---
license: other
license_name: openmdw-1.1
license_link: https://openmdw.ai/license/1-1/
library_name: onnxruntime
pipeline_tag: automatic-speech-recognition
tags:
  - onnx
  - directml
  - speaker-diarization
  - streaming-asr
  - multilingual
base_model:
  - nvidia/Nemotron-3-Diarization
  - nvidia/nemotron-3.5-asr-streaming-0.6b
---

# Parley speech models

ONNX models used by **Parley**, a desktop app for live speaker-aware transcription and
translation. Parley downloads one variant on first run and runs it natively with ONNX Runtime
(DirectML on any DirectX 12 GPU, CPU fallback).

| Variant | Size | Precision | Best for |
|---|---|---|---|
| `gpu16/` | 1.55 GB | fp16 weights and compute, LayerNorms in fp32 | GPUs (DirectML), about 1.6 GB VRAM |
| `standard/` | 2.99 GB | fp32 | GPUs (DirectML), about 3 GB VRAM |
| `compact/` | 0.79 GB | int8 (dynamic, per-channel) | CPU-only machines |

`gpu16` is built from `standard` by `export_fp16.py`: inputs and outputs stay fp32, and every
LayerNorm runs in fp32 because DirectML's fp16 ReduceMean overflows on the conformer's variance.
On the 97.6 s eight-speaker demo it produces the same transcript as `standard` (796 vs 797 tokens,
identical utterances) and 99.9% diarization agreement.

Each variant folder has a `manifest.json` with the size and SHA-256 of every file.

## Contents

### `diarization/` — NVIDIA Nemotron-3-Diarization (streaming Sortformer, up to 8 speakers)

Exported from the 🤗 Transformers implementation of
[nvidia/Nemotron-3-Diarization](https://huggingface.co/nvidia/Nemotron-3-Diarization):

- `diar_embed.onnx` — feature stacking: log-mel `(1, T, 128)`, `T % 8 == 0` → embeddings `(1, T/8, 512)`
- `diar_step.onnx` — encoder + head: embeddings `(1, L, 512)` and padding mask `(1, L)` →
  speaker probabilities `(1, L, 8)` pooled to 80 ms frames. Right padding is exact (masked
  attention, zeroed before the upsampling conv), so callers can use fixed shapes on DirectML.
- `silence_embeds.f32` — learned silence embedding for the speaker cache
- `diarization.json` — Arrival-Order Speaker Cache / FIFO parameters and streaming modes
- `mel_filters_128x257.f32` — slaney mel filter bank (shared with the ASR front end)

The streaming state (speaker cache + FIFO, with its score-based compression) is not part of the
graphs; Parley implements it in Rust, ported from `Nemotron3DiarizationSpeakerCache`.
Streaming output matches the Transformers reference exactly on the fp32 variant (100% frame
agreement on the model card's example); the int8 variant agrees on 99.5% of frames.

### `asr/` — NVIDIA Nemotron 3.5 ASR (cache-aware FastConformer RNN-T, 40 locales, 560 ms chunks)

NeMo cache-aware streaming export of
[nvidia/nemotron-3.5-asr-streaming-0.6b](https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b)
by the [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) project
(`csukuangfj2/sherpa-onnx-nemotron-3.5-asr-streaming-0.6b-560ms[-int8]-2026-06-11`):
`encoder.onnx` (+ `encoder.data`), `decoder.onnx`, `joiner.onnx`, `tokens.txt`. Chunk geometry,
cache shapes and the language-prompt dictionary are in the encoder's metadata.

## Front end

Both models consume the same features: 16 kHz mono, pre-emphasis 0.97, 512-point FFT, 400-sample
symmetric Hann window, hop 160, power spectrum, 128 slaney mel bins, `ln(x + 2^-24)`, no
normalization.

## License and attribution

The model weights are NVIDIA's, released under the
[OpenMDW License Agreement 1.1](https://openmdw.ai/license/1-1/); these are format conversions
and quantizations of those weights, redistributed under the same terms.

- Nemotron-3-Diarization and Nemotron 3.5 ASR © NVIDIA Corporation.
- ASR ONNX export by the sherpa-onnx project (Apache-2.0 tooling).
- Diarization export and quantization by Parley.