qwen3-forced-aligner-0.6b-GGUF

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

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:

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

# 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

# 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:

1. 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.

2. 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:

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

@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.