Property	Value
Base model	NVIDIA Nemotron 3.5 ASR Streaming 0.6B
Encoder layers	24
Hidden size	1024
Quantization	INT8 k-quant (block_size=32, accuracy_level=4)
Chunk size	0.56s (8,960 samples @ 16kHz)
Sample rate	16,000 Hz
Vocab size	13,088
Languages	80+ (multilingual)
VAD	Silero VAD included
Total size	~1,021 MB

Quick Start

C# / .NET (onnxruntime-genai)

csharp

using NemotronSpeech;

using var session = new ModelSession(
    modelPath: "DimQ1/nemotron-3.5-asr-streaming-0.6b-onnx-int8-cpu",
    executionProvider: "cpu",
    langId: "11",  // Russian
    useVad: true
);

Python (onnxruntime-genai)

python

import onnxruntime_genai as og
import numpy as np

model = og.Model("DimQ1/nemotron-3.5-asr-streaming-0.6b-onnx-int8-cpu")
processor = og.StreamingProcessor(model)
tokenizer = og.Tokenizer(model)
params = og.GeneratorParams(model)
generator = og.Generator(model, params)

generator.set_runtime_option("lang_id", "11")  # Russian

chunk = np.zeros(8960, dtype=np.float32)  # 0.56s @ 16kHz
inputs = processor.process(chunk)
if inputs is not None:
    generator.set_inputs(inputs)
    while not generator.is_done():
        generator.generate_next_token()

Conversion Recipe

bash

cd converter
python src/optimize.py \
  --model-name nemotron-3.5-asr-streaming-0.6b.nemo \
  --encoder-precision int8 \
  --execution-provider cpu \
  --output-dir build/onnx_models_int8_cpu

See nemotron-speech-csharp for the full C# demo app with VoiceType WPF UI.

Files

File	Size
`encoder.onnx` + `.data`	922 MB
`decoder.onnx` + `.data`	57 MB
`joint.onnx` + `.data`	36 MB
`silero_vad.onnx`	2.1 MB
`tokenizer.json`	0.7 MB

Available Variants

Model	Precision	Encoder	Total	Quality
FP32	FP32	2,380 MB	2,479 MB	⭐ Best
INT8	INT8 k-quant	922 MB	1,021 MB	Good
INT4	INT4 k-quant	658 MB	757 MB	⚡ Fastest

License

This model inherits cc-by-nc-4.0 from the base NVIDIA Nemotron model.