[Misc] Optional installation of audio related packages (#8063)

requirements-common.txt

@@ -22,9 +22,7 @@ typing_extensions >= 4.10
 filelock >= 3.10.4 # filelock starts to support `mode` argument from 3.10.4
 pyzmq
 msgspec
-librosa # Required for audio processing
-soundfile # Required for audio processing
 gguf == 0.9.1
 importlib_metadata
 mistral_common >= 1.3.4
-pyyaml
+pyyaml

requirements-test.txt

@@ -13,10 +13,12 @@ pytest-shard
 awscli
 einops # required for MPT, qwen-vl and Mamba
 httpx
+librosa # required for audio test
 peft
 requests
 ray
 sentence-transformers # required for embedding
+soundfile # required for audio test
 compressed-tensors==0.4.0 # required for compressed-tensors
 timm # required for internvl test
 transformers_stream_generator # required for qwen-vl test
@@ -30,4 +32,4 @@ aiohttp
 
 # quantization
 bitsandbytes==0.42.0
-buildkite-test-collector==0.1.8
+buildkite-test-collector==0.1.8

setup.py

@@ -501,6 +501,7 @@ setup(
     ext_modules=ext_modules,
     extras_require={
         "tensorizer": ["tensorizer>=2.9.0"],
+        "audio": ["librosa", "soundfile"]  # Required for audio processing
     },
     cmdclass={"build_ext": cmake_build_ext} if len(ext_modules) > 0 else {},
     package_data=package_data,

tests/models/test_ultravox.py

@@ -1,11 +1,9 @@
 from typing import List, Optional, Tuple, Type
 
-import librosa
 import numpy as np
 import pytest
 from transformers import AutoModel, AutoTokenizer, BatchEncoding
 
-from vllm.assets.audio import AudioAsset
 from vllm.sequence import SampleLogprobs
 from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
 
@@ -21,6 +19,7 @@ AudioTuple = Tuple[np.ndarray, int]
 
 @pytest.fixture(scope="session")
 def audio_and_sample_rate():
+    from vllm.assets.audio import AudioAsset
     return AudioAsset("mary_had_lamb").audio_and_sample_rate
 
 
@@ -109,6 +108,7 @@ def run_test(
                    dtype=dtype,
                    postprocess_inputs=process,
                    auto_cls=AutoModel) as hf_model:
+        import librosa
 
         hf_outputs_per_audio = [
             hf_model.generate_greedy_logprobs_limit(

vllm/model_executor/models/ultravox.py

@@ -8,7 +8,6 @@ from functools import lru_cache
 from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
                     TypedDict, Union, cast)
 
-import librosa
 import numpy as np
 import torch
 import torch.utils.checkpoint
@@ -107,6 +106,11 @@ def input_mapper_for_ultravox(ctx: InputContext, data: object):
         feature_extractor = whisper_feature_extractor(ctx)
 
         if sr != feature_extractor.sampling_rate:
+            try:
+                import librosa
+            except ImportError:
+                raise ImportError(
+                    "Please install vllm[audio] for audio support.") from None
             audio = librosa.resample(audio,
                                      orig_sr=sr,
                                      target_sr=feature_extractor.sampling_rate)

vllm/multimodal/utils.py

@@ -1,11 +1,9 @@
 import base64
 from functools import lru_cache
 from io import BytesIO
-from typing import List, Optional, Tuple, TypeVar, Union
+from typing import Any, List, Optional, Tuple, TypeVar, Union
 
-import librosa
 import numpy as np
-import soundfile
 from PIL import Image
 
 from vllm.connections import global_http_connection
@@ -73,10 +71,22 @@ async def async_fetch_image(image_url: str,
     return image.convert(image_mode)
 
 
+def try_import_audio_packages() -> Tuple[Any, Any]:
+    try:
+        import librosa
+        import soundfile
+    except ImportError:
+        raise ImportError(
+            "Please install vllm[audio] for audio support.") from None
+    return librosa, soundfile
+
+
 def fetch_audio(audio_url: str) -> Tuple[np.ndarray, Union[int, float]]:
     """
     Load audio from a URL.
     """
+    librosa, _ = try_import_audio_packages()
+
     if audio_url.startswith("http"):
         audio_bytes = global_http_connection.get_bytes(
             audio_url, timeout=VLLM_AUDIO_FETCH_TIMEOUT)
@@ -95,6 +105,8 @@ async def async_fetch_audio(
     """
     Asynchronously fetch audio from a URL.
     """
+    librosa, _ = try_import_audio_packages()
+
     if audio_url.startswith("http"):
         audio_bytes = await global_http_connection.async_get_bytes(
             audio_url, timeout=VLLM_AUDIO_FETCH_TIMEOUT)
@@ -123,6 +135,8 @@ def encode_audio_base64(
     sampling_rate: int,
 ) -> str:
     """Encode audio as base64."""
+    _, soundfile = try_import_audio_packages()
+
     buffered = BytesIO()
     soundfile.write(buffered, audio, sampling_rate, format="WAV")