Add initial model card

README.md

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+---
+license: cc-by-4.0
+language:
+- en
+library_name: voxrt
+pipeline_tag: automatic-speech-recognition
+tags:
+- speech-recognition
+- streaming
+- asr
+- fastconformer
+- nemo
+- nvidia
+- on-device
+- edge
+- arm
+- neon
+- voxrt
+base_model:
+- nvidia/stt_en_fastconformer_hybrid_medium_streaming_80ms_pc
+---
+
+# 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](https://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](https://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`](https://github.com/VoxRT/voxrt-asr-android) (JitPack)
+- iOS — [`voxrt-asr-ios`](https://github.com/VoxRT/voxrt-asr-ios) (Swift Package)
+- Linux aarch64 — available on request (contact `help@voxrt.com`)
+
+## Kotlin example
+
+```kotlin
+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](https://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](https://huggingface.co/VoxRT/wake-word-hey-assistant-vxrt)**
+- Voice activity detection: **[Silero VAD in `.vxrt`](https://huggingface.co/VoxRT/silero-vad-vxrt)**
+
+Commercial integration / custom-model packaging: `help@voxrt.com`
+· [voxrt.com](https://voxrt.com)