---
license: apache-2.0
language:
- en
- zh
- ja
- ko
- de
- fr
- es
- it
- ru
tags:
- coreml
- forced-alignment
- speech
- qwen3
- timestamps
- neural-engine
base_model: Qwen/Qwen3-ForcedAligner-0.6B
base_model_relation: quantized
pipeline_tag: audio-classification
---

# Qwen3-ForcedAligner-0.6B — CoreML INT8

CoreML conversion of [Qwen/Qwen3-ForcedAligner-0.6B](https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B) with INT8 kmeans palettization on the two transformer bundles, for Apple Silicon.

Predicts word-level timestamps for audio + text pairs in a single non-autoregressive forward pass.

## Files

| File | Role | Size |
|---|---|---|
| `audio_encoder.mlmodelc` / `.mlpackage` | 24-layer block-attention encoder (INT8-palettized); fixed 30 s mel input | ~304 MB |
| `text_decoder.mlmodelc` / `.mlpackage` | 28-layer non-AR decoder + 5000-class classify head (INT8-palettized); fixed T=768 | ~427 MB |
| `embed_tokens.fp16.bin` | Raw little-endian fp16 token embedding table, shape [152 064, 1024] (not palettized) | ~297 MB |
| `config.json` | Runtime config — variant, classify_num, timestamp_segment_time, fixed shapes | small |
| `vocab.json`, `merges.txt`, `tokenizer_config.json` | Qwen3 BPE tokenizer files | ~5 MB |

The embed-tokens table is shipped as a raw fp16 binary rather than a CoreML package — the Swift runtime memory-maps the file and gathers rows with `vImageConvert_Planar16FtoPlanarF`, which costs ~0.5 ms per alignment instead of the ~70 ms an mlpackage round-trip would cost. Keeping the embedding at fp16 (not palettized) preserves classify-time accuracy across the full vocabulary; the palettization budget is spent on the two large transformer bundles, where it matters most for disk and ANE memory.

## Usage (Swift via [speech-swift](https://github.com/soniqo/speech-swift))

```swift
import Qwen3ASR

let aligner = try await CoreMLForcedAligner.fromPretrained(
    modelId: "aufklarer/Qwen3-ForcedAligner-0.6B-CoreML-INT8")
let aligned = try aligner.align(
    audio: pcmSamples,
    text: "Can you guarantee that the replacement part will be shipped tomorrow?",
    sampleRate: 16000,
    language: "English")
```

CLI: `speech align audio.wav --engine coreml --aligner-model aufklarer/Qwen3-ForcedAligner-0.6B-CoreML-INT8`

## Performance (M2 Max, 64 GB, debug build, 20 s clip)

| Metric | Value |
|---|---|
| RTF | 0.014 (69× faster than real-time) |
| Peak RSS | 697 MB |
| Median alignment time | ~289 ms |

Per-stage profile (`COREML_ALIGN_PROFILE=1`):

```
mel=150ms  encoder=78ms  embedding=0.5ms  splice=0ms  decoder=54ms  argmax=7ms
```

The decoder uses `MLComputeUnits = .all` because its 28 layers exceed the ~26-layer ANE graph cap; the encoder runs on `.cpuAndNeuralEngine`.

## Architecture

Same model as the upstream Qwen3-ForcedAligner. Inference is non-autoregressive: one forward pass over a chat-template prompt (system + audio + assistant + `<ts>` word `<ts>` slots), then argmax at the timestamp positions, then LIS monotonicity correction.

The causal mask is baked into the exported graph as a constant with a finite ``-1e4`` fill value (not ``-inf``) so the fp16 softmax cannot produce NaN.

Conversion source: [soniqo/speech-models/models/forced-aligner/export/convert_coreml.py](https://github.com/soniqo/speech-models).

## Variants

- This repo: **INT8** — kmeans-palettized text decoder + audio encoder, ~880 MB, similar RTF, ~35% lower peak memory than FP16.
- Companion: [`aufklarer/Qwen3-ForcedAligner-0.6B-CoreML-FP16`](https://huggingface.co/aufklarer/Qwen3-ForcedAligner-0.6B-CoreML-FP16) — full precision, ~1.7 GB, highest fidelity.

For pure MLX paths (no CoreML), see the `4bit`, `5bit`, `8bit`, and `bf16` siblings under the same HuggingFace org.