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import numpy as np
from tensorflow.signal import frame as tf_split_signal_into_chunks
from iSparrow import preprocessor_base as ppb
# README: work in progress - will be completed in separate issue
class Preprocessor(ppb.PreprocessorBase):
"""
Preprocessor Preprocess audio data into resampled chunks for analysis.
"""
def __init__(
self,
sample_rate: int = 32000,
sample_secs: float = 5.0,
resample_type: str = "kaiser_fast",
**kwargs
):
"""
__init__ Construct a new preprocesssor for custom birdnet classifiers from given parameters, and use defaults for the ones not present.
Args:
sample_rate (int, optional): The sample rate used to resample the read audio file. Defaults to 48000.
sample_secs (float, optional): Length of chunks to be analyzed at once. Defaults to 3.0.
resample_type (str, optional): Resampling method used when reading from file. Defaults to "kaiser_fast".
**kwargs: Additional keyword arguments that are not used in this class.
"""
# README: this class does not have an overlap attribute because the model it works with does not want it.
super().__init__(
"google_perch",
sample_rate=sample_rate,
sample_secs=sample_secs,
resample_type=resample_type,
**kwargs
)
def process_audio_data(self, rawdata: np.array) -> np.array:
"""
process_audio_data Process raw, resampled audio data into chunks that then can be analyzed
Args:
data (np.ndarray): raw, resampled audio data as returned from 'read_audio'
Returns:
list: chunked audio data
"""
print("process audio data custom", flush=True)
self.chunks = []
# raise when sampling rate is unequal.
if self.actual_sampling_rate != self.sample_rate:
raise RuntimeError(
"Sampling rate is not the desired one. Desired sampling rate: {self.sample_rate}, actual sampling rate: {self.actual_sampling_rate}"
)
frame_length = int(self.sample_secs * self.sample_rate)
step_length = int(self.sample_secs - self.overlap) * self.sample_rate
self.chunks = tf_split_signal_into_chunks(
rawdata, frame_length, step_length, pad_end=True
).numpy()
print(
"process audio data google: complete, read ",
str(len(self.chunks)),
"chunks.",
flush=True
)
return self.chunks
@classmethod
def from_cfg(cls, cfg: dict):
# make sure there are no more than the allowed keyword arguments in the cfg
allowed = [
"sample_rate",
"sample_secs",
"resample_type",
"duration",
"actual_sampling_rate",
]
if len([key for key in cfg if key not in allowed]) > 0:
raise RuntimeError("Erroneous keyword arguments in preprocessor config")
return cls(**cfg)
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