valoomba/Qwen3-ForcedAligner-0.6B-ONNX

Qwen3 Forced Aligner ONNX

This repository contains a dynamic ONNX export of `Qwen/Qwen3-ForcedAligner-0.6B` for forced alignment.

It preserves the original tokenizer, processor, and configuration files while replacing the PyTorch weights with an ONNX Runtime graph that supports:

• —dynamic batch size
• —dynamic text sequence length
• —dynamic audio feature length
• —browser loading with transformers.js v4

Base model

This is a format conversion of `Qwen/Qwen3-ForcedAligner-0.6B`. It does not introduce new training or fine-tuning.

Files

• —onnx/model.onnx
• —onnx/model.onnx_data
• —onnx/model_q4.onnx
• —config.json
• —generation_config.json
• —tokenizer and processor files
• —export_metadata.json

Usage

Python (qwen_asr ONNX backend)

from qwen_asr import Qwen3ForcedAligner

aligner = Qwen3ForcedAligner.from_pretrained(
    "valoomba/Qwen3-ForcedAligner-0.6B-ONNX",
    backend="onnx",
)

Browser / Node (transformers.js v4)

Use AutoTokenizer, AutoFeatureExtractor, and AutoModel with the repo root. The ONNX graph lives at onnx/model.onnx, and config.json includes transformers.js_config.use_external_data_format for the external data sidecar.

For browser-friendly loading, this repo also includes a q4 variant at onnx/model_q4.onnx.

const model = await AutoModel.from_pretrained("valoomba/Qwen3-ForcedAligner-0.6B-ONNX", { dtype: "q4" });

Export provenance

• —Source model: Qwen/Qwen3-ForcedAligner-0.6B
• —Generated at: 2026-03-31 09:04:54 UTC
• —Export tool: examples/export_qwen3_forced_aligner_onnx.py