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import warnings |
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warnings.filterwarnings("ignore") |
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import os |
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import numpy as np |
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import pandas as pd |
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from typing import Iterable |
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import gradio as gr |
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from gradio.themes.base import Base |
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from gradio.themes.utils import colors, fonts, sizes |
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import torch |
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import librosa |
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import torch.nn.functional as F |
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from audio_class_predictor import predict_class |
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from bird_ast_model import birdast_preprocess, birdast_inference |
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from bird_ast_seq_model import birdast_seq_preprocess, birdast_seq_inference |
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from utils import plot_wave, plot_mel, download_model, bandpass_filter |
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ASSET_DIR = "./assets" |
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DEFUALT_SR = 16_000 |
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DEFUALT_HIGH_CUT = 8_000 |
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DEFUALT_LOW_CUT = 1_000 |
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu" |
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print(f"Use Device: {DEVICE}") |
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if not os.path.exists(ASSET_DIR): |
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os.makedirs(ASSET_DIR) |
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birdast_assets = { |
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"model_weights": [ |
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f"https://huggingface.co/shiyi-li/BirdAST/resolve/main/BirdAST_Baseline_GroupKFold_fold_{i}.pth" |
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for i in range(5) |
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], |
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"label_mapping": "https://huggingface.co/shiyi-li/BirdAST/resolve/main/BirdAST_Baseline_GroupKFold_label_map.csv", |
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"preprocess_fn": birdast_preprocess, |
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"inference_fn": birdast_inference, |
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} |
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birdast_seq_assets = { |
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"model_weights": [ |
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f"https://huggingface.co/shiyi-li/BirdAST_Seq/resolve/main/BirdAST_SeqPool_GroupKFold_fold_{i}.pth" |
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for i in range(5) |
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], |
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"label_mapping": "https://huggingface.co/shiyi-li/BirdAST_Seq/resolve/main/BirdAST_SeqPool_GroupKFold_label_map.csv", |
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"preprocess_fn": birdast_seq_preprocess, |
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"inference_fn": birdast_seq_inference, |
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} |
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ASSET_DICT = { |
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"BirdAST": birdast_assets, |
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"BirdAST_Seq": birdast_seq_assets, |
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} |
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def run_inference_with_model(audio_clip, sr, model_name): |
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assets = ASSET_DICT[model_name] |
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model_weights_url = assets["model_weights"] |
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label_map_url = assets["label_mapping"] |
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preprocess_fn = assets["preprocess_fn"] |
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inference_fn = assets["inference_fn"] |
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model_weights = [] |
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for model_weight in model_weights_url: |
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weight_file = os.path.join(ASSET_DIR, model_weight.split("/")[-1]) |
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if not os.path.exists(weight_file): |
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download_model(model_weight, weight_file) |
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model_weights.append(weight_file) |
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label_map_csv = os.path.join(ASSET_DIR, label_map_url.split("/")[-1]) |
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if not os.path.exists(label_map_csv): |
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download_model(label_map_url, label_map_csv) |
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label_mapping = pd.read_csv(label_map_csv) |
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species_id_to_name = {row["species_id"]: row["scientific_name"] for _, row in label_mapping.iterrows()} |
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spectrogram = preprocess_fn(audio_clip, sr=sr) |
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predictions = inference_fn(model_weights, spectrogram, device=DEVICE) |
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final_predicts = predictions.mean(axis=0) |
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topk_values, topk_indices = torch.topk(torch.from_numpy(final_predicts), 10) |
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results = [] |
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for idx, scores in zip(topk_indices, topk_values): |
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species_name = species_id_to_name[idx.item()] |
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probability = scores.item() * 100 |
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results.append([species_name, probability]) |
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return results |
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def predict(audio, start, end, model_name="BirdAST_Seq"): |
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raw_sr, audio_array = audio |
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if audio_array.ndim > 1: |
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audio_array = audio_array.mean(axis=1) |
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print(f"Audio shape raw: {audio_array.shape}, sr: {raw_sr}") |
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len_audio = audio_array.shape[0] / raw_sr |
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if start >= end: |
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raise gr.Error(f"`start` ({start}) must be smaller than end ({end}s)") |
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if audio_array.shape[0] < start * raw_sr: |
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raise gr.Error(f"`start` ({start}) must be smaller than audio duration ({len_audio:.0f}s)") |
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if audio_array.shape[0] > end * raw_sr: |
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end = audio_array.shape[0] / (1.0*raw_sr) |
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audio_array = np.array(audio_array, dtype=np.float32) / 32768.0 |
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audio_array = audio_array[int(start*raw_sr) : int(end*raw_sr)] |
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if raw_sr != DEFUALT_SR: |
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audio_array = bandpass_filter(audio_array, DEFUALT_LOW_CUT, DEFUALT_HIGH_CUT, raw_sr) |
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audio_array = librosa.resample(audio_array, orig_sr=raw_sr, target_sr=DEFUALT_SR) |
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print(f"Resampled Audio shape: {audio_array.shape}") |
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audio_array = audio_array.astype(np.float32) |
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audio_class = predict_class(audio_array) |
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fig_spectrogram = plot_mel(DEFUALT_SR, audio_array) |
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fig_waveform = plot_wave(DEFUALT_SR, audio_array) |
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print(f"Running inference with model: {model_name}") |
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species_class = run_inference_with_model(audio_array, DEFUALT_SR, model_name) |
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return audio_class, species_class, fig_waveform, fig_spectrogram |
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DESCRIPTION = """ |
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<div align="center"> |
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<b>Team Members: </b> |
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Amro Abdrabo [[email protected] | [LinkedIn](https://www.linkedin.com/in/amroabdrabo/)] |
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Shiyi Li [[email protected] | [LinkedIn](www.linkedin.com/in/shiyili01)] |
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Thomas Radinger [ [email protected] | [LinkedIn](https://www.linkedin.com/in/thomas-radinger-743958142/) ] |
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</div> |
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# Introduction |
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Birds are key indicators of ecosystem health and play pivotal roles in maintaining biodiversity [1]. To monitor and protect bird species, automatic bird sound recognition systems are essential. These systems can help in identifying bird species, monitoring their populations, and understanding their behavior. However, building such systems is challenging due to the diversity of bird sounds, complex acoustic interference and limited labeled data. |
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To tackle these challenges, we expored the potential of deep learning models for bird sound recognition. In our work, we developed two Audio Spectrogram Transformer (AST) based models: BirdAST and BirdAST_Seq, to predict bird species from audio recordings. We evaluated the models on a dataset of 728 bird species and achieved promising results. Details of the models and evaluation results are provided in the table below. As the field-recordings may contain various types of audio rather than only bird songs/calls, we also employed an Audio Masked AutoEncoder (AudioMAE) model to pre-classify audio clips into bird, insects, rain, environmental noise, and other types [2]. |
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Our contributions have shown the potential of deep learning models for bird sound recognition. We hope that our work can contribute to the development of automatic bird sound recognition systems and help in monitoring and protecting bird species. |
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<div align="center"> |
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<b>Model Details</b> |
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| Model name | Architecture | ROC-AUC Score | |
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| --------------- |:------------------------------:|:-------------:| |
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| BirdAST | AST* + MLP | 0.6825 | |
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| BirdAST_Seq | AST* + Sequence Pooling + MLP | 0.7335 | |
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</div> |
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# How to use the space: |
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1. Choose a model from the dropdown list. It will download the model weights automatically if not already downloaded (~30 seconds). |
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2. Upload an audio clip and specify the start and end time for prediction. |
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3. Click on the "Predict" button to get the predictions. |
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4. In the output, you will get the audio type classification (e.g., bird, insects, rain, etc.) in the panel "Class Prediction" and the predicted bird species in the panel "Species Prediction". |
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* The audio types are predicted as multi-lable classification based on the AudioMAE model. The predicted classes indicate the possible presence of different types of audio in the recording. |
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* The bird species are predicted as a multi-class classification using the selected model. The predicted classes indicate the most possible bird species present in the recording. |
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5. The waveform and spectrogram of the audio clip are displayed in the respective panels. |
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**Notes:** |
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- For an unknown bird species, the model may predict the most similar bird species based on the training data. |
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- If an audio clip contains non-bird sounds (predicted by the AudioMAE), the bird species prediction may not be accurate. |
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**Disclaimer**: The model predictions are based on the training data and may not be accurate for all audio clips. The model is trained on a dataset of 728 bird species and may not generalize well to all bird species. |
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<div align="center"> |
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<b>Enjoy the Bird Songs! 🐦🎶 |
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</div> |
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""" |
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css = """ |
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#gradio-animation { |
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font-size: 2em; |
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font-weight: bold; |
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text-align: center; |
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margin-bottom: 20px; |
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} |
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.logo-container img { |
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width: 14%; /* Adjust width as necessary */ |
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display: block; |
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margin: auto; |
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} |
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.number-input { |
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height: 100%; |
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padding-bottom: 60px; /* Adust the value as needed for more or less space */ |
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} |
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.full-height { |
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height: 100%; |
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} |
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.column-container { |
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height: 100%; |
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} |
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""" |
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class Seafoam(Base): |
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def __init__( |
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self, |
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*, |
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primary_hue: colors.Color | str = colors.emerald, |
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secondary_hue: colors.Color | str = colors.blue, |
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neutral_hue: colors.Color | str = colors.gray, |
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spacing_size: sizes.Size | str = sizes.spacing_md, |
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radius_size: sizes.Size | str = sizes.radius_md, |
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text_size: sizes.Size | str = sizes.text_lg, |
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font: fonts.Font |
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| str |
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| Iterable[fonts.Font | str] = ( |
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fonts.GoogleFont("Quicksand"), |
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"ui-sans-serif", |
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"sans-serif", |
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), |
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font_mono: fonts.Font |
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| str |
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| Iterable[fonts.Font | str] = ( |
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fonts.GoogleFont("IBM Plex Mono"), |
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"ui-monospace", |
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"monospace", |
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), |
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): |
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super().__init__( |
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primary_hue=primary_hue, |
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secondary_hue=secondary_hue, |
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neutral_hue=neutral_hue, |
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spacing_size=spacing_size, |
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radius_size=radius_size, |
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text_size=text_size, |
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font=font, |
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font_mono=font_mono, |
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) |
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seafoam = Seafoam() |
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js = """ |
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function createGradioAnimation() { |
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var container = document.getElementById('gradio-animation'); |
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var text = 'Voice of Jungle'; |
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for (var i = 0; i < text.length; i++) { |
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(function(i){ |
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setTimeout(function(){ |
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var letter = document.createElement('span'); |
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letter.style.opacity = '0'; |
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letter.style.transition = 'opacity 0.5s'; |
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letter.innerText = text[i]; |
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container.appendChild(letter); |
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setTimeout(function() { |
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letter.style.opacity = '1'; |
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}, 50); |
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}, i * 250); |
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})(i); |
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} |
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} |
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""" |
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REFERENCES = """ |
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# References |
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[1] Torkington, S. (2023, February 7). 50% of the global economy is under threat from biodiversity loss. World Economic Forum. Retrieved from https://www.weforum.org/agenda/2023/02/biodiversity-nature-loss-cop15/. |
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[2] Huang, P.-Y., Xu, H., Li, J., Baevski, A., Auli, M., Galuba, W., Metze, F., & Feichtenhofer, C. (2022). Masked Autoencoders that Listen. In NeurIPS. |
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[3] https://www.kaggle.com/code/dima806/bird-species-by-sound-detection |
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""" |
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def handle_model_selection(model_name, download_status): |
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print(f"Downloading model weights for {model_name}...") |
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if model_name is None: |
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model_name = "BirdAST" |
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assets = ASSET_DICT[model_name] |
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model_weights_url = assets["model_weights"] |
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download_flag = True |
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try: |
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total_files = len(model_weights_url) |
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for idx, model_weight in enumerate(model_weights_url): |
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weight_file = os.path.join(ASSET_DIR, model_weight.split("/")[-1]) |
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print(weight_file) |
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if not os.path.exists(weight_file): |
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download_status = f"Downloading {idx + 1} of {total_files}" |
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download_model(model_weight, weight_file) |
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if not os.path.exists(weight_file): |
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download_flag = False |
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break |
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if download_flag: |
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download_status = f"Model <{model_name}> is ready! 🎉🎉🎉\nUsing Device: {DEVICE.upper()}" |
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else: |
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download_status = f"An error occurred while downloading model weights." |
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except Exception as e: |
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download_status = f"An error occurred while downloading model weights." |
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return download_status |
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with gr.Blocks(theme = seafoam, css = css, js = js) as demo: |
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gr.Markdown('<div class="logo-container"><img src="https://i.ibb.co/vcG9kr0/vojlogo.jpg" width="50px" alt="vojlogo"></div>') |
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gr.Markdown('<div id="gradio-animation"></div>') |
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gr.Markdown(DESCRIPTION) |
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model_names = ['BirdAST', 'BirdAST_Seq'] |
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model_dropdown = gr.Dropdown(label="Choose a model", choices=model_names) |
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download_status = gr.Textbox(label="Model Status", lines=3, value='', interactive=False) |
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model_dropdown.change(handle_model_selection, inputs=[model_dropdown, download_status], outputs=download_status) |
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with gr.Row(): |
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with gr.Column(elem_classes="column-container"): |
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start_time_input = gr.Number(label="Start Time", value=0, elem_classes="number-input full-height") |
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end_time_input = gr.Number(label="End Time", value=10, elem_classes="number-input full-height") |
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with gr.Column(): |
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audio_input = gr.Audio(label="Input Audio", elem_classes="full-height") |
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with gr.Row(): |
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raw_class_output = gr.Dataframe(headers=["Class", "Score [%]"], row_count=10, label="Class Prediction") |
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species_output = gr.Dataframe(headers=["Class", "Score [%]"], row_count=10, label="Species Prediction") |
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with gr.Row(): |
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waveform_output = gr.Plot(label="Waveform") |
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spectrogram_output = gr.Plot(label="Spectrogram") |
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gr.Examples( |
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examples=[ |
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["XC226833-Chestnut-belted_20Chat-Tyrant_20A_2010989.mp3", 0, 10], |
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["XC812290-Many-striped-Canastero_Teaben_Pe_1jul2022_FSchmitt_1.mp3", 0, 10], |
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["XC763511-Synallaxis-maronica_Bagua-grande_MixPre-1746.mp3", 0, 10] |
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], |
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inputs=[audio_input, start_time_input, end_time_input] |
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) |
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gr.Button("Predict").click(predict, [audio_input, start_time_input, end_time_input, model_dropdown], [raw_class_output, species_output, waveform_output, spectrogram_output]) |
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gr.Markdown(REFERENCES) |
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demo.launch(share = True) |
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