qwen-aligner-toolkit 0.1.1

Production toolkit around Qwen3-ForcedAligner: VAD pre-segmentation, multi-language word/char-level alignment, and speaker diarization.

qwen-aligner-toolkit

Production toolkit around Qwen3-ForcedAligner: VAD pre-segmentation, multi-language word/char-level forced alignment, and speaker diarization.

Why

qwen-asr ships an excellent forced aligner (Qwen3-ForcedAligner-0.6B) that handles multiple languages with one model — no per-language wav2vec2 needed, no kanji vocab gaps. This toolkit packages it with the surrounding pieces you typically need in production:

• VAD (pyannote/segmentation-3.0) to chunk long audio
• Diarization (pyannote/speaker-diarization-community-1) with per-word speaker assignment and run smoothing
• Audio utilities (path / URL / bytes / np.ndarray → 16 kHz mono)
• AVX-less CPU compatibility via a nagisa char-level stub

The toolkit does not do ASR. Bring your own transcript (from Whisper via vLLM, faster-whisper, qwen-asr itself, or any other system) and the toolkit will time-align it and label speakers.

Install

pip install qwen-aligner-toolkit
pip install qwen-aligner-toolkit[full]    # with VAD + diarization

Usage

Simple alignment (short audio)

from qwen_aligner_toolkit import Aligner

aligner = Aligner.from_pretrained()
words = aligner.align(
    text="甚至出现交易几乎停滞的情况。",
    audio="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav",
    language="Chinese",
)
for w in words:
    print(w.start_time, w.end_time, w.text)

ASR segments → word-level + speakers

from qwen_aligner_toolkit import Pipeline

pipeline = Pipeline.from_pretrained(hf_token="hf_...", device="cuda")

segments = [
    {"text": "おはようございます。", "start": 0.0, "end": 2.3},
    {"text": "今日は良い天気ですね。", "start": 2.3, "end": 5.1},
]

result = pipeline.align_segments(
    segments=segments,
    audio="audio.wav",
    language="Japanese",
    diarize=True,
)

for w in result.words:
    print(f"{w.start_time:.2f}-{w.end_time:.2f} [{w.speaker}] {w.text}")

VAD only

from qwen_aligner_toolkit import VAD

vad = VAD.from_pretrained(hf_token="hf_...")
chunks = vad.detect("audio.wav")

CPU compatibility (AVX requirement)

qwen-asr depends on nagisa, which ships DyNet38 compiled with AVX. On AVX-less CPUs (Intel Celeron G-series, some embedded SoCs), importing nagisa crashes with SIGILL.

If that affects you, install the char-level stub:

qwen-aligner-toolkit install-nagisa-stub
# or
python -m qwen_aligner_toolkit.compat install-stub

This downgrades Japanese tokenization from morpheme-level to character-level (other languages are unaffected). For forced alignment the difference is mostly cosmetic; speaker boundaries may even be detected at finer granularity.

For Docker:

RUN pip install qwen-aligner-toolkit && \
    qwen-aligner-toolkit install-nagisa-stub

License

Apache 2.0. See LICENSE.