OpenASR/qwen3-forced-aligner-0.6b

Qwen3-ForcedAligner 0.6B · OpenASR

Word-level forced alignment for OpenASR transcripts -- a non-autoregressive Qwen3 audio+text model that refines per-word timestamps

A capability-pack support model for the OpenASR runtime — pure-Rust inference, no Python at inference time. Not a standalone transcription model: it augments another OpenASR ASR model's own decode path.

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✨ Highlights

• 🎯 Refined word timestamps — consumes a finished transcript's text plus the source audio and replaces a model family's own approximate per-word timestamps with aligner-refined spans (--word-timestamps=aligned)
• ⚡ Non-autoregressive — a single forward pass over interleaved audio/text with argmax at <timestamp> positions (5000 80ms-wide bins), not incremental greedy decoding, so it is not dispatched through the qwen3-asr runtime
• 🧩 Shares its backbone with Qwen3-ASR — the same audio-encoder + LM thinker tensor layout, byte-for-byte; only the final head differs (an independent 5000-way classification head instead of the tied vocabulary head)
• 🔌 Attribution-ready dependency — used explicitly by --word-timestamps=aligned and can supply the word anchors a universal speaker-attribution pipeline needs to split coarse ASR segments at speaker changes
• 🦀 Native Q4_K runtime — the single production tier runs locally with no Python; Auto uses CPU on Apple Metal and retains supported discrete-GPU paths
• 🦀 Native in OpenASR — .oasr packs run with no Python at inference, engineered for peak performance on CPU & GPU

🚀 Quickstart

# 1. Install the OpenASR CLI  ·  https://openasr.org
# 2. Pull the pack
openasr pull qwen3-forced-aligner-0.6b:q4

# 3. Use it as an opt-in refinement for another model's transcribe call
openasr transcribe meeting.wav --model <asr-model> --word-timestamps=aligned

📦 Pack

Quant	File (.oasr)	Size
q4_k	qwen3-forced-aligner-0.6b-q4_k.oasr	529 MB

🧠 About Qwen3-ForcedAligner 0.6B

Qwen3-ForcedAligner-0.6B is a word-level forced-alignment model from Qwen, sharing its audio-encoder + LM thinker tensor layout byte-for-byte with Qwen3-ASR (same Qwen3ASRForConditionalGeneration architecture). The only structural difference is the final head: instead of a tied vocabulary lm_head, it uses an independent Linear(hidden_size, 5000) classification head over 80ms-wide timestamp bins. Given a transcript's text and its source audio, it runs a single non-autoregressive forward pass and reads off word-boundary timestamps at argmax <timestamp> positions -- refining a model family's own (typically decode-time-approximate) per-word timestamps. This OpenASR repo repackages the weights as .oasr packs that run natively in the OpenASR runtime -- no Python at inference, all decoding local. OpenASR ships one production q4_k tier for this capability pack.

Not a standalone transcription model. This pack cannot transcribe audio by itself; it is an alignment dependency consumed explicitly via openasr transcribe <audio> --model <asr-model> --word-timestamps=aligned. The same word anchors can support speaker-attribution pipelines that must split a coarse ASR segment at speaker changes.

Verification: local verification covers a tensor-parity check against the real upstream checkpoint -- every source safetensors tensor maps 1:1 to a destination tensor, plus the two synthesized frontend tensors. The production q4_k pack additionally passes the quant-floor audit and real Chinese meeting-audio attribution verification.

⚙️ How this pack was made

Converted from Qwen/Qwen3-ForcedAligner-0.6B with the OpenASR local-source importer (openasr model-pack import qwen-forced-aligner ...).

The .oasr container is GGUF-backed; each shipped quant stores weights at the requested precision while parity-sensitive tensors stay f32 where required.

⚖️ License

This pack inherits the upstream model's license: Apache-2.0 (source). OpenASR packaging retains the upstream copyright; the only modifications are format conversion and quantization.

🙏 Acknowledgements

This pack is a redistribution of Qwen3-ForcedAligner-0.6B, created and open-sourced by Qwen (Qwen/Qwen3-ForcedAligner-0.6B). All credit for the original architecture, training, and weights belongs to the authors; the license is inherited from and identical to the upstream model (Apache-2.0). OpenASR only performs format conversion, quantization, runtime verification, and local-inference adaptation.

🔗 Links

• 🦀 OpenASR — https://github.com/QuintinShaw/openasr
• 🌐 Website — https://openasr.org
• 🤗 Upstream model — Qwen/Qwen3-ForcedAligner-0.6B