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 Elephant Enterprises LLC IP. Redistribution allowed as an unmodified part of the VoxRT SDKs above.

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