[Feature] Add Qwen3-ForcedAligner support via token classification pooling (#35367)

Signed-off-by: haosdent <haosdent@gmail.com>

examples/pooling/token_classify/forced_alignment_offline.py

@@ -0,0 +1,90 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Adapted from Qwen3-ForcedAligner inference:
+# https://github.com/QwenLM/Qwen3-ASR
+
+"""
+Offline forced alignment example using Qwen3-ForcedAligner-0.6B.
+
+Forced alignment takes audio and reference text as input and produces
+word-level timestamps. The model predicts a time bin at each <timestamp>
+token position; multiplying by ``timestamp_segment_time`` gives milliseconds.
+
+Usage::
+
+    python forced_alignment_offline.py \
+        --model Qwen/Qwen3-ForcedAligner-0.6B
+"""
+
+from argparse import Namespace
+
+import numpy as np
+
+from vllm import LLM, EngineArgs
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+
+def parse_args():
+    parser = FlexibleArgumentParser()
+    parser = EngineArgs.add_cli_args(parser)
+    parser.set_defaults(
+        model="Qwen/Qwen3-ForcedAligner-0.6B",
+        runner="pooling",
+        enforce_eager=True,
+        hf_overrides={"architectures": ["Qwen3ASRForcedAlignerForTokenClassification"]},
+    )
+    return parser.parse_args()
+
+
+def build_prompt(words: list[str]) -> str:
+    """Build the forced alignment prompt from a word list.
+
+    Format: <|audio_start|><|audio_pad|><|audio_end|>
+            word1<timestamp><timestamp>word2<timestamp><timestamp>...
+    """
+    body = "<timestamp><timestamp>".join(words) + "<timestamp><timestamp>"
+    return f"<|audio_start|><|audio_pad|><|audio_end|>{body}"
+
+
+def main(args: Namespace):
+    llm = LLM(**vars(args))
+
+    config = llm.llm_engine.vllm_config.model_config.hf_config
+    timestamp_token_id = config.timestamp_token_id
+    timestamp_segment_time = config.timestamp_segment_time
+
+    # Example: align these words against a 5-second audio clip
+    words = ["Hello", "world"]
+    prompt = build_prompt(words)
+
+    # Use a 5-second silent audio as placeholder (replace with real audio)
+    sample_rate = 16000
+    audio = np.zeros(sample_rate * 5, dtype=np.float32)
+
+    outputs = llm.encode(
+        [{"prompt": prompt, "multi_modal_data": {"audio": audio}}],
+        pooling_task="token_classify",
+    )
+
+    for output in outputs:
+        logits = output.outputs.data  # [num_tokens, classify_num]
+        predictions = logits.argmax(dim=-1)
+        token_ids = output.prompt_token_ids
+
+        # Extract timestamps at <timestamp> positions
+        ts_predictions = [
+            pred.item() * timestamp_segment_time
+            for tid, pred in zip(token_ids, predictions)
+            if tid == timestamp_token_id
+        ]
+
+        # Pair up start/end times per word
+        for i, word in enumerate(words):
+            start_ms = ts_predictions[i * 2]
+            end_ms = ts_predictions[i * 2 + 1]
+            print(f"{word:15s} {start_ms / 1000:.3f}s - {end_ms / 1000:.3f}s")
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)