qwen-asr
Qwen-ASR python package
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you need multilingual ASR (especially for Chinese dialects or non-English languages) and can tolerate a fresh Python 3.9+ environment with 11 runtime dependencies. The package is young (released 2026-01-29) and marked aging, so expect possible API changes; no known security vulnerabilities. Permissive Apache-2.0 license poses no legal barrier. Requires GPU for practical inference speed and manual model download in offline environments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python ≥3.9.
- Model weights download automatically; offline environments need manual download via modelscope or huggingface-cli.
- GPU recommended for reasonable inference speed.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
last release 2026-01-30 (196 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 752,019 downloads/mo, #5,154 on PyPI
Alternatives
Verify before relying
pip install qwen-asr
from qwen_asr import Qwen3ASRModel
model = Qwen3ASRModel.from_pretrained('Qwen/Qwen3-ASR-0.6B')
result = model.transcribe('audio.wav')- Actual throughput and latency figures for the 0.6B model at stated concurrency levels
- Accuracy benchmarks against proprietary APIs mentioned in description
- Memory requirements for each model variant on typical hardware
- Whether vLLM backend is required for streaming inference or optional
What it is and what it does
Qwen3-ASR is a Python package wrapping Qwen's speech recognition models (1.7B and 0.6B variants) that transcribe audio to text in 52 languages and dialects, identify the spoken language automatically, and optionally align text to speech timestamps. It supports both streaming and offline inference, handles various audio formats (speech, singing, songs with background music), and provides multiple backends: a transformers-based inference engine and an optional vLLM backend for faster batch processing.
The package is designed for developers who need multilingual ASR in production or research settings. It depends on transformers, librosa, soundfile, sox, and other audio/ML libraries; optional vLLM support requires additional installation. The forced-aligner model (Qwen3-ForcedAligner-0.6B) is a separate, non-autoregressive component for timestamping arbitrary text units within speech up to 5 minutes long across 11 languages.
Use it for
- Transcribe multilingual customer support calls or meeting recordings without language detection overhead
- Build a voice-to-text pipeline for content in Chinese dialects, Arabic, or other underrepresented languages
- Generate precise word-level timestamps for subtitle generation or speech-to-text alignment in video editing
- Deploy a real-time streaming transcription service using the vLLM backend for high-concurrency scenarios
- Fine-tune the model on domain-specific audio (medical, legal, technical) using the provided training framework
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you need multilingual ASR (especially for Chinese dialects or non-English languages) and can tolerate a fresh Python 3.9+ environment with 11 runtime dependencies. The package is young (released 2026-01-29) and marked aging, so expect possible API changes; no known security vulnerabilities. Permissive Apache-2.0 license poses no legal barrier. Requires GPU for practical inference speed and manual model download in offline environments.
Install
qwen-asr on PyPI
Before you install
Low friction: pure Python wheel with no compiled dependencies. Aging status (196 days since release) but recent activity; supports Python 3.9–3.13. Eleven runtime dependencies include transformers, librosa, soundfile, sox, and optional vLLM backend—typical for ML inference packages.
Requires Python ≥3.9. Model weights download automatically; offline environments need manual download via modelscope or huggingface-cli. GPU recommended for reasonable inference speed.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
Quickstart
pip install qwen-asr
from qwen_asr import Qwen3ASRModel
model = Qwen3ASRModel.from_pretrained('Qwen/Qwen3-ASR-0.6B')
result = model.transcribe('audio.wav')Verify before relying
- Actual throughput and latency figures for the 0.6B model at stated concurrency levels
- Accuracy benchmarks against proprietary APIs mentioned in description
- Memory requirements for each model variant on typical hardware
- Whether vLLM backend is required for streaming inference or optional
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packagestransformersnagisasoynlpaccelerateqwen-omni-utilslibrosasoundfilesoxgradioflaskpytz |
| Maintenance | Aging 196 days since the last release |
| First released | |
| Downloads | 752,019 / month, #5,154 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9 |
Evidence: qwen_asr-0.0.6-py3-none-any.whl
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See also qwen-tts · vosk · funasr · openai-whisper · sherpa-onnx · SpeechRecognition · onnx-asr · qwen-agent · nameparser · qwen-omni-utils