[Feat] Streaming TTS word-level timestamps via forced aligner (#4034)

Signed-off-by: wjinxu <1299461899@qq.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

examples/offline_inference/text_to_speech/README.md

@@ -369,6 +369,20 @@ python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py \
 ```
 Streaming requires `async_chunk: true` in the stage config.
 
+### Word Timestamps
+Generate a WAV offline and a JSON sidecar with word-level timestamps from
+`Qwen/Qwen3-ForcedAligner-0.6B`:
+```bash
+python examples/offline_inference/text_to_speech/qwen3_tts/word_timestamps.py \
+    --model Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
+    --forced-aligner Qwen/Qwen3-ForcedAligner-0.6B \
+    --text "Hello world." \
+    --output-dir /tmp/qwen3_tts_timestamps
+```
+The script writes `qwen3_tts_word_timestamps.wav` and
+`qwen3_tts_word_timestamps.json`. On machines without a local CUDA toolkit,
+set `VLLM_USE_FLASHINFER_SAMPLER=0` to avoid FlashInfer sampler JIT.
+
 ### Batched decoding
 The Code2Wav stage supports batched decoding through the SpeechTokenizer. Pass multiple prompts via `--txt-prompts` and set `--batch-size` accordingly. To raise `max_num_seqs` on either stage, point `--stage-configs-path` at a stage configs YAML with the desired values (see `vllm_omni/model_executor/stage_configs/` for templates):
 ```bash

examples/offline_inference/text_to_speech/qwen3_tts/word_timestamps.py

@@ -0,0 +1,172 @@
+"""Offline Qwen3-TTS example with word-level timestamps.
+
+This demo runs Qwen3-TTS offline, aligns the synthesized audio in memory with
+the same shared forced aligner used by the streaming server path, then writes
+the WAV and JSON sidecar:
+
+    python examples/offline_inference/text_to_speech/qwen3_tts/word_timestamps.py \
+        --model Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
+        --forced-aligner Qwen/Qwen3-ForcedAligner-0.6B
+
+On machines without a local CUDA toolkit, set ``VLLM_USE_FLASHINFER_SAMPLER=0``
+to avoid FlashInfer sampler JIT during warmup.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import os
+from pathlib import Path
+from typing import Any
+
+os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+# The in-process offline aligner consumes token_classify outputs. Keeping V1
+# multiprocessing off avoids msgspec serialization issues for token-wise logits.
+os.environ.setdefault("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
+
+import numpy as np
+import soundfile as sf
+import torch
+from end2end import _estimate_prompt_len
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+from vllm_omni import Omni
+from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
+from vllm_omni.utils.forced_aligner import align, build_forced_aligner_config
+
+
+def _default_stage_config() -> str:
+    repo_root = Path(__file__).resolve().parents[4]
+    return str(repo_root / "vllm_omni" / "deploy" / "qwen3_tts.yaml")
+
+
+def _build_custom_voice_input(args: Any) -> dict[str, Any]:
+    additional_information = {
+        "task_type": ["CustomVoice"],
+        "text": [args.text],
+        "language": [args.language],
+        "speaker": [args.speaker],
+        "instruct": [args.instructions],
+        "max_new_tokens": [args.max_new_tokens],
+    }
+    return {
+        "prompt_token_ids": [0] * _estimate_prompt_len(additional_information, args.model),
+        "additional_information": additional_information,
+    }
+
+
+def _audio_tensor_and_sample_rate(mm: dict[str, Any]) -> tuple[torch.Tensor, int]:
+    audio_data = mm["audio"]
+    sr_raw = mm["sr"]
+    sr_val = sr_raw[-1] if isinstance(sr_raw, list) and sr_raw else sr_raw
+    sample_rate = int(sr_val.item()) if hasattr(sr_val, "item") else int(sr_val)
+    audio_tensor = torch.cat(audio_data, dim=-1) if isinstance(audio_data, list) else audio_data
+    return audio_tensor.float().detach().cpu().flatten(), sample_rate
+
+
+def _float_audio_to_pcm16_bytes(audio: torch.Tensor) -> bytes:
+    samples = audio.numpy()
+    pcm = (np.clip(samples, -1.0, 1.0) * 32767.0).astype("<i2")
+    return pcm.tobytes()
+
+
+async def _run_alignment(args: Any, audio: torch.Tensor, sample_rate: int) -> list[dict[str, Any]] | None:
+    aligner_config = build_forced_aligner_config(args)
+    if aligner_config is None:
+        raise ValueError("--forced-aligner or --forced-aligner-config is required")
+    timestamps = await align(
+        audio=_float_audio_to_pcm16_bytes(audio),
+        text=args.text,
+        sample_rate=sample_rate,
+        config=aligner_config,
+        language=args.language,
+    )
+    if timestamps is None:
+        return None
+    return [
+        {
+            "word": item.word,
+            "start_ms": item.start_ms,
+            "end_ms": item.end_ms,
+        }
+        for item in timestamps
+    ]
+
+
+def main(args: Any) -> None:
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    omni_kwargs = vars(args).copy()
+    for key in (
+        "forced_aligner",
+        "text",
+        "language",
+        "speaker",
+        "instructions",
+        "max_new_tokens",
+        "output_dir",
+    ):
+        omni_kwargs.pop(key, None)
+    omni_kwargs["stage_configs_path"] = args.stage_configs_path or _default_stage_config()
+    omni_kwargs["log_stats"] = args.log_stats
+    omni = Omni(**omni_kwargs)
+
+    prompt = _build_custom_voice_input(args)
+    final_output = None
+    for stage_outputs in omni.generate([prompt]):
+        final_output = stage_outputs.request_output
+    if final_output is None:
+        raise RuntimeError("Qwen3-TTS did not produce an output.")
+
+    mm = final_output.outputs[0].multimodal_output
+    audio, sample_rate = _audio_tensor_and_sample_rate(mm)
+
+    wav_path = output_dir / "qwen3_tts_word_timestamps.wav"
+    # Align the in-memory tensor, not the saved WAV. The WAV is only a demo
+    # artifact so users can listen to the same audio referenced by the sidecar.
+    timestamps = asyncio.run(_run_alignment(args, audio, sample_rate))
+    sf.write(wav_path, audio.numpy(), samplerate=sample_rate, format="WAV")
+
+    sidecar = {
+        "text": args.text,
+        "sample_rate": sample_rate,
+        "audio_path": str(wav_path),
+        "timestamps": timestamps,
+    }
+    json_path = output_dir / "qwen3_tts_word_timestamps.json"
+    json_path.write_text(json.dumps(sidecar, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+
+    print(f"Saved audio: {wav_path}")
+    print(f"Saved timestamps: {json_path}")
+    print(json.dumps(sidecar["timestamps"], ensure_ascii=False, indent=2))
+
+
+def parse_args() -> Any:
+    parser = FlexibleArgumentParser(description="Offline Qwen3-TTS word timestamp example")
+    parser.add_argument("--model", default="Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", help="Qwen3-TTS model path/name")
+    parser.add_argument(
+        "--forced-aligner",
+        default=None,
+        help="Qwen3 forced aligner model path/name",
+    )
+    parser.add_argument(
+        "--forced-aligner-config",
+        default=None,
+        help="Optional YAML file for forced aligner settings (incl. gpu_memory_utilization)",
+    )
+    parser.add_argument("--stage-configs-path", default=None, help="Qwen3-TTS deploy YAML")
+    parser.add_argument("--text", default="Hello world.", help="Text to synthesize and align")
+    parser.add_argument("--language", default="English", help="Qwen3-TTS language field")
+    parser.add_argument("--speaker", default="Vivian", help="CustomVoice speaker name")
+    parser.add_argument("--instructions", default="", help="Optional speaking style instruction")
+    parser.add_argument("--max-new-tokens", type=int, default=2048, help="TTS max_new_tokens")
+    parser.add_argument("--output-dir", default="output_audio", help="Directory for WAV and JSON sidecar")
+    parser.add_argument("--log-stats", action="store_true", default=False, help="Enable vLLM-Omni stats logging")
+    nullify_stage_engine_defaults(parser)
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    main(parse_args())

examples/online_serving/text_to_speech/README.md

@@ -504,6 +504,35 @@ python qwen3_tts/streaming_speech_client.py --text "Hello world. How are you? I
 python qwen3_tts/streaming_speech_client.py --text "..." --simulate-stt --stt-delay 0.1
 ```
 
+To receive word-level timestamps, launch the server with a forced aligner:
+```bash
+vllm-omni serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
+    --omni \
+    --deploy-config vllm_omni/deploy/qwen3_tts.yaml \
+    --trust-remote-code \
+    --forced-aligner Qwen/Qwen3-ForcedAligner-0.6B
+```
+Then request PCM JSON sidecar chunks:
+```bash
+python qwen3_tts/streaming_speech_client.py \
+    --text "Hello world. How are you?" \
+    --stream-audio \
+    --response-format pcm \
+    --word-timestamps
+```
+The client writes one PCM file per sentence and a matching
+`sentence_XXX_timestamps.json` sidecar.
+
+To *see* the alignment instead of reading a JSON sidecar, run the
+word-timestamp Gradio demo (server must be launched with `--forced-aligner`):
+```bash
+python qwen3_tts/word_timestamps_demo.py --api-base http://localhost:8091
+```
+Each sentence's audio plays in an `<audio>` element while its text is rendered
+as inline word spans; the current word highlights as `audio.currentTime`
+crosses each `start_ms`. The **Stop (barge-in)** button cuts playback and
+reports the last-spoken word, useful for the voice-agent barge-in case.
+
 ### Gradio demos
 ```bash
 ./qwen3_tts/run_gradio_demo.sh                              # CustomVoice (default)

examples/online_serving/text_to_speech/qwen3_tts/streaming_speech_client.py

@@ -18,6 +18,21 @@ Usage:
         --text "Hello world. How are you? I am fine." \
         --simulate-stt --stt-delay 0.1
 
+    # Receive JSON sidecar chunks with word-level timestamps
+    python streaming_speech_client.py \
+        --text "Hello world. How are you?" \
+        --stream-audio \
+        --response-format pcm \
+        --word-timestamps
+
+    # Also save every received audio.chunk frame as individual PCM/WAV files
+    python streaming_speech_client.py \
+        --text "Hello world. How are you?" \
+        --stream-audio \
+        --response-format pcm \
+        --word-timestamps \
+        --save-chunks
+
     # VoiceDesign task
     python streaming_speech_client.py \
         --text "Today is a great day. The weather is nice." \
@@ -37,8 +52,10 @@ Requirements:
 
 import argparse
 import asyncio
+import base64
 import json
 import os
+import wave
 
 try:
     import websockets
@@ -47,6 +64,37 @@ except ImportError:
     raise SystemExit(1)
 
 
+def save_audio_file(output_dir: str, sentence_index: int, response_format: str, chunks: list[bytes]) -> str:
+    audio_bytes = b"".join(chunks)
+    if response_format == "pcm":
+        raw_path = os.path.join(output_dir, f"sentence_{sentence_index:03d}.pcm")
+        with open(raw_path, "wb") as f:
+            f.write(audio_bytes)
+
+        wav_path = os.path.join(output_dir, f"sentence_{sentence_index:03d}.wav")
+        save_pcm_wav(wav_path, audio_bytes)
+        return wav_path
+
+    filename = os.path.join(output_dir, f"sentence_{sentence_index:03d}.{response_format}")
+    with open(filename, "wb") as f:
+        f.write(audio_bytes)
+    return filename
+
+
+def save_pcm_wav(path: str, audio_bytes: bytes, sample_rate: int = 24000) -> None:
+    with wave.open(path, "wb") as wf:
+        wf.setnchannels(1)
+        wf.setsampwidth(2)
+        wf.setframerate(sample_rate)
+        wf.writeframes(audio_bytes)
+
+
+def timestamp_words_text(timestamps: list[dict] | None) -> str:
+    if not timestamps:
+        return ""
+    return " ".join(str(item.get("word", "")) for item in timestamps if item.get("word"))
+
+
 async def stream_tts(
     url: str,
     text: str,
@@ -54,6 +102,7 @@ async def stream_tts(
     output_dir: str,
     simulate_stt: bool = False,
     stt_delay: float = 0.1,
+    save_chunks: bool = False,
 ) -> None:
     """Connect to the streaming TTS endpoint and process audio responses."""
     os.makedirs(output_dir, exist_ok=True)
@@ -99,8 +148,14 @@ async def stream_tts(
         sender_task = asyncio.create_task(send_text())
 
         response_format = config.get("response_format", "wav")
+        word_timestamps = bool(config.get("word_timestamps", False))
         current_sentence_index = 0
+        current_sentence_text = ""
         current_chunks: list[bytes] = []
+        current_timestamps: list[dict] = []
+        # Distinguish `null` (aligner failed) from `[]` (silence / no tokens):
+        # the sentence is a failure only if no frame ever carried timestamps.
+        alignment_frame_seen = False
 
         try:
             while True:
@@ -116,22 +171,94 @@ async def stream_tts(
 
                     if msg_type == "audio.start":
                         current_sentence_index = msg["sentence_index"]
+                        current_sentence_text = msg.get("sentence_text", "")
                         current_chunks = []
-                        print(f"  [sentence {msg['sentence_index']}] Generating: {msg['sentence_text']!r}")
+                        current_timestamps = []
+                        alignment_frame_seen = False
+                        print(f"  [sentence {msg['sentence_index']}] Generating: {current_sentence_text!r}")
+                    elif msg_type == "audio.chunk":
+                        audio = base64.b64decode(msg["audio_b64"])
+                        current_chunks.append(audio)
+                        chunk_timestamps = msg.get("timestamps")
+                        # Sentence-level alignment sends a trailing timestamp-only
+                        # frame with empty audio; don't write an empty chunk file.
+                        if save_chunks and audio:
+                            chunk_base = os.path.join(
+                                output_dir,
+                                f"sentence_{msg['sentence_index']:03d}_chunk_{msg['chunk_id']:03d}",
+                            )
+                            chunk_pcm = f"{chunk_base}.pcm"
+                            chunk_wav = f"{chunk_base}.wav"
+                            chunk_json = f"{chunk_base}_timestamps.json"
+                            with open(chunk_pcm, "wb") as f:
+                                f.write(audio)
+                            save_pcm_wav(chunk_wav, audio, int(msg.get("sample_rate", 24000)))
+                            with open(chunk_json, "w", encoding="utf-8") as f:
+                                json.dump(
+                                    {
+                                        "sentence_index": msg["sentence_index"],
+                                        "chunk_id": msg["chunk_id"],
+                                        "chunk_start_ms": msg.get("chunk_start_ms"),
+                                        "chunk_end_ms": msg.get("chunk_end_ms"),
+                                        "sentence_text": current_sentence_text,
+                                        "covered_text": timestamp_words_text(chunk_timestamps),
+                                        "timestamps": chunk_timestamps,
+                                    },
+                                    f,
+                                    ensure_ascii=False,
+                                    indent=2,
+                                )
+                        if chunk_timestamps is None:
+                            print(
+                                f"  [sentence {msg['sentence_index']}] chunk {msg['chunk_id']}: "
+                                f"{len(audio)} bytes, timestamps unavailable"
+                            )
+                        else:
+                            alignment_frame_seen = True
+                            current_timestamps.extend(chunk_timestamps)
+                            print(
+                                f"  [sentence {msg['sentence_index']}] chunk {msg['chunk_id']}: "
+                                f"{len(audio)} bytes, {len(chunk_timestamps)} timestamp(s)"
+                            )
+                        if save_chunks and audio:
+                            print(f"    saved chunk -> {chunk_wav}")
                     elif msg_type == "audio.done":
-                        filename = os.path.join(
+                        filename = save_audio_file(
                             output_dir,
-                            f"sentence_{msg['sentence_index']:03d}.{response_format}",
+                            msg["sentence_index"],
+                            response_format,
+                            current_chunks,
                         )
-                        with open(filename, "wb") as f:
-                            f.write(b"".join(current_chunks))
+                        if word_timestamps:
+                            # `null` on failure, else the accumulated list (`[]` for silence).
+                            sentence_timestamps = current_timestamps if alignment_frame_seen else None
+                            ts_filename = os.path.join(
+                                output_dir,
+                                f"sentence_{msg['sentence_index']:03d}_timestamps.json",
+                            )
+                            with open(ts_filename, "w", encoding="utf-8") as f:
+                                json.dump(
+                                    {
+                                        "sentence_index": msg["sentence_index"],
+                                        "sentence_text": current_sentence_text,
+                                        "covered_text": timestamp_words_text(sentence_timestamps),
+                                        "timestamps": sentence_timestamps,
+                                    },
+                                    f,
+                                    ensure_ascii=False,
+                                    indent=2,
+                                )
                         print(
                             f"  [sentence {msg['sentence_index']}] Done"
                             f" bytes={msg.get('total_bytes', len(b''.join(current_chunks)))}"
                             f" error={msg.get('error', False)}"
                             f" -> {filename}"
                         )
+                        if word_timestamps:
+                            print(f"  [sentence {msg['sentence_index']}] Timestamps -> {ts_filename}")
                         current_chunks = []
+                        current_timestamps = []
+                        alignment_frame_seen = False
                     elif msg_type == "session.done":
                         print(f"\nSession complete: {msg['total_sentences']} sentence(s) generated")
                         break
@@ -189,6 +316,16 @@ def main():
         action="store_true",
         help="Receive one or more PCM chunks per sentence (requires --response-format pcm)",
     )
+    parser.add_argument(
+        "--word-timestamps",
+        action="store_true",
+        help="Receive JSON sidecar chunks with word-level timestamps (requires --stream-audio --response-format pcm)",
+    )
+    parser.add_argument(
+        "--save-chunks",
+        action="store_true",
+        help="Save each audio.chunk frame as sentence_XXX_chunk_YYY.{pcm,wav,json}",
+    )
     parser.add_argument("--speed", type=float, default=1.0, help="Playback speed (0.25-4.0)")
     parser.add_argument("--max-new-tokens", type=int, default=None, help="Max tokens")
 
@@ -238,6 +375,10 @@ def main():
             config[key] = val
     if args.stream_audio:
         config["stream_audio"] = True
+    if args.word_timestamps:
+        config["word_timestamps"] = True
+        config["stream_audio"] = True
+        config["response_format"] = "pcm"
     if args.x_vector_only_mode:
         config["x_vector_only_mode"] = True
 
@@ -249,6 +390,7 @@ def main():
             output_dir=args.output_dir,
             simulate_stt=args.simulate_stt,
             stt_delay=args.stt_delay,
+            save_chunks=args.save_chunks,
         )
     )
 

examples/online_serving/text_to_speech/qwen3_tts/word_timestamps_demo.py

@@ -0,0 +1,457 @@
+"""Gradio demo for streaming TTS word-level timestamps.
+
+Connects to the WebSocket endpoint ``/v1/audio/speech/stream`` with
+``word_timestamps: true`` and visualizes the alignment: each sentence's
+audio plays in a native ``<audio>`` element while its text is rendered as
+inline word spans, the current word highlighting as ``audio.currentTime``
+crosses each ``start_ms``.
+
+A "Stop (barge-in)" button cuts playback and reports the last-spoken word,
+demonstrating the voice-agent barge-in use case.
+
+Timestamps are sentence-level: audio streams in real time, then the forced
+aligner runs once per sentence and the complete word timestamps arrive in a
+trailing frame. The demo therefore plays one sentence block at a time.
+
+Usage:
+    # Launch the server with a forced aligner first, e.g.:
+    #   vllm-omni serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --omni \
+    #       --deploy-config vllm_omni/deploy/qwen3_tts.yaml --trust-remote-code \
+    #       --forced-aligner Qwen/Qwen3-ForcedAligner-0.6B
+    # Then:
+    python word_timestamps_demo.py --api-base http://localhost:8000
+"""
+
+import argparse
+import asyncio
+import json
+import logging
+
+try:
+    import gradio as gr
+except ImportError:
+    raise ImportError("gradio is required to run this demo. Install it with: pip install 'vllm-omni[demo]'") from None
+try:
+    import websockets
+except ImportError:
+    raise ImportError("websockets is required to run this demo. Install it with: pip install websockets") from None
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from tts_common import (
+    PCM_SAMPLE_RATE,
+    SUPPORTED_LANGUAGES,
+    TASK_TYPES,
+    add_common_args,
+    fetch_voices,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _upstream_ws_url(api_base: str) -> str:
+    """Map an http(s) API base to its WebSocket streaming endpoint."""
+    base = api_base.rstrip("/")
+    if base.startswith("https://"):
+        base = "wss://" + base[len("https://") :]
+    elif base.startswith("http://"):
+        base = "ws://" + base[len("http://") :]
+    else:
+        base = "ws://" + base
+    return base + "/v1/audio/speech/stream"
+
+
+# ── Front-end ────────────────────────────────────────────────────────
+PLAYER_HTML = """
+<div id="wt-root">
+  <div style="display:flex; align-items:center; gap:10px;">
+    <div id="wt-dot" style="width:10px;height:10px;border-radius:50%;background:#ccc;flex-shrink:0;"></div>
+    <span id="wt-status" style="font-weight:600;font-size:1.05em;">Ready</span>
+    <button id="wt-stop" onclick="window.wtStop()"
+      style="display:none; margin-left:auto; padding:5px 16px; border-radius:6px; border:1px solid #EF5552;
+             background:#fff; color:#EF5552; cursor:pointer; font-size:0.85em;">Stop (barge-in)</button>
+  </div>
+  <div id="wt-barge" style="display:none; margin-top:10px; padding:8px 12px; border-radius:6px;
+       background:#fff5f5; border:1px solid #f3c2c2; color:#b02a2a; font-size:0.9em;"></div>
+  <div id="wt-sentences" style="margin-top:12px; display:flex; flex-direction:column; gap:14px;"></div>
+</div>
+"""
+
+
+def _build_head_js(sample_rate: int) -> str:
+    """Browser-side player: WebSocket -> per-sentence <audio> + word highlight."""
+    return f"""
+<script>
+const WT_SR = {sample_rate};
+let wtWS = null, wtQueue = [], wtPlaying = false, wtCur = null, wtLastWord = "";
+
+function wtSet(text, color) {{
+    const s = document.getElementById('wt-status');
+    const d = document.getElementById('wt-dot');
+    if (s) s.textContent = text;
+    if (d) d.style.background = color || '#ccc';
+}}
+
+function wtStopBtn(show) {{
+    const b = document.getElementById('wt-stop');
+    if (b) b.style.display = show ? 'inline-block' : 'none';
+}}
+
+// Build a WAV Blob (16-bit mono PCM) from concatenated Int16 samples.
+function wtPcmToWav(int16, sr) {{
+    const n = int16.length;
+    const buf = new ArrayBuffer(44 + n * 2);
+    const v = new DataView(buf);
+    const w = (off, s) => {{ for (let i = 0; i < s.length; i++) v.setUint8(off + i, s.charCodeAt(i)); }};
+    w(0, 'RIFF'); v.setUint32(4, 36 + n * 2, true); w(8, 'WAVE');
+    w(12, 'fmt '); v.setUint32(16, 16, true); v.setUint16(20, 1, true); v.setUint16(22, 1, true);
+    v.setUint32(24, sr, true); v.setUint32(28, sr * 2, true); v.setUint16(32, 2, true); v.setUint16(34, 16, true);
+    w(36, 'data'); v.setUint32(40, n * 2, true);
+    let off = 44;
+    for (let i = 0; i < n; i++) {{ v.setInt16(off, int16[i], true); off += 2; }}
+    return new Blob([buf], {{ type: 'audio/wav' }});
+}}
+
+function wtConcat(chunks) {{
+    let total = 0;
+    for (const c of chunks) total += c.length;
+    const out = new Int16Array(total);
+    let off = 0;
+    for (const c of chunks) {{ out.set(c, off); off += c.length; }}
+    return out;
+}}
+
+// Render one finished sentence: <audio> + inline word spans.
+function wtRenderSentence(sentence) {{
+    const wrap = document.getElementById('wt-sentences');
+    if (!wrap) return;
+    const block = document.createElement('div');
+    block.style.cssText = 'border:1px solid #e3e8ef;border-radius:8px;padding:10px 12px;';
+
+    const head = document.createElement('div');
+    head.style.cssText = 'font-size:0.75em;color:#888;margin-bottom:6px;';
+    head.textContent = 'Sentence ' + sentence.index +
+        (sentence.timestamps ? ' (' + sentence.timestamps.length + ' words)' : ' (no timestamps)');
+    block.appendChild(head);
+
+    const textEl = document.createElement('div');
+    textEl.style.cssText = 'font-size:1.15em;line-height:1.8;margin-bottom:8px;';
+    if (sentence.timestamps && sentence.timestamps.length) {{
+        sentence.spans = [];
+        for (const ts of sentence.timestamps) {{
+            const span = document.createElement('span');
+            span.textContent = ts.word;
+            span.dataset.start = ts.start_ms;
+            span.dataset.end = ts.end_ms;
+            span.style.cssText = 'padding:1px 2px;border-radius:3px;transition:background 0.1s;';
+            textEl.appendChild(span);
+            // Keep spacing for spaced languages; CJK chars sit flush.
+            textEl.appendChild(document.createTextNode(' '));
+            sentence.spans.push(span);
+        }}
+    }} else {{
+        textEl.textContent = sentence.text || '(timestamps unavailable)';
+    }}
+    block.appendChild(textEl);
+
+    const audio = document.createElement('audio');
+    audio.controls = true;
+    audio.style.cssText = 'width:100%;';
+    audio.src = URL.createObjectURL(wtPcmToWav(wtConcat(sentence.chunks), WT_SR));
+    block.appendChild(audio);
+    sentence.audio = audio;
+
+    // Highlight the current word. Driven by requestAnimationFrame (~60fps)
+    // rather than the <audio> 'timeupdate' event, which only fires ~4x/sec
+    // and is too coarse for short words. We light the latest word whose
+    // start_ms has passed, so there is always exactly one active word (no
+    // inter-word flicker) and wtLastWord stays well-defined for barge-in.
+    let raf = null;
+    function highlight() {{
+        if (!sentence.spans) return;
+        const t = audio.currentTime * 1000;
+        let idx = -1;
+        for (let i = 0; i < sentence.spans.length; i++) {{
+            if (t >= +sentence.spans[i].dataset.start) idx = i;
+            else break;
+        }}
+        sentence.spans.forEach((sp, i) => {{
+            const on = i === idx;
+            sp.style.background = on ? '#ffe680' : 'transparent';
+            sp.style.fontWeight = on ? '700' : '400';
+        }});
+        if (idx >= 0) wtLastWord = sentence.spans[idx].textContent;
+    }}
+    function loop() {{
+        highlight();
+        if (!audio.paused && !audio.ended) raf = requestAnimationFrame(loop);
+    }}
+    audio.addEventListener('play', () => {{
+        wtCur = audio;
+        wtSet('Playing sentence ' + sentence.index + '...', '#64dd17');
+        cancelAnimationFrame(raf);
+        raf = requestAnimationFrame(loop);
+    }});
+    audio.addEventListener('pause', () => cancelAnimationFrame(raf));
+    audio.addEventListener('seeked', highlight);
+    audio.addEventListener('ended', () => {{
+        cancelAnimationFrame(raf);
+        highlight();
+        wtPlaying = false;
+        wtPlayNext();
+    }});
+
+    wrap.appendChild(block);
+    wtQueue.push(audio);
+    wtPlayNext();
+}}
+
+function wtPlayNext() {{
+    if (wtPlaying) return;
+    const next = wtQueue.shift();
+    if (!next) {{
+        if (wtWS === null) {{ wtSet('Done', '#64dd17'); wtStopBtn(false); }}
+        return;
+    }}
+    wtPlaying = true;
+    next.play().catch(() => {{ wtPlaying = false; }});
+}}
+
+window.wtStop = function() {{
+    // Barge-in: cut playback, stop generation, report the last-spoken word.
+    if (wtCur) {{ try {{ wtCur.pause(); }} catch (e) {{}} }}
+    wtQueue = [];
+    wtPlaying = false;
+    if (wtWS) {{ try {{ wtWS.close(); }} catch (e) {{}} wtWS = null; }}
+    wtSet('Stopped (barge-in)', '#999');
+    wtStopBtn(false);
+    const b = document.getElementById('wt-barge');
+    if (b) {{
+        b.style.display = 'block';
+        b.textContent = wtLastWord
+            ? 'Barge-in at word: "' + wtLastWord + '"  (a voice agent would resume the conversation from here)'
+            : 'Barge-in before any word was spoken.';
+    }}
+}};
+
+window.wtGenerate = function(payload) {{
+    if (wtWS) {{ try {{ wtWS.close(); }} catch (e) {{}} }}
+    wtQueue = []; wtPlaying = false; wtCur = null; wtLastWord = "";
+    document.getElementById('wt-sentences').innerHTML = '';
+    const barge = document.getElementById('wt-barge');
+    if (barge) {{ barge.style.display = 'none'; barge.textContent = ''; }}
+    wtStopBtn(true);
+    wtSet('Connecting...', '#4A90D9');
+
+    const proto = location.protocol === 'https:' ? 'wss:' : 'ws:';
+    const ws = new WebSocket(proto + '//' + location.host + '/proxy/stream');
+    wtWS = ws;
+    const sentences = {{}};
+
+    ws.onopen = () => {{
+        wtSet('Streaming...', '#4A90D9');
+        ws.send(JSON.stringify(Object.assign({{ type: 'session.config' }}, payload.config)));
+        ws.send(JSON.stringify({{ type: 'input.text', text: payload.text }}));
+        ws.send(JSON.stringify({{ type: 'input.done' }}));
+    }};
+
+    ws.onmessage = (ev) => {{
+        let msg;
+        try {{ msg = JSON.parse(ev.data); }} catch (e) {{ return; }}
+        if (msg.type === 'audio.start') {{
+            sentences[msg.sentence_index] = {{
+                index: msg.sentence_index, text: msg.sentence_text || '',
+                chunks: [], timestamps: null
+            }};
+        }} else if (msg.type === 'audio.chunk') {{
+            const s = sentences[msg.sentence_index];
+            if (!s) return;
+            if (msg.audio_b64) {{
+                const bin = atob(msg.audio_b64);
+                const bytes = new Uint8Array(bin.length);
+                for (let i = 0; i < bin.length; i++) bytes[i] = bin.charCodeAt(i);
+                const usable = bytes.length - (bytes.length % 2);
+                if (usable > 0) s.chunks.push(new Int16Array(bytes.buffer, 0, usable / 2));
+            }}
+            if (msg.timestamps != null) s.timestamps = msg.timestamps;
+        }} else if (msg.type === 'audio.done') {{
+            const s = sentences[msg.sentence_index];
+            if (s) wtRenderSentence(s);
+        }} else if (msg.type === 'session.done') {{
+            wtWS = null;
+            try {{ ws.close(); }} catch (e) {{}}
+            wtPlayNext();
+        }} else if (msg.type === 'error') {{
+            wtSet('Error: ' + (msg.message || 'unknown'), '#EF5552');
+        }}
+    }};
+
+    ws.onerror = () => wtSet('WebSocket error', '#EF5552');
+    ws.onclose = () => {{ if (wtWS === ws) wtWS = null; }};
+}};
+</script>
+"""
+
+
+def create_app(api_base: str) -> FastAPI:
+    """FastAPI app with a same-origin WebSocket proxy + Gradio UI."""
+    fastapi_app = FastAPI()
+    upstream_url = _upstream_ws_url(api_base)
+
+    @fastapi_app.websocket("/proxy/stream")
+    async def proxy_stream(client_ws: WebSocket):
+        await client_ws.accept()
+        try:
+            async with websockets.connect(upstream_url, max_size=None) as upstream:
+
+                async def client_to_upstream():
+                    try:
+                        while True:
+                            await upstream.send(await client_ws.receive_text())
+                    except (WebSocketDisconnect, Exception):
+                        await upstream.close()
+
+                async def upstream_to_client():
+                    try:
+                        async for msg in upstream:
+                            if isinstance(msg, bytes):
+                                await client_ws.send_bytes(msg)
+                            else:
+                                await client_ws.send_text(msg)
+                    except Exception:
+                        pass
+                    finally:
+                        try:
+                            await client_ws.close()
+                        except Exception:
+                            pass
+
+                await asyncio.gather(client_to_upstream(), upstream_to_client())
+        except Exception as exc:
+            logger.exception("WS proxy error")
+            try:
+                await client_ws.send_text(json.dumps({"type": "error", "message": f"proxy: {exc}"}))
+                await client_ws.close()
+            except Exception:
+                pass
+
+    voices = fetch_voices(api_base)
+
+    with gr.Blocks(title="Qwen3-TTS Word Timestamps") as demo:
+        gr.HTML(f"""
+        <div style="margin-bottom:8px;">
+          <h1 style="margin:0; font-size:1.5em;">Qwen3-TTS Streaming Word Timestamps</h1>
+          <span style="font-size:0.85em; color:#666;">
+            Served by <a href="https://github.com/vllm-project/vllm-omni" target="_blank">vLLM-Omni</a>
+            &nbsp;&middot;&nbsp; <code>{api_base}</code> &nbsp;&middot;&nbsp;
+            launch the server with <code>--forced-aligner</code>.
+          </span>
+        </div>
+        """)
+
+        with gr.Row():
+            with gr.Column(scale=3):
+                text_input = gr.Textbox(
+                    label="Text to Synthesize",
+                    value="Hello world. How are you today? I am doing great.",
+                    lines=3,
+                )
+                with gr.Row():
+                    task_type = gr.Radio(choices=TASK_TYPES, value="CustomVoice", label="Task Type", scale=2)
+                    language = gr.Dropdown(choices=SUPPORTED_LANGUAGES, value="Auto", label="Language", scale=1)
+                voice = gr.Dropdown(
+                    choices=voices,
+                    value=voices[0] if voices else None,
+                    label="Speaker",
+                    allow_custom_value=True,
+                )
+                instructions = gr.Textbox(
+                    label="Instructions",
+                    placeholder="Optional style/emotion (required for VoiceDesign)",
+                    lines=1,
+                )
+                with gr.Column(visible=False) as ref_group:
+                    ref_audio_url = gr.Textbox(
+                        label="Reference Audio URL (Base task)",
+                        placeholder="https://example.com/reference.wav",
+                        lines=1,
+                    )
+                    ref_text = gr.Textbox(label="Reference Transcript", lines=1)
+                generate_btn = gr.Button("Generate + Align", variant="primary", size="lg")
+
+            with gr.Column(scale=2):
+                gr.HTML(value=PLAYER_HTML)
+
+        hidden_payload = gr.Textbox(visible=False, elem_id="wt-payload")
+
+        def on_task_change(tt):
+            return gr.update(visible=(tt == "Base"))
+
+        task_type.change(fn=on_task_change, inputs=[task_type], outputs=[ref_group])
+
+        def on_generate(text, tt, voice_v, lang_v, instr_v, ref_url, ref_t):
+            if not text or not text.strip():
+                raise gr.Error("Please enter text to synthesize.")
+            config: dict = {
+                "task_type": tt,
+                "word_timestamps": True,
+                "stream_audio": True,
+                "response_format": "pcm",
+            }
+            if lang_v:
+                config["language"] = lang_v
+            if tt == "CustomVoice":
+                if voice_v:
+                    config["voice"] = voice_v
+                if instr_v and instr_v.strip():
+                    config["instructions"] = instr_v.strip()
+            elif tt == "VoiceDesign":
+                if not instr_v or not instr_v.strip():
+                    raise gr.Error("VoiceDesign requires voice style instructions.")
+                config["instructions"] = instr_v.strip()
+            elif tt == "Base":
+                if not ref_url or not ref_url.strip():
+                    raise gr.Error("Base (voice clone) needs a reference audio URL.")
+                config["ref_audio"] = ref_url.strip()
+                if ref_t and ref_t.strip():
+                    config["ref_text"] = ref_t.strip()
+            return json.dumps({"config": config, "text": text.strip()})
+
+        generate_btn.click(
+            fn=on_generate,
+            inputs=[text_input, task_type, voice, language, instructions, ref_audio_url, ref_text],
+            outputs=[hidden_payload],
+        ).then(
+            fn=lambda p: p,
+            inputs=[hidden_payload],
+            outputs=[hidden_payload],
+            js="(p) => { if (p && p.trim()) window.wtGenerate(JSON.parse(p)); return p; }",
+        )
+
+        demo.queue()
+
+    return gr.mount_gradio_app(
+        fastapi_app,
+        demo,
+        path="/",
+        head=_build_head_js(PCM_SAMPLE_RATE),
+    )
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Gradio demo for streaming TTS word-level timestamps.")
+    add_common_args(parser)
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    logging.basicConfig(level=logging.INFO)
+    print(f"Connecting to vLLM server at: {args.api_base}")
+    app = create_app(args.api_base)
+
+    import uvicorn
+
+    uvicorn.run(app, host=args.host, port=args.port)
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -76,6 +76,10 @@ demo = [
     "cn2an>=0.5.22",  # Chinese number normalization for TTS (e.g. "1次" -> "一次")
 ]
 
+forced-aligner = [
+    "qwen-asr>=0.0.6",  # faithful Japanese/Korean word segmentation for the TTS forced aligner
+]
+
 # Local-test-only extras
 local = [
     "FlagEmbedding",
@@ -304,3 +308,6 @@ CANN = "CANN"
 thi = "thi"
 ND = "ND"
 byt5 = "byt5"
+# "seeked" is the standard HTML media element DOM event name (fires when a
+# seek completes), used by the word-timestamps Gradio demo. Not a typo.
+seeked = "seeked"

tests/entrypoints/openai_api/test_serving_speech_stream.py

@@ -1,4 +1,6 @@
 import asyncio
+import base64
+from types import SimpleNamespace
 
 import pytest
 from fastapi import FastAPI, WebSocket
@@ -9,6 +11,7 @@ from starlette.websockets import WebSocketDisconnect
 from vllm_omni.entrypoints.openai import serving_speech_stream as streaming_speech_module
 from vllm_omni.entrypoints.openai.serving_speech import OmniOpenAIServingSpeech
 from vllm_omni.entrypoints.openai.serving_speech_stream import OmniStreamingSpeechHandler
+from vllm_omni.utils.forced_aligner import WordTimestamp
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
@@ -25,10 +28,11 @@ def _build_test_app(
         speech_service = mocker.MagicMock(spec=OmniOpenAIServingSpeech)
         speech_service._generate_audio_bytes = mocker.AsyncMock(return_value=(b"RIFF" + b"\x00" * 32, "audio/wav"))
         speech_service._prepare_speech_generation = mocker.AsyncMock(return_value=("req-1", object(), {}))
+        speech_service.forced_aligner_config = None
 
-        async def mock_generate_pcm_chunks(_generator, _request_id):
+        async def mock_generate_pcm_chunks(_generator, _request_id, *, include_sample_rate=False):
             for chunk in (b"\x01\x02", b"\x03\x04\x05"):
-                yield chunk
+                yield (chunk, 24000) if include_sample_rate else chunk
 
         speech_service._generate_pcm_chunks = mock_generate_pcm_chunks
         speech_service.engine_client = mocker.MagicMock()
@@ -82,6 +86,7 @@ class TestStreamingSpeechWebSocket:
         speech_service._generate_audio_bytes = mocker.AsyncMock(return_value=(b"", "audio/wav"))
         speech_service.engine_client = mocker.MagicMock()
         speech_service.engine_client.abort = mocker.AsyncMock()
+        speech_service.forced_aligner_config = None
 
         async def mock_prepare_speech_generation(request):
             captured_requests.append(request)
@@ -89,9 +94,9 @@ class TestStreamingSpeechWebSocket:
 
         speech_service._prepare_speech_generation = mock_prepare_speech_generation
 
-        async def mock_generate_pcm_chunks(_generator, _request_id):
+        async def mock_generate_pcm_chunks(_generator, _request_id, *, include_sample_rate=False):
             for chunk in (b"\x01\x02", b"\x03\x04\x05", b"\x06"):
-                yield chunk
+                yield (chunk, 24000) if include_sample_rate else chunk
 
         speech_service._generate_pcm_chunks = mock_generate_pcm_chunks
         app, _ = _build_test_app(speech_service)
@@ -130,6 +135,164 @@ class TestStreamingSpeechWebSocket:
         assert captured_requests[0].initial_codec_chunk_frames == 12
         assert speech_service._generate_audio_bytes.await_count == 0
 
+    def test_word_timestamps_requires_configured_aligner(self, mocker: MockerFixture):
+        app, _ = _build_test_app(mocker=mocker)
+
+        with TestClient(app) as client:
+            with client.websocket_connect("/v1/audio/speech/stream") as ws:
+                ws.send_json(
+                    {
+                        "type": "session.config",
+                        "voice": "Vivian",
+                        "stream_audio": True,
+                        "response_format": "pcm",
+                        "word_timestamps": True,
+                    }
+                )
+                ws.send_json({"type": "input.text", "text": "Hello world. "})
+
+                error = ws.receive_json()
+                assert error["type"] == "error"
+                assert "without --forced-aligner" in error["message"]
+
+    def test_word_timestamps_emit_sidecar_json_frame(self, mocker: MockerFixture):
+        captured_requests = []
+        speech_service = mocker.MagicMock(spec=OmniOpenAIServingSpeech)
+        speech_service._generate_audio_bytes = mocker.AsyncMock(return_value=(b"", "audio/wav"))
+        speech_service.engine_client = mocker.MagicMock()
+        speech_service.engine_client.abort = mocker.AsyncMock()
+        speech_service.forced_aligner_config = SimpleNamespace(model="aligner")
+
+        async def mock_prepare_speech_generation(request):
+            captured_requests.append(request)
+            return "req-stream", object(), {}
+
+        speech_service._prepare_speech_generation = mock_prepare_speech_generation
+
+        first_chunk = b"\x01" * 1000
+        second_chunk = b"\x02" * 1000
+
+        async def mock_generate_pcm_chunks(_generator, _request_id, *, include_sample_rate=False):
+            for chunk in (first_chunk, second_chunk):
+                yield (chunk, 1000) if include_sample_rate else chunk
+
+        speech_service._generate_pcm_chunks = mock_generate_pcm_chunks
+        # Sentence-level: aligner runs once over the whole sentence audio.
+        mock_align = mocker.AsyncMock(
+            return_value=[
+                WordTimestamp("Hello", 0, 200),
+                WordTimestamp("world", 200, 900),
+            ]
+        )
+        mocker.patch.object(streaming_speech_module, "forced_align", mock_align)
+        app, _ = _build_test_app(speech_service)
+
+        with TestClient(app) as client:
+            with client.websocket_connect("/v1/audio/speech/stream") as ws:
+                ws.send_json(
+                    {
+                        "type": "session.config",
+                        "voice": "Vivian",
+                        "stream_audio": True,
+                        "response_format": "pcm",
+                        "word_timestamps": True,
+                    }
+                )
+                ws.send_json({"type": "input.text", "text": "Hello world. "})
+
+                start = ws.receive_json()
+                assert start["type"] == "audio.start"
+                assert start["word_timestamps"] is True
+
+                # Audio streams first; timestamps are null until the sentence
+                # is fully aligned.
+                chunk = ws.receive_json()
+                assert chunk["type"] == "audio.chunk"
+                assert chunk["chunk_id"] == 0
+                assert chunk["chunk_start_ms"] == 0
+                assert chunk["chunk_end_ms"] == 500
+                assert chunk["sample_rate"] == 1000
+                assert base64.b64decode(chunk["audio_b64"]) == first_chunk
+                assert chunk["timestamps"] is None
+
+                chunk = ws.receive_json()
+                assert chunk["type"] == "audio.chunk"
+                assert chunk["chunk_id"] == 1
+                assert chunk["chunk_start_ms"] == 500
+                assert chunk["chunk_end_ms"] == 1000
+                assert chunk["sample_rate"] == 1000
+                assert base64.b64decode(chunk["audio_b64"]) == second_chunk
+                assert chunk["timestamps"] is None
+
+                # Final frame: empty audio carrying the whole-sentence timestamps.
+                chunk = ws.receive_json()
+                assert chunk["type"] == "audio.chunk"
+                assert chunk["chunk_id"] == 2
+                assert chunk["chunk_start_ms"] == 0
+                assert chunk["chunk_end_ms"] == 1000
+                assert chunk["sample_rate"] == 1000
+                assert base64.b64decode(chunk["audio_b64"]) == b""
+                assert chunk["timestamps"] == [
+                    {"word": "Hello", "start_ms": 0, "end_ms": 200},
+                    {"word": "world", "start_ms": 200, "end_ms": 900},
+                ]
+
+                done = ws.receive_json()
+                assert done == {"type": "audio.done", "sentence_index": 0, "total_bytes": 2000, "error": False}
+
+        assert captured_requests[0].word_timestamps is True
+        assert mock_align.await_count == 1
+
+    def test_word_timestamps_emit_word_dicts(self, mocker: MockerFixture):
+        # The streaming layer forwards the aligner's (already monotonic,
+        # non-overlapping) words as JSON dicts in the trailing frame.
+        speech_service = mocker.MagicMock(spec=OmniOpenAIServingSpeech)
+        speech_service._generate_audio_bytes = mocker.AsyncMock(return_value=(b"", "audio/wav"))
+        speech_service.engine_client = mocker.MagicMock()
+        speech_service.engine_client.abort = mocker.AsyncMock()
+        speech_service.forced_aligner_config = SimpleNamespace(model="aligner")
+        speech_service._prepare_speech_generation = mocker.AsyncMock(return_value=("req", object(), {}))
+
+        async def mock_generate_pcm_chunks(_generator, _request_id, *, include_sample_rate=False):
+            chunk = b"\x01" * 1000
+            yield (chunk, 1000) if include_sample_rate else chunk
+
+        speech_service._generate_pcm_chunks = mock_generate_pcm_chunks
+        mock_align = mocker.AsyncMock(
+            return_value=[
+                WordTimestamp("Hello", 0, 1000),
+                WordTimestamp("world", 1000, 1200),
+            ]
+        )
+        mocker.patch.object(streaming_speech_module, "forced_align", mock_align)
+        app, _ = _build_test_app(speech_service)
+
+        with TestClient(app) as client:
+            with client.websocket_connect("/v1/audio/speech/stream") as ws:
+                ws.send_json(
+                    {
+                        "type": "session.config",
+                        "voice": "Vivian",
+                        "stream_audio": True,
+                        "response_format": "pcm",
+                        "word_timestamps": True,
+                    }
+                )
+                ws.send_json({"type": "input.text", "text": "Hello world. "})
+
+                assert ws.receive_json()["type"] == "audio.start"
+                # Real-time audio chunk (timestamps null), then the timestamp frame.
+                assert ws.receive_json()["timestamps"] is None
+                final = ws.receive_json()
+                assert final["type"] == "audio.chunk"
+                timestamps = final["timestamps"]
+                assert timestamps == [
+                    {"word": "Hello", "start_ms": 0, "end_ms": 1000},
+                    {"word": "world", "start_ms": 1000, "end_ms": 1200},
+                ]
+                for ts in timestamps:
+                    assert ts["end_ms"] >= ts["start_ms"]
+
     def test_flush_on_input_done(self, mocker: MockerFixture):
         app, _ = _build_test_app(mocker=mocker)
 
@@ -249,6 +412,7 @@ class TestStreamingSpeechWebSocket:
         speech_service._generate_pcm_chunks = mocker.AsyncMock()
         speech_service.engine_client = mocker.MagicMock()
         speech_service.engine_client.abort = mocker.AsyncMock()
+        speech_service.forced_aligner_config = None
         app, _ = _build_test_app(speech_service)
 
         with TestClient(app) as client:
@@ -269,8 +433,9 @@ class TestStreamingSpeechWebSocket:
         speech_service._prepare_speech_generation = mocker.AsyncMock(return_value=("req-stream-err", object(), {}))
         speech_service.engine_client = mocker.MagicMock()
         speech_service.engine_client.abort = mocker.AsyncMock()
+        speech_service.forced_aligner_config = None
 
-        async def mock_generate_pcm_chunks(_generator, _request_id):
+        async def mock_generate_pcm_chunks(_generator, _request_id, *, include_sample_rate=False):
             yield b"\x01\x02"
             raise RuntimeError("stream boom")
 
@@ -361,8 +526,9 @@ class TestStreamingSpeechWebSocket:
         speech_service._prepare_speech_generation = mocker.AsyncMock(return_value=("req-abort", object(), {}))
         speech_service.engine_client = mocker.MagicMock()
         speech_service.engine_client.abort = mocker.AsyncMock()
+        speech_service.forced_aligner_config = None
 
-        async def mock_generate_pcm_chunks(_generator, _request_id):
+        async def mock_generate_pcm_chunks(_generator, _request_id, *, include_sample_rate=False):
             yield b"\x01\x02"
 
         speech_service._generate_pcm_chunks = mock_generate_pcm_chunks
@@ -385,7 +551,9 @@ class TestStreamingSpeechWebSocket:
         config.ref_audio = None
         config.ref_text = None
         config.x_vector_only_mode = None
+        config.speaker_embedding = None
         config.stream_audio = True
+        config.word_timestamps = False
 
         with pytest.raises(WebSocketDisconnect):
             asyncio.run(handler._generate_and_send(websocket, config, "Hello world.", 0))

tests/utils/test_forced_aligner.py

@@ -0,0 +1,165 @@
+import numpy as np
+import pytest
+
+from vllm_omni.utils import forced_aligner
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def test_decode_timestamps_maps_boundary_bins_to_words():
+    logits = np.zeros((4, 5), dtype=np.float32)
+    logits[0, 0] = 1.0
+    logits[1, 2] = 1.0
+    logits[2, 2] = 1.0
+    logits[3, 4] = 1.0
+
+    timestamps = forced_aligner._decode_timestamps(
+        logits=logits,
+        words=["hello", "world"],
+        timestamp_positions=[0, 1, 2, 3],
+        classify_num=5,
+        audio_duration_ms=1000,
+    )
+
+    assert timestamps == [
+        forced_aligner.WordTimestamp("hello", 0, 400),
+        forced_aligner.WordTimestamp("world", 400, 800),
+    ]
+
+
+def test_decode_timestamps_repairs_non_monotonic_bins():
+    # Word 1's end bin (1) dips below its start bin (2); fix_timestamp should
+    # snap it back so the frame never carries end_ms < start_ms.
+    logits = np.zeros((4, 5), dtype=np.float32)
+    logits[0, 0] = 1.0  # word0 start -> bin 0
+    logits[1, 2] = 1.0  # word0 end   -> bin 2
+    logits[2, 1] = 1.0  # word1 start -> bin 1 (out of order)
+    logits[3, 3] = 1.0  # word1 end   -> bin 3
+
+    timestamps = forced_aligner._decode_timestamps(
+        logits=logits,
+        words=["hello", "world"],
+        timestamp_positions=[0, 1, 2, 3],
+        classify_num=5,
+        timestamp_segment_time_ms=200,
+        audio_duration_ms=1000,
+    )
+
+    for ts in timestamps:
+        assert ts.end_ms >= ts.start_ms
+    # bins [0, 2, 1, 3] -> repaired [0, 2, 2, 3] -> ms x200
+    assert timestamps == [
+        forced_aligner.WordTimestamp("hello", 0, 400),
+        forced_aligner.WordTimestamp("world", 400, 600),
+    ]
+
+
+def test_decode_timestamps_clamps_to_audio_duration_and_stays_ordered():
+    # Bins run past the 1000 ms audio; the decoder must cap each bound at the
+    # audio length and keep words non-overlapping (start <= end, monotonic).
+    logits = np.zeros((4, 5), dtype=np.float32)
+    logits[0, 0] = 1.0  # word0 start -> bin 0
+    logits[1, 3] = 1.0  # word0 end   -> bin 3 (1200 ms, past audio)
+    logits[2, 3] = 1.0  # word1 start -> bin 3 (1200 ms, past audio)
+    logits[3, 4] = 1.0  # word1 end   -> bin 4 (1600 ms, past audio)
+
+    timestamps = forced_aligner._decode_timestamps(
+        logits=logits,
+        words=["a", "b"],
+        timestamp_positions=[0, 1, 2, 3],
+        classify_num=5,
+        timestamp_segment_time_ms=400,
+        audio_duration_ms=1000,
+    )
+
+    assert timestamps == [
+        forced_aligner.WordTimestamp("a", 0, 1000),
+        forced_aligner.WordTimestamp("b", 1000, 1000),
+    ]
+
+
+def test_decode_timestamps_rejects_marker_count_mismatch():
+    logits = np.zeros((2, 5), dtype=np.float32)
+
+    timestamps = forced_aligner._decode_timestamps(
+        logits=logits,
+        words=["hello", "world"],
+        timestamp_positions=[0, 1],
+        classify_num=5,
+        audio_duration_ms=1000,
+    )
+
+    assert timestamps == []
+
+
+def test_build_config_from_yaml(tmp_path):
+    cfg = tmp_path / "forced_aligner.yaml"
+    cfg.write_text(
+        """
+forced_aligner:
+  model: Qwen/Qwen3-ForcedAligner-0.6B
+  gpu_memory_utilization: 0.42
+  dtype: float16
+  max_model_len: 2048
+  trust_remote_code: false
+""",
+        encoding="utf-8",
+    )
+    args = type("Args", (), {"forced_aligner": None, "forced_aligner_config": str(cfg)})()
+
+    out = forced_aligner.build_forced_aligner_config(args)
+
+    assert out == forced_aligner.ForcedAlignerConfig(
+        model="Qwen/Qwen3-ForcedAligner-0.6B",
+        runner="pooling",
+        architecture="Qwen3ASRForcedAlignerForTokenClassification",
+        pooling_task="token_classify",
+        gpu_memory_utilization=0.42,
+        dtype="float16",
+        max_model_len=2048,
+        trust_remote_code=False,
+    )
+
+
+def test_build_config_cli_model_overrides_yaml(tmp_path):
+    cfg = tmp_path / "forced_aligner.yaml"
+    cfg.write_text(
+        "forced_aligner:\n  model: old\n  gpu_memory_utilization: 0.2\n  dtype: float16\n",
+        encoding="utf-8",
+    )
+    args = type(
+        "Args",
+        (),
+        {
+            "forced_aligner": "new",
+            "forced_aligner_config": str(cfg),
+        },
+    )()
+
+    out = forced_aligner.build_forced_aligner_config(args)
+
+    assert out is not None
+    # --forced-aligner overrides the YAML model; gpu_memory_utilization/dtype
+    # come from the user YAML (there is no longer a CLI flag for gpu mem).
+    assert out.model == "new"
+    assert out.gpu_memory_utilization == 0.2
+    assert out.dtype == "float16"
+    assert out.runner == "pooling"
+
+
+def test_build_config_from_cli_model_uses_default_yaml():
+    args = type(
+        "Args",
+        (),
+        {
+            "forced_aligner": "local-aligner",
+            "forced_aligner_config": None,
+        },
+    )()
+
+    out = forced_aligner.build_forced_aligner_config(args)
+
+    assert out is not None
+    assert out.model == "local-aligner"
+    assert out.runner == "pooling"
+    assert out.architecture == "Qwen3ASRForcedAlignerForTokenClassification"

tests/utils/test_qwen3_force_align_processor.py

@@ -0,0 +1,65 @@
+import pytest
+
+from vllm_omni.utils import qwen3_force_align_processor as processor
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def test_build_prompt_has_boundary_timestamp_markers():
+    prompt = processor.build_prompt(["hello", "world"])
+
+    assert prompt.count("<timestamp>") == 4
+    assert "hello<timestamp><timestamp>world" in prompt
+    assert prompt.endswith("<|im_start|>assistant\n")
+
+
+@pytest.mark.parametrize(
+    "text,expected",
+    [
+        # Punctuation inside a whitespace segment is stripped, not split on:
+        # the hand-rolled splitter used to break these into many fake words.
+        ("U.S.A", ["USA"]),
+        ("hello, world!", ["hello", "world"]),
+        ("don't stop", ["don't", "stop"]),
+        # CJK characters peel off as individual tokens, Latin runs stay whole.
+        ("你好world", ["你", "好", "world"]),
+        ("我爱 China", ["我", "爱", "China"]),
+        ("   spaced   out   ", ["spaced", "out"]),
+    ],
+)
+def test_tokenize_space_lang_matches_official_segmentation(text, expected):
+    assert processor._tokenize_space_lang(text) == expected
+
+
+def test_segment_words_falls_back_to_port_without_qwen_asr(monkeypatch):
+    # With qwen_asr unavailable, segmentation must use the built-in port and
+    # still produce the faithful result for the common (non-JP/KO) case.
+    monkeypatch.setattr(processor, "_get_official_processor", lambda: None)
+
+    assert processor.segment_words("U.S.A is here", "auto") == ["USA", "is", "here"]
+
+
+def test_fix_timestamp_repairs_dip_onto_monotonic_sequence():
+    # 200 dips below the preceding 400; it snaps back up to 400.
+    assert processor.fix_timestamp([0, 400, 200, 800]) == [0, 400, 400, 800]
+
+
+def test_fix_timestamp_passes_through_monotonic():
+    assert processor.fix_timestamp([0, 100, 100, 250]) == [0, 100, 100, 250]
+
+
+def test_resolve_timestamp_token_id_defaults_to_marker_token():
+    # Regression: the default must resolve the same <timestamp> marker that
+    # build_prompt inserts, not None (which would degrade every request).
+    seen = {}
+
+    class FakeTokenizer:
+        def convert_tokens_to_ids(self, token):
+            seen["token"] = token
+            return 151705
+
+    tid = processor.resolve_timestamp_token_id(FakeTokenizer())
+
+    assert tid == 151705
+    assert seen["token"] == processor.TIMESTAMP_TOKEN
+    assert processor.TIMESTAMP_TOKEN in processor.build_prompt(["hello", "world"])

vllm_omni/deploy/qwen3_tts_forced_aligner.yaml

@@ -0,0 +1,19 @@
+# Reference deploy knobs for Qwen/Qwen3-ForcedAligner-0.6B, the shared forced
+# aligner for streaming TTS word timestamps. Pass with --forced-aligner-config;
+# --forced-aligner overrides the model path without editing this file.
+#
+# Run as LLM(runner="pooling") with pooling_task="token_classify". Prompt tokens,
+# chat template and word segmentation are Qwen-specific and live in
+# qwen3_force_align_processor (not configurable here).
+
+forced_aligner:
+  model: Qwen/Qwen3-ForcedAligner-0.6B
+  runner: pooling
+  architecture: Qwen3ASRForcedAlignerForTokenClassification
+  pooling_task: token_classify
+
+  gpu_memory_utilization: 0.30
+
+  dtype: bfloat16
+  max_model_len: 4096
+  trust_remote_code: true

vllm_omni/entrypoints/cli/serve.py

@@ -216,6 +216,29 @@ class OmniServeCommand(CLISubcommand):
             help="Default task type for TTS models (CustomVoice, VoiceDesign, or Base). "
             "If not specified, will be inferred from model path.",
         )
+        # Forced aligner / word timestamps. --forced-aligner is the opt-in
+        # toggle; heavier knobs (gpu_memory_utilization, dtype, max_model_len)
+        # live in the deploy YAML passed via --forced-aligner-config.
+        omni_config_group.add_argument(
+            "--forced-aligner",
+            type=str,
+            default=None,
+            help=(
+                "Enable streaming TTS word timestamps via a forced aligner. "
+                "Pass the aligner model path/name, e.g. 'Qwen/Qwen3-ForcedAligner-0.6B'. "
+                "Disabled when omitted."
+            ),
+        )
+        omni_config_group.add_argument(
+            "--forced-aligner-config",
+            type=str,
+            default=None,
+            help=(
+                "Optional YAML file for forced aligner settings (model, runner, "
+                "gpu_memory_utilization, dtype, max_model_len). The --forced-aligner "
+                "flag, when set, overrides the YAML model field."
+            ),
+        )
         # TODO(@lishunyang12): deprecate once all models migrate to --deploy-config
         omni_config_group.add_argument(
             "--stage-configs-path",

vllm_omni/entrypoints/openai/api_server.py

@@ -144,6 +144,7 @@ from vllm_omni.entrypoints.openai.video_api_utils import decode_audio_url, decod
 from vllm_omni.entrypoints.openpi.serving import ServingRealtimeRobotOpenPI
 from vllm_omni.errors import OmniClientError
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniTextPrompt
+from vllm_omni.utils.forced_aligner import build_forced_aligner_config
 from vllm_omni.utils.tracking_parser import TrackingArgumentParser, TrackingNamespace
 
 logger = init_logger(__name__)
@@ -1058,7 +1059,11 @@ async def omni_init_app_state(
     )
 
     state.openai_serving_speech = OmniOpenAIServingSpeech(
-        engine_client, state.openai_serving_models, request_logger=request_logger, model_name=model_name
+        engine_client,
+        state.openai_serving_models,
+        request_logger=request_logger,
+        model_name=model_name,
+        forced_aligner_config=build_forced_aligner_config(args),
     )
 
     # Warm up speech pipeline (CUDA Graph capture, torch.compile) so the first

vllm_omni/entrypoints/openai/protocol/audio.py

@@ -145,6 +145,15 @@ class OpenAICreateSpeechRequest(BaseModel):
         default=None,
         description=("Optional model-specific parameters passed directly to the model's extra_args."),
     )
+    word_timestamps: bool = Field(
+        default=False,
+        description=(
+            "When true, the server runs a shared forced aligner alongside the streamed "
+            "audio and emits per-chunk word timestamps. Requires the server to be "
+            "launched with --forced-aligner pointing at an aligner model. No effect "
+            "when streaming is off."
+        ),
+    )
 
     @field_validator("stream_format")
     @classmethod
@@ -463,6 +472,15 @@ class StreamingSpeechSessionConfig(BaseModel):
             "'clause' also splits on CJK commas ， and semicolons ；."
         ),
     )
+    word_timestamps: bool = Field(
+        default=False,
+        description=(
+            "When true, audio chunks are wrapped in JSON 'audio.chunk' frames carrying "
+            "base64-encoded PCM plus aligned word timestamps. Requires the server to be "
+            "launched with --forced-aligner. When false, audio is sent as raw binary "
+            "frames (existing behavior)."
+        ),
+    )
 
     @model_validator(mode="after")
     def validate_streaming_constraints(self) -> "StreamingSpeechSessionConfig":

vllm_omni/entrypoints/openai/serving_speech.py

@@ -411,6 +411,7 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
 
     def __init__(self, *args, **kwargs):
         self.model_name = kwargs.pop("model_name", None)
+        self.forced_aligner_config: Any | None = kwargs.pop("forced_aligner_config", None)
         super().__init__(*args, **kwargs)
         self._init_speaker_storage()
 
@@ -2586,6 +2587,7 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
         response_format: str = "pcm",
         raw_request: Request | None = None,
         request_start_s: float | None = None,
+        include_sample_rate: bool = False,
     ):
         """Generate audio chunks for streaming response.
 
@@ -2662,7 +2664,11 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
                     )
                     if first_audio_chunk_s is None:
                         first_audio_chunk_s = time.perf_counter()
-                    yield self.create_audio(audio_obj).audio_data
+                    audio_bytes = self.create_audio(audio_obj).audio_data
+                    if include_sample_rate:
+                        yield audio_bytes, sample_rate_val
+                    else:
+                        yield audio_bytes
             self._mark_ref_audio_artifact_ready_for_request(request_id)
             artifact_ready = True
             total_ms = (time.perf_counter() - stream_start_s) * 1000.0
@@ -3508,13 +3514,18 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
         self._track_ref_audio_artifact_warmup(request_id, qwen3_ref_audio_warmup_artifact_key)
         return request_id, generator, tts_params
 
-    async def _generate_pcm_chunks(self, generator, request_id: str):
+    async def _generate_pcm_chunks(self, generator, request_id: str, *, include_sample_rate: bool = False):
         """Yield raw PCM byte chunks from the engine generator.
 
         Delegates to ``_generate_audio_chunks`` with ``response_format="pcm"``.
         Used by the WebSocket streaming handler and ``_iter_pcm_audio_bytes``.
         """
-        async for chunk in self._generate_audio_chunks(generator, request_id, response_format="pcm"):
+        async for chunk in self._generate_audio_chunks(
+            generator,
+            request_id,
+            response_format="pcm",
+            include_sample_rate=include_sample_rate,
+        ):
             yield chunk
 
     async def _iter_pcm_audio_bytes(self, request: OpenAICreateSpeechRequest):
@@ -3810,6 +3821,13 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
 
         try:
             if request.stream:
+                if request.word_timestamps:
+                    return self.create_error_response(
+                        "word_timestamps=true is currently supported by the WebSocket "
+                        "/v1/audio/speech/stream path. Use session.config with "
+                        "stream_audio=true and response_format='pcm'."
+                    )
+
                 # Determine response format and media type for streaming
                 response_format = (request.response_format or "wav").lower()
 

vllm_omni/entrypoints/openai/serving_speech_stream.py

@@ -9,15 +9,26 @@ Protocol:
         {"type": "input.text", "text": "..."} # Text chunks
         {"type": "input.done"}            # End of input
 
-    Server -> Client:
+    Server -> Client (default, word_timestamps=false):
         {"type": "audio.start", "sentence_index": 0, "sentence_text": "...", "format": "wav"}
         <binary frame: audio bytes>
+        ...
         {"type": "audio.done", "sentence_index": 0}
         {"type": "session.done", "total_sentences": N}
         {"type": "error", "message": "..."}
+
+    Server -> Client (when word_timestamps=true):
+        {"type": "audio.start", "sentence_index": 0, "sentence_text": "...", "format": "pcm"}
+        {"type": "audio.chunk", "sentence_index": 0, "chunk_id": 0, "audio_b64": "<base64 PCM>", "timestamps": null}
+        ...
+        {"type": "audio.chunk", "audio_b64": "", "timestamps": [{"word", "start_ms", "end_ms"}, ...]}
+        {"type": "audio.done", "sentence_index": 0}
+        # Audio is JSON base64 PCM (not binary). A trailing empty-audio chunk carries the
+        # full sentence-relative alignment. timestamps: list = aligned, [] = silence, null = failed.
 """
 
 import asyncio
+import base64
 import json
 from contextlib import aclosing
 
@@ -35,12 +46,15 @@ from vllm_omni.entrypoints.openai.text_splitter import (
     SPLIT_SENTENCE,
     SentenceSplitter,
 )
+from vllm_omni.utils.forced_aligner import ForcedAlignerLoadError
+from vllm_omni.utils.forced_aligner import align as forced_align
 
 logger = init_logger(__name__)
 
 _DEFAULT_IDLE_TIMEOUT = 30.0  # seconds
 _DEFAULT_CONFIG_TIMEOUT = 10.0  # seconds
 _PCM_SAMPLE_RATE = 24000
+_BYTES_PER_SAMPLE = 2  # 16-bit mono PCM
 _MAX_CONFIG_MESSAGE_SIZE = 4 * 1024 * 1024  # allow large ref_audio payloads
 _MAX_INPUT_TEXT_MESSAGE_SIZE = 128 * 1024
 
@@ -210,6 +224,24 @@ class OmniStreamingSpeechHandler:
         """Generate audio for a single sentence and send it over WebSocket."""
         response_format = config.response_format or "wav"
 
+        # Reject unmet word-timestamps preconditions early with a clear reason.
+        if config.word_timestamps:
+            if self._speech_service.forced_aligner_config is None:
+                await self._send_error(
+                    websocket,
+                    "word_timestamps=true but the server was launched without "
+                    "--forced-aligner; either restart the server with that flag "
+                    "or set word_timestamps=false in session.config.",
+                )
+                return
+            if not (config.stream_audio and response_format == "pcm"):
+                await self._send_error(
+                    websocket,
+                    "word_timestamps=true requires stream_audio=true and "
+                    "response_format='pcm' (the aligner consumes raw PCM).",
+                )
+                return
+
         request = OpenAICreateSpeechRequest(
             input=sentence_text,
             model=config.model,
@@ -227,6 +259,7 @@ class OmniStreamingSpeechHandler:
             x_vector_only_mode=config.x_vector_only_mode,
             speaker_embedding=config.speaker_embedding,
             stream=config.stream_audio,
+            word_timestamps=config.word_timestamps,
         )
 
         start_payload = {
@@ -236,7 +269,11 @@ class OmniStreamingSpeechHandler:
             "format": response_format,
         }
         if config.stream_audio and response_format == "pcm":
+            # Nominal stream rate; each audio.chunk carries the authoritative
+            # per-chunk sample_rate.
             start_payload["sample_rate"] = _PCM_SAMPLE_RATE
+        if config.word_timestamps:
+            start_payload["word_timestamps"] = True
         await websocket.send_json(start_payload)
 
         total_bytes = 0
@@ -245,10 +282,20 @@ class OmniStreamingSpeechHandler:
         try:
             if config.stream_audio:
                 request_id, generator, _ = await self._speech_service._prepare_speech_generation(request)
-                async with aclosing(self._speech_service._generate_pcm_chunks(generator, request_id)) as stream:
-                    async for chunk in stream:
-                        total_bytes += len(chunk)
-                        await websocket.send_bytes(chunk)
+                if config.word_timestamps:
+                    total_bytes = await self._stream_audio_with_alignments(
+                        websocket=websocket,
+                        request_id=request_id,
+                        generator=generator,
+                        sentence_text=sentence_text,
+                        sentence_index=sentence_index,
+                        language=config.language,
+                    )
+                else:
+                    async with aclosing(self._speech_service._generate_pcm_chunks(generator, request_id)) as stream:
+                        async for chunk in stream:
+                            total_bytes += len(chunk)
+                            await websocket.send_bytes(chunk)
             else:
                 audio_bytes, _ = await self._speech_service._generate_audio_bytes(request)
                 total_bytes = len(audio_bytes)
@@ -277,6 +324,115 @@ class OmniStreamingSpeechHandler:
             except Exception:
                 logger.debug("Failed to send audio.done for sentence %d", sentence_index, exc_info=True)
 
+    async def _stream_audio_with_alignments(
+        self,
+        *,
+        websocket: WebSocket,
+        request_id: str,
+        generator,
+        sentence_text: str,
+        sentence_index: int,
+        language: str | None = None,
+    ) -> int:
+        """Stream PCM as JSON ``audio.chunk`` frames, aligned per sentence.
+
+        Forward each PCM chunk live (``timestamps: null``) while buffering the
+        sentence audio, then run the forced aligner once over the whole
+        sentence and emit a final empty-audio ``audio.chunk`` with the word
+        timestamps. On aligner failure timestamps is ``null``; for silence it
+        is ``[]`` (audio always flows regardless).
+        """
+        aligner_config = self._speech_service.forced_aligner_config
+        assert aligner_config is not None  # gated by the precondition check
+
+        sentence_audio = bytearray()
+        total_bytes = 0
+        sample_rate = _PCM_SAMPLE_RATE
+        chunk_id = 0
+
+        async def send_chunk(
+            chunk: bytes,
+            chunk_sample_rate: int,
+            timestamps_payload: list[dict] | None,
+            chunk_start_ms: int,
+            chunk_end_ms: int,
+        ) -> None:
+            nonlocal chunk_id
+            await websocket.send_json(
+                {
+                    "type": "audio.chunk",
+                    "sentence_index": sentence_index,
+                    "chunk_id": chunk_id,
+                    "chunk_start_ms": chunk_start_ms,
+                    "chunk_end_ms": chunk_end_ms,
+                    "sample_rate": chunk_sample_rate,
+                    "audio_b64": base64.b64encode(chunk).decode("ascii"),
+                    "timestamps": timestamps_payload,
+                }
+            )
+            chunk_id += 1
+
+        async with aclosing(
+            self._speech_service._generate_pcm_chunks(
+                generator,
+                request_id,
+                include_sample_rate=True,
+            )
+        ) as stream:
+            async for chunk, chunk_sample_rate in stream:
+                sample_rate = chunk_sample_rate
+                chunk_start_ms = int(round((len(sentence_audio) / _BYTES_PER_SAMPLE / sample_rate) * 1000.0))
+                sentence_audio.extend(chunk)
+                chunk_end_ms = int(round((len(sentence_audio) / _BYTES_PER_SAMPLE / sample_rate) * 1000.0))
+                total_bytes += len(chunk)
+                # Audio first, timestamps after the whole sentence is aligned.
+                await send_chunk(chunk, chunk_sample_rate, None, chunk_start_ms, chunk_end_ms)
+
+        # Single alignment pass over the full sentence, then emit timestamps.
+        # A load/config failure is permanent, so surface the reason once; audio
+        # has already streamed, so the trailing frame still carries null.
+        try:
+            timestamps_payload = await self._align_sentence(
+                audio=bytes(sentence_audio),
+                text=sentence_text,
+                sample_rate=sample_rate,
+                config=aligner_config,
+                language=language,
+            )
+        except ForcedAlignerLoadError as exc:
+            await self._send_error(websocket, f"forced aligner unavailable: {exc}")
+            timestamps_payload = None
+        sentence_end_ms = int(round((len(sentence_audio) / _BYTES_PER_SAMPLE / sample_rate) * 1000.0))
+        await send_chunk(b"", sample_rate, timestamps_payload, 0, sentence_end_ms)
+
+        return total_bytes
+
+    @staticmethod
+    async def _align_sentence(
+        *,
+        audio: bytes,
+        text: str,
+        sample_rate: int,
+        config,
+        language: str | None = None,
+    ) -> list[dict] | None:
+        """Convert a sentence alignment into JSON word-timestamp dicts.
+
+        Returns ``None`` on aligner failure, ``[]`` when it ran but produced no
+        tokens. Monotonic, non-overlapping bounds are guaranteed by the decoder.
+        ``language`` is forwarded to word segmentation.
+        """
+        aligned = await forced_align(
+            audio=audio,
+            text=text,
+            sample_rate=sample_rate,
+            config=config,
+            language=language,
+        )
+        if aligned is None:
+            return None
+        return [{"word": ts.word, "start_ms": ts.start_ms, "end_ms": ts.end_ms} for ts in aligned]
+
     @staticmethod
     async def _send_error(websocket: WebSocket, message: str) -> None:
         """Send an error message to the client."""

vllm_omni/utils/forced_aligner.py

@@ -0,0 +1,399 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Shared forced aligner for streaming TTS word timestamps.
+
+Hosts one lazy-loaded, in-process pooling ``vllm.LLM`` shared by the TTS
+frontend. ``llm.encode`` is sync/blocking, so :func:`align` runs it in
+``asyncio.to_thread``, serialized on a lock since the offline LLM is not
+thread-safe. See :func:`align` for the return-value contract.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import threading
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import yaml
+
+from vllm_omni.utils import qwen3_force_align_processor as _processor
+
+logger = logging.getLogger(__name__)
+
+
+_DEFAULT_CONFIG_PATH = Path(__file__).resolve().parents[1] / "deploy" / "qwen3_tts_forced_aligner.yaml"
+
+
+@dataclass(frozen=True, slots=True)
+class WordTimestamp:
+    """Internal alignment record. Serialized to a plain JSON object
+    (``{"word", "start_ms", "end_ms"}``) at the WebSocket boundary.
+    """
+
+    word: str
+    start_ms: int
+    end_ms: int
+
+
+@dataclass(frozen=True, slots=True)
+class ForcedAlignerConfig:
+    """Plain-data config for constructing the aligner ``vllm.LLM`` (lazy-loaded)."""
+
+    model: str
+    runner: str | None = None
+    architecture: str | None = None
+    pooling_task: str | None = None
+    gpu_memory_utilization: float | None = None
+    dtype: str | None = None
+    max_model_len: int | None = None
+    trust_remote_code: bool | None = None
+    extra_llm_kwargs: dict[str, Any] = field(default_factory=dict)
+
+
+def _load_forced_aligner_yaml(path: str | Path) -> dict[str, Any]:
+    with Path(path).open(encoding="utf-8") as f:
+        raw = yaml.safe_load(f) or {}
+    return dict(raw.get("forced_aligner", raw))
+
+
+def build_forced_aligner_config(args: Any) -> ForcedAlignerConfig | None:
+    """Build a config from CLI args, or ``None`` when the feature is off.
+
+    Precedence (lowest to highest): the packaged default YAML
+    (:data:`_DEFAULT_CONFIG_PATH`, Qwen deploy defaults) -> a user YAML passed
+    via ``--forced-aligner-config`` -> the ``--forced-aligner`` model path. The
+    feature is off (returns ``None``) unless a model resolves from this chain.
+    Per-field overrides such as ``gpu_memory_utilization`` live in the YAML.
+    """
+    config_path = getattr(args, "forced_aligner_config", None)
+    config_data: dict[str, Any] = {}
+    model = getattr(args, "forced_aligner", None)
+    if config_path or model:
+        config_data.update(_load_forced_aligner_yaml(_DEFAULT_CONFIG_PATH))
+    if config_path:
+        config_data.update(_load_forced_aligner_yaml(config_path))
+    if model:
+        config_data["model"] = str(model)
+    model = config_data.get("model")
+    if not model:
+        return None
+    allowed = set(ForcedAlignerConfig.__dataclass_fields__)
+    return ForcedAlignerConfig(**{k: v for k, v in config_data.items() if k in allowed})
+
+
+class ForcedAlignerLoadError(RuntimeError):
+    """The aligner model/config could not be loaded (permanent until restart).
+
+    Distinguished from a per-request alignment failure so the streaming layer
+    can report a clear reason once rather than degrading every request to
+    ``timestamps: null``.
+    """
+
+
+# --- Singleton state ---
+# A single LLM serves the whole API server. ``_lock`` guards the lazy
+# constructor; ``_encode_lock`` serializes ``encode`` because the offline
+# ``vllm.LLM`` is a sync batch interface that is not safe to call from multiple
+# threads at once (concurrent WebSocket sessions otherwise race the engine).
+_lock = threading.Lock()
+_encode_lock = asyncio.Lock()
+_llm: Any = None
+_classify_num: int | None = None
+_timestamp_token_id: int | None = None
+_timestamp_segment_time_ms: float | None = None
+_loaded_config: ForcedAlignerConfig | None = None
+
+
+async def align(
+    *,
+    audio: bytes,
+    text: str,
+    sample_rate: int,
+    config: ForcedAlignerConfig,
+    language: str | None = None,
+) -> list[WordTimestamp] | None:
+    """Run one forced-alignment pass.
+
+    Args:
+        audio: Signed-int16 little-endian mono PCM bytes.
+        text: Ground-truth text whose words to align.
+        sample_rate: Sample rate of ``audio`` in Hz.
+        config: Aligner config (same instance for every call across the
+            server's lifetime; reload requires a server restart).
+        language: Optional language hint for word segmentation. ``None`` /
+            ``"auto"`` use the space + Chinese-mixed path; ``"japanese"`` /
+            ``"korean"`` (or their codes) need ``qwen_asr`` installed for
+            faithful tokenisation, else they degrade to whitespace splitting.
+
+    Returns:
+        List of :class:`WordTimestamp` on success (possibly empty for
+        silence / no aligned tokens), ``None`` on a per-request alignment
+        failure (decode error).
+
+    Raises:
+        ForcedAlignerLoadError: the model/config could not be loaded. This is
+            permanent until restart, so callers surface it once instead of
+            degrading every request to ``None``.
+    """
+    async with _encode_lock:
+        try:
+            await asyncio.to_thread(_ensure_loaded, config)
+        except Exception as exc:  # noqa: BLE001
+            logger.exception("Forced aligner failed to load")
+            raise ForcedAlignerLoadError(str(exc)) from exc
+        try:
+            return await asyncio.to_thread(_align_sync, audio, text, sample_rate, config, language)
+        except Exception:  # noqa: BLE001
+            logger.exception("Forced aligner failed for text=%r", text)
+            return None
+
+
+def _align_sync(
+    audio: bytes,
+    text: str,
+    sample_rate: int,
+    config: ForcedAlignerConfig,
+    language: str | None = None,
+) -> list[WordTimestamp]:
+    # Precondition: align() has already run _ensure_loaded(config) under
+    # _encode_lock and owns the load-failure -> ForcedAlignerLoadError mapping.
+    # Don't reload here, so a load error can't be swallowed as a per-request None.
+    audio_arr = _pcm_bytes_to_float32(audio)
+    if audio_arr.size == 0:
+        return []
+    audio_duration_ms = (audio_arr.size / sample_rate) * 1000.0
+
+    # Segment once and reuse for both the prompt and the decode: the word
+    # units MUST match between the two or the markers drift out of sync.
+    words = _processor.segment_words(text, language)
+    prompt = _processor.build_prompt(words)
+    request = {
+        "prompt": prompt,
+        "multi_modal_data": {"audio": (audio_arr, sample_rate)},
+    }
+
+    from vllm.pooling_params import PoolingParams
+
+    outputs = _llm.encode(  # type: ignore[union-attr]
+        [request],
+        pooling_params=PoolingParams(),
+        pooling_task=config.pooling_task or "token_classify",
+        use_tqdm=False,
+    )
+    if not outputs:
+        return []
+
+    result = outputs[0]
+    logits = result.outputs.data  # [n_token, classify_num]
+    prompt_token_ids = list(result.prompt_token_ids)
+    timestamp_positions = [i for i, tid in enumerate(prompt_token_ids) if tid == _timestamp_token_id]
+    if not timestamp_positions:
+        logger.warning(
+            "No <|timestamp|> tokens found in prompt for text=%r; aligner returned %d rows.",
+            text,
+            logits.shape[0] if hasattr(logits, "shape") else len(logits),
+        )
+        return []
+
+    return _decode_timestamps(
+        logits=logits,
+        words=words,
+        timestamp_positions=timestamp_positions,
+        classify_num=_classify_num,
+        timestamp_segment_time_ms=_timestamp_segment_time_ms,
+        audio_duration_ms=audio_duration_ms,
+    )
+
+
+def _ensure_loaded(config: ForcedAlignerConfig) -> None:
+    """Lazy-load the singleton ``vllm.LLM`` under lock; idempotent."""
+    global _llm, _classify_num, _timestamp_token_id, _timestamp_segment_time_ms, _loaded_config
+
+    if _llm is not None:
+        if _loaded_config is not None and _loaded_config.model != config.model:
+            # Multiple configs from different requests — refuse rather
+            # than swap models silently. A server restart is required.
+            raise RuntimeError(
+                f"Forced aligner already loaded with config={_loaded_config!r}; "
+                f"cannot serve a request that asks for {config!r}. "
+                "Restart the server to change the aligner config."
+            )
+        return
+
+    with _lock:
+        if _llm is not None:
+            return  # raced; another caller did the load
+
+        # Lazy import: vllm pulls torch + CUDA, which we want to avoid at
+        # module import time.
+        from vllm import LLM
+
+        logger.info(
+            "Loading forced aligner %s (gpu_memory_utilization=%s)",
+            config.model,
+            config.gpu_memory_utilization if config.gpu_memory_utilization is not None else "default",
+        )
+        llm_kwargs: dict[str, Any] = {"model": config.model}
+        if config.runner is not None:
+            llm_kwargs["runner"] = config.runner
+        if config.architecture is not None:
+            llm_kwargs["hf_overrides"] = {"architectures": [config.architecture]}
+        if config.gpu_memory_utilization is not None:
+            llm_kwargs["gpu_memory_utilization"] = config.gpu_memory_utilization
+        if config.trust_remote_code is not None:
+            llm_kwargs["trust_remote_code"] = config.trust_remote_code
+        if config.dtype is not None:
+            llm_kwargs["dtype"] = config.dtype
+        if config.max_model_len is not None:
+            llm_kwargs["max_model_len"] = config.max_model_len
+        llm_kwargs.update(config.extra_llm_kwargs)
+
+        # Force the aligner engine in-process: in multiprocessing mode the
+        # token_classify ``pooling_output`` fails to round-trip over the
+        # engine-core IPC on current vLLM. Hard-set (not setdefault) so a
+        # globally exported ``=1`` can't push it into a subprocess; save/restore
+        # to avoid leaking the override to anything built later in this process.
+        prev_mp = os.environ.get("VLLM_ENABLE_V1_MULTIPROCESSING")
+        os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"
+        try:
+            llm = LLM(**llm_kwargs)
+        finally:
+            if prev_mp is None:
+                os.environ.pop("VLLM_ENABLE_V1_MULTIPROCESSING", None)
+            else:
+                os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = prev_mp
+
+        thinker_config = getattr(llm.llm_engine.model_config.hf_config, "thinker_config", None)
+        if thinker_config is None or not hasattr(thinker_config, "classify_num"):
+            raise RuntimeError(
+                "Loaded aligner has no thinker_config.classify_num; "
+                "expected a Qwen3ASRForcedAlignerForTokenClassification checkpoint."
+            )
+        timestamp_segment_time_ms = getattr(llm.llm_engine.model_config.hf_config, "timestamp_segment_time", None)
+        if timestamp_segment_time_ms is None:
+            raise RuntimeError(
+                "Loaded aligner has no timestamp_segment_time; expected a Qwen3ASR forced aligner checkpoint."
+            )
+
+        tokenizer = llm.get_tokenizer()
+        timestamp_token_id = _processor.resolve_timestamp_token_id(tokenizer)
+
+        # Publish in this order so a concurrent reader either sees
+        # _llm == None (will block on the lock) or sees a fully
+        # initialized aligner.
+        _classify_num = int(thinker_config.classify_num)
+        _timestamp_token_id = timestamp_token_id
+        _timestamp_segment_time_ms = float(timestamp_segment_time_ms)
+        _loaded_config = config
+        _llm = llm
+
+        logger.info(
+            "Forced aligner ready: timestamp_token_id=%d, classify_num=%d, timestamp_segment_time_ms=%.1f",
+            timestamp_token_id,
+            _classify_num,
+            _timestamp_segment_time_ms,
+        )
+
+
+# --- pure helpers (testable without a GPU / vllm) ---
+#
+# Word segmentation, prompt building, timestamp repair and marker-token
+# lookup are Qwen-specific and live in
+# :mod:`vllm_omni.utils.qwen3_force_align_processor`. This module owns only
+# the generic vLLM orchestration.
+
+
+def _pcm_bytes_to_float32(audio: bytes) -> np.ndarray:
+    """Decode signed-int16 mono PCM bytes into a [-1, 1] float32 array."""
+    if not audio:
+        return np.zeros(0, dtype=np.float32)
+    if len(audio) % 2 != 0:
+        # Drop a trailing odd byte rather than raise; keeps streaming
+        # robust against off-by-one chunk boundaries.
+        audio = audio[:-1]
+    pcm = np.frombuffer(audio, dtype=np.int16)
+    return (pcm.astype(np.float32) / 32768.0).copy()
+
+
+def _decode_timestamps(
+    *,
+    logits: Any,
+    words: list[str],
+    timestamp_positions: list[int],
+    classify_num: int,
+    audio_duration_ms: float,
+    timestamp_segment_time_ms: float | None = None,
+) -> list[WordTimestamp]:
+    """Translate ``[n_token, classify_num]`` logits into word timestamps.
+
+    ``words`` must be the exact segmentation used to build the prompt (see
+    :func:`qwen3_force_align_processor.segment_words`); each word owns two
+    consecutive markers.
+    """
+    arr = logits.detach().cpu().numpy() if hasattr(logits, "detach") else np.asarray(logits)
+    if arr.ndim != 2:
+        raise ValueError(f"Expected 2D logits [n_token, classify_num]; got shape {arr.shape}")
+    if arr.shape[1] != classify_num:
+        raise ValueError(
+            f"Logits last dim {arr.shape[1]} != classify_num {classify_num}; "
+            "model config and prompt template may be out of sync."
+        )
+
+    expected = len(words) * 2
+    if len(timestamp_positions) != expected:
+        logger.warning(
+            "Got %d timestamp positions but text has %d words (expected %d start/end markers); "
+            "returning empty alignment.",
+            len(timestamp_positions),
+            len(words),
+            expected,
+        )
+        return []
+
+    marker_logits = arr[timestamp_positions, :]
+    bin_idx = marker_logits.argmax(axis=-1)
+    bin_size_ms = (
+        float(timestamp_segment_time_ms)
+        if timestamp_segment_time_ms is not None
+        else (audio_duration_ms / classify_num if classify_num > 0 else 0.0)
+    )
+
+    # Repair non-monotonic bins across the whole start/end sequence before
+    # pairing, so each (start, end) pair stays ordered and one bad bin can't
+    # corrupt its neighbours.
+    marker_ms = _processor.fix_timestamp([int(round(int(b) * bin_size_ms)) for b in bin_idx])
+
+    # Pair markers into per-word intervals, enforcing the wire contract here so
+    # callers don't have to: monotonic and non-overlapping across words, with
+    # start <= end and nothing past the audio length.
+    duration_ms = int(round(audio_duration_ms))
+    out: list[WordTimestamp] = []
+    prev_end_ms = 0
+    for i, word in enumerate(words):
+        start_ms = min(max(marker_ms[i * 2], prev_end_ms), duration_ms)
+        end_ms = min(max(marker_ms[i * 2 + 1], start_ms), duration_ms)
+        out.append(WordTimestamp(word=word, start_ms=start_ms, end_ms=end_ms))
+        prev_end_ms = end_ms
+    return out
+
+
+# Test hooks ---------------------------------------------------------------
+# Tests need a way to reset module state without restarting Python. Not
+# part of the public API; do not call in production code.
+
+
+def _reset_for_tests() -> None:
+    """Drop the cached aligner state so the next call reloads."""
+    global _llm, _classify_num, _timestamp_token_id, _timestamp_segment_time_ms, _loaded_config
+    with _lock:
+        _llm = None
+        _classify_num = None
+        _timestamp_token_id = None
+        _timestamp_segment_time_ms = None
+        _loaded_config = None
+    _processor._reset_for_tests()

vllm_omni/utils/qwen3_force_align_processor.py

@@ -0,0 +1,285 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Adapted from:
+# https://github.com/QwenLM/Qwen3-ASR/blob/main/qwen_asr/inference/qwen3_forced_aligner.py
+#
+# Copyright 2026 The Alibaba Qwen team. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+"""Qwen3 forced-aligner text/timestamp processor.
+
+This is the model-specific half of upstream's ``Qwen3ForceAlignProcessor``:
+it turns text into the aligner's word units and prompt, repairs the predicted
+timestamp bins, and resolves the marker token id. The generic "run a vLLM
+pooling model" half lives in :mod:`vllm_omni.utils.forced_aligner`, which
+calls into this module.
+
+Keeping the Qwen-specific pieces here marks the seam for the model-agnostic
+aligner the issue asks for: a different aligner family would supply its own
+processor exposing the same small surface — :func:`segment_words`,
+:func:`build_prompt`, :func:`fix_timestamp`, :func:`resolve_timestamp_token_id`.
+
+Word segmentation prefers qwen_asr's official ``Qwen3ForceAlignProcessor``
+when installed (full multilingual fidelity, incl. Japanese/Korean) and
+otherwise uses the faithful port below: exact for whitespace-delimited and
+Chinese-mixed text; Japanese/Korean degrade to whitespace splitting.
+"""
+
+from __future__ import annotations
+
+import logging
+import unicodedata
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+# Prompt tokens for the Qwen3 forced aligner. Baked in (not config) because the
+# prompt template and the word segmentation are inherently Qwen-specific: a
+# different aligner family needs a different processor module regardless.
+AUDIO_PLACEHOLDER = "<|audio_start|><|audio_pad|><|audio_end|>"
+TIMESTAMP_TOKEN = "<timestamp>"
+
+_CJK_RANGES = (
+    (0x4E00, 0x9FFF),
+    (0x3400, 0x4DBF),
+    (0x20000, 0x2A6DF),
+    (0x2A700, 0x2B73F),
+    (0x2B740, 0x2B81F),
+    (0x2B820, 0x2CEAF),
+    (0xF900, 0xFAFF),
+)
+
+# Map common language codes to the names qwen_asr's processor expects.
+_LANG_ALIASES = {
+    "ja": "japanese",
+    "jp": "japanese",
+    "jpn": "japanese",
+    "ko": "korean",
+    "kr": "korean",
+    "kor": "korean",
+}
+
+
+def build_prompt(words: list[str]) -> str:
+    """Wrap segmented words in the Qwen3 aligner prompt template.
+
+    Each word gets two trailing ``<timestamp>`` markers (start + end); the
+    model classifies each marker into an audio time bin.
+    """
+    if not words:
+        # Pad with one timestamp so the decoder always has something to
+        # read; an empty result still surfaces as "[]" upstream.
+        body = TIMESTAMP_TOKEN
+    else:
+        body = f"{TIMESTAMP_TOKEN}{TIMESTAMP_TOKEN}".join(words) + f"{TIMESTAMP_TOKEN}{TIMESTAMP_TOKEN}"
+    return f"<|im_start|>user\n{AUDIO_PLACEHOLDER}{body}<|im_end|>\n<|im_start|>assistant\n"
+
+
+# --- word segmentation (port of Qwen3ForceAlignProcessor) ----------------
+#
+# The marker count fed to the model must match the segmentation the aligner
+# was trained with, or the decode drifts and returns empty.
+
+
+def _is_cjk_char(ch: str) -> bool:
+    code = ord(ch)
+    return any(lo <= code <= hi for lo, hi in _CJK_RANGES)
+
+
+def _is_kept_char(ch: str) -> bool:
+    """Keep letters/numbers (any script) and the apostrophe; drop punctuation."""
+    if ch == "'":
+        return True
+    cat = unicodedata.category(ch)
+    return cat.startswith("L") or cat.startswith("N")
+
+
+def _clean_token(token: str) -> str:
+    return "".join(ch for ch in token if _is_kept_char(ch))
+
+
+def _split_segment_with_chinese(seg: str) -> list[str]:
+    """Split a cleaned segment so each CJK char is its own token."""
+    tokens: list[str] = []
+    buf: list[str] = []
+
+    def flush() -> None:
+        if buf:
+            tokens.append("".join(buf))
+            buf.clear()
+
+    for ch in seg:
+        if _is_cjk_char(ch):
+            flush()
+            tokens.append(ch)
+        else:
+            buf.append(ch)
+    flush()
+    return tokens
+
+
+def _tokenize_space_lang(text: str) -> list[str]:
+    """Whitespace + Chinese-mixed segmentation (Qwen3ForceAlignProcessor port).
+
+    Splits on whitespace, strips punctuation from each segment (keeping
+    cross-script letters/digits and the apostrophe), then peels off CJK
+    characters as individual tokens.
+    """
+    tokens: list[str] = []
+    for seg in text.split():
+        cleaned = _clean_token(seg)
+        if cleaned:
+            tokens.extend(_split_segment_with_chinese(cleaned))
+    return tokens
+
+
+# Cached qwen_asr processor (or a sentinel that it is unavailable). Importing
+# it eagerly pulls nagisa + the Qwen3ASR transformers backend, so we only try
+# once, lazily, and remember the outcome.
+_official_processor: Any = None
+_official_processor_unavailable = False
+
+
+def _get_official_processor() -> Any | None:
+    global _official_processor, _official_processor_unavailable
+    if _official_processor is not None:
+        return _official_processor
+    if _official_processor_unavailable:
+        return None
+    try:
+        from qwen_asr.inference.qwen3_forced_aligner import (  # type: ignore[import-not-found]
+            Qwen3ForceAlignProcessor,
+        )
+
+        _official_processor = Qwen3ForceAlignProcessor()
+    except Exception:  # noqa: BLE001 - any import/init error means "use the port"
+        _official_processor_unavailable = True
+        logger.info(
+            "qwen_asr not available; using the built-in word segmentation "
+            "(exact for whitespace/Chinese-mixed text; Japanese/Korean fall "
+            "back to whitespace splitting). Install qwen-asr for full fidelity."
+        )
+        return None
+    return _official_processor
+
+
+def segment_words(text: str, language: str | None = None) -> list[str]:
+    """Split ``text`` into the aligner's word units.
+
+    Prefers qwen_asr's official processor (full multilingual fidelity); falls
+    back to the built-in port otherwise. ``language`` follows the official
+    naming — ``"japanese"`` / ``"korean"`` (or codes like ``ja`` / ``ko``)
+    trigger language-specific tokenisers; anything else (incl. ``None`` /
+    ``"auto"``) uses the whitespace + Chinese-mixed path.
+    """
+    lang = (language or "auto").strip().lower()
+    lang = _LANG_ALIASES.get(lang, lang)
+    processor = _get_official_processor()
+    if processor is not None:
+        try:
+            word_list, _ = processor.encode_timestamp(text, lang)
+            return list(word_list)
+        except Exception:  # noqa: BLE001
+            logger.warning(
+                "Official Qwen3ForceAlignProcessor failed for language=%r; falling back to built-in segmentation.",
+                lang,
+                exc_info=True,
+            )
+    if lang in ("japanese", "korean"):
+        logger.warning(
+            "language=%r needs qwen_asr (nagisa/soynlp) for faithful word "
+            "segmentation; falling back to whitespace splitting.",
+            lang,
+        )
+    return _tokenize_space_lang(text)
+
+
+def fix_timestamp(values: list[int]) -> list[int]:
+    """Repair non-monotonic timestamp bins via LIS + interpolation.
+
+    The model occasionally emits out-of-order bins; this snaps anomalies back
+    onto the longest non-decreasing subsequence (interpolating longer runs)
+    so paired start/end markers stay ordered. Port of
+    ``Qwen3ForceAlignProcessor.fix_timestamp`` (Apache-2.0).
+    """
+    n = len(values)
+    if n == 0:
+        return []
+
+    dp = [1] * n
+    parent = [-1] * n
+    for i in range(1, n):
+        for j in range(i):
+            if values[j] <= values[i] and dp[j] + 1 > dp[i]:
+                dp[i] = dp[j] + 1
+                parent[i] = j
+
+    idx = dp.index(max(dp))
+    lis: list[int] = []
+    while idx != -1:
+        lis.append(idx)
+        idx = parent[idx]
+    is_normal = [False] * n
+    for k in lis:
+        is_normal[k] = True
+
+    result = [float(v) for v in values]
+    i = 0
+    while i < n:
+        if is_normal[i]:
+            i += 1
+            continue
+        j = i
+        while j < n and not is_normal[j]:
+            j += 1
+        count = j - i
+        left = next((result[k] for k in range(i - 1, -1, -1) if is_normal[k]), None)
+        right = next((result[k] for k in range(j, n) if is_normal[k]), None)
+        if count <= 2:
+            for k in range(i, j):
+                if left is None:
+                    result[k] = right
+                elif right is None:
+                    result[k] = left
+                else:
+                    result[k] = left if (k - (i - 1)) <= (j - k) else right
+        elif left is not None and right is not None:
+            step = (right - left) / (count + 1)
+            for k in range(i, j):
+                result[k] = left + step * (k - i + 1)
+        elif left is not None:
+            for k in range(i, j):
+                result[k] = left
+        elif right is not None:
+            for k in range(i, j):
+                result[k] = right
+        i = j
+
+    return [int(r) for r in result]
+
+
+def resolve_timestamp_token_id(tokenizer: Any, timestamp_token: str = TIMESTAMP_TOKEN) -> int:
+    """Look up the integer id of the timestamp special token."""
+    convert = getattr(tokenizer, "convert_tokens_to_ids", None)
+    if not callable(convert):
+        raise RuntimeError("Aligner tokenizer has no convert_tokens_to_ids method.")
+    tid = convert(timestamp_token)
+    if isinstance(tid, list):
+        tid = tid[0] if tid else None
+    if tid is None or (isinstance(tid, int) and tid < 0):
+        raise RuntimeError(
+            f"Aligner tokenizer does not recognise {timestamp_token!r} (got id={tid}). "
+            "Check the model card; the marker token may use a different name."
+        )
+    return int(tid)
+
+
+def _reset_for_tests() -> None:
+    """Drop the cached official-processor probe so the next call re-checks."""
+    global _official_processor, _official_processor_unavailable
+    _official_processor = None
+    _official_processor_unavailable = False