Merge pull request #422 from ianwh02/feature/batch-inference

Add cross-client GPU batch inference for faster_whisper backend

run_server.py

@@ -66,6 +66,24 @@ if __name__ == "__main__":
         default=None,
         help="Comma-separated list of allowed CORS origins (e.g., 'http://localhost:3000,http://example.com'). Defaults to localhost/127.0.0.1 on the WebSocket port."
     )
+    parser.add_argument(
+        '--batch_inference',
+        action='store_true',
+        help='Enable batched GPU inference for concurrent sessions. '
+             'Batches multiple sessions into a single GPU call for higher throughput.'
+    )
+    parser.add_argument(
+        '--batch_max_size',
+        type=int,
+        default=8,
+        help='Maximum batch size for batched inference (default: 8).'
+    )
+    parser.add_argument(
+        '--batch_window_ms',
+        type=int,
+        default=50,
+        help='Maximum time in ms to wait for batch to fill (default: 50).'
+    )
     args = parser.parse_args()
 
     if args.backend == "tensorrt":
@@ -92,4 +110,7 @@ if __name__ == "__main__":
         rest_port=args.rest_port,
         enable_rest=args.enable_rest,
         cors_origins=args.cors_origins,
+        batch_enabled=args.batch_inference,
+        batch_max_size=args.batch_max_size,
+        batch_window_ms=args.batch_window_ms,
     )

tests/test_batch_inference.py

@@ -0,0 +1,163 @@
+import time
+import unittest
+from unittest import mock
+from unittest.mock import MagicMock
+
+import numpy as np
+
+from whisper_live.batch_inference import BatchInferenceWorker, BatchRequest
+
+
+class TestBatchInferenceWorker(unittest.TestCase):
+    def setUp(self):
+        self.mock_transcriber = MagicMock()
+        self.worker = BatchInferenceWorker(
+            transcriber=self.mock_transcriber,
+            max_batch_size=8,
+            batch_window_ms=200,
+        )
+        self.worker.start()
+
+    def tearDown(self):
+        self.worker.stop()
+
+    def _make_audio(self, duration_s=1.0):
+        return np.random.randn(int(16000 * duration_s)).astype(np.float32)
+
+    def test_single_request_uses_transcribe(self):
+        """Single request should fall back to transcriber.transcribe()."""
+        fake_segment = MagicMock()
+        fake_info = MagicMock()
+        self.mock_transcriber.transcribe.return_value = ([fake_segment], fake_info)
+
+        req = BatchRequest(audio=self._make_audio(), language="en", use_vad=False)
+        self.worker.submit(req)
+        req.future.wait(timeout=5)
+
+        self.assertTrue(req.future.is_set())
+        self.assertIsNone(req.error)
+        self.assertEqual(req.result, [fake_segment])
+        self.assertEqual(req.info, fake_info)
+        self.mock_transcriber.transcribe.assert_called_once()
+
+    @mock.patch('whisper_live.batch_inference.get_suppressed_tokens', return_value=[-1])
+    @mock.patch('whisper_live.batch_inference.Tokenizer')
+    def test_multiple_requests_batched(self, mock_tokenizer_cls, mock_suppress):
+        """Multiple concurrent requests should go through the batched GPU path."""
+        # Mock tokenizer
+        mock_tok = MagicMock()
+        mock_tok.decode.return_value = "hello world"
+        mock_tokenizer_cls.return_value = mock_tok
+
+        # Mock feature extractor
+        self.mock_transcriber.feature_extractor.return_value = np.zeros(
+            (80, 3000), dtype=np.float32
+        )
+        self.mock_transcriber.feature_extractor.sampling_rate = 16000
+
+        # Mock encode
+        self.mock_transcriber.encode.return_value = np.zeros(
+            (3, 1500, 512), dtype=np.float32
+        )
+
+        # Mock model.generate — one result per item
+        gen_result = MagicMock()
+        gen_result.sequences_ids = [[50257, 50362, 1234, 50256]]
+        gen_result.scores = [np.float32(-1.0)]
+        gen_result.no_speech_prob = 0.1
+        self.mock_transcriber.model.generate.return_value = [gen_result] * 3
+
+        # Mock remaining model attributes
+        self.mock_transcriber.model.is_multilingual = False
+        self.mock_transcriber.max_length = 448
+        self.mock_transcriber.frames_per_second = 50
+        self.mock_transcriber.get_prompt.return_value = [50258]
+        self.mock_transcriber._split_segments_by_timestamps.return_value = (
+            [{"start": 0.0, "end": 1.0, "tokens": [1234], "seek": 0}],
+            None,
+            None,
+        )
+
+        requests = [
+            BatchRequest(audio=self._make_audio(), language="en", use_vad=False)
+            for _ in range(3)
+        ]
+        for req in requests:
+            self.worker.submit(req)
+        for req in requests:
+            req.future.wait(timeout=5)
+
+        for req in requests:
+            self.assertTrue(req.future.is_set())
+            self.assertIsNone(req.error)
+            self.assertIsNotNone(req.result)
+
+        # Verify the batched encode path was used (not transcribe)
+        self.mock_transcriber.encode.assert_called()
+        self.mock_transcriber.transcribe.assert_not_called()
+
+    def test_error_propagation(self):
+        """Transcriber errors should propagate to the request without crashing the worker."""
+        self.mock_transcriber.transcribe.side_effect = RuntimeError("GPU OOM")
+
+        req = BatchRequest(audio=self._make_audio(), language="en", use_vad=False)
+        self.worker.submit(req)
+        req.future.wait(timeout=5)
+
+        self.assertTrue(req.future.is_set())
+        self.assertIsInstance(req.error, RuntimeError)
+        self.assertIn("GPU OOM", str(req.error))
+
+        # Worker should still be alive — submit another request
+        self.mock_transcriber.transcribe.side_effect = None
+        self.mock_transcriber.transcribe.return_value = ([MagicMock()], MagicMock())
+
+        req2 = BatchRequest(audio=self._make_audio(), language="en", use_vad=False)
+        self.worker.submit(req2)
+        req2.future.wait(timeout=5)
+
+        self.assertIsNone(req2.error)
+        self.assertIsNotNone(req2.result)
+
+    def test_worker_stop(self):
+        """Worker thread should exit cleanly when stop() is called."""
+        self.assertTrue(self.worker._thread.is_alive())
+        self.worker.stop()
+        self.assertFalse(self.worker._thread.is_alive())
+
+    def test_batch_respects_max_size(self):
+        """Batches should not exceed max_batch_size."""
+        self.worker.stop()  # Stop the default worker
+
+        observed_batch_sizes = []
+        original_process = BatchInferenceWorker._process_batch
+
+        def tracking_process(self_inner, batch):
+            observed_batch_sizes.append(len(batch))
+            original_process(self_inner, batch)
+
+        self.worker = BatchInferenceWorker(
+            transcriber=self.mock_transcriber,
+            max_batch_size=2,
+            batch_window_ms=100,
+        )
+
+        self.mock_transcriber.transcribe.return_value = ([MagicMock()], MagicMock())
+
+        with mock.patch.object(
+            BatchInferenceWorker, '_process_batch', tracking_process
+        ):
+            self.worker.start()
+
+            requests = [
+                BatchRequest(audio=self._make_audio(), language="en", use_vad=False)
+                for _ in range(4)
+            ]
+            for req in requests:
+                self.worker.submit(req)
+            for req in requests:
+                req.future.wait(timeout=5)
+
+        for size in observed_batch_sizes:
+            self.assertLessEqual(size, 2)
+        self.assertTrue(all(req.future.is_set() for req in requests))

whisper_live/backend/faster_whisper_backend.py

@@ -14,6 +14,7 @@ from whisper_live.backend.base import ServeClientBase
 class ServeClientFasterWhisper(ServeClientBase):
     SINGLE_MODEL = None
     SINGLE_MODEL_LOCK = threading.Lock()
+    BATCH_WORKER = None
 
     def __init__(
         self,
@@ -202,6 +203,26 @@ class ServeClientFasterWhisper(ServeClientBase):
             depends on the implementation of the `transcriber.transcribe` method but typically
             includes the transcribed text.
         """
+        # Batch inference path: submit to central queue and wait
+        if ServeClientFasterWhisper.BATCH_WORKER is not None:
+            from whisper_live.batch_inference import BatchRequest
+            request = BatchRequest(
+                audio=input_sample,
+                language=self.language,
+                task=self.task,
+                initial_prompt=self.initial_prompt,
+                use_vad=self.use_vad,
+                vad_parameters=self.vad_parameters if self.use_vad else None,
+            )
+            ServeClientFasterWhisper.BATCH_WORKER.submit(request)
+            request.future.wait(timeout=30)
+            if request.error:
+                raise request.error
+            if self.language is None and request.info is not None:
+                self.set_language(request.info)
+            return request.result
+
+        # Original lock-based path (backward compatible)
         if ServeClientFasterWhisper.SINGLE_MODEL:
             ServeClientFasterWhisper.SINGLE_MODEL_LOCK.acquire()
         result, info = self.transcriber.transcribe(

whisper_live/batch_inference.py

@@ -0,0 +1,397 @@
+"""
+Batch inference scheduler for WhisperLive.
+
+Replaces the per-session SINGLE_MODEL_LOCK with a queue-based batch system.
+Multiple sessions submit audio to a central queue; a single dedicated thread
+collects pending requests and runs them as a GPU batch via CTranslate2's
+batched encode() + generate() API.
+
+For batch_size=1, falls back to standard transcriber.transcribe() for
+identical behavior to the non-batched path.
+
+Usage:
+    Enable via ``--batch_inference`` CLI flag. The batch worker is lazily
+    started after the first client connects and the shared model is loaded.
+
+Thread safety:
+    - ``queue.Queue`` is stdlib thread-safe.
+    - Each ``BatchRequest.future`` (``threading.Event``) is written by the
+      batch worker BEFORE ``.set()``, read by the session thread AFTER
+      ``.wait()`` — no data race.
+    - Only the batch worker thread touches the GPU model — zero lock
+      contention between session threads.
+"""
+
+import logging
+import queue
+import threading
+import time
+from dataclasses import dataclass, field
+from math import ceil
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+
+from faster_whisper.audio import pad_or_trim
+from faster_whisper.tokenizer import Tokenizer
+from faster_whisper.vad import (
+    VadOptions,
+    collect_chunks,
+    get_speech_timestamps,
+)
+
+from whisper_live.transcriber.transcriber_faster_whisper import (
+    Segment,
+    TranscriptionInfo,
+    get_compression_ratio,
+    get_suppressed_tokens,
+)
+
+
+@dataclass
+class BatchRequest:
+    """A single inference request submitted by a session thread.
+
+    The session thread creates this, calls ``BatchInferenceWorker.submit()``,
+    then blocks on ``future.wait()``.  The batch worker fills ``result``
+    and/or ``error``, then signals ``future.set()``.
+
+    Attributes:
+        audio: Raw audio samples (float32, 16 kHz mono).
+        language: ISO language code or None for auto-detection.
+        task: ``"transcribe"`` or ``"translate"``.
+        initial_prompt: Optional prompt for Whisper conditioning.
+        use_vad: Whether to apply Voice Activity Detection.
+        vad_parameters: Parameters forwarded to ``VadOptions``.
+        future: Event signaled when the result is ready.
+        result: List of ``Segment`` objects (filled by worker).
+        info: ``TranscriptionInfo`` metadata (filled by worker).
+        error: Exception instance if processing failed.
+    """
+    audio: np.ndarray
+    language: Optional[str] = None
+    task: str = "transcribe"
+    initial_prompt: Optional[str] = None
+    use_vad: bool = True
+    vad_parameters: Optional[Dict] = None
+    # Signaling
+    future: threading.Event = field(default_factory=threading.Event)
+    # Results (filled by batch worker)
+    result: Optional[Any] = None
+    info: Optional[Any] = None
+    error: Optional[Exception] = None
+
+
+class BatchInferenceWorker:
+    """Central batch inference scheduler for the faster_whisper backend.
+
+    Owns a single daemon thread that is the **only** thread touching the GPU
+    model.  Per-session transcription threads submit ``BatchRequest`` objects
+    and block on ``future.wait()`` instead of competing for
+    ``SINGLE_MODEL_LOCK``.
+
+    The worker loop:
+
+    1. Blocks until the first request arrives from the queue.
+    2. Waits up to ``batch_window_ms`` for additional requests (up to
+       ``max_batch_size``).
+    3. Processes the collected batch:
+       - **batch_size == 1**: delegates to ``transcriber.transcribe()`` for
+         identical behavior to the non-batched path.
+       - **batch_size > 1**: runs a custom batched GPU path using
+         CTranslate2's ``encode()`` + ``generate()`` APIs.
+
+    Args:
+        transcriber: The shared ``WhisperModel`` instance.
+        max_batch_size: Maximum number of requests per batch.
+        batch_window_ms: Maximum time (ms) to wait for the batch to fill
+            after the first request arrives.
+    """
+
+    def __init__(
+        self,
+        transcriber,
+        max_batch_size: int = 8,
+        batch_window_ms: int = 50,
+    ):
+        self.transcriber = transcriber
+        self.max_batch_size = max_batch_size
+        self.batch_window_ms = batch_window_ms
+        self._queue: queue.Queue = queue.Queue()
+        self._stop_event = threading.Event()
+        self._thread: Optional[threading.Thread] = None
+
+    def start(self):
+        """Start the background batch worker thread."""
+        self._thread = threading.Thread(target=self._worker_loop, daemon=True)
+        self._thread.start()
+        logging.info(
+            f"[BatchInference] Started (max_batch={self.max_batch_size}, "
+            f"window={self.batch_window_ms}ms)"
+        )
+
+    def stop(self):
+        """Signal the worker to stop and wait for it to finish."""
+        self._stop_event.set()
+        if self._thread:
+            self._thread.join(timeout=5)
+
+    def submit(self, request: BatchRequest):
+        """Submit an inference request to the batch queue.
+
+        Args:
+            request: The ``BatchRequest`` to enqueue.  The caller should
+                then call ``request.future.wait()`` to block until the
+                result is ready.
+        """
+        self._queue.put(request)
+
+    # -------------------------------------------------------------------------
+    # Worker loop
+    # -------------------------------------------------------------------------
+
+    def _worker_loop(self):
+        """Main loop: collect requests into batches and process them."""
+        while not self._stop_event.is_set():
+            batch: List[BatchRequest] = []
+
+            # Block until first request arrives
+            try:
+                first = self._queue.get(timeout=0.5)
+                batch.append(first)
+            except queue.Empty:
+                continue
+
+            # Collect more requests within the batch window
+            deadline = time.monotonic() + (self.batch_window_ms / 1000.0)
+            while len(batch) < self.max_batch_size:
+                remaining = deadline - time.monotonic()
+                if remaining <= 0:
+                    break
+                try:
+                    item = self._queue.get(timeout=remaining)
+                    batch.append(item)
+                except queue.Empty:
+                    break
+
+            # Process the collected batch
+            try:
+                self._process_batch(batch)
+            except Exception as e:
+                logging.error(f"[BatchInference] Batch processing error: {e}")
+                for req in batch:
+                    if not req.future.is_set():
+                        req.error = e
+                        req.future.set()
+
+    # -------------------------------------------------------------------------
+    # Batch processing
+    # -------------------------------------------------------------------------
+
+    def _process_batch(self, batch: List[BatchRequest]):
+        """Dispatch to single or multi-item processing."""
+        if len(batch) == 1:
+            self._process_single(batch[0])
+            return
+
+        logging.info(f"[BatchInference] Processing batch of {len(batch)}")
+        self._process_multi(batch)
+
+    def _process_single(self, req: BatchRequest):
+        """Process a single request using standard ``transcriber.transcribe()``.
+
+        This path is used when only one request is available in the batch
+        window, ensuring identical behavior to the non-batched code path.
+        """
+        try:
+            result, info = self.transcriber.transcribe(
+                req.audio,
+                language=req.language,
+                task=req.task,
+                initial_prompt=req.initial_prompt,
+                vad_filter=req.use_vad,
+                vad_parameters=req.vad_parameters if req.use_vad else None,
+            )
+            # Materialize the generator into a list
+            req.result = list(result)
+            req.info = info
+        except Exception as e:
+            req.error = e
+        finally:
+            req.future.set()
+
+    def _process_multi(self, batch: List[BatchRequest]):
+        """Batched GPU path: encode + generate for multiple sessions at once.
+
+        Pipeline:
+            1. Per-item CPU preprocessing (VAD filtering + mel feature extraction)
+            2. Batch GPU encode — single ``transcriber.encode()`` call
+            3. Per-item prompt construction (handles different languages/tasks)
+            4. Batch GPU generate — single ``transcriber.model.generate()`` call
+            5. Per-item segment parsing and result dispatch
+        """
+        # Step 1: Per-item CPU preprocessing (VAD + feature extraction)
+        preprocessed = []
+        for req in batch:
+            try:
+                audio = req.audio
+                speech_chunks = None
+
+                if req.use_vad:
+                    vad_params = req.vad_parameters or {}
+                    vad_opts = VadOptions(**vad_params) if isinstance(vad_params, dict) else vad_params
+                    speech_chunks = get_speech_timestamps(audio, vad_opts)
+                    if speech_chunks:
+                        audio_chunks, _ = collect_chunks(audio, speech_chunks)
+                        audio = np.concatenate(audio_chunks, axis=0) if audio_chunks else audio
+
+                if audio.shape[0] == 0:
+                    # No speech detected — return empty result immediately
+                    req.result = []
+                    req.info = self._make_info(req, 0.0, 0.0)
+                    req.future.set()
+                    continue
+
+                duration = audio.shape[0] / self.transcriber.feature_extractor.sampling_rate
+                features = self.transcriber.feature_extractor(audio)
+                features = pad_or_trim(features)  # -> [n_mels, 3000]
+                preprocessed.append((req, features, audio, duration, speech_chunks))
+            except Exception as e:
+                req.error = e
+                req.future.set()
+
+        if not preprocessed:
+            return
+
+        try:
+            # Step 2: Batch GPU encode
+            feature_batch = np.stack([p[1] for p in preprocessed])  # [B, n_mels, 3000]
+            encoder_output = self.transcriber.encode(feature_batch)
+
+            # Step 3: Build per-item prompts (handles different languages/tasks)
+            tokenizers_list = []
+            prompts = []
+            resolved_languages = []
+
+            for i, (req, features, audio, duration, speech_chunks) in enumerate(preprocessed):
+                lang = req.language
+                # If language unknown, detect from encoder output
+                if lang is None:
+                    try:
+                        lang_results = self.transcriber.model.detect_language(encoder_output)
+                        if lang_results and len(lang_results) > i:
+                            detected = lang_results[i]
+                            if detected:
+                                lang = detected[0][0].strip("<|>")
+                    except Exception:
+                        lang = "en"  # fallback
+
+                resolved_languages.append(lang or "en")
+
+                tokenizer = Tokenizer(
+                    self.transcriber.hf_tokenizer,
+                    self.transcriber.model.is_multilingual,
+                    task=req.task,
+                    language=lang or "en",
+                )
+
+                previous_tokens = []
+                if req.initial_prompt:
+                    previous_tokens = tokenizer.encode(" " + req.initial_prompt.strip())
+
+                prompt = self.transcriber.get_prompt(
+                    tokenizer,
+                    previous_tokens=previous_tokens,
+                    without_timestamps=False,
+                )
+                tokenizers_list.append(tokenizer)
+                prompts.append(prompt)
+
+            # Step 4: Batch GPU generate
+            suppress_tokens = get_suppressed_tokens(tokenizers_list[0], [-1])
+
+            results = self.transcriber.model.generate(
+                encoder_output,
+                prompts,
+                beam_size=5,
+                patience=1,
+                length_penalty=1,
+                max_length=self.transcriber.max_length,
+                suppress_blank=True,
+                suppress_tokens=suppress_tokens,
+                return_scores=True,
+                return_no_speech_prob=True,
+                sampling_temperature=0.0,
+                repetition_penalty=1,
+                no_repeat_ngram_size=0,
+            )
+
+            # Step 5: Per-item segment parsing and result dispatch
+            for i, (req, features, audio, duration, speech_chunks) in enumerate(preprocessed):
+                try:
+                    tokenizer = tokenizers_list[i]
+                    gen_result = results[i]
+
+                    tokens = gen_result.sequences_ids[0]
+                    seq_len = len(tokens)
+                    cum_logprob = gen_result.scores[0] * seq_len
+                    avg_logprob = cum_logprob / (seq_len + 1) if seq_len > 0 else 0.0
+
+                    segment_size = int(ceil(duration) * self.transcriber.frames_per_second)
+
+                    subsegments, _, _ = self.transcriber._split_segments_by_timestamps(
+                        tokenizer=tokenizer,
+                        tokens=tokens,
+                        time_offset=0,
+                        segment_size=segment_size,
+                        segment_duration=duration,
+                        seek=0,
+                    )
+
+                    segments = []
+                    for seg_idx, subseg in enumerate(subsegments):
+                        text = tokenizer.decode(subseg["tokens"]).strip()
+                        if not text:
+                            continue
+                        segments.append(Segment(
+                            id=seg_idx,
+                            seek=subseg.get("seek", 0),
+                            start=subseg["start"],
+                            end=subseg["end"],
+                            text=text,
+                            tokens=subseg["tokens"],
+                            avg_logprob=avg_logprob,
+                            compression_ratio=get_compression_ratio(text),
+                            no_speech_prob=gen_result.no_speech_prob,
+                            words=None,
+                            temperature=0.0,
+                        ))
+
+                    req.result = segments
+                    req.info = self._make_info(
+                        req, duration, duration,
+                        language=resolved_languages[i],
+                    )
+                except Exception as e:
+                    req.error = e
+                finally:
+                    req.future.set()
+
+        except Exception as e:
+            logging.error(f"[BatchInference] GPU batch error: {e}")
+            for req, *_ in preprocessed:
+                if not req.future.is_set():
+                    req.error = e
+                    req.future.set()
+
+    def _make_info(self, req, duration, duration_after_vad, language=None):
+        """Build a ``TranscriptionInfo`` for the given request."""
+        return TranscriptionInfo(
+            language=language or req.language or "en",
+            language_probability=1.0,
+            duration=duration,
+            duration_after_vad=duration_after_vad,
+            all_language_probs=None,
+            transcription_options=None,
+            vad_options=None,
+        )

whisper_live/server.py

@@ -159,6 +159,7 @@ class TranscriptionServer:
         self.no_voice_activity_chunks = 0
         self.use_vad = True
         self.single_model = False
+        self.batch_config = None
 
     def initialize_client(
         self, websocket, options, faster_whisper_custom_model_path,
@@ -277,6 +278,18 @@ class TranscriptionServer:
                 )
 
                 logging.info("Running faster_whisper backend.")
+
+                # Start batch inference worker on first client (after model is loaded)
+                if (self.batch_config is not None
+                        and ServeClientFasterWhisper.BATCH_WORKER is None
+                        and ServeClientFasterWhisper.SINGLE_MODEL is not None):
+                    from whisper_live.batch_inference import BatchInferenceWorker
+                    worker = BatchInferenceWorker(
+                        transcriber=ServeClientFasterWhisper.SINGLE_MODEL,
+                        **self.batch_config,
+                    )
+                    worker.start()
+                    ServeClientFasterWhisper.BATCH_WORKER = worker
         except Exception as e:
             logging.error(e)
             return
@@ -415,13 +428,25 @@ class TranscriptionServer:
             cache_path="~/.cache/whisper-live/",
             rest_port=8000,
             enable_rest=False,
-            cors_origins: Optional[str] = None):
+            cors_origins: Optional[str] = None,
+            batch_enabled=False,
+            batch_max_size=8,
+            batch_window_ms=50):
         """
         Run the transcription server.
 
         Args:
             host (str): The host address to bind the server.
             port (int): The port number to bind the server.
+            batch_enabled (bool): Enable cross-client GPU batch inference for
+                the faster_whisper backend. When enabled, ``single_model`` is
+                forced to True and a ``BatchInferenceWorker`` is started after
+                the first client connects. Defaults to False.
+            batch_max_size (int): Maximum number of requests per GPU batch.
+                Defaults to 8.
+            batch_window_ms (int): Maximum time in milliseconds to wait for
+                the batch to fill after the first request arrives. Defaults
+                to 50.
         """
         self.cache_path = cache_path
         self.client_manager = ClientManager(max_clients, max_connection_time)
@@ -430,6 +455,18 @@ class TranscriptionServer:
                 raise ValueError(f"Custom faster_whisper model '{faster_whisper_custom_model_path}' is not a valid path or HuggingFace model.")
         if whisper_tensorrt_path is not None and not os.path.exists(whisper_tensorrt_path):
             raise ValueError(f"TensorRT model '{whisper_tensorrt_path}' is not a valid path.")
+
+        # Batch inference config
+        if batch_enabled:
+            single_model = True  # Batch mode requires shared model
+            self.batch_config = {
+                'max_batch_size': batch_max_size,
+                'batch_window_ms': batch_window_ms,
+            }
+            logging.info(f"Batch inference enabled (max_batch={batch_max_size}, window={batch_window_ms}ms)")
+        else:
+            self.batch_config = None
+
         if single_model:
             if faster_whisper_custom_model_path or whisper_tensorrt_path:
                 logging.info("Custom model option was provided. Switching to single model mode.")