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cstr/
qwen3-forced-aligner-0.6b-GGUF

Name: qwen3-forced-aligner-0.6b-GGUF
Availability: InStock

automatic-speech-recognition

yue

Qwen/Qwen3-ForcedAligner-0.6B

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Qwen3-ForcedAligner-0.6B — GGUF (CrispASR)

GGUF conversions of Qwen/Qwen3-ForcedAligner-0.6B — a single-pass forced aligner that takes any (audio, transcript) pair and predicts per-word/per-token timestamps. Reuses the Qwen3-ASR audio encoder + 28-layer LLM body but swaps the lm_head from (vocab, d) to (5000, d): each <timestamp> placeholder you embed in the input gets a 5000-class softmax over class * 80 ms timestamps.

Plug it into CrispASR via -am qwen3-forced-aligner-*.gguf to get word-level timing on any transcription backend — voxtral, voxtral4b, qwen3-asr, granite, parakeet, canary, cohere, even whisper. It's an alternative to the existing canary-ctc-aligner second-pass with broader language coverage and 80-ms resolution.

What's in the box

File	Size	Quantization	Notes
`qwen3-forced-aligner-0.6b-f16.gguf`	1.84 GB	F16	Reference precision; matches PyTorch bfloat16 within float-noise tolerance
`qwen3-forced-aligner-0.6b-q8_0.gguf`	0.99 GB	Q8_0	Effectively lossless
`qwen3-forced-aligner-0.6b-q5_0.gguf`	0.64 GB	Q5_0	Slightly slower than Q4_K but a bit more accurate on edge cases
`qwen3-forced-aligner-0.6b-q4_k.gguf`	0.53 GB	Q4_K	3.5× compressed; smallest reasonable choice

All four contain:

The full audio encoder (24 layers, d_model 1024, 16 heads, 4096 ff)
The Qwen3 0.6B LLM body (28 layers, d_model 1024, 16 heads / 8 KV heads, 3072 ff, 152K vocab, RoPE θ=1e6)
The 5000-class forced-alignment lm_head (instead of the 152K-class lm_head used by the regular ASR variants)
Full GPT-2-style BPE vocab + merges, mel filterbank, and Hann window

How it differs from the ASR models

Same body, different head:

	Qwen3-ASR-0.6B / 1.7B	Qwen3-ForcedAligner-0.6B
Audio encoder	24-layer, d_model 1024	identical
Text decoder	Qwen3 28-layer	identical body
`lm_head` shape	`(vocab=152K, d)`	`(5000, d)` — timestamp classes
Inference mode	Autoregressive (decode token by token)	Single forward pass over the whole input
Use case	Audio → text	(Audio, text) → per-word timestamps
Output	Generated tokens	argmax(lm_head)·80 ms at each `<timestamp>` placeholder

The CrispASR C++ runtime auto-detects which variant a loaded GGUF is by reading the lm_head shape from output.weight.ne[1] — no separate backend, no separate library.

Use with CrispASR

bash

# Build crispasr (one-time)
git clone https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(nproc) --target whisper-cli

# Word-level SRT from any transcription backend, using FA for timing.
# `-am` (--aligner-model) auto-routes to the qwen3-fa path when the
# filename contains "forced-aligner" (case-insensitive).

# voxtral 3B + Qwen3-FA timing
./build/bin/crispasr --backend voxtral \
    -m voxtral-mini-3b-2507-q8_0.gguf \
    -f my_audio.wav \
    -am qwen3-forced-aligner-0.6b-q4_k.gguf \
    -osrt -ml 1

# parakeet + Qwen3-FA (parakeet has its own native word timestamps, but
# you can override them with FA on the same audio)
./build/bin/crispasr --backend parakeet \
    -m parakeet-tdt-0.6b-v3-q4_k.gguf \
    -f my_audio.wav \
    -am qwen3-forced-aligner-0.6b-q4_k.gguf \
    -osrt -ml 1

# Granite, qwen3-asr, voxtral4b, cohere, canary all work the same way.

The Python equivalent on the upstream side is Qwen3ForcedAligner.align(audio, text, language) from qwen-asr. Our C++ wrapper does the whole pipeline (mel → encoder → prompt build with <timestamp> placeholders → embed + audio splice → single FA forward → argmax at placeholder positions → ms conversion) in one call to qwen3_asr_align_words(ctx, samples, n_samples, words[], n_words, out_start_ms, out_end_ms).

Languages

Same as upstream Qwen3-ForcedAligner: Chinese, English, Cantonese, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish (11 languages).

The current C++ wrapper uses whitespace pre-tokenization for splitting the transcript into words. This works well for English and the other Latin/Cyrillic-script languages but is sub-optimal for Chinese / Japanese where the upstream Python uses character-level / morphological tokenizers (tokenize_japanese, tokenize_korean via soynlp). Adding char-level tokenization for CJK languages is a follow-up tracked in the CrispASR repo.

How it was made

bash

# 1. Download the base model from HF
hf download Qwen/Qwen3-ForcedAligner-0.6B --local-dir ./Qwen3-ForcedAligner-0.6B

# 2. Convert to F16 GGUF (the qwen3-asr converter handles both ASR and
#    ForcedAligner variants — sizes are read from config.json so the
#    same script handles both checkpoints)
python models/convert-qwen3-asr-to-gguf.py \
    --input ./Qwen3-ForcedAligner-0.6B \
    --output qwen3-forced-aligner-0.6b-f16.gguf

# 3. Quantize
./build/bin/crispasr-quantize qwen3-forced-aligner-0.6b-f16.gguf qwen3-forced-aligner-0.6b-q8_0.gguf q8_0
./build/bin/crispasr-quantize qwen3-forced-aligner-0.6b-f16.gguf qwen3-forced-aligner-0.6b-q5_0.gguf q5_0
./build/bin/crispasr-quantize qwen3-forced-aligner-0.6b-f16.gguf qwen3-forced-aligner-0.6b-q4_k.gguf q4_k

The C++ side needed two small additions to support FA models alongside ASR models in the existing qwen3 backend:

Flexible lm_head shape. qwen3_asr_load_model now reads the actual output.weight.ne[1] instead of asserting it equals llm.vocab_size. For ASR models the two are equal (152K); for FA models the head is 5000 wide.
Single-pass aligner forward. A new qwen3_asr_run_aligner() extern "C" entry point runs build_graph_llm_kv(..., last_token_only=false) so the lm_head sees every token position, not just the last. The result is a (5000, T) logit matrix; qwen3_asr_align_words() reads argmax at the positions where input_id == 151705 (<timestamp> placeholder) and converts to ms via class * 80.

Verification

End-to-end on samples/jfk.wav with voxtral as the transcription backend:

bash

crispasr --backend voxtral -m voxtral-mini-3b-2507-q8_0.gguf \
    -f samples/jfk.wav \
    -am qwen3-forced-aligner-0.6b-q4_k.gguf -ml 1

[00:00:00.320 --> 00:00:00.560]  And
[00:00:00.960 --> 00:00:00.960]  so,
[00:00:00.960 --> 00:00:01.280]  my
[00:00:01.360 --> 00:00:01.680]  fellow
[00:00:02.080 --> 00:00:02.160]  Americans,
... (10 s total, 21 words)

Same audio with all four quants produces near-identical timing — the worst spread between F16 and Q4_K on this clip is 80 ms (one alignment-class step).

License

Apache-2.0, same as upstream Qwen3-ForcedAligner-0.6B.

Citation

bibtex

@misc{qwen3asr,
    title  = {Qwen3-ASR},
    author = {Qwen Team},
    year   = {2026},
    url    = {https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B}
}

Provenance and EU AI Act Art. 53 note

Upstream model: Qwen/Qwen3-ForcedAligner-0.6B — published by Qwen.
Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.

cstr/qwen3-forced-aligner-0.6b-GGUF

Qwen3-ForcedAligner-0.6B — GGUF (CrispASR)

What's in the box

How it differs from the ASR models

Use with CrispASR

Languages

How it was made

Verification

License

Citation

Provenance and EU AI Act Art. 53 note

cstr/
qwen3-forced-aligner-0.6b-GGUF