fix

pyproject.toml

@@ -40,6 +40,7 @@ dependencies = [
     # core deps
     "torch>=2.4.0",
     "torchvision>=0.19.0",
+    "torchaudio>=2.4.0",
     "transformers>=4.49.0,<=4.56.2,!=4.52.0; python_version < '3.10'",
     "transformers>=4.49.0,<=4.57.1,!=4.52.0,!=4.57.0; python_version >= '3.10'",
     "datasets>=2.16.0,<=4.0.0",
@@ -63,6 +64,7 @@ dependencies = [
     "hf-transfer",
     "safetensors",
     # python
+    "av",
     "fire",
     "omegaconf",
     "packaging",
@@ -72,10 +74,7 @@ dependencies = [
     # api
     "uvicorn",
     "fastapi",
-    "sse-starlette",
-    # media
-    "av",
-    "librosa"
+    "sse-starlette"
 ]
 
 [project.optional-dependencies]

src/llamafactory/data/mm_plugin.py

@@ -26,6 +26,7 @@ from typing import TYPE_CHECKING, BinaryIO, Literal, Optional, TypedDict, Union
 
 import numpy as np
 import torch
+import torchaudio
 from transformers.image_utils import get_image_size, is_valid_image, to_numpy_array
 from transformers.models.mllama.processing_mllama import (
     convert_sparse_cross_attention_mask_to_dense,
@@ -34,16 +35,7 @@ from transformers.models.mllama.processing_mllama import (
 from typing_extensions import NotRequired, override
 
 from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
-from ..extras.packages import (
-    is_librosa_available,
-    is_pillow_available,
-    is_pyav_available,
-    is_transformers_version_greater_than,
-)
-
-
-if is_librosa_available():
-    import librosa
+from ..extras.packages import is_pillow_available, is_pyav_available, is_transformers_version_greater_than
 
 
 if is_pillow_available():
@@ -316,7 +308,14 @@ class MMPluginMixin:
         results, sampling_rates = [], []
         for audio in audios:
             if not isinstance(audio, np.ndarray):
-                audio, sampling_rate = librosa.load(audio, sr=sampling_rate)
+                audio, sr = torchaudio.load(audio)
+                if audio.shape[0] > 1:
+                    audio = audio.mean(dim=0, keepdim=True)
+
+                if sr != sampling_rate:
+                    audio = torchaudio.functional.resample(audio, sr, sampling_rate)
+
+                audio = audio.squeeze(0).numpy()
 
             results.append(audio)
             sampling_rates.append(sampling_rate)