Changed the signature of api.process_file, au.load_audio and du.process_file. This allows users to use the same args for processing data as librosa.load()

batdetect2/api.py

@@ -97,7 +97,7 @@ consult the API documentation in the code.
 
 """
 import warnings
-from typing import List, Optional, Tuple
+from typing import List, Optional, Tuple, BinaryIO, Any, Union
 
 import numpy as np
 import torch
@@ -120,6 +120,10 @@ from batdetect2.types import (
 )
 from batdetect2.utils.detector_utils import list_audio_files, load_model
 
+import audioread
+import os 
+import soundfile as sf
+
 # Remove warnings from torch
 warnings.filterwarnings("ignore", category=UserWarning, module="torch")
 
@@ -238,32 +242,41 @@ def generate_spectrogram(
 
 
 def process_file(
-    audio_file: str,
+    path:  Union[
+        str, int, os.PathLike[Any], sf.SoundFile, audioread.AudioFile, BinaryIO
+    ],
     model: DetectionModel = MODEL,
     config: Optional[ProcessingConfiguration] = None,
     device: torch.device = DEVICE,
+    file_id: str | None = None
 ) -> du.RunResults:
     """Process audio file with model.
 
     Parameters
     ----------
-    audio_file : str
+    path :  Union[
-        Path to audio file.
+        str, int, os.PathLike[Any], sf.SoundFile, audioread.AudioFile, BinaryIO
+    ]
+        Path to audio data.
     model : DetectionModel, optional
         Detection model. Uses default model if not specified.
     config : Optional[ProcessingConfiguration], optional
         Processing configuration, by default None (uses default parameters).
     device : torch.device, optional
         Device to use, by default tries to use GPU if available.
+    file_id: Optional[str],
+        Give the data an id. If path is a string path to a file this can be ignored and
+        the file_id will be the basename of the file.
     """
     if config is None:
         config = CONFIG
 
     return du.process_file(
-        audio_file,
+        path,
         model,
         config,
         device,
+        file_id
     )
 
 

batdetect2/utils/audio_utils.py

@@ -1,11 +1,15 @@
 import warnings
-from typing import Optional, Tuple
+from typing import Optional, Tuple, Union, Any, BinaryIO
 
 import librosa
 import librosa.core.spectrum
 import numpy as np
 import torch
 
+import audioread
+import os 
+import soundfile as sf
+
 from batdetect2.detector import parameters
 
 from . import wavfile
@@ -140,21 +144,29 @@ def generate_spectrogram(
 
     return spec, spec_for_viz
 
+def get_samplerate(
+    path:  Union[
+        str, int, os.PathLike[Any], sf.SoundFile, audioread.AudioFile, BinaryIO
+    ]):
+    with sf.SoundFile(path) as f:
+        return f.samplerate
 
 def load_audio(
-    audio_file: str,
+    path:  Union[
+        str, int, os.PathLike[Any], sf.SoundFile, audioread.AudioFile, BinaryIO
+    ],
     time_exp_fact: float,
     target_samp_rate: int,
     scale: bool = False,
     max_duration: Optional[float] = None,
-) -> Tuple[int, np.ndarray]:
+) -> Tuple[int, np.ndarray ]:
     """Load an audio file and resample it to the target sampling rate.
 
     The audio is also scaled to [-1, 1] and clipped to the maximum duration.
     Only mono files are supported.
 
     Args:
-        audio_file (str): Path to the audio file.
+        path (string, int, pathlib.Path, soundfile.SoundFile, audioread object, or file-like object): path to the input file.
         target_samp_rate (int): Target sampling rate.
         scale (bool): Whether to scale the audio to [-1, 1].
         max_duration (float): Maximum duration of the audio in seconds.
@@ -170,16 +182,16 @@ def load_audio(
     with warnings.catch_warnings():
         warnings.filterwarnings("ignore", category=wavfile.WavFileWarning)
         # sampling_rate, audio_raw = wavfile.read(audio_file)
-        audio_raw, sampling_rate = librosa.load(
+        audio_raw, file_sampling_rate = librosa.load(
-            audio_file,
+            path,
             sr=None,
             dtype=np.float32,
         )
-
+    
     if len(audio_raw.shape) > 1:
         raise ValueError("Currently does not handle stereo files")
 
-    sampling_rate = sampling_rate * time_exp_fact
+    sampling_rate = file_sampling_rate * time_exp_fact
 
     # resample - need to do this after correcting for time expansion
     sampling_rate_old = sampling_rate

batdetect2/utils/detector_utils.py

@@ -1,6 +1,6 @@
 import json
 import os
-from typing import Any, Iterator, List, Optional, Tuple, Union
+from typing import Any, Iterator, List, Optional, Tuple, Union, BinaryIO
 
 import librosa
 import numpy as np
@@ -31,6 +31,11 @@ from batdetect2.types import (
     SpectrogramParameters,
 )
 
+import audioread
+import os 
+import soundfile as sf
+
+
 __all__ = [
     "load_model",
     "list_audio_files",
@@ -729,10 +734,13 @@ def process_audio_array(
 
 
 def process_file(
-    audio_file: str,
+    path:  Union[
+        str, int, os.PathLike[Any], sf.SoundFile, audioread.AudioFile, BinaryIO
+    ],
     model: DetectionModel,
     config: ProcessingConfiguration,
     device: torch.device,
+    file_id: str | None = None
 ) -> Union[RunResults, Any]:
     """Process a single audio file with detection model.
 
@@ -741,7 +749,7 @@ def process_file(
 
     Parameters
     ----------
-    audio_file : str
+    path : str, int, os.PathLike[Any], sf.SoundFile, audioread.AudioFile, BinaryIO
         Path to audio file.
 
     model : torch.nn.Module
@@ -762,18 +770,17 @@ def process_file(
     cnn_feats = []
     spec_slices = []
 
-    # Get original sampling rate
-    file_samp_rate = librosa.get_samplerate(audio_file)
-    orig_samp_rate = file_samp_rate * (config.get("time_expansion") or 1)
-
     # load audio file
     sampling_rate, audio_full = au.load_audio(
-        audio_file,
+        path,
         time_exp_fact=config.get("time_expansion", 1) or 1,
         target_samp_rate=config["target_samp_rate"],
         scale=config["scale_raw_audio"],
         max_duration=config.get("max_duration"),
     )
+    file_samp_rate = au.get_samplerate(path)
+
+    orig_samp_rate = file_samp_rate * (config.get("time_expansion") or 1)
 
     # loop through larger file and split into chunks
     # TODO: fix so that it overlaps correctly and takes care of
@@ -823,9 +830,13 @@ def process_file(
         spec_slices,
     )
 
+    _file_id = file_id
+    if _file_id is None:
+        _file_id = os.path.basename(path) if isinstance(path, str) else "unknown"
+
     # convert results to a dictionary in the right format
     results = convert_results(
-        file_id=os.path.basename(audio_file),
+        file_id=_file_id,
         time_exp=config.get("time_expansion", 1) or 1,
         duration=audio_full.shape[0] / float(sampling_rate),
         params=config,

tests/test_audio_utils.py

@@ -6,7 +6,8 @@ from hypothesis import strategies as st
 
 from batdetect2.detector import parameters
 from batdetect2.utils import audio_utils, detector_utils
-
+import io
+import requests
 
 @given(duration=st.floats(min_value=0.1, max_value=2))
 def test_can_compute_correct_spectrogram_width(duration: float):
@@ -134,3 +135,11 @@ def test_pad_audio_with_fixed_width(duration: float, width: int):
         resize_factor=params["resize_factor"],
     )
     assert expected_width == width
+
+def test_get_samplerate_using_bytesio():
+    audio_url="https://anon.erda.au.dk/share_redirect/e5c7G2AWmg/F1/20240724/2MU02597/BIOBD01_20240626_231650.wav"
+    
+    sample_rate = audio_utils.get_samplerate(io.BytesIO(requests.get(audio_url).content))
+
+    expected_sample_rate = 256000
+    assert expected_sample_rate == sample_rate