VoxRT/streaming-medium-pc-vxrt

NeMo FastConformer streaming-medium-pc on the VoxRT runtime

The stt_en_fastconformer_hybrid_medium_streaming_80ms_pc model from NVIDIA NeMo, packaged as a .vxrt file for the VoxRT on-device inference runtime. Same weights, repackaged so the 80 ms cache-aware streaming ASR runs on Android or iOS in real-time — with punctuation and capitalisation output straight from the model, no post-processing layer required.

This is not our model. The weights are NVIDIA's nvidia/stt_en_fastconformer_hybrid_medium_streaming_80ms_pc checkpoint, released under CC-BY-4.0. What we ship is the runtime that makes it fast on cheap ARM hardware plus the .vxrt container that the runtime consumes.

Runtime performance

Cumulative RTF on a single CPU core, arm64 release builds, post-warmup. Live-mic figures are the production-realistic ones (scheduler jitter + capture overhead included):

Device	CPU	Decoder	Mode	RTF
Xiaomi Redmi 9C (Android)	Cortex-A73	RNN-T	file replay	0.302
Xiaomi Redmi 9C (Android)	Cortex-A73	RNN-T	live mic	0.353
iPhone 13 Pro Max (iOS)	Apple A15	RNN-T	live mic	0.08–0.10

For the same weights, RNN-T decoding costs ~50 ms of CPU per 1.12 s chunk on SD662; the CTC head is ~5 ms per chunk with a minor WER hit. The SDK exposes both decoders — pick per your battery / accuracy trade-off.

Chunked streaming granularity is 80 ms cache-aware look-ahead. Inherent end-to-end buffering is one chunk (≈ 1.12 s at chunk_size=112) before text emission begins.

Model quality

Empirically validated on LibriSpeech test-clean (500-utterance subset, matches the SDK repos' reported numbers):

Decoder	WER	Notes
RNN-T ★	3.267 %	Recommended default. Higher accuracy.
CTC	4.895 %	~15 % cheaper per chunk; long-session friendly.

Model architecture, training data, and topline WER claims are NVIDIA's — see the upstream checkpoint at huggingface.co/nvidia/stt_en_fastconformer_hybrid_medium_streaming_80ms_pc.

Download & use

The .vxrt file on this HF repo is byte-identical to the one at github.com/VoxRT/voxrt-asr-models/releases. Either source is fine.

.vxrt files cannot be loaded with transformers, nemo_toolkit, or any standard HF library — they are a proprietary container the VoxRT runtime reads. Use one of our SDKs:

• Android — voxrt-asr-android (JitPack)
• iOS — voxrt-asr-ios (Swift Package)
• Linux aarch64 — available on request (contact help@voxrt.com)

Kotlin example

import com.voxrt.asr.VoxrtAsrNative
import com.voxrt.asr.VoxrtAsrStreamingEngine

val engine = VoxrtAsrStreamingEngine.fromAssetFd(modelFd)
// Or explicitly pick CTC:
// val engine = VoxrtAsrStreamingEngine.fromAssetFd(modelFd, VoxrtAsrNative.DECODE_CTC)

val delta = engine.processPcm(pcmFloatArray)   // text emitted this call
val tail  = engine.stop()                       // drain remaining text
engine.close()

engine.processPcm / stop / reset / close are synchronous and stateful — the engine doesn't own a worker thread. Drive it from your own capture / IO thread; marshal text deltas back to UI via runOnUiThread / a Flow / your preferred concurrency. RNN-T (default) survives chunk boundaries via its LSTM state; CTC dedupes across chunks internally.

Licensing

• Model weights are derived from nvidia/stt_en_fastconformer_hybrid_medium_streaming_80ms_pc, © NVIDIA Corporation, CC-BY-4.0 licensed.
• Repackaging into the .vxrt container preserves the CC-BY-4.0 obligations attached to the weights. Full notice lives at github.com/VoxRT/voxrt-asr-models/blob/main/LICENSE.
• The VoxRT runtime and .vxrt container format are proprietary Wiseport Ltd. (trading as VoxRT) IP, redistributable in unmodified form as incorporated into applications or products that use the VoxRT SDKs above. Nothing here restricts your CC-BY-4.0 rights in the NVIDIA-derived weights.

Attribution required by CC-BY-4.0:

Speech recognition powered by NVIDIA NeMo FastConformer (streaming, medium, 80 ms look-ahead, P&C), © NVIDIA Corporation, licensed under CC-BY-4.0.

Include this line in your product's UI, docs, or credits when you ship a product that runs this model.

About VoxRT

VoxRT is a from-scratch on-device inference runtime tuned for streaming audio on commodity ARM CPUs — no GPU, no NPU, no vendor accelerator required. Sister products on the same runtime:

• Wake-word: "Hey Assistant" model + custom-phrase tier
• Voice activity detection: Silero VAD in .vxrt

Commercial integration / custom-model packaging: help@voxrt.com · voxrt.com