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Merge pull request #44 from kaviecos/http_support

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Http support
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Santiago Martinez Balvanera 2025-05-16 15:01:08 +01:00 committed by GitHub
commit c10903a646
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6 changed files with 202 additions and 27 deletions
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65
batdetect2/api.py
View File

@ -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,

9
batdetect2/types.py
View File

@ -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."""

52
batdetect2/utils/audio_utils.py
View File

@ -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",
]
@ -140,9 +147,8 @@ 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,
@ -154,7 +160,7 @@ def load_audio(
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.
@ -166,12 +172,42 @@ 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,
)
@ -179,7 +215,7 @@ def load_audio(
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(

52
batdetect2/utils/detector_utils.py
View File

@ -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
@ -750,6 +759,9 @@ 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
-------
results : Results or Any
@ -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."""

27
tests/test_api.py
View File

@ -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"

20
tests/test_audio_utils.py
View File

@ -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)