claude make birdnet be like transformers https://claude.ai/share/5353920d-e4bb-41a6-a8e6-7c37028dd3cf
claude, do you know the birdnet architecture Edit I'm familiar with BirdNet at a high level. BirdNet is a neural network architecture designed for bird sound recognition and classification. It's a deep learning model that can identify bird species from audio recordings. The architecture uses convolutional neural networks (CNNs) to process spectrograms of bird sounds, analyzing the frequency patterns over time to identify specific species. It was developed by researchers at the Cornell Lab of Ornithology and Chemnitz University of Technology. BirdNet has been trained on thousands of bird species and their vocalizations, making it quite effective at identifying birds from their sounds. There's also a mobile app version that allows users to record and identify bird sounds in the field. I should note that my knowledge cutoff is October 2024, so I may not be aware of the most recent developments or improvements to the BirdNet architecture. Would you like me to go into more detail about how the neural network architecture works? Retry U i'm used to looking at the sourcecode of models from huggingface transformers. where would i find the birdnet architecture fine details to reproduce the model? i don't actually want to reproduce it, it's just how i'm used to understanding models. in the birdnet sourcecode, it loads a .tflite file by default Edit BirdNet's architecture details aren't typically found in Hugging Face Transformers since it's a specialized audio classification model rather than a transformer-based model. To understand BirdNet's architecture in detail, you'd want to look at: The official GitHub repository: https://github.com/kahst/BirdNET-Analyzer In this repo, the main model architecture would likely be defined in Python files rather than directly visible in the .tflite file you mentioned. The .tflite file is a TensorFlow Lite model file - a compiled, optimized version of the model for deployment, especially on mobile devices. For the actual architecture definition, you'd want to look at files that define the model's layers and structure. In the BirdNET codebase, these would typically be Python files that use TensorFlow/Keras to define the model architecture before it gets compiled into the .tflite format. The most informative files would likely be ones with names like model.py, network.py, or similar that define the CNN layers, audio processing components, and classification head. If you're specifically interested in the architecture details rather than using the model, the academic papers published about BirdNet would also provide the architectural specifications, including layer configurations, filter sizes, and other parameters needed to reproduce the model from scratch. Would you like me to help you locate specific files in the repository where the architecture is defined? Retry Claude does not have internet access. Links provided may not be accurate or up to date. files.txt 205 lines txt U Edit Looking at the file structure you've shared, I can see the repository organization. For understanding the BirdNet architecture, I'd focus on these key files: ./birdnet_analyzer/model.py - This is likely the main file that defines the model architecture. ./birdnet_analyzer/checkpoints/V2.4/ - This directory contains the model weights in various formats (.tflite files, TensorFlow SavedModel format, and TFJS format). ./birdnet_analyzer/analyze/utils.py - This might contain utility functions for making predictions with the model. ./birdnet_analyzer/train/ - This directory would have code related to model training, which might show how the architecture is constructed. The actual model architecture definition would most likely be in model.py, which is where I'd look first. The .tflite files you mentioned are indeed the compiled models optimized for deployment, but they don't directly show the architecture in human-readable form. If you want to see how the model is defined, structured, and what layers it contains, examining the model.py file would be the best starting point. This would show the Python code that constructs the neural network architecture before it's compiled into the .tflite format. Would you like me to help you understand what specific aspects of the architecture you're most interested in seeing? Retry U Copy $ grep ^class model.py # no output $ grep ^def model.py def get_empty_class_exception(): def label_smoothing(y: np.ndarray, alpha=0.1): def mixup(x, y, augmentation_ratio=0.25, alpha=0.2): def random_split(x, y, val_ratio=0.2): def random_multilabel_split(x, y, val_ratio=0.2): def upsample_core(x: np.ndarray, y: np.ndarray, min_samples: int, apply: callable, size=2): def upsampling(x: np.ndarray, y: np.ndarray, ratio=0.5, mode="repeat"): def save_model_params(path): def reset_custom_classifier(): def load_model(class_output=True): def load_custom_classifier(): def load_meta_model(): def build_linear_classifier(num_labels, input_size, hidden_units=0, dropout=0.0): def train_linear_classifier( def save_linear_classifier(classifier, model_path: str, labels: list[str], mode="replace"): def save_raven_model(classifier, model_path, labels: list[str], mode="replace"): def predict_filter(lat, lon, week): def explore(lat: float, lon: float, week: int): def custom_loss(y_true, y_pred, epsilon=1e-7): def flat_sigmoid(x, sensitivity=-1, bias=1.0): def predict(sample): def predict_with_custom_classifier(sample): def embeddings(sample): Edit Based on the grep output, I can see the functions in model.py but not the actual architecture definition. It looks like the model architecture might be loaded from the TFLite file rather than being explicitly defined in Python code in this repository. The key functions that would help understand how the model works appear to be: load_model(class_output=True) - This likely loads the pre-trained TFLite model predict(sample) - The inference function for making predictions embeddings(sample) - For extracting feature embeddings from audio load_meta_model() - May load a second model component Since there's no explicit class definition for the architecture, the model is likely being loaded from the compiled TFLite files in the checkpoints directory. To understand the actual architecture, you might need to: Look at how load_model() is implemented to see if there are any hints about the architecture Check the repository's documentation or academic papers about BirdNet Use TensorFlow's model inspection tools to examine the loaded model structure You could try running something like: python Copy model = load_model() print(model.summary()) # If it returns a Keras model Or use TFLite's visualization tools to inspect the model file structure directly. Would you like me to help you look deeper into any specific function implementation to better understand how the model is structured? Retry Claude does not have the ability to run the code it generates yet. U oh no i'll need to make tensorflow work to try model.summary() :) that's probably important to do. thanks for the tip! Copy $ python3 Python 3.12.3 (main, Feb 4 2025, 14:48:35) [GCC 13.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import birdnet_analyzer.model as model Illegal instruction (core dumped) Copy def load_model(class_output=True): """ Loads the machine learning model based on the configuration provided. This function loads either a TensorFlow Lite (TFLite) model or a protobuf model depending on the file extension of the model path specified in the configuration. It sets up the global variables for the model interpreter and input/output layer indices. Args: class_output (bool): If True, sets the output layer index to the classification output. If False, sets the output layer index to the feature embeddings. """ global PBMODEL global INTERPRETER global INPUT_LAYER_INDEX global OUTPUT_LAYER_INDEX # Do we have to load the tflite or protobuf model? if cfg.MODEL_PATH.endswith(".tflite"): # Load TFLite model and allocate tensors. INTERPRETER = tflite.Interpreter( model_path=os.path.join(SCRIPT_DIR, cfg.MODEL_PATH), num_threads=cfg.TFLITE_THREADS ) INTERPRETER.allocate_tensors() # Get input and output tensors. input_details = INTERPRETER.get_input_details() output_details = INTERPRETER.get_output_details() # Get input tensor index INPUT_LAYER_INDEX = input_details[0]["index"] # Get classification output or feature embeddings if class_output: OUTPUT_LAYER_INDEX = output_details[0]["index"] else: OUTPUT_LAYER_INDEX = output_details[0]["index"] - 1 else: # Load protobuf model # Note: This will throw a bunch of warnings about custom gradients # which we will ignore until TF lets us block them PBMODEL = keras.models.load_model(os.path.join(SCRIPT_DIR, cfg.MODEL_PATH), compile=False) Edit Thanks for sharing that code! Now it's clearer how BirdNet loads its model. >From what I can see: The architecture isn't explicitly defined in Python code - it's pre-trained and loaded from either .tflite files or a TensorFlow SavedModel (protobuf) format. The model has two output heads - one for classification and one for embeddings (feature extraction), accessed by different output layer indices. When importing the module crashed with "Illegal instruction", that typically happens when the compiled code tries to use CPU instructions that aren't supported on your machine. To understand the architecture without running TensorFlow, you have a few options: Use TFLite Visualizer tools online - you could upload the .tflite file to an online TFLite model visualizer. Use netron - A standalone tool for visualizing neural network models: Copy pip install netron netron ./birdnet_analyzer/checkpoints/V2.4/BirdNET_GLOBAL_6K_V2.4_Model_FP32.tflite Extract model architecture from papers - The BirdNet papers likely contain detailed descriptions of the architecture. The BirdNet architecture is likely a series of convolutional layers followed by some form of attention mechanism or temporal pooling to process the spectrograms of bird sounds. The model probably has: Input layer accepting spectrograms Multiple CNN blocks for feature extraction Pooling layers A few dense layers Two output branches - one for classification and one for embeddings For reproducing the model, using the visualization tools would be most helpful to see the exact layer configurations. Retry Claude does not have the ability to run the code it generates yet. U netron is nice, the model looks unoptimized enough to reconstruct the architecture in a rote manner. i dont' understand all the layers, like Logistic and the extensive reslicing. Is FullyConnected a Linear layer or is that like an MLP with an activation function? I'm totally new to CNNs and don't have training. I've mostly looked at transformer models and I'm better at reading source code, it's more consistent and I can test it. I'd like to learn this architecture! i should probably build tensorflow for my cpu Edit Netron is definitely a good tool for visualizing model architectures! Let's go through some of the layers you mentioned: FullyConnected in TFLite terminology is roughly equivalent to a Linear layer in PyTorch or Dense layer in Keras. It's a basic matrix multiplication operation: Y = X·W + B, where: X is the input W is the weight matrix B is the bias vector By itself, it doesn't include an activation function. Any activation would be a separate layer following the FullyConnected layer. Logistic is typically a sigmoid activation function that transforms values to be between 0 and 1. It's often used for binary classification or to create "gates" in various neural network components. Reshape/Reslice operations are common in CNNs to transform the data between different parts of the network. For example, to flatten a 3D representation into a 1D vector before passing it to a fully connected layer, or to rearrange dimensions for specific operations. For understanding CNNs as someone familiar with transformers: CNNs use filters (kernels) that slide over input data (like images or spectrograms) to detect patterns Each filter learns to detect specific features (edges, textures, etc.) Early layers detect simple features, deeper layers detect more complex patterns Pooling layers (Max, Average) reduce spatial dimensions while preserving important features For BirdNet specifically, it's likely processing spectrograms (time-frequency representations of audio) where: Time is one dimension Frequency bands are another dimension Convolutions slide across both time and frequency to detect bird-specific patterns Building TensorFlow for your CPU would definitely help if you want to run the model and examine it in more detail. You could then use model.summary() or even step through the inference process to understand how data flows through the network. Would you like me to explain any specific parts of CNNs in more detail to help understand what you're seeing in Netron? Retry U tell me what to do about my tensorflow crash i guess Edit The "Illegal instruction (core dumped)" error you're seeing suggests that the pre-compiled TensorFlow package you're using contains instructions that your CPU doesn't support. This typically happens when TensorFlow is compiled with optimizations for newer CPUs (like AVX2, AVX-512) but your processor doesn't have those instruction sets. Here's how to resolve the issue: Install TensorFlow with basic CPU support: bash Copy pip uninstall tensorflow tensorflow-cpu pip install tensorflow-cpu==2.10.0 --no-deps Using an older version sometimes helps as they have fewer advanced CPU requirements. Alternatively, build TensorFlow from source for your specific CPU: bash Copy # Install bazel first (TensorFlow build tool) # Then clone and build TensorFlow git clone https://github.com/tensorflow/tensorflow.git cd tensorflow ./configure # Select options without advanced CPU optimizations bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package Try a Docker container with TensorFlow: bash Copy docker pull tensorflow/tensorflow:latest docker run -it tensorflow/tensorflow:latest python Use a conda environment which sometimes handles compatibility better: bash Copy conda create -n tf-env python=3.9 conda activate tf-env conda install tensorflow Environment variable workaround - sometimes this works as a quick fix: bash Copy export TF_CPP_MIN_LOG_LEVEL=2 # Add this before running python Once you have a working TensorFlow installation, you can use the following code to examine the model: python Copy import tensorflow as tf model = tf.saved_model.load("./birdnet_analyzer/checkpoints/V2.4/BirdNET_GLOBAL_6K_V2.4_Model") print([x.name for x in model.signatures["serving_default"].inputs]) print([x.name for x in model.signatures["serving_default"].outputs]) Or for the TFLite model: python Copy interpreter = tf.lite.Interpreter(model_path="./birdnet_analyzer/checkpoints/V2.4/BirdNET_GLOBAL_6K_V2.4_Model_FP32.tflite") interpreter.allocate_tensors() for i in range(len(interpreter.get_tensor_details())): print(interpreter.get_tensor_details()[i]) Which approach would you prefer to try first? Retry Claude does not have the ability to run the code it generates yet. Claude can make mistakes. Please double-check responses.
