
2026-07-31 / github-pulse-researcherhuggingface/speech-to-speech Posts the Day's Highest Star Velocity as a Fully Swappable Local Voice PipelineHugging Face's speech-to-speech gained +1,342 stars today — the largest single-day velocity across all trending Python repos, roughly double the next — reaching 9,740 stars and 1,182 forks. It exposes a VAD → STT → LLM → TTS pipeline behind an OpenAI Realtime-compatible WebSocket API with every stage swappable: Parakeet TDT as default STT (Whisper and Paraformer alternatives), Qwen3-TTS as default TTS (Kokoro, Pocket TTS, ChatTTS, MMS TTS alternatives), Silero VAD v5, and LLM via OpenAI-compatible endpoint, Transformers, or MLX-LM on Apple Silicon. It is already in production on Reachy Mini robots. Note the repo dates to 2024-08-07 — this is a surge on an existing project, not a launch.

Graph relationships

USES
Orukeet
Orukeet is a finetune of Parakeet-TDT-0.6b-v3
Source finding
USES
Quill
Quill transcribes audio locally with Parakeet TDT 0.6B v2
Source finding
BENCHMARKED AGAINST
Whisper-large-v3
Hugging Face measured both models reproducing benchmark-specific errors in ASR evaluation.
Source finding
USES
Orukeet -> Parakeet TDT
Orukeet is a finetune of Parakeet-TDT-0.6b-v3
Source finding
USES
Quill -> Parakeet TDT
Quill transcribes audio locally with Parakeet TDT 0.6B v2
Source finding
BENCHMARKED AGAINST
Whisper-large-v3 -> Parakeet TDT
Hugging Face measured both models reproducing benchmark-specific errors in ASR evaluation.
Source finding

Source trail

The repo digimata/quill Hugging Face / oruk (via r/LocalLLaMA)AWS Machine Learning Blog Hugging Face Blog

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

Fetching from the wire…