| --- |
| license: cc-by-4.0 |
| base_model: FluidVoice-Labs/fluid-speech-pro |
| base_model_relation: quantized |
| pipeline_tag: automatic-speech-recognition |
| language: |
| - en |
| - bg |
| - hr |
| - cs |
| - da |
| - nl |
| - et |
| - fi |
| - fr |
| - de |
| - el |
| - hu |
| - it |
| - lv |
| - lt |
| - mt |
| - pl |
| - pt |
| - ro |
| - sk |
| - sl |
| - es |
| - sv |
| - ru |
| - uk |
| tags: |
| - glade |
| - core-ai |
| - parakeet |
| - speech-recognition |
| --- |
| |
| # Fluid Speech Pro for Glade |
|
|
| Complete [Fluid Speech Pro](https://huggingface.co/FluidVoice-Labs/fluid-speech-pro) |
| converted for Glade with Apple Core AI. Both distributions include the learned |
| FrameCleaner block. The model has **629,125,766 primary parameters**, including |
| 2,117,632 in FrameCleaner, and supports the upstream checkpoint's 25 languages. |
|
|
| Source revision: `46027a3bffc5817ce8f9b29add0a062610134a90`. |
| The weights retain **CC-BY-4.0**; see LICENSE and NOTICE. |
|
|
| ## Choose a mode |
|
|
| | Directory | Behavior | Download | |
| | --- | --- | ---: | |
| | `clean/` | Edited transcription: removes fillers and repetitions | 806.4 MB | |
| | `verbatim/` | Word-for-word transcription, retaining fillers and repetitions | 806.4 MB | |
|
|
| Download one complete directory. Each works independently. Clean uses the |
| checkpoint's trained rank-32 LoRA adapter and learned predictor/joint offsets, |
| merged into the weights **before export**. It is not a text postprocessor and |
| adds no inference operations. The original FP32 NeMo adapter is merged before |
| encoder quantization. Both modes have the same graph topology, operation count |
| and primary parameter count, with separate specialization caches. |
|
|
| ## Runtime |
|
|
| Requires Glade on physical Apple silicon with iOS 27 or macOS 27. |
|
|
| - Mono 16 kHz input and the checkpoint's complete 128-bin log-mel frontend. |
| - A 24-layer, 1,024-channel, eight-head FastConformer, with the full residual |
| FrameCleaner after subsampling; a two-layer 640-channel LSTM TDT predictor. |
| - W8 encoder projections and FP16 graph tensors. Native host decoder sidecars |
| are included. Quantization and finite-frame conventions are explicit metadata. |
| - One 384-position encoder shape, split into six four-layer assets. Attention |
| remains fully bidirectional within each chunk; staging does not restrict it. |
| - Contiguous quiet-boundary chunks of up to 30 seconds preserve every audio |
| sample, including silence. No VAD head is supplied by this checkpoint. |
| - Two concurrent encoder requests and up to 32 batched decoder lanes. |
| - Upstream-default greedy TDT decoding, durations 0–4, at most ten symbols per |
| acoustic frame. These are beam-1 results, not the publisher's beam-4 leaderboard. |
| - Native word timing has an 80 ms acoustic frame step; it is not forced alignment. |
|
|
| Glade handles file reading, chunk planning, batching, ordered results and cleanup: |
|
|
| ```swift |
| import Foundation |
| import Glade |
| |
| let bundle = try ParakeetModelBundle(directory: modelDirectory) |
| let recording = try STTRecordingTranscriber(runtime: bundle.makeTranscriber()) |
| try await recording.transcribe(fileURL: audioURL) { segment in |
| print(segment.text) |
| } |
| ``` |
|
|
| `metadata.json` identifies the transcription style, shapes and numerical |
| conventions; `runtime.json` carries scheduling. A bundle's style can be read as |
| `bundle.transcriptionStyle`. Loading one mode does not prepare the other. |
|
|
| ## Performance |
|
|
| November 14, 2026. Complete 1,095.320125-second JFK Rice speech; one complete |
| warmup, then three measured passes. RTFx is input audio seconds / processing |
| seconds. Reference WPM is the 2,220 normalized reference words × 60 / processing |
| seconds. Medians are reported; loading and warmup are excluded. |
|
|
| | Device | Mode | Processing | RTFx | Reference WPM | |
| | --- | --- | ---: | ---: | ---: | |
| | MacBook Pro, M5 Max, 64 GB | Verbatim | 2.446 s | 447.9× | 54,463 | |
| | MacBook Pro, M5 Max, 64 GB | Clean | 2.447 s | 447.7× | 54,440 | |
| | iPhone Duo, A20 Pro, 12 GB | Verbatim | 2.274 s | 481.7× | 58,581 | |
| | iPhone Duo, A20 Pro, 12 GB | Clean | 2.323 s | 471.4× | 57,329 | |
| | iPhone 15 Pro Max, A17 Pro, 8 GB | Verbatim | 3.564 s | 307.3× | 37,373 | |
| | iPhone 15 Pro Max, A17 Pro, 8 GB | Clean | 3.563 s | 307.5× | 37,389 | |
|
|
| The Mac runs use prepared, preloaded PCM and exclude file reading and planning. |
| The phones use the public recording API, whose timer additionally includes |
| bounded WAV conversion and planning. All use the same 43 contiguous cuts and |
| consume every sample. The Mac uses Automatic power mode, macOS 27.2.1 (26B8132), |
| Xcode 27.2 (27B52). Duo uses iOS 27.3 (24C5037i); 15 Pro Max uses iOS 27.2 |
| (24B119). Duo Verbatim and all 15 Pro Max passes were thermally nominal; Duo |
| Clean passes were fair. |
|
|
| Batching matters. A separate, matched 15-second-window Mac control measured |
| **114.4× with serial chunks / one decoder lane** and **488.1× with two encoder |
| requests / up to 32 decoder lanes**. That control uses a different input shape |
| from these 30-second distributions and is not the default download. The gain |
| combines decoder batching and encoder concurrency; it does not isolate either. |
| Short single-chunk latency does not scale like full-recording throughput. |
|
|
| ## First preparation |
|
|
| First observed preparation includes Core AI model/function preparation. Global |
| caches were not cleared; cached values are subsequent loads of these assets. |
| The first-use clock excludes offline conversion and any settling wait. |
|
|
| | Device | First Verbatim | First Clean | Cached preparation | |
| | --- | ---: | ---: | ---: | |
| | M5 Max | 34.5 s | 35.1 s | 0.07 s | |
| | iPhone Duo | 82.5 s | 83.9 s | 0.12–0.18 s | |
| | iPhone 15 Pro Max | 53.2 s | 53.2 s | 0.17–0.19 s | |
|
|
| ## Qualification |
|
|
| Verbatim has **54/2,220 errors (2.43% WER)** on this recording with |
| whisper-normalizer 0.1.15 and jiwer 4.0.0, on all three devices. Clean matches |
| the normalized independent FP32 clean reference exactly on these cuts. Clean |
| agreement measures conversion fidelity; intended edits are not scored as errors |
| against a verbatim human reference. English and a short FLEURS French recording |
| were checked; this is not a broad multilingual accuracy evaluation. |
|
|
| Separate Mac and 15 Pro Max traces capture 594 unique ANE hardware predictions |
| for a complete recording pass and no target GPU intervals. Concurrent phone |
| scopes can overlap, so per-call ownership is not always unique. The Duo trace |
| shows ANE-framework evaluation calls and no target GPU intervals, but its |
| hardware-counter table is empty on that iOS build. This establishes backend |
| invocation, not arithmetic utilization. |
|
|
| Attribution: **Fluid Speech Pro by FluidVoice. Built on NVIDIA Parakeet TDT 0.6B |
| v3 and Moondream Parakeet Ultra.** This conversion is not endorsed by those |
| publishers. |
|
|
