diff --git a/funasr/utils/load_utils.py b/funasr/utils/load_utils.py
index b689d65ec..7c93dd34b 100644
--- a/funasr/utils/load_utils.py
+++ b/funasr/utils/load_utils.py
@@ -140,7 +140,11 @@ def load_audio_text_image_video(
                 except:
                     if hasattr(data_or_path_or_list, "seek"):
                         data_or_path_or_list.seek(0)
-                    data_or_path_or_list = _load_audio_ffmpeg(data_or_path_or_list, sr=fs)
+                    data_or_path_or_list = _load_audio_ffmpeg(
+                        data_or_path_or_list, sr=fs, input_sr=audio_fs
+                    )
+                    # FFmpeg has already resampled its output to the target rate.
+                    audio_fs = fs
                     data_or_path_or_list = torch.from_numpy(
                         data_or_path_or_list
                     ).squeeze()  # [n_samples,]
@@ -438,7 +442,7 @@ def extract_fbank(data, data_len=None, data_type: str = "sound", frontend=None,
     return data.to(torch.float32), data_len.to(torch.int32)
 
 
-def _load_audio_ffmpeg(file, sr: int = 16000):
+def _load_audio_ffmpeg(file, sr: int = 16000, input_sr=None):
     """
     Open an audio file and read as mono waveform, resampling as necessary
 
@@ -450,6 +454,10 @@ def _load_audio_ffmpeg(file, sr: int = 16000):
     sr: int
         The sample rate to resample the audio if necessary
 
+    input_sr: int or None
+        The source sample rate for raw PCM files. Defaults to sr; ignored for
+        containers, which provide their own sample rate.
+
     Returns
     -------
     A NumPy array containing the audio waveform, in float32 dtype.
@@ -470,7 +478,7 @@ def _load_audio_ffmpeg(file, sr: int = 16000):
     if isinstance(input_source, str) and input_source.lower().endswith('.pcm'):
         pcm_params = [
             "-f", "s16le",
-            "-ar", str(sr),
+            "-ar", str(sr if input_sr is None else input_sr),
             "-ac", "1"
         ]
 
diff --git a/tests/test_load_audio_bytes.py b/tests/test_load_audio_bytes.py
index d23ae04a7..6cbeb6eca 100644
--- a/tests/test_load_audio_bytes.py
+++ b/tests/test_load_audio_bytes.py
@@ -11,6 +11,8 @@
 import wave
 
 import numpy as np
+import pytest
+import soundfile as sf
 
 
 ROOT = Path(__file__).resolve().parents[1]
@@ -47,6 +49,13 @@ def _load_utils_module():
 
 LOAD_UTILS = _load_utils_module()
 
+
+def _consume_then_fail(value, *args, **kwargs):
+    if hasattr(value, "read"):
+        value.read()
+    raise RuntimeError("decoder unavailable")
+
+
 # 80 ms, 16 kHz mono, generated without ID3 or Xing metadata by ffmpeg 6.1.1.
 NO_ID3_MP3 = base64.b64decode(
     "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"
@@ -231,5 +240,110 @@ def test_corrupt_container_error_is_actionable(self):
             LOAD_UTILS.load_bytes(corrupt_wav)
 
 
+@pytest.mark.skipif(not LOAD_UTILS.is_ffmpeg_installed(), reason="ffmpeg is required")
+@pytest.mark.parametrize("source_rate", [8000, 16000, 48000])
+@pytest.mark.parametrize("target_rate", [16000, 24000])
+@pytest.mark.parametrize("rate_hint", [8000, 16000, 48000])
+@pytest.mark.parametrize("file_like", [False, True])
+def test_ffmpeg_fallback_uses_decoded_rate(
+    source_rate, target_rate, rate_hint, file_like, tmp_path, monkeypatch
+):
+    samples = _sine_pcm(source_rate, duration=1.0)
+    wav_data = _wav_bytes(samples, source_rate)
+    path = tmp_path / "audio.wav"
+    path.write_bytes(wav_data)
+    source = io.BytesIO(wav_data) if file_like else str(path)
+    expected = LOAD_UTILS._load_audio_ffmpeg(str(path), sr=target_rate)
+    monkeypatch.setattr(LOAD_UTILS.torchaudio, "load", _consume_then_fail)
+    monkeypatch.setattr(sf, "read", _consume_then_fail)
+
+    actual = LOAD_UTILS.load_audio_text_image_video(
+        source, fs=target_rate, audio_fs=rate_hint
+    )
+
+    assert len(actual) == target_rate
+    np.testing.assert_array_equal(actual, expected)
+
+
+@pytest.mark.skipif(not LOAD_UTILS.is_ffmpeg_installed(), reason="ffmpeg is required")
+@pytest.mark.parametrize("source_rate", [8000, 48000])
+def test_ffmpeg_fallback_without_torchaudio(source_rate, monkeypatch):
+    source = io.BytesIO(_wav_bytes(_sine_pcm(source_rate, 1.0), source_rate))
+    expected = LOAD_UTILS._load_audio_ffmpeg(source, sr=16000)
+    monkeypatch.setattr(LOAD_UTILS, "torchaudio", None)
+    monkeypatch.setattr(sf, "read", _consume_then_fail)
+
+    actual = LOAD_UTILS.load_audio_text_image_video(
+        source, fs=16000, audio_fs=source_rate
+    )
+
+    assert len(actual) == 16000
+    np.testing.assert_array_equal(actual, expected)
+
+
+@pytest.mark.parametrize("source_rate", [8000, 16000, 48000])
+@pytest.mark.parametrize("target_rate", [16000, 24000])
+@pytest.mark.parametrize("decoder", ["torchaudio", "soundfile"])
+def test_normal_decoders_use_container_rate(
+    source_rate, target_rate, decoder, tmp_path, monkeypatch
+):
+    samples = _sine_pcm(source_rate, duration=1.0)
+    path = tmp_path / "audio.wav"
+    path.write_bytes(_wav_bytes(samples, source_rate))
+    if decoder == "soundfile":
+        monkeypatch.setattr(LOAD_UTILS.torchaudio, "load", _consume_then_fail)
+    else:
+        # torchaudio 2.9+ requires the optional torchcodec package to load audio.
+        try:
+            LOAD_UTILS.torchaudio.load(str(path))
+        except (ImportError, RuntimeError) as error:
+            pytest.skip(f"torchaudio decoder unavailable: {error}")
+        monkeypatch.setattr(sf, "read", mock.Mock(side_effect=AssertionError))
+    fallback = mock.Mock(side_effect=AssertionError("unexpected ffmpeg fallback"))
+    monkeypatch.setattr(LOAD_UTILS, "_load_audio_ffmpeg", fallback)
+
+    actual = LOAD_UTILS.load_audio_text_image_video(
+        str(path), fs=target_rate, audio_fs=12345
+    )
+
+    assert len(actual) == target_rate
+    expected = _sine_pcm(target_rate, 1.0).astype(np.float32) / 32768.0
+    np.testing.assert_allclose(actual[100:-100], expected[100:-100], atol=0.001)
+    fallback.assert_not_called()
+
+
+@pytest.mark.skipif(not LOAD_UTILS.is_ffmpeg_installed(), reason="ffmpeg is required")
+@pytest.mark.parametrize("source_rate", [8000, 16000, 48000])
+@pytest.mark.parametrize("target_rate", [16000, 24000])
+def test_ffmpeg_raw_pcm_preserves_source_rate(
+    source_rate, target_rate, tmp_path, monkeypatch
+):
+    samples = _sine_pcm(source_rate, duration=1.0)
+    path = tmp_path / "audio.pcm"
+    path.write_bytes(samples.astype("<i2").tobytes())
+    monkeypatch.setattr(LOAD_UTILS.torchaudio, "load", _consume_then_fail)
+    monkeypatch.setattr(sf, "read", _consume_then_fail)
+
+    actual = LOAD_UTILS.load_audio_text_image_video(
+        str(path), fs=target_rate, audio_fs=source_rate
+    )
+
+    assert len(actual) == target_rate
+    expected = _sine_pcm(target_rate, 1.0).astype(np.float32) / 32768.0
+    np.testing.assert_allclose(actual[100:-100], expected[100:-100], atol=0.001)
+
+
+@pytest.mark.skipif(not LOAD_UTILS.is_ffmpeg_installed(), reason="ffmpeg is required")
+@pytest.mark.parametrize("sample_rate", [8000, 16000, 48000])
+def test_direct_ffmpeg_raw_pcm_defaults_to_requested_rate(sample_rate, tmp_path):
+    samples = _sine_pcm(sample_rate, duration=1.0)
+    path = tmp_path / "audio.pcm"
+    path.write_bytes(samples.astype("<i2").tobytes())
+
+    actual = LOAD_UTILS._load_audio_ffmpeg(str(path), sr=sample_rate)
+
+    np.testing.assert_array_equal(actual, samples.astype(np.float32) / 32768.0)
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_pcm_input_format.py b/tests/test_pcm_input_format.py
index 432834bc3..795f79335 100644
--- a/tests/test_pcm_input_format.py
+++ b/tests/test_pcm_input_format.py
@@ -256,6 +256,41 @@ def test_vad_source_rates_are_not_reapplied_to_resampled_segments(
         np.testing.assert_allclose(loaded, expected, atol=1e-6, rtol=0)
 
 
+@pytest.mark.skipif(not load_utils.is_ffmpeg_installed(), reason="ffmpeg is required")
+@pytest.mark.parametrize("source_rate", [8000, 48000])
+@pytest.mark.parametrize("batch", [False, True])
+def test_vad_ffmpeg_fallback_preserves_resampled_audio(
+    source_rate, batch, tmp_path, monkeypatch
+):
+    import soundfile as sf
+
+    samples = np.round(
+        np.sin(2 * np.pi * 440 * np.arange(source_rate) / source_rate) * 12000
+    ).astype("<i2")
+    path = tmp_path / "audio.wav"
+    sf.write(path, samples, source_rate, subtype="PCM_16")
+    expected = load_utils._load_audio_ffmpeg(str(path), sr=16000)
+
+    def decoder_unavailable(*args, **kwargs):
+        raise RuntimeError("decoder unavailable")
+
+    monkeypatch.setattr(load_utils.torchaudio, "load", decoder_unavailable)
+    monkeypatch.setattr(sf, "read", decoder_unavailable)
+    wrapper = _wrapper(vad=True)
+    wrapper.vad_model.segments = [[0, 1000]]
+    value = [str(path), str(path)] if batch else str(path)
+
+    results = wrapper.generate(input=value, fs=source_rate)
+
+    assert len(results) == (2 if batch else 1)
+    for _, vad_options in wrapper.vad_model.calls:
+        assert vad_options["fs"] == source_rate
+    for segments, asr_options in wrapper.model.calls:
+        assert asr_options["fs"] == 16000
+        assert len(segments[0]) == 16000
+        np.testing.assert_array_equal(segments[0], expected)
+
+
 class _RecordingSpeaker(torch.nn.Module):
     """Use CAMPPlus preprocessing with a stand-in embedding network."""