add all tflite models

birdnet_custom_v2.4/README.md

@@ -0,0 +1,20 @@
+# README 
+
+This folder provides a model that has been obtained from the unchanged birdnetv2.4 model available [here](https://github.com/kahst/BirdNET-Analyzer) under `checkpoints`, originally developed by the birdnet team 
+and published here
+
+```
+@article{kahl2021birdnet,
+  title={BirdNET: A deep learning solution for avian diversity monitoring},
+  author={Kahl, Stefan and Wood, Connor M and Eibl, Maximilian and Klinck, Holger},
+  journal={Ecological Informatics},
+  volume={61},
+  pages={101236},
+  year={2021},
+  publisher={Elsevier}
+}
+```
+and distributed under the [ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
+It has been obtained by following the [`Training`](https://github.com/kahst/BirdNET-Analyzer?tab=readme-ov-file#training) section in the readme of birdnet-analyzer. 
+Thus, it is merely a toy model and not useful for any serious inference. 

birdnet_custom_v2.4/default.yml

@@ -0,0 +1,29 @@
+Analysis: 
+  Recording:
+    species_presence_threshold: 0.03
+    min_conf: 0.25
+    file_check_poll_interval: 1
+  Model:
+    sigmoid_sensitivity: 1.0
+    num_threads: 2
+    species_list_file: null
+
+  SpeciesPresence: 
+    threshold: 0.03
+    num_threads: 1
+    use_cache: true
+
+  modelname: birdnet_default
+  pattern: ".wav"
+  check_time: 1
+  delete_recordings: "never"
+  model_dir: ~/iSparrow/models 
+
+Data: 
+  input: ~/iSparrow_data
+  output: ~/iSparrow_output
+  Preprocessor:
+    sample_rate: 48000
+    overlap: 0.0
+    sample_secs: 3.0
+    resample_type: kaiser_fast

birdnet_custom_v2.4/labels.txt

@@ -0,0 +1,4 @@
+Cardinalis cardinalis_Northern Cardinal
+Cyanocitta cristata_Blue Jay
+Junco hyemalis_Dark-eyed Junco
+Poecile atricapillus_Black-capped Chickadee

birdnet_custom_v2.4/model.py

@@ -0,0 +1,215 @@
+from pathlib import Path
+import numpy as np
+
+try:
+    import tflite_runtime.interpreter as tflite
+except Exception:
+    from tensorflow import lite as tflite
+
+from birdnetlib.analyzer import AnalyzerConfigurationError
+
+from iSparrow.model_base import ModelBase
+from iSparrow import utils
+
+
+class Model(ModelBase):
+
+    def _check_classifier_path_integrity(
+        self, classifier_model_path: str, classifier_labels_path: str
+    ):
+        """checks if custom classifier/labels are both given if one is present and the files they point to exist"""
+
+        if (classifier_model_path is not None and classifier_labels_path is None) or (
+            classifier_model_path is None and classifier_labels_path is not None
+        ):
+            raise AnalyzerConfigurationError(
+                "Model and label file paths must be specified to use a custom classifier"
+            )
+
+        if (
+            classifier_model_path is not None
+            and Path(classifier_model_path).exists() is False
+        ):
+            raise AnalyzerConfigurationError(
+                f"Custom classifier model could not be found at the provided path {classifier_model_path}"
+            )
+
+        if (
+            classifier_model_path is not None
+            and Path(classifier_labels_path).exists() is False
+        ):
+            raise AnalyzerConfigurationError(
+                f"Custom classifier labels could not be found at the provided path {classifier_labels_path}"
+            )
+
+    #
+    def __init__(
+        self,
+        default_model_path: str = None,
+        model_path: str = None,
+        sigmoid_sensitivity: float = 1.0,
+        num_threads: int = 1,
+        **kwargs 
+    ):
+
+        self.default_model_path = str(Path(default_model_path) / "model.tflite")
+        self.default_labels_path = str(Path(default_model_path) / "labels.txt")
+
+        classifier_model_path = str(Path(model_path) / "model.tflite")
+        classifier_labels_path = str(Path(model_path) / "labels.txt")
+
+        # check custom classifier paths through function due to higher complexity
+        self._check_classifier_path_integrity(
+            classifier_model_path, classifier_labels_path
+        )
+        # need to call this custom because the super class has no prefix..
+        self.custom_classifier = None
+        self.custom_input_layer_index = None
+        self.custom_output_layer_index = None
+
+        self.input_layer_index = None
+        self.output_layer_index = None
+        self.sigmoid_sensitivity = sigmoid_sensitivity
+
+        # use the super class for handling the default models and load the custom ones in this one
+        super().__init__(
+            "birdnet_custom",
+            model_path=classifier_model_path,
+            labels_path=classifier_labels_path,
+            num_threads=num_threads,
+            **kwargs
+        )
+
+    def load_model(self):
+        """
+        load_model Load the default model for making feature embeddings and the custom classifier for classifying them into species.
+
+        """
+
+        # this overrides the base method because we need to load the default models to provide
+        # the feature embeddings and the custom classifier to apply to them to get the actual
+        # classification
+
+        # load the default model
+        self.model = utils.load_model_from_file_tflite(
+            self.default_model_path, num_threads=self.num_threads
+        )
+        self.model.allocate_tensors()
+
+        # Get input and output tensors.
+        input_details = self.model.get_input_details()
+        output_details = self.model.get_output_details()
+
+        # Get input tensor index
+        self.input_layer_index = input_details[0]["index"]
+
+        # Get  feature embeddings
+        self.output_layer_index = output_details[0]["index"] - 1
+        print("Default classifier loaded", flush = True)
+
+        # now load the custom classifier
+        self.custom_classifier = tflite.Interpreter(
+            model_path=str(self.model_path), num_threads=self.num_threads
+        )
+        self.custom_classifier.allocate_tensors()
+
+        # Get input and output tensors.
+        custom_input_details = self.custom_classifier.get_input_details()
+        custom_output_details = self.custom_classifier.get_output_details()
+
+        self.custom_input_layer_index = custom_input_details[0]["index"]
+        self.custom_output_layer_index = custom_output_details[0]["index"]
+
+        print("Custom classifier loaded")
+
+    def load_species_list(self):
+        # TODO
+        pass
+
+    def get_embeddings(self, data: np.array) -> np.array:
+        """
+        get_embeddings Extract feature embedding from audio file without immediatelly classifying the species.
+                       These can in a second step be used with a custom classifier to find species not
+                       included in the default training data.
+
+        Args:
+            data (np.array): Preprocessed audio snippet to extract features from
+
+        Returns:
+            np.array: Feature embedding produces by the default birdnet CNN.
+        """
+        print(" get embeddings", flush = True)
+        self.model.resize_tensor_input(
+            self.input_layer_index, [len(data), *data[0].shape]
+        )
+
+        self.model.allocate_tensors()
+
+        # Extract feature embeddings
+        self.model.set_tensor(self.input_layer_index, np.array(data, dtype="float32"))
+
+        self.model.invoke()
+
+        features = self.model.get_tensor(self.output_layer_index)
+
+        return features
+
+    def predict(self, sample: np.array) -> np.array:
+        """
+        predict Make inference about the bird species for the preprocessed data passed to this function as arguments.
+
+        Args:
+            data (np.array): list of preprocessed data chunks
+        Returns:
+            list: List of (label, inferred_probability)
+        """
+        data = np.array([sample], dtype="float32")
+
+        input_details = self.custom_classifier.get_input_details()
+
+        input_size = input_details[0]["shape"][-1]
+
+        feature_vector = self.get_embeddings(data) if input_size != 144000 else data
+
+        self.custom_classifier.resize_tensor_input(
+            self.custom_input_layer_index,
+            [len(feature_vector), *feature_vector[0].shape],
+        )
+
+        self.custom_classifier.allocate_tensors()
+
+        # Make a prediction
+        self.custom_classifier.set_tensor(
+            self.custom_input_layer_index, np.array(feature_vector, dtype="float32")
+        )
+
+        self.custom_classifier.invoke()
+
+        prediction = self.custom_classifier.get_tensor(self.custom_output_layer_index)
+
+        # map to probabilities
+        confidence = self._sigmoid(np.array(prediction), -self.sigmoid_sensitivity)
+
+        return confidence
+
+    @classmethod
+    def from_cfg(cls, iSparrow_dir: str, cfg: dict):
+        """
+        from_cfg Create a new instance from a dictionary containing keyword arguments. Usually loaded from a config file.
+
+        Args:
+            iSparrow_dir (str): Installation directory of the iSparrow package
+            cfg (dict): Dictionary containing the keyword arguments
+
+        Returns:
+            Model: New model instance created with the supplied kwargs.
+        """
+
+        # preprocess config because we need two models here
+        cfg["default_model_path"] = str(
+            Path(iSparrow_dir) / Path("models") / Path("birdnet_default")
+        )
+        cfg["model_path"] = str(
+            Path(iSparrow_dir) / Path("models") / Path(cfg["model_path"])
+        )
+        return cls(**cfg)

birdnet_custom_v2.4/model.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5eee37652430c54490321e0d2096d55817b4ac4da2301b594f03fcb9d52af741
+size 25008856

birdnet_custom_v2.4/params.csv

@@ -0,0 +1,2 @@
+Hidden units,Dropout,Batchsize,Learning rate,Crop mode,Crop overlap,Upsamling mode,Upsamling ratio,use mixup,use label smoothing
+0,0,32,0.001,center,0.0,repeat,0,False,False

birdnet_custom_v2.4/preprocessor.py

@@ -0,0 +1,103 @@
+import numpy as np
+import iSparrow.preprocessor_base as ppb
+
+
+class Preprocessor(ppb.PreprocessorBase):
+    """
+    Preprocessor Preprocess audio data into resampled chunks for analysis.
+
+    """
+
+    def __init__(
+        self,
+        sample_rate: int = 48000,
+        overlap: float = 0.0,
+        sample_secs: int = 3.0,
+        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.
+            overlap (float, optional): Overlap between chunks to be analyzed. Defaults to 0.0.
+            sample_secs (int, 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".
+        """
+
+        super().__init__(
+            "birdnet_custom",
+            sample_rate=sample_rate,
+            overlap=overlap,
+            sample_secs=sample_secs,
+            resample_type=resample_type,
+            **kwargs
+        )
+
+    def process_audio_data(self, rawdata: np.ndarray) -> list:
+        """
+        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)
+        seconds = self.sample_secs
+        minlen = 1.5
+
+        self.chunks = []
+
+        for i in range(
+            0, len(rawdata), int((seconds - self.overlap) * self.sample_rate)
+        ):
+
+            split = rawdata[i : (i + int(seconds * self.actual_sampling_rate))]
+
+            # End of signal?
+            if len(split) < int(minlen * self.actual_sampling_rate):
+                break
+
+            # Signal chunk too short? Fill with zeros.
+            if len(split) < int(self.actual_sampling_rate * seconds):
+                temp = np.zeros((int(self.actual_sampling_rate * seconds)))
+                temp[: len(split)] = split
+                split = temp
+
+            self.chunks.append(split)
+
+        print(
+            "process audio data custom: complete, read ",
+            str(len(self.chunks)),
+            "chunks.",
+            flush=True
+        )
+
+        return self.chunks
+
+    @classmethod
+    def from_cfg(cls, cfg: dict):
+        """
+        from_cfg Construct a new preprocessor from a given dictionary. This represents typically a config node read from a YAML file.
+
+        Args:
+            cfg (dict): Config node read from a YAML file
+
+        Returns:  new preprocessor instance
+        """
+        allowed = [
+            "sample_rate",
+            "overlap",
+            "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)

birdnet_default_v2.4/README.md

@@ -0,0 +1,17 @@
+# README 
+
+This folder provides the unchanged birdnetv2.4 model available [here](https://github.com/kahst/BirdNET-Analyzer) under `checkpoints`, originally developed by the birdnet team 
+and published here
+
+```
+@article{kahl2021birdnet,
+  title={BirdNET: A deep learning solution for avian diversity monitoring},
+  author={Kahl, Stefan and Wood, Connor M and Eibl, Maximilian and Klinck, Holger},
+  journal={Ecological Informatics},
+  volume={61},
+  pages={101236},
+  year={2021},
+  publisher={Elsevier}
+}
+```
+and distributed under the [ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/)

birdnet_default_v2.4/default.yml

@@ -0,0 +1,29 @@
+Analysis: 
+  Recording:
+    species_presence_threshold: 0.03
+    min_conf: 0.25
+    file_check_poll_interval: 1
+  Model:
+    sigmoid_sensitivity: 1.0
+    num_threads: 2
+    species_list_file: null
+
+  SpeciesPresence: 
+    threshold: 0.03
+    num_threads: 1
+    use_cache: true
+
+  modelname: birdnet_default
+  pattern: ".wav"
+  check_time: 1
+  delete_recordings: "never"
+  model_dir: ~/iSparrow/models 
+
+Data: 
+  input: ~/iSparrow_data
+  output: ~/iSparrow_output
+  Preprocessor:
+    sample_rate: 48000
+    overlap: 0.0
+    sample_secs: 3.0
+    resample_type: kaiser_fast

birdnet_default_v2.4/model.py

@@ -0,0 +1,110 @@
+from pathlib import Path
+import numpy as np
+
+from iSparrow.model_base import ModelBase
+
+# from iSparrow import utils
+
+
+class Model(ModelBase):
+    """
+    Model Model class representing the the default birdnet model. Does currently not support custom species list or species prediction based on coordinates.
+
+    """
+
+    def __init__(
+        self,
+        model_path: str,
+        num_threads: int = 1,
+        sigmoid_sensitivity: float = 1.0,
+        species_list_file: str = None,
+        **kwargs
+    ):
+        """
+        __init__ Create a new model instance that uses birdnet-analyzer models for bird species classification
+
+        Args:
+            model_path (str): Path to the location of the model file to be loaded
+            num_threads (int, optional): Number of threads used for inference. Defaults to 1.
+            sigmoid_sensitivity (float, optional): Parameter of the sigmoid function used to compute probabilities. Defaults to 1.0.
+
+        Raises:
+            AnalyzerConfigurationError: The model file 'model.tflite' doesn't exist at the given path.
+            AnalyzerConfigurationError: The labels file 'labels.txt' doesn't exist at the given path.
+        """
+
+        labels_path = str(Path(model_path) / "labels.txt")
+
+        model_path = str(Path(model_path) / "model.tflite")
+
+        # base class loads the model and labels
+        super().__init__(
+            "birdnet_default",
+            model_path,
+            labels_path,
+            num_threads=num_threads,
+            **kwargs
+        )
+
+        self.sigmoid_sensitivity = sigmoid_sensitivity
+
+        # store input and output index to not have to retrieve them each time an inference is made
+        input_details = self.model.get_input_details()
+
+        output_details = self.model.get_output_details()
+
+        # Get input tensor index
+        self.input_layer_index = input_details[0]["index"]
+
+        # Get classification output or feature embeddings as output, depending on presence fo custom classifier
+        self.output_layer_index = output_details[0]["index"]
+
+    def load_species_list(self):
+        # TODO
+        pass
+
+    def predict(self, sample: np.array) -> np.array:
+        """
+        predict Make inference about the bird species for the preprocessed data passed to this function as arguments.
+
+        Args:
+            data (np.array): list of preprocessed data chunks
+        Returns:
+            list: List of (label, inferred_probability)
+        """
+        data = np.array([sample], dtype="float32")
+
+        self.model.resize_tensor_input(
+            self.input_layer_index, [len(data), *data[0].shape]
+        )
+        self.model.allocate_tensors()
+
+        # Make a prediction (Audio only for now)
+        self.model.set_tensor(self.input_layer_index, np.array(data, dtype="float32"))
+        self.model.invoke()
+
+        prediction = self.model.get_tensor(self.output_layer_index)
+
+        confidence = self._sigmoid(
+            np.array(prediction), sigmoid_sensitivity=-self.sigmoid_sensitivity
+        )
+
+        return confidence
+
+    @classmethod
+    def from_cfg(cls, iSparrow_folder: str, cfg: dict):
+        """
+        from_cfg Create a new instance from a dictionary containing keyword arguments. Usually loaded from a config file.
+
+        Args:
+            iSparrow_dir (str): Installation directory of the iSparrow package
+            cfg (dict): Dictionary containing the keyword arguments
+
+        Returns:
+            Model: New model instance created with the supplied kwargs.
+        """
+        cfg["model_path"] = str(
+            Path(iSparrow_folder) / Path("models") / cfg["model_path"]
+        )
+
+        return cls(**cfg)

birdnet_default_v2.4/model.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55f3e4055b1a13bfa9a2452731d0d34f6a02d6b775a334362665892794165e4c
+size 51726412

birdnet_default_v2.4/preprocessor.py

@@ -0,0 +1,101 @@
+import numpy as np
+import iSparrow.preprocessor_base as ppb
+
+
+class Preprocessor(ppb.PreprocessorBase):
+    """
+    Preprocessor Preprocess audio data into resampled chunks for analysis.
+
+    """
+
+    def __init__(
+        self,
+        sample_rate: int = 48000,
+        overlap: float = 0.0,
+        sample_secs: int = 3.0,
+        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.
+            overlap (float, optional): Overlap between chunks to be analyzed. Defaults to 0.0.
+            sample_secs (int, 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".
+        """
+
+        super().__init__(
+            "birdnet_default",
+            sample_rate=sample_rate,
+            overlap=overlap,
+            sample_secs=sample_secs,
+            resample_type=resample_type,
+            **kwargs
+        )
+
+    def process_audio_data(self, rawdata: np.ndarray) -> list:
+        """
+        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 default", flush=True)
+        seconds = self.sample_secs
+        minlen = 1.5
+
+        self.chunks = []
+
+        for i in range(
+            0, len(rawdata), int((seconds - self.overlap) * self.sample_rate)
+        ):
+
+            split = rawdata[i : (i + int(seconds * self.actual_sampling_rate))]
+
+            # End of signal?
+            if len(split) < int(minlen * self.actual_sampling_rate):
+                break
+
+            # Signal chunk too short? Fill with zeros.
+            if len(split) < int(self.actual_sampling_rate * seconds):
+                temp = np.zeros((int(self.actual_sampling_rate * seconds)))
+                temp[: len(split)] = split
+                split = temp
+
+            self.chunks.append(split)
+
+        print(
+            "process audio data default: complete, read ",
+            str(len(self.chunks)),
+            "chunks.",flush=True)
+
+        return self.chunks
+
+    @classmethod
+    def from_cfg(cls, cfg: dict):
+        """
+        from_cfg Construct a new preprocessor from a given dictionary. This represents typically a config node read from a YAML file.
+
+        Args:
+            cfg (dict): Config node read from a YAML file
+
+        Returns:  new preprocessor instance
+        """
+        allowed = [
+            "sample_rate",
+            "overlap",
+            "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)

birdnet_default_v2.4/species_presence_model.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1226f23fc20362617deb09178f366111b70cf085c2c82893f2814e5acedce6c2
+size 14770468

google_perch_lite/Readme.md

@@ -0,0 +1,11 @@
+# Google Perch 
+
+The model has been pulled from [here](https://www.kaggle.com/models/google/bird-vocalization-classifier/TensorFlow2/bird-vocalization-classifier/2) and 
+is distributed under the apache 2.0 licence there and here. 
+
+Please refer to the above link for more information.
+
+This is a conversion of the original model obtained from the above link that has 
+been converted to tflite format. 
+This has been done using the notebook 'tf_conversion.ipynb' supplied in this repository folder. 
+Please refer to its content for details. 

google_perch_lite/default.yml

@@ -0,0 +1,31 @@
+Analysis: 
+  Recording:
+    species_presence_threshold: 0.03
+    min_conf: 0.25
+    file_check_poll_interval: 1
+    lat: null
+    lon: null
+    date: null
+  Model:
+    num_threads: 2
+    species_list_file: null
+
+  SpeciesPresence: 
+    threshold: 0.03
+    num_threads: 1
+    use_cache: true
+
+  modelname: google_perch_lite
+  pattern: ".wav"
+  check_time: 1
+  delete_recordings: "never"
+  model_dir: ~/iSparrow/models 
+
+Data: 
+  input: ~/iSparrow_data
+  output: ~/iSparrow_output
+  Preprocessor:
+    sample_rate: 32000
+    overlap: 0.0
+    sample_secs: 5.0
+    resample_type: kaiser_fast

google_perch_lite/labels.txt

@@ -0,0 +1,10932 @@
+aakspa1
+abbbab1
+abbboo2
+abbsta2
+abbwar1
+abcwin1
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google_perch_lite/licence.md

@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
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+   limitations under the License.

google_perch_lite/model.py

@@ -0,0 +1,95 @@
+from iSparrow.model_base import ModelBase
+
+try:
+    import tflite_runtime.interpreter as tflite
+except ImportError:
+    import tensorflow.lite as tflite
+
+from iSparrow import utils
+from iSparrow import ModelBase
+
+import numpy as np
+from pathlib import Path
+from scipy.special import softmax
+
+
+class Model(ModelBase):
+    """
+    Model Implementation of a iSparrow model that uses the google perch tflite model.
+
+    Args:
+        ModelBase (iSparrow.ModelBase): Model base class that provides the interface through which to interact with iSparrow.
+    """
+
+    def __init__(self, model_path: str, num_threads: int = 1, **kwargs):
+        """
+        __init__ Create a new model instance that uses the google perch tflite converted model.
+
+        Args:
+            model_path (str): path to where the google perch tflite model is stored
+            num_threads (int, optional): number of threads to use. Defaults to 1.
+        """
+        labels_path = str(Path(model_path) / "labels.txt")
+        model_path = str(Path(model_path) / "model.tflite")
+
+        # base class loads the model and labels
+        super().__init__(
+            "google_perch_lite",
+            model_path,
+            labels_path,
+            num_threads=num_threads,
+            **kwargs
+        )
+
+        # store input and output index to not have to retrieve them each time an inference is made
+        input_details = self.model.get_input_details()
+
+        output_details = self.model.get_output_details()
+
+        self.input_layer_index = input_details[0]["index"]
+
+        self.output_layer_index = output_details[1]["index"]
+
+    def predict(self, sample: np.array) -> np.array:
+        """
+        predict Make inference about the bird species for the preprocessed data passed to this function as arguments.
+
+        Args:
+            data (np.array): list of preprocessed data chunks
+        Returns:
+            numpy array: array of probabilities per class
+        """
+        data = np.array([sample], dtype="float32")
+
+        self.model.resize_tensor_input(
+            self.input_layer_index, [len(data), *data[0].shape]
+        )
+        self.model.allocate_tensors()
+
+        # Make a prediction
+        self.model.set_tensor(self.input_layer_index, data)
+        self.model.invoke()
+
+        logits = self.model.get_tensor(self.output_layer_index)
+
+        confidence = softmax(logits)
+
+        return confidence
+
+    @classmethod
+    def from_cfg(cls, iSparrow_folder: str, cfg: dict):
+        """
+        from_cfg Create a new instance from a dictionary containing keyword arguments. Usually loaded from a config file.
+
+        Args:
+            iSparrow_dir (str): Installation directory of the iSparrow package
+            cfg (dict): Dictionary containing the keyword arguments
+
+        Returns:
+            Model: New model instance created with the supplied kwargs.
+        """
+        cfg["model_name"] = str(
+            Path(iSparrow_folder) / Path("models") / cfg["model_name"]
+        )
+
+        return cls(**cfg)

google_perch_lite/model.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e97549582d6a0b763a23f4feb661ac17f12f59a73e63452f77503f233ee1daf
+size 84405592

google_perch_lite/preprocessor.py

@@ -0,0 +1,78 @@
+import numpy as np
+import iSparrow.preprocessor_base as ppb
+
+
+class Preprocessor(ppb.PreprocessorBase):
+
+    def __init__(
+        self,
+        sample_rate: int = 32000,
+        sample_secs: float = 5.0,
+        resample_type: str = "kaiser_fast",
+        **kwargs
+    ):
+
+        super().__init__(
+            "google_perch_lite",
+            sample_rate=sample_rate,
+            sample_secs=sample_secs,
+            resample_type=resample_type,
+            **kwargs
+        )
+
+    def process_audio_data(self, rawdata: np.array) -> np.array:
+
+        # 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}"
+            )
+
+        seconds = self.sample_secs
+        minlen = 1.5
+
+        self.chunks = []
+
+        for i in range(
+            0, len(rawdata), int((seconds - self.overlap) * self.sample_rate)
+        ):
+
+            split = rawdata[i : (i + int(seconds * self.actual_sampling_rate))]
+
+            # End of signal?
+            if len(split) < int(minlen * self.actual_sampling_rate):
+                break
+
+            # Signal chunk too short? Fill with zeros.
+            if len(split) < int(self.actual_sampling_rate * seconds):
+                temp = np.zeros((int(self.actual_sampling_rate * seconds)))
+                temp[: len(split)] = split
+                split = temp
+
+            self.chunks.append(split)
+
+        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)

google_perch_lite/tf_conversion.ipynb

@@ -0,0 +1,537 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(tf.lite.TFLiteConverter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(tf.lite.TargetSpec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_dir = \"/path/to/original/google_bird_classification/google_perch\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "converter = tf.lite.TFLiteConverter.from_saved_model(model_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tflite_model = converter.convert()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The inclusion of the tensorflow ops makes the conversion work, but increases the size of the model substantially. It would be better to have an alternative, perhaps obtainable using the the google perch repo: https://github.com/google-research/perch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "converter.target_spec.supported_ops = [\n",
+    "  tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.\n",
+    "  tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tflite_model = converter.convert()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(model_dir + \"/converted_model.tflite\", \"wb\") as modelfile: \n",
+    "    modelfile.write(tflite_model)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Try out our new model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.signal import frame as tf_split_signal_into_chunks\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tflite_runtime.interpreter as tflite\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_dir = \"/path/to/model/google_bird_classification/google_perch\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interpreter = tflite.Interpreter(model_path=model_dir + \"/model.tflite\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audiopath = \"/path/to/sample/soundfile/soundscape.wav\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np  \n",
+    "import librosa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess(path: str, sample_rate: int, sample_secs: float, overlap: float) -> list:\n",
+    "    chunks = []\n",
+    "    data, actual_sampling_rate = librosa.load(\n",
+    "        path, sr=sample_rate, mono=True, res_type=\"kaiser_fast\"\n",
+    "    )\n",
+    "\n",
+    "    duration = librosa.get_duration(y=data, sr=sample_rate)\n",
+    "    # raise when sampling rate is unequal.\n",
+    "    if actual_sampling_rate != sample_rate:\n",
+    "        raise RuntimeError(\n",
+    "            \"Sampling rate is not the desired one. Desired sampling rate: {sample_rate}, actual sampling rate: {actual_sampling_rate}\"\n",
+    "        )\n",
+    "\n",
+    "    frame_length = int(sample_secs * sample_rate)\n",
+    "    step_length = int(sample_secs - overlap) * sample_rate\n",
+    "\n",
+    "    chunks = tf_split_signal_into_chunks(\n",
+    "        data, frame_length, step_length, pad_end=True\n",
+    "    ).numpy()\n",
+    "\n",
+    "    print(\n",
+    "        \"process audio data google: complete, read \",\n",
+    "        str(len(chunks)),\n",
+    "        \"chunks.\",\n",
+    "        flush=True\n",
+    "    )\n",
+    "\n",
+    "    return chunks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = preprocess(audiopath, 32000, 5.0, 0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_signature = interpreter.get_signature_runner()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_details = my_signature.get_input_details()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_details = my_signature.get_output_details()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_details\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = my_signature(inputs = tf.constant([1], shape = (1, 160000), dtype=tf.float32 ))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output[\"output_0\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output[\"output_1\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = my_signature(inputs = data[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interpreter = tflite.Interpreter(model_path=model_dir + \"/model.tflite\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interpreter.allocate_tensors()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get input and output tensors.\n",
+    "input_details = interpreter.get_input_details()\n",
+    "output_details = interpreter.get_output_details()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_shape = input_details[0]['shape']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interpreter.set_tensor(input_details[0]['index'], input_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interpreter.invoke()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_data = interpreter.get_tensor(output_details[0]['index'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = np.array([data[0]], dtype=\"float32\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interpreter.set_tensor(input_details[0]['index'], sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interpreter.invoke()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logits = interpreter.get_tensor(output_details[1]['index'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_data = tf.nn.softmax(logits).numpy()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# try to run the same thing with the original model and then compare the output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_model_from_file_pb(path: str, _):\n",
+    "\n",
+    "    if \".\" in Path(path).name or \".pb\" in Path(path).name:\n",
+    "        # tensorflow assumes a model file to be named \"saved_model.pb\" and the path given to be a directory\n",
+    "        path = Path(path).parent\n",
+    "\n",
+    "    if Path(path).exists() is False:\n",
+    "        raise FileNotFoundError(\"The desired model file does not exist\")\n",
+    "\n",
+    "    try:\n",
+    "        model = tf.saved_model.load(path)\n",
+    "        return model\n",
+    "    except Exception as e:\n",
+    "        raise RuntimeError from e\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_model = load_model_from_file_pb(model_dir, None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logits, _ = other_model.infer_tf(\n",
+    "    sample\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = tf.nn.softmax(logits).numpy()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.max(np.abs(results - output_data))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}