---
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
base_model: Luigi/nemotron-3.5-asr-streaming-0.6b-zhtw
language: [zh, en, de, fr, es, ar, ja, ko]
tags: [litert, tflite, automatic-speech-recognition, streaming-asr, FastConformer, RNNT, zh-tw, q4]
pipeline_tag: automatic-speech-recognition
library_name: litert
---

# Nemotron-3.5-ASR — LiteRT q4-mix, zh-TW fine-tuned (v2)

On-device (Android) LiteRT build of
[Luigi/nemotron-3.5-asr-streaming-0.6b-zhtw](https://huggingface.co/Luigi/nemotron-3.5-asr-streaming-0.6b-zhtw)
— the zh-TW **fine-tuned** Nemotron (3.2× better Taiwan Mandarin than base).
**q4-mix**: INT4 encoder + fp16 decoder/joint + fp32 prompt-fusion. Bundle **663 MB**.

Supersedes v1 (which packaged the zero-training warm-start).

## Accuracy

| build | CV zh-TW CER (n=120) |
|---|---|
| fp32 fine-tuned model (reference) | 11.33 |
| **this q4-mix LiteRT** | **13.20** (+1.9 INT4 cost) |
| v1 q4-mix (base warm-start) | ~38 |

Per language-prompt slot on this build — all within ~0.7 CER, pick either:

| slot | q4-mix CER | fp32 CER |
|---|---|---|
| `zh-CN` (4) | **13.20** | 11.57 |
| `auto` (101) | 13.79 | **11.33** |
| `zh-TW` (5) | 13.90 | 12.03 |

`auto` was the *worst* slot on the base model (50.58 CER) and is repaired by the
fine-tune — training used `prompt_mode: unified`, which trains the auto path
alongside the explicit language ID. Use `auto` when the speaker may switch
languages, or an explicit slot when you know it.

Other languages (fp32 FT, vs base): ko/de/ja/hi/en all improve, ar flat,
fr/es +1.5 — see the [source model card](https://huggingface.co/Luigi/nemotron-3.5-asr-streaming-0.6b-zhtw).

## Files

| file | role | precision | size |
|---|---|---|---|
| `nemotron_encoder_q4.tflite` | FastConformer encoder | INT4 FC (blockwise-128) + fp32 convs | 596 MB |
| `nemotron_prompt_fuse_fp32.tflite` | 128-slot language-prompt fusion | fp32 | 18 MB |
| `nemotron_decoder_fp16.tflite` | RNN-T prediction net | fp16 | 31 MB |
| `nemotron_joint_fp16.tflite` | RNN-T joint (→ vocab 13088) | fp16 | 18 MB |
| `tokenizer.json` / `processor_config.json` / `config.json` | runtime | — | — |

## Run

Port, runner and integration note:
https://github.com/vieenrose/LiteRT/tree/nemotron/litert/samples/asr/nemotron

```bash
python -m nemotron.runner --models ./ --wav clip.wav --lang zh-CN --s2t --itn
# --lang auto also works well (see slot table); --s2t gives Traditional output
```

**Output is Simplified Chinese** — the tokenizer cannot represent many common
Traditional characters, so apply OpenCC `s2t` (`--s2t`) for Traditional.

The INT4 encoder needs the LiteRT-Next `CompiledModel` runtime (Android NNAPI /
XNNPACK-QD8); the classic `Interpreter` cannot allocate dynamic-range INT4.
Set the CPU thread count explicitly (big-core count on big.LITTLE).

## Provenance

Base: nvidia/nemotron-3.5-asr-streaming-0.6b (NVIDIA Open Model License),
fine-tuned for zh-TW (see source model card), then exported with
`litert_torch` 0.9.1 + `ai_edge_quantizer` 0.7.0 (INT4 weights pre-baked to the
export grid before quantization).