{"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":"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\n# 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\")\nmodel = 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\"\n# 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)\ntrain = pd.read_csv(\"/kaggle/input/birdclef-2023/train_metadata.csv\")\ntrain.head()\ncompetition_classes = sorted(train.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        \nforced_defaults\ndef frame_audio(\n      audio_array: np.ndarray,\n      window_size_s: float = 5.0,\n      hop_size_s: float = 5.0,\n      sam_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 * sam_rate)\n    hop_length = int(hop_size_s * sam_rate)\n    framed_audio = tf.signal.frame(audio_array, frame_length, hop_length, pad_end=True)\n    return framed_audio\n\ndef ensure_sam_rate(waveform, original_sam_rate,\n                       desired_sam_rate=32000):\n    \"\"\"Resample waveform if required.\"\"\"\n    if original_sam_rate != desired_sam_rate:\n        waveform = tfio.audio.resample(waveform, original_sam_rate, desired_sam_rate)\n    return desired_sam_rate, waveform\naudio, sam_rate = librosa.load(\"/kaggle/input/birdclef-2023/train_audio/afghor1/XC156639.ogg\")\nsam_rate, wav_data = ensure_sam_rate(audio, sam_rate)\nAudio(wav_data, rate=sam_rate)\nfixed_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]}\")\ndef predict_for_sam(filename, sam_submission, frame_limit_secs=None):\n    file_id = filename.split(\".ogg\")[0].split(\"/\")[-1]\n    \n    audio, sam_rate = librosa.load(filename)\n    sam_rate, wav_data = ensure_sam_rate(audio, sam_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        sam_submission.loc[sam_submission.row_id == file_id + \"_\" + str(frame), competition_classes] = probabilities[competition_class_map]\n        frame += 5\ntest_samples = list(glob.glob(\"/kaggle/input/birdclef-2023/test_soundscapes/*.ogg\"))\ntest_samples  \nsam_sub = pd.read_csv(\"/kaggle/input/birdclef-2023/sample_submission.csv\")\nsam_sub[competition_classes] = sam_sub[competition_classes].astype(np.float32)\nsam_sub.head()\nframe_limit_secs = 15 if sam_sub.shape[0] == 3 else None\nfor sam_filename in test_samples:\n    predict_for_sam(sam_filename, sam_sub, frame_limit_secs=15)\nsam_sub\nsam_sub.to_csv(\"submission.csv\", index=False)\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-05-24T11:01:19.502175Z","iopub.execute_input":"2023-05-24T11:01:19.502573Z","iopub.status.idle":"2023-05-24T11:01:52.374876Z","shell.execute_reply.started":"2023-05-24T11:01:19.502543Z","shell.execute_reply":"2023-05-24T11:01:52.373980Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}