diff --git a/setup.py b/setup.py
index 83b8b008ab45..5218b6eff4fc 100644
--- a/setup.py
+++ b/setup.py
@@ -976,6 +976,7 @@ def _read_requirements(filename: str) -> list[str]:
             "soundfile",
             "mistral_common[audio]",
             "av",
+            "torchcodec",
         ],  # Required for audio processing
         "video": [],  # Kept for backwards compatibility
         "flashinfer": [],  # Kept for backwards compatibility
diff --git a/vllm/entrypoints/openai/speech_to_text/speech_to_text.py b/vllm/entrypoints/openai/speech_to_text/speech_to_text.py
index ac621270d660..31902bfa7f0f 100644
--- a/vllm/entrypoints/openai/speech_to_text/speech_to_text.py
+++ b/vllm/entrypoints/openai/speech_to_text/speech_to_text.py
@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import asyncio
-import io
 import math
 import time
 import zlib
@@ -11,7 +10,6 @@
 
 import numpy as np
 from fastapi import Request
-from soundfile import LibsndfileError
 from transformers import PreTrainedTokenizerBase
 
 import vllm.envs as envs
@@ -37,6 +35,7 @@
     TranslationSegment,
     TranslationStreamResponse,
 )
+from vllm.entrypoints.openai.speech_to_text.utils import load_audio_bytes
 from vllm.entrypoints.utils import get_max_tokens
 from vllm.exceptions import VLLMValidationError
 from vllm.inputs import EncoderDecoderInputs, ProcessorInputs
@@ -56,14 +55,6 @@
 except ImportError:
     librosa = PlaceholderModule("librosa")  # type: ignore[assignment]
 
-# Public libsndfile error codes exposed via `soundfile.LibsndfileError.code`, soundfile
-# being librosa's main backend. Used to validate if an audio loading error is due to a
-# server error vs a client error (invalid audio file).
-# 1 = unrecognised format      (file is not a supported audio container)
-# 3 = malformed file           (corrupt or structurally invalid audio)
-# 4 = unsupported encoding     (codec not supported by this libsndfile build)
-_BAD_SF_CODES = {1, 3, 4}
-
 SpeechToTextResponse: TypeAlias = TranscriptionResponse | TranslationResponse
 SpeechToTextResponseVerbose: TypeAlias = (
     TranscriptionResponseVerbose | TranslationResponseVerbose
@@ -202,16 +193,12 @@ async def _preprocess_speech_to_text(
                 value=len(audio_data) / 1024**2,
             )
 
-        with io.BytesIO(audio_data) as bytes_:
-            try:
-                # NOTE resample to model SR here for efficiency. This is also a
-                # pre-requisite for chunking, as it assumes Whisper SR.
-                y, sr = librosa.load(bytes_, sr=self.asr_config.sample_rate)
-            except LibsndfileError as exc:
-                # Distinguish client errors (invalid audio) from server errors
-                if exc.code in _BAD_SF_CODES:
-                    raise ValueError("Invalid or unsupported audio file.") from exc
-                raise
+        # Decode audio bytes.  For container formats (MP4, M4A, WebM) that
+        # soundfile cannot detect from a BytesIO stream, _load_audio_bytes
+        # transparently falls back to ffmpeg via an in-memory fd.
+        # NOTE resample to model SR here for efficiency. This is also a
+        # pre-requisite for chunking, as it assumes Whisper SR.
+        y, sr = load_audio_bytes(audio_data, sr=self.asr_config.sample_rate)
 
         duration = librosa.get_duration(y=y, sr=sr)
         do_split_audio = (
diff --git a/vllm/entrypoints/openai/speech_to_text/utils.py b/vllm/entrypoints/openai/speech_to_text/utils.py
new file mode 100644
index 000000000000..ec82cdc3c2d5
--- /dev/null
+++ b/vllm/entrypoints/openai/speech_to_text/utils.py
@@ -0,0 +1,106 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Audio decoding utilities for the speech-to-text endpoints."""
+
+import io
+
+import numpy as np
+import torchaudio
+
+from vllm.logger import init_logger
+from vllm.utils.import_utils import PlaceholderModule
+
+try:
+    import librosa
+except ImportError:
+    librosa = PlaceholderModule("librosa")  # type: ignore[assignment]
+
+try:
+    import soundfile as sf
+except ImportError:
+    sf = PlaceholderModule("soundfile")  # type: ignore[assignment]
+
+logger = init_logger(__name__)
+
+# Public libsndfile error codes exposed via ``soundfile.LibsndfileError.code``.
+# soundfile is librosa's primary backend.  These codes indicate that the audio
+# data itself is problematic (unrecognised container, corrupt file, or
+# unsupported encoding) rather than a transient server error.
+# 1 = unrecognised format, 3 = malformed file, 4 = unsupported encoding
+_BAD_SF_CODES = {1, 3, 4}
+
+
+def _decode_audio_bytes_torchaudio(
+    audio_data: bytes,
+    sr: int,
+) -> tuple[np.ndarray, int]:
+    """Decode audio bytes to mono float32 PCM via torchaudio, in-process.
+
+    ``torchaudio.load`` (backed by TorchCodec / FFmpeg) can decode
+    container formats (MP4, M4A, WebM) directly from a ``BytesIO``
+    buffer without spawning a subprocess.  The decoded waveform is
+    down-mixed to mono and resampled to *sr* Hz, matching the return
+    convention of ``librosa.load``.
+    """
+    buf = io.BytesIO(audio_data)
+    waveform, orig_sr = torchaudio.load(buf)
+
+    # Down-mix to mono (average across channels).
+    if waveform.shape[0] > 1:
+        waveform = waveform.mean(dim=0, keepdim=True)
+
+    # Resample to the target sample rate when necessary.
+    if orig_sr != sr:
+        waveform = torchaudio.functional.resample(
+            waveform, orig_freq=orig_sr, new_freq=sr
+        )
+
+    # Squeeze channel dim → 1-D float32 numpy array (same as librosa.load).
+    y = waveform.squeeze(0).numpy()
+    if y.size == 0:
+        raise RuntimeError(
+            "torchaudio produced no audio samples (file may be empty or corrupt)"
+        )
+    return y, sr
+
+
+def load_audio_bytes(
+    audio_data: bytes,
+    sr: int | float,
+) -> tuple[np.ndarray, int]:
+    """Load audio from raw bytes, with an in-process torchaudio fallback.
+
+    First tries ``librosa.load(BytesIO(...))`` which works for formats
+    that *soundfile* can auto-detect (WAV, FLAC, MP3, OGG, ...).  If
+    that fails with a ``LibsndfileError`` indicating an unrecognised or
+    unsupported format (typically container formats like MP4/M4A/WebM),
+    the bytes are decoded in-process via ``torchaudio`` (backed by
+    TorchCodec / FFmpeg) which handles these containers natively.
+    """
+    sr = int(sr)
+
+    # Fast path: librosa + soundfile (works for most formats).
+    try:
+        with io.BytesIO(audio_data) as buf:
+            return librosa.load(buf, sr=sr)  # type: ignore[return-value]
+    except sf.LibsndfileError as exc:
+        # Only fall back for known format-detection failures.
+        # Re-raise anything else (e.g. corrupt but recognised format).
+        if exc.code not in _BAD_SF_CODES:
+            raise
+        logger.debug(
+            "librosa/soundfile could not decode audio from BytesIO "
+            "(code=%s: %s); falling back to torchaudio in-process decode",
+            exc.code,
+            exc,
+        )
+
+    # Fallback: torchaudio in-process decode (no subprocess overhead).
+    try:
+        return _decode_audio_bytes_torchaudio(audio_data, sr)
+    except Exception as ta_exc:
+        logger.debug(
+            "torchaudio fallback also failed: %s",
+            ta_exc,
+        )
+        raise ValueError("Invalid or unsupported audio file.") from ta_exc