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Cataloged from Qwen/Qwen3-ForcedAligner-0.6B-hf
All models by Qwen β
The Qwen3-ASR family includes Qwen3-ASR-1.7B and Qwen3-ASR-0.6B, which support language identification and ASR for 52 languages and dialects. Both leverage large-scale speech training data and the strong audio understanding capability of their foundation model, Qwen3-Omni. The 1.7B version achieves state-of-the-art performance among open-source ASR models and is competitive with the strongest proprietary commercial APIs.
Key features:
Until Qwen3-ForcedAligner is part of an official Transformers release, install from source:
Transcribe with the ASR model, then pass the transcript and audio to the forced aligner.
The forced aligner accepts transcripts from any ASR system. Below is a batch inference example using NVIDIA Parakeet CTC for transcription.
The forced aligner is an especially good fit for torch.compile because it runs a single forward pass with no autoregressive decoding. This makes it ideal for bulk audio timestamping: transcribe with any ASR model, then batch-align with the compiled forced aligner for maximum throughput.
On an A100, we observed a speed-up of ~2.5Γ for a batch size of 4 (benchmark script).
pip install git+https://github.com/huggingface/transformersimport torchfrom transformers import AutoProcessor, AutoModelForMultimodalLM, AutoModelForTokenClassification
asr_model_id = "Qwen/Qwen3-ASR-0.6B-hf"aligner_model_id = "Qwen/Qwen3-ForcedAligner-0.6B-hf"
asr_processor = AutoProcessor.from_pretrained(asr_model_id)asr_model = AutoModelForMultimodalLM.from_pretrained(asr_model_id, device_map="auto")
aligner_processor = AutoProcessor.from_pretrained(aligner_model_id)aligner_model = AutoModelForTokenClassification.from_pretrained( aligner_model_id, dtype=torch.bfloat16, device_map="auto")
audio_url = "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav"
# Step 1: Transcribeinputs = asr_processor.apply_transcription_request(audio=audio_url)inputs = inputs.to(asr_model.device, asr_model.dtype)output_ids = asr_model.generate(**inputs, max_new_tokens=256)generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]parsed = asr_processor.decode(generated_ids, return_format="parsed")[0]transcript = parsed["transcription"]language = parsed["language"] or "English"
# Step 2: Prepare alignment inputsaligner_inputs, word_lists = aligner_processor.prepare_forced_aligner_inputs( audio=audio_url, transcript=transcript, language=language,)aligner_inputs = aligner_inputs.to(aligner_model.device, aligner_model.dtype)
# Step 3: Run forced alignerwith torch.inference_mode(): outputs = aligner_model(**aligner_inputs)
# Step 4: Decode timestampstimestamps = aligner_processor.decode_forced_alignment( logits=outputs.logits, input_ids=aligner_inputs["input_ids"], word_lists=word_lists, timestamp_token_id=aligner_model.config.timestamp_token_id,)[0]
for item in timestamps: print(f"{item['text']:<20} {item['start_time']:>8.3f}s β {item['end_time']:>8.3f}s")
"""Word Start (s) End (s)------------------------------------------Mr 0.560 0.800Quilter 0.800 1.280is 1.280 1.440the 1.440 1.520apostle 1.520 2.080..."""import torchfrom datasets import Audio, load_datasetfrom transformers import AutoModelForCTC, AutoProcessor, AutoModelForTokenClassification
parakeet_processor = AutoProcessor.from_pretrained("nvidia/parakeet-ctc-1.1b")parakeet_model = AutoModelForCTC.from_pretrained( "nvidia/parakeet-ctc-1.1b", dtype="auto", device_map="cuda",)
aligner_model_id = "Qwen/Qwen3-ForcedAligner-0.6B-hf"aligner_processor = AutoProcessor.from_pretrained(aligner_model_id)aligner_model = AutoModelForTokenClassification.from_pretrained( aligner_model_id, dtype=torch.bfloat16, device_map="cuda",)
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")ds = ds.cast_column("audio", Audio(sampling_rate=parakeet_processor.feature_extractor.sampling_rate))audio_arrays = [ds[i]["audio"]["array"] for i in range(3)]sr = ds[0]["audio"]["sampling_rate"]
# Batch transcribe with Parakeetinputs = parakeet_processor(audio_arrays, sampling_rate=sr, return_tensors="pt", padding=True).to( parakeet_model.device, dtype=parakeet_model.dtype)with torch.inference_mode(): outputs = parakeet_model.generate(**inputs)transcripts = parakeet_processor.decode(outputs)
# Batch align with Qwen3 Forced Aligneraligner_inputs, word_lists = aligner_processor.prepare_forced_aligner_inputs( audio=audio_arrays, transcript=transcripts, language="English",)aligner_inputs = aligner_inputs.to(aligner_model.device, aligner_model.dtype)
with torch.inference_mode(): aligner_outputs = aligner_model(**aligner_inputs)
batch_timestamps = aligner_processor.decode_forced_alignment( logits=aligner_outputs.logits, input_ids=aligner_inputs["input_ids"], word_lists=word_lists, timestamp_token_id=aligner_model.config.timestamp_token_id,)
for i, (transcript, timestamps) in enumerate(zip(transcripts, batch_timestamps)): print(f"\n[Sample {i}] {transcript}") for item in timestamps[:5]: print(f" {item['text']:<20} {item['start_time']:>8.3f}s β {item['end_time']:>8.3f}s") if len(timestamps) > 5: print(f" ... ({len(timestamps) - 5} more words)")import torchfrom transformers import AutoProcessor, AutoModelForTokenClassification
model_id = "Qwen/Qwen3-ForcedAligner-0.6B-hf"num_warmup = 5batch_size = 4
processor = AutoProcessor.from_pretrained(model_id)model = AutoModelForTokenClassification.from_pretrained(model_id, dtype=torch.bfloat16).to("cuda")
audio_url = "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav"transcript = "Mr. Quilter is the apostle of the middle classes."
aligner_inputs, word_lists = processor.prepare_forced_aligner_inputs( audio=[audio_url] * batch_size, transcript=[transcript] * batch_size, language=["English"] * batch_size,)aligner_inputs = aligner_inputs.to("cuda", torch.bfloat16)
model.forward = torch.compile(model.forward)
# Warmupwith torch.no_grad(): for _ in range(num_warmup): _ = model(**aligner_inputs)
# Inferencewith torch.no_grad(): outputs = model(**aligner_inputs)@article{Qwen3-ASR, title={Qwen3-ASR Technical Report}, author={Xian Shi, Xiong Wang, Zhifang Guo, Yongqi Wang, Pei Zhang, Xinyu Zhang, Zishan Guo, Hongkun Hao, Yu Xi, Baosong Yang, Jin Xu, Jingren Zhou, Junyang Lin}, journal={arXiv preprint arXiv:2601.21337}, year={2026}}