{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"In this notebook, we'll use a pre-trained machine learning model to generate a submission to the [BirdClef2023 competition](https://www.kaggle.com/c/birdclef-2023).  The goal of the competition is to identify Eastern African bird species by sound.","metadata":{}},{"cell_type":"markdown","source":"## Step 1: Imports","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nimport tensorflow_hub as hub\nimport tensorflow_io as tfio\n\nimport pandas as pd\nimport numpy as np\nimport librosa\nimport glob\n\nimport csv\nimport io\n\nfrom IPython.display import Audio","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-04-04T13:12:48.743152Z","iopub.execute_input":"2023-04-04T13:12:48.743630Z","iopub.status.idle":"2023-04-04T13:13:00.284895Z","shell.execute_reply.started":"2023-04-04T13:12:48.743588Z","shell.execute_reply":"2023-04-04T13:13:00.283346Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Step 2: Explore the training data\n\nWe'll start by loading a couple of training examples and using the IPython.display.Audio module to play them!","metadata":{}},{"cell_type":"code","source":"# Load a sample audio files from two different species\naudio_abe, sr_abe = librosa.load(\"/kaggle/input/birdclef-2023/train_audio/abethr1/XC128013.ogg\")\naudio_abh, sr_abh = librosa.load(\"/kaggle/input/birdclef-2023/train_audio/abhori1/XC127317.ogg\")","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:13:00.287326Z","iopub.execute_input":"2023-04-04T13:13:00.288075Z","iopub.status.idle":"2023-04-04T13:13:13.695811Z","shell.execute_reply.started":"2023-04-04T13:13:00.288034Z","shell.execute_reply":"2023-04-04T13:13:13.694412Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Play the audio\nAudio(data=audio_abe, rate=sr_abe)","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:13:13.697651Z","iopub.execute_input":"2023-04-04T13:13:13.698754Z","iopub.status.idle":"2023-04-04T13:13:13.780000Z","shell.execute_reply.started":"2023-04-04T13:13:13.698707Z","shell.execute_reply":"2023-04-04T13:13:13.778921Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Play the audio\nAudio(data=audio_abh, rate=sr_abh)","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:14:43.574968Z","iopub.execute_input":"2023-04-04T13:14:43.575534Z","iopub.status.idle":"2023-04-04T13:14:43.652130Z","shell.execute_reply.started":"2023-04-04T13:14:43.575488Z","shell.execute_reply":"2023-04-04T13:14:43.649615Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Step 3: Match the model's output with the bird species in the competition\n\nThe competition includes 264 classes of birds, 261 of which exist in this model. We'll set up a way to map the model's output logits to our competition.","metadata":{}},{"cell_type":"code","source":"model = hub.load('https://kaggle.com/models/google/bird-vocalization-classifier/frameworks/tensorFlow2/variations/bird-vocalization-classifier/versions/1')\nlabels_path = hub.resolve('https://kaggle.com/models/google/bird-vocalization-classifier/frameworks/tensorFlow2/variations/bird-vocalization-classifier/versions/1') + \"/assets/label.csv\"","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:15:42.966356Z","iopub.execute_input":"2023-04-04T13:15:42.966882Z","iopub.status.idle":"2023-04-04T13:15:51.379282Z","shell.execute_reply.started":"2023-04-04T13:15:42.966835Z","shell.execute_reply":"2023-04-04T13:15:51.378227Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Find the name of the class with the top score when mean-aggregated across frames.\ndef class_names_from_csv(class_map_csv_text):\n    \"\"\"Returns list of class names corresponding to score vector.\"\"\"\n    with open(labels_path) as csv_file:\n        csv_reader = csv.reader(csv_file, delimiter=',')\n        class_names = [mid for mid, desc in csv_reader]\n        return class_names[1:]\n\n## note that the bird classifier classifies a much larger set of birds than the\n## competition, so we need to load the model's set of class names or else our \n## indices will be off.\nclasses = class_names_from_csv(labels_path)","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:17:26.025647Z","iopub.execute_input":"2023-04-04T13:17:26.026153Z","iopub.status.idle":"2023-04-04T13:17:26.050513Z","shell.execute_reply.started":"2023-04-04T13:17:26.026111Z","shell.execute_reply":"2023-04-04T13:17:26.048868Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_metadata = pd.read_csv(\"/kaggle/input/birdclef-2023/train_metadata.csv\")\ntrain_metadata.head()\ncompetition_classes = sorted(train_metadata.primary_label.unique())\n\nforced_defaults = 0\ncompetition_class_map = []\nfor c in competition_classes:\n    try:\n        i = classes.index(c)\n        competition_class_map.append(i)\n    except:\n        competition_class_map.append(0)\n        forced_defaults += 1\n        \n## this is the count of classes not supported by our pretrained model\n## you could choose to simply not predict these, set a default as above,\n## or create your own model using the pretrained model as a base.\nforced_defaults","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:18:09.495758Z","iopub.execute_input":"2023-04-04T13:18:09.496274Z","iopub.status.idle":"2023-04-04T13:18:09.681194Z","shell.execute_reply.started":"2023-04-04T13:18:09.496230Z","shell.execute_reply":"2023-04-04T13:18:09.679887Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Step 4: Preprocess the data\n\nThe following functions are one way to load the audio provided and break it up into the five-second samples with a sample rate of 32,000 required by the competition.","metadata":{}},{"cell_type":"code","source":"def frame_audio(\n      audio_array: np.ndarray,\n      window_size_s: float = 5.0,\n      hop_size_s: float = 5.0,\n      sample_rate = 32000,\n      ) -> np.ndarray:\n    \n    \"\"\"Helper function for framing audio for inference.\"\"\"\n    \"\"\" using tf.signal \"\"\"\n    if window_size_s is None or window_size_s < 0:\n        return audio_array[np.newaxis, :]\n    frame_length = int(window_size_s * sample_rate)\n    hop_length = int(hop_size_s * sample_rate)\n    framed_audio = tf.signal.frame(audio_array, frame_length, hop_length, pad_end=True)\n    return framed_audio\n\ndef ensure_sample_rate(waveform, original_sample_rate,\n                       desired_sample_rate=32000):\n    \"\"\"Resample waveform if required.\"\"\"\n    if original_sample_rate != desired_sample_rate:\n        waveform = tfio.audio.resample(waveform, original_sample_rate, desired_sample_rate)\n    return desired_sample_rate, waveform","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:18:44.731110Z","iopub.execute_input":"2023-04-04T13:18:44.731675Z","iopub.status.idle":"2023-04-04T13:18:44.742723Z","shell.execute_reply.started":"2023-04-04T13:18:44.731622Z","shell.execute_reply":"2023-04-04T13:18:44.740949Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Below we load one training sample - use the Audio function to listen to the samples inside the notebook!","metadata":{}},{"cell_type":"code","source":"audio, sample_rate = librosa.load(\"/kaggle/input/birdclef-2023/train_audio/afghor1/XC156639.ogg\")\nsample_rate, wav_data = ensure_sample_rate(audio, sample_rate)\nAudio(wav_data, rate=sample_rate)","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:19:11.908679Z","iopub.execute_input":"2023-04-04T13:19:11.909104Z","iopub.status.idle":"2023-04-04T13:19:12.718981Z","shell.execute_reply.started":"2023-04-04T13:19:11.909066Z","shell.execute_reply":"2023-04-04T13:19:12.716977Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Step 5: Make predictions\n\nEach test sample is cut into 5-second chunks. We use the pretrained model to return probabilities for all 10k birds included in the model, then pull out the classes used in this competition to create a final submission row. Note that we are NOT doing anything special to handle the 3 missing classes; those will need fine-tuning / transfer learning, which will be handled in a separate notebook.","metadata":{}},{"cell_type":"code","source":"fixed_tm = frame_audio(wav_data)\nlogits, embeddings = model.infer_tf(fixed_tm[:1])\nprobabilities = tf.nn.softmax(logits)\nargmax = np.argmax(probabilities)\nprint(f\"The audio is from the class {classes[argmax]} (element:{argmax} in the label.csv file), with probability of {probabilities[0][argmax]}\")","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:19:28.090045Z","iopub.execute_input":"2023-04-04T13:19:28.090547Z","iopub.status.idle":"2023-04-04T13:19:40.149236Z","shell.execute_reply.started":"2023-04-04T13:19:28.090500Z","shell.execute_reply":"2023-04-04T13:19:40.147818Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def predict_for_sample(filename, sample_submission, frame_limit_secs=None):\n    file_id = filename.split(\".ogg\")[0].split(\"/\")[-1]\n    \n    audio, sample_rate = librosa.load(filename)\n    sample_rate, wav_data = ensure_sample_rate(audio, sample_rate)\n    \n    fixed_tm = frame_audio(wav_data)\n    \n    frame = 5\n    all_logits, all_embeddings = model.infer_tf(fixed_tm[:1])\n    for window in fixed_tm[1:]:\n        if frame_limit_secs and frame > frame_limit_secs:\n            continue\n        \n        logits, embeddings = model.infer_tf(window[np.newaxis, :])\n        all_logits = np.concatenate([all_logits, logits], axis=0)\n        frame += 5\n    \n    frame = 5\n    all_probabilities = []\n    for frame_logits in all_logits:\n        probabilities = tf.nn.softmax(frame_logits).numpy()\n        \n        ## set the appropriate row in the sample submission\n        sample_submission.loc[sample_submission.row_id == file_id + \"_\" + str(frame), competition_classes] = probabilities[competition_class_map]\n        frame += 5","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:19:44.375136Z","iopub.execute_input":"2023-04-04T13:19:44.375605Z","iopub.status.idle":"2023-04-04T13:19:44.386244Z","shell.execute_reply.started":"2023-04-04T13:19:44.375553Z","shell.execute_reply":"2023-04-04T13:19:44.384616Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Step 6: Generate a submission\n\nNow we process all of the test samples as discussed above, creating output rows, and saving them in the provided `sample_submission.csv`. Finally, we save these rows to our final output file: `submission.csv`. This is the file that gets submitted and scored when you submit the notebook.","metadata":{}},{"cell_type":"code","source":"test_samples = list(glob.glob(\"/kaggle/input/birdclef-2023/test_soundscapes/*.ogg\"))\ntest_samples","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:19:48.293841Z","iopub.execute_input":"2023-04-04T13:19:48.294669Z","iopub.status.idle":"2023-04-04T13:19:48.308157Z","shell.execute_reply.started":"2023-04-04T13:19:48.294618Z","shell.execute_reply":"2023-04-04T13:19:48.306314Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_sub = pd.read_csv(\"/kaggle/input/birdclef-2023/sample_submission.csv\")\nsample_sub[competition_classes] = sample_sub[competition_classes].astype(np.float32)\nsample_sub.head()","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:19:49.896556Z","iopub.execute_input":"2023-04-04T13:19:49.897025Z","iopub.status.idle":"2023-04-04T13:19:50.014876Z","shell.execute_reply.started":"2023-04-04T13:19:49.896985Z","shell.execute_reply":"2023-04-04T13:19:50.013551Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"frame_limit_secs = 15 if sample_sub.shape[0] == 3 else None\nfor sample_filename in test_samples:\n    predict_for_sample(sample_filename, sample_sub, frame_limit_secs=15)","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:19:52.493214Z","iopub.execute_input":"2023-04-04T13:19:52.494155Z","iopub.status.idle":"2023-04-04T13:20:05.585087Z","shell.execute_reply.started":"2023-04-04T13:19:52.494080Z","shell.execute_reply":"2023-04-04T13:20:05.583643Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_sub","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:20:05.587336Z","iopub.execute_input":"2023-04-04T13:20:05.587778Z","iopub.status.idle":"2023-04-04T13:20:05.619336Z","shell.execute_reply.started":"2023-04-04T13:20:05.587737Z","shell.execute_reply":"2023-04-04T13:20:05.617760Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_sub.to_csv(\"submission.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2023-04-04T13:20:05.621063Z","iopub.execute_input":"2023-04-04T13:20:05.621486Z","iopub.status.idle":"2023-04-04T13:20:05.650349Z","shell.execute_reply.started":"2023-04-04T13:20:05.621430Z","shell.execute_reply":"2023-04-04T13:20:05.649078Z"},"trusted":true},"execution_count":null,"outputs":[]}]}