diff --git a/batdetect2/api.py b/batdetect2/api.py index 4d04f42..86bf55b 100644 --- a/batdetect2/api.py +++ b/batdetect2/api.py @@ -97,8 +97,9 @@ consult the API documentation in the code. """ import warnings -from typing import List, Optional, Tuple +from typing import List, Optional, Tuple, BinaryIO, Any, Union +from .types import AudioPath import numpy as np import torch @@ -120,6 +121,12 @@ from batdetect2.types import ( ) from batdetect2.utils.detector_utils import list_audio_files, load_model +import audioread +import os +import soundfile as sf +import requests +import io + # Remove warnings from torch warnings.filterwarnings("ignore", category=UserWarning, module="torch") @@ -238,34 +245,82 @@ def generate_spectrogram( def process_file( - audio_file: str, + path: AudioPath, model: DetectionModel = MODEL, config: Optional[ProcessingConfiguration] = None, device: torch.device = DEVICE, + file_id: Optional[str] = None ) -> du.RunResults: """Process audio file with model. Parameters ---------- - audio_file : str - Path to audio file. + path : AudioPath + 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 ) +def process_url( + url: str, + model: DetectionModel = MODEL, + config: Optional[ProcessingConfiguration] = None, + device: torch.device = DEVICE, + file_id: Optional[str] = None +) -> du.RunResults: + """Process audio file with model. + + Parameters + ---------- + url : str + HTTP URL to load the audio data from + 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. Defaults to the URL + """ + if config is None: + config = CONFIG + + if file_id is None: + file_id = url + + response = requests.get(url) + + # Raise exception on HTTP error + response.raise_for_status() + + # Retrieve body as raw bytes + raw_audio_data = response.content + + return du.process_file( + io.BytesIO(raw_audio_data), + model, + config, + device, + file_id + ) def process_spectrogram( spec: torch.Tensor, diff --git a/batdetect2/types.py b/batdetect2/types.py index 57a60b4..3f22862 100644 --- a/batdetect2/types.py +++ b/batdetect2/types.py @@ -1,6 +1,10 @@ """Types used in the code base.""" -from typing import List, NamedTuple, Optional, Union +from typing import List, NamedTuple, Optional, Union, Any, BinaryIO + +import audioread +import os +import soundfile as sf import numpy as np import torch @@ -40,6 +44,9 @@ __all__ = [ "SpectrogramParameters", ] +AudioPath = Union[ + str, int, os.PathLike[Any], sf.SoundFile, audioread.AudioFile, BinaryIO + ] class SpectrogramParameters(TypedDict): """Parameters for generating spectrograms.""" diff --git a/batdetect2/utils/audio_utils.py b/batdetect2/utils/audio_utils.py index a60ea94..b89cdca 100644 --- a/batdetect2/utils/audio_utils.py +++ b/batdetect2/utils/audio_utils.py @@ -1,17 +1,24 @@ import warnings -from typing import Optional, Tuple +from typing import Optional, Tuple, Union, Any, BinaryIO + +from ..types import AudioPath 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 __all__ = [ "load_audio", + "load_audio_and_samplerate", "generate_spectrogram", "pad_audio", ] @@ -140,21 +147,20 @@ def generate_spectrogram( return spec, spec_for_viz - def load_audio( - audio_file: str, + path: AudioPath, 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. @@ -166,20 +172,50 @@ def load_audio( Raises: ValueError: If the audio file is stereo. + """ + sample_rate, audio_data, _ = load_audio_and_samplerate(path, time_exp_fact, target_samp_rate, scale, max_duration) + return sample_rate, audio_data + +def load_audio_and_samplerate( + path: AudioPath, + time_exp_fact: float, + target_samp_rate: int, + scale: bool = False, + max_duration: Optional[float] = None, +) -> Tuple[int, np.ndarray, Union[float, int]]: + """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: + 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. + + Returns: + sampling_rate: The sampling rate of the audio. + audio_raw: The audio signal in a numpy array. + file_sampling_rate: The original sampling rate of the audio + + Raises: + ValueError: If the audio file is stereo. + """ 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_file, + audio_raw, file_sampling_rate = librosa.load( + 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 @@ -207,7 +243,7 @@ def load_audio( audio_raw = audio_raw - audio_raw.mean() audio_raw = audio_raw / (np.abs(audio_raw).max() + 10e-6) - return sampling_rate, audio_raw + return sampling_rate, audio_raw, file_sampling_rate def compute_spectrogram_width( diff --git a/batdetect2/utils/detector_utils.py b/batdetect2/utils/detector_utils.py index 63643b6..f96c5d7 100644 --- a/batdetect2/utils/detector_utils.py +++ b/batdetect2/utils/detector_utils.py @@ -1,8 +1,9 @@ import json import os -from typing import Any, Iterator, List, Optional, Tuple, Union +from typing import Any, Iterator, List, Optional, Tuple, Union, BinaryIO + +from ..types import AudioPath -import librosa import numpy as np import pandas as pd import torch @@ -31,6 +32,13 @@ from batdetect2.types import ( SpectrogramParameters, ) +import audioread +import os +import io +import soundfile as sf +import hashlib +import uuid + __all__ = [ "load_model", "list_audio_files", @@ -729,10 +737,11 @@ def process_audio_array( def process_file( - audio_file: str, + path: AudioPath, model: DetectionModel, config: ProcessingConfiguration, device: torch.device, + file_id: Optional[str] = None ) -> Union[RunResults, Any]: """Process a single audio file with detection model. @@ -741,7 +750,7 @@ def process_file( Parameters ---------- - audio_file : str + path : AudioPath Path to audio file. model : torch.nn.Module @@ -749,6 +758,9 @@ def process_file( config : ProcessingConfiguration Configuration for processing. + + file_id: Optional[str], + Give the data an id. Defaults to the filename if path is a string. Otherwise an md5 will be calculated from the binary data. Returns ------- @@ -762,19 +774,17 @@ 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, + sampling_rate, audio_full, file_samp_rate = au.load_audio_and_samplerate( + 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"), ) + 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 # duplicate detections at borders @@ -823,9 +833,13 @@ def process_file( spec_slices, ) + _file_id = file_id + if _file_id is None: + _file_id = _generate_id(path) + # 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, @@ -845,6 +859,22 @@ def process_file( return results +def _generate_id(path: AudioPath) -> str: + """ Generate an id based on the path. + + If the path is a str or PathLike it will parsed as the basename. + This should ensure backwards compatibility with previous versions. + """ + if isinstance(path, str) or isinstance(path, os.PathLike): + return os.path.basename(path) + elif isinstance(path, (BinaryIO, io.BytesIO)): + path.seek(0) + md5 = hashlib.md5(path.read()).hexdigest() + path.seek(0) + return md5 + else: + return str(uuid.uuid4()) + def summarize_results(results, predictions, config): """Print summary of results.""" diff --git a/tests/test_api.py b/tests/test_api.py index e828c9e..d46786d 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -10,11 +10,13 @@ import torch from torch import nn from batdetect2 import api +import io PKG_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) TEST_DATA_DIR = os.path.join(PKG_DIR, "example_data", "audio") TEST_DATA = glob(os.path.join(TEST_DATA_DIR, "*.wav")) +DATA_DIR = os.path.join(os.path.dirname(__file__), "data") def test_load_model_with_default_params(): """Test loading model with default parameters.""" @@ -280,3 +282,28 @@ def test_process_file_with_empty_predictions_does_not_fail( assert results is not None assert len(results["pred_dict"]["annotation"]) == 0 + +def test_process_file_file_id_defaults_to_basename(): + """Test that process_file assigns basename as an id if no file_id is provided.""" + # Recording donated by @@kdarras + basename = "20230322_172000_selec2.wav" + path = os.path.join(DATA_DIR, basename) + + output = api.process_file(path) + predictions = output["pred_dict"] + id = predictions["id"] + assert id == basename + +def test_bytesio_file_id_defaults_to_md5(): + """Test that process_file assigns an md5 sum as an id if no file_id is provided when using binary data.""" + # Recording donated by @@kdarras + basename = "20230322_172000_selec2.wav" + path = os.path.join(DATA_DIR, basename) + + with open(path, "rb") as f: + data = io.BytesIO(f.read()) + + output = api.process_file(data) + predictions = output["pred_dict"] + id = predictions["id"] + assert id == "7ade9ebf1a9fe5477ff3a2dc57001929" diff --git a/tests/test_audio_utils.py b/tests/test_audio_utils.py index 1b489bc..ed64b15 100644 --- a/tests/test_audio_utils.py +++ b/tests/test_audio_utils.py @@ -6,7 +6,10 @@ from hypothesis import strategies as st from batdetect2.detector import parameters from batdetect2.utils import audio_utils, detector_utils +import io +import os +DATA_DIR = os.path.join(os.path.dirname(__file__), "data") @given(duration=st.floats(min_value=0.1, max_value=2)) def test_can_compute_correct_spectrogram_width(duration: float): @@ -134,3 +137,20 @@ def test_pad_audio_with_fixed_width(duration: float, width: int): resize_factor=params["resize_factor"], ) assert expected_width == width + + +def test_load_audio_using_bytesio(): + basename = "20230322_172000_selec2.wav" + path = os.path.join(DATA_DIR, basename) + + with open(path, "rb") as f: + data = io.BytesIO(f.read()) + + sample_rate, audio_data, file_sample_rate = audio_utils.load_audio_and_samplerate(data, time_exp_fact=1, target_samp_rate=parameters.TARGET_SAMPLERATE_HZ) + + expected_sample_rate, expected_audio_data, exp_file_sample_rate = audio_utils.load_audio_and_samplerate(path, time_exp_fact=1, target_samp_rate=parameters.TARGET_SAMPLERATE_HZ) + + assert expected_sample_rate == sample_rate + assert exp_file_sample_rate == file_sample_rate + + assert np.array_equal(audio_data, expected_audio_data) \ No newline at end of file