A cache-aware streaming FastConformer-RNNT model covering 40 language-locales with native punctuation and capitalisation. The encoder exposes a prompt_index input, so a single graph serves every language; the target language is selected at runtime (or left to auto-detect).

Files

File	Size	Purpose
`encoder.onnx` + `encoder.onnx.data`	~627 MB	int8 streaming encoder with the prompt-kernel head
`decoder_joint.onnx`	~23 MB	int8 RNNT decoder + joint network
`tokenizer.model`	~0.4 MB	SentencePiece tokenizer (vocab 13088, includes language tags)
`config.json`	~3 KB	streaming params, cache shapes, prompt dictionary
`nemotron-3.5-asr-streaming-0.6b-int8.tar.gz`	~432 MB	the above bundled for Undertone's downloader

All files must live in the same directory. The parakeet-rs loader auto-detects the multilingual variant by spotting the prompt_index encoder input.

Usage (parakeet-rs)

rust

use parakeet_rs::{Nemotron, NemotronMode};

let mut model = Nemotron::from_pretrained("./nemotron-3.5-asr-streaming-0.6b-int8", None)?;
if model.mode() == NemotronMode::Multilingual {
    model.set_target_lang("es-ES")?; // or "auto", "en-US", "ja-JP", "zh-CN", ...
}

// 560ms chunks at 16kHz mono f32
const CHUNK: usize = 8960;
for chunk in audio.chunks(CHUNK) {
    print!("{}", model.transcribe_chunk(chunk)?);
}

How this was produced

The two scripts used to produce this repo are included for reproducibility:

export_nemotron_streaming_multilingual.py - NeMo .nemo -> FP32 ONNX
quantize_and_package.py - FP32 ONNX -> int8 + tar.gz

Steps:

python export_nemotron_streaming_multilingual.py nemotron-3.5-asr-streaming-0.6b.nemo ./onnx_fp32 - exports FP32 ONNX (the encoder's per-language prompt_index is exposed as a real ONNX input rather than baked in).
python quantize_and_package.py ./onnx_fp32 ./out --tar ./out/nemotron-3.5-asr-streaming-0.6b-int8.tar.gz - quantises encoder.onnx and decoder_joint.onnx to int8 via onnxruntime.quantization.quantize_dynamic (QuantType.QInt8).
Encoder/NeMo parity was verified across en-US, es-ES and ja-JP before quantising (max abs diff < 1e-4).

Input: 16kHz mono f32. Output: text with native punctuation; no word-level timestamps.

Licence and attribution

This is a derivative of NVIDIA's nemotron-3.5-asr-streaming-0.6b, distributed under the OpenMDW 1.1 licence. All model credit belongs to NVIDIA; this repository only re-packages the weights as int8 ONNX.

smcleod/nemotron-3.5-asr-streaming-0.6b-int8

Nemotron 3.5 ASR Streaming 0.6B (Multilingual) - ONNX int8

Files

Usage (parakeet-rs)

How this was produced

Licence and attribution

smcleod/
nemotron-3.5-asr-streaming-0.6b-int8