---
library_name: onnxruntime
pipeline_tag: automatic-speech-recognition
tags:
- onnx
- ctc
- forced-alignment
- word-timestamps
- mobile
- flutter
---

# Fluency NeMo Conformer CTC Aligner

This repository contains the mobile-distribution assets for Fluency's English word-level forced alignment pipeline.

The model is not used as a standalone ASR decoder in the app. Whisper provides the transcript, this model provides frame-level CTC log-probabilities, and the app runs constrained CTC Viterbi to recover word timestamps.

## Runtime Pipeline

```text
Whisper transcript
→ text normalization
→ SentencePiece tokenizer
→ native log-mel feature extraction
→ ONNX Runtime Mobile
→ constrained CTC Viterbi
→ word timestamps
```

## Required App Assets

```text
stt_en_conformer_ctc_small_features.onnx
metadata.json
tokenizer/tokenizer.model
tokenizer/vocab.txt
manifest.json
```

Optional debugging assets are under `parity/`.

## ONNX Contract

- Input `processed_signal`: `[batch, 80, feature_frames]` float32
- Input `processed_signal_length`: `[batch]` int64
- Output `log_probs`: `[batch, encoded_frames, 1025]` float32
- Output `encoded_len`: `[batch]` int64
- CTC blank id: `1024`

## Feature Contract

- Sample rate: `16000 Hz`
- Channels: mono
- Feature type: 80-bin log-mel spectrogram
- Window size: `25 ms`
- Window stride: `10 ms`
- FFT: `512`
- Window: Hann
- Normalization: `per_feature`

The ONNX graph intentionally starts after feature extraction. Mobile clients must implement NeMo-compatible log-mel features before invoking ONNX Runtime.

## Validation

Validated on a 375.838s video using raw Whisper `text` fields only:

- Windows checked: 7
- Segments checked: 110
- Matched words checked: 1029
- PyTorch feature+model total: 3.106s
- ONNX model total: 5.396s
- Log-prob mean abs diff: 0.000014
- Word center delta p50/p95/max: 0.0ms / 0.0ms / 0.0ms

See `parity/parity_1gzKSyOBpZg.json` and `parity/parity_1gzKSyOBpZg.csv` for details.

## Download URLs

```text
https://huggingface.co/iamzhangship/fluency-nemo-conformer-ctc-aligner/resolve/main/stt_en_conformer_ctc_small_features.onnx
https://huggingface.co/iamzhangship/fluency-nemo-conformer-ctc-aligner/resolve/main/metadata.json
https://huggingface.co/iamzhangship/fluency-nemo-conformer-ctc-aligner/resolve/main/tokenizer/tokenizer.model
https://huggingface.co/iamzhangship/fluency-nemo-conformer-ctc-aligner/resolve/main/tokenizer/vocab.txt
https://huggingface.co/iamzhangship/fluency-nemo-conformer-ctc-aligner/resolve/main/manifest.json
```

## Checksums

- ONNX sha256: `8998fd44bc2374c7d085f7c709fbfbd28ea6db09998622077071faf478db24fd`
- ONNX size: `53197388` bytes

Use `manifest.json` for full file sizes and sha256 checksums.

## Source Model

- NeMo pretrained model: `stt_en_conformer_ctc_small`
- Export graph: Conformer encoder + CTC projection/log-softmax
- Export metadata model bytes: `53197388`