{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "339f789c-7f6f-4178-bd05-098c8de462f8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/kuacc/users/bbiner21/.local/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "\n", "from CLAPWrapper import CLAPWrapper\n", "from esc50_dataset import ESC50\n", "import torch.nn.functional as F" ] }, { "cell_type": "code", "execution_count": 73, "id": "e7989310-f8f1-4e41-a8ae-137a7d96e055", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using downloaded and verified file: ../esc50/ESC-50-master.zip\n", "Loading audio files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2000it [00:00, 8426.53it/s]\n" ] } ], "source": [ "# Load ESC50 dataset\n", "dataset = ESC50(root=\"../esc50\", download=False) # set download=True when dataset is not downloaded\n", "audio_file, target, one_hot_target = dataset[1000]\n", "audio_file = [audio_file]\n", "prompt = 'this is a sound of '\n", "y = [prompt + x for x in dataset.classes]\n" ] }, { "cell_type": "code", "execution_count": null, "id": "67da3392-1f63-4688-8e91-bcaed6edb348", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 74, "id": "076711bb-beeb-4720-85b4-2ff0537b4739", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['../esc50/ESC-50-master/audio/3-151212-A-24.wav']" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "audio_file\n", "# one_hot_target\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "1a824331-814d-44b4-9ba9-9e79440ea35f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/kuacc/users/bbiner21/.conda/envs/clap/lib/python3.8/site-packages/torchlibrosa/stft.py:193: FutureWarning: Pass size=1024 as keyword args. From version 0.10 passing these as positional arguments will result in an error\n", " fft_window = librosa.util.pad_center(fft_window, n_fft)\n", "Downloading (…)lve/main/config.json: 100%|██████████| 570/570 [00:00<00:00, 35.2kB/s]\n", "Downloading (…)\"pytorch_model.bin\";: 100%|██████████| 440M/440M [01:14<00:00, 5.93MB/s] \n", "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']\n", "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Downloading (…)okenizer_config.json: 100%|██████████| 28.0/28.0 [00:00<00:00, 1.83kB/s]\n", "Downloading (…)solve/main/vocab.txt: 100%|██████████| 232k/232k [00:00<00:00, 404kB/s]\n", "Downloading (…)/main/tokenizer.json: 100%|██████████| 466k/466k [00:00<00:00, 657kB/s]\n", "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n", "/kuacc/users/bbiner21/.conda/envs/clap/lib/python3.8/site-packages/transformers/tokenization_utils_base.py:2339: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n", " warnings.warn(\n" ] } ], "source": [ "# Load and initialize CLAP\n", "weights_path = \"../clap_weight/CLAP_weights_2022.pth\"\n", "\n", "# Setting use_cuda = True will load the model on a GPU using CUDA\n", "clap_model = CLAPWrapper(weights_path, use_cuda=False)\n", "\n" ] }, { "cell_type": "code", "execution_count": 93, "id": "0c1d6fba-1826-4aa0-a1bf-337cde3466a7", "metadata": {}, "outputs": [], "source": [ "audio_file = \"/datasets/audio-image/audios/audio_10s/FXdfIJ1XrCc.wav\"\n", "audio_file = [audio_file]" ] }, { "cell_type": "code", "execution_count": 94, "id": "1482419d-315c-477f-9cfd-d8d81dc42eeb", "metadata": {}, "outputs": [], "source": [ "# compute text embeddings from natural text \n", "text_embeddings = clap_model.get_text_embeddings(y)\n", "\n", "# compute the audio embeddings from an audio file \n", "audio_embeddings = clap_model.get_audio_embeddings(audio_file, resample=True)" ] }, { "cell_type": "code", "execution_count": 95, "id": "3ab4c165-43a4-4cbf-b98b-9baab0f7c55a", "metadata": {}, "outputs": [], "source": [ "# compute the similarity between audio_embeddings and text_embeddings\n", "similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings)\n", "\n", "\n", "similarity = F.softmax(similarity, dim=1)\n", "values, indices = similarity[0].topk(5)\n" ] }, { "cell_type": "code", "execution_count": 96, "id": "188013e0-0c59-4c56-b9e7-1a00aab4a20d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ground Truth: coughing\n", "Top predictions:\n", "\n", " crackling fire: 82.46%\n", " rain: 14.87%\n", " wind: 0.82%\n", " water drops: 0.51%\n", " thunderstorm: 0.33%\n" ] }, { "data": { "text/plain": [ "'\\nThe output (the exact numbers may vary):\\n\\nGround Truth: coughing\\nTop predictions:\\n\\n coughing: 86.34%\\n sneezing: 9.30%\\ndrinking sipping: 1.31%\\n laughing: 1.20%\\n glass breaking: 0.81%\\n'" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# view the results\n", "print(\"Ground Truth: {}\".format(target))\n", "print(\"Top predictions:\\n\")\n", "for value, index in zip(values, indices):\n", " print(f\"{dataset.classes[index]:>16s}: {100 * value.item():.2f}%\")\n", "\n", "\"\"\"\n", "The output (the exact numbers may vary):\n", "\n", "Ground Truth: coughing\n", "Top predictions:\n", "\n", " coughing: 86.34%\n", " sneezing: 9.30%\n", "drinking sipping: 1.31%\n", " laughing: 1.20%\n", " glass breaking: 0.81%\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 92, "id": "ac39ef4c-0347-4892-8002-a53a52866ac6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#play audio\n", "import IPython.display as ipd\n", "\n", "ipd.Audio(audio_file[0])" ] }, { "cell_type": "code", "execution_count": null, "id": "1d6ff7da-16ef-4812-997e-cdb4dade099e", "metadata": {}, "outputs": [], "source": [ "Audio" ] }, { "cell_type": "code", "execution_count": 154, "id": "4344c241-44c0-4d7a-ae43-793c9ccc4c68", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv(\"../landscape/landscape_final.csv\")\n", "\n", "classes = list(set(df[\"label\"]))" ] }, { "cell_type": "code", "execution_count": 155, "id": "e35ed954-f284-487d-9c26-1d2bd1acadf9", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 156, "id": "69032cec-961a-4012-9db3-61dc53c9c87c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['splashing water',\n", " 'thunder',\n", " 'wind noise',\n", " 'waterfall burbling',\n", " 'fire crackling',\n", " 'explosion',\n", " 'raining',\n", " 'underwater bubbling',\n", " 'squishing water']" ] }, "execution_count": 156, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classes" ] }, { "cell_type": "code", "execution_count": 2, "id": "a0875b74-61ab-4735-afc4-b18b85472fc4", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv(\"../landscape/trekking.csv\")\n", "\n", "# classes = list(set(df[\"label\"]))" ] }, { "cell_type": "code", "execution_count": 4, "id": "cfa9d458-8272-4b36-9d47-978efe0548c2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['ID', 'Snow', 'Forest', 'Rain', 'Duration'], dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 15, "id": "5b72b7e7-d7d2-447a-9886-e0cfe057940f", "metadata": {}, "outputs": [], "source": [ "import glob \n", "import os\n", "\n", "# paths_to_audio = glob.glob('/datasets/audio-image/audios/yt-audios/*.wav')\n", "wav_names = [os.path.basename(x) for x in glob.glob('/datasets/audio-image/audios/yt-audios/*.wav')]\n", "wav_names = [wav[:-4] for wav in wav_names]\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "fa4437f6-1faa-468a-9a43-7c915d2a1b89", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['nD3fpMkcmM0037',\n", " 'yqT_t3qBPJk278',\n", " 'wUTGZgtqouw049',\n", " 'UBMHK4IQGHE080',\n", " 'P___KhTylcE031',\n", " 'ynLpZGegiJE064',\n", " 'NeGLTM0TRjk170',\n", " 'vu-xeuqXflE135',\n", " 'iSGXKN9X8LM026',\n", " '_OQ2n6-ABl8408',\n", " 'f3lVkdM3fGQ396',\n", " 'Ij-8Ilis4Ic201',\n", " '6nBen_asE_A018',\n", " 'SuLyeN2XkDk150',\n", " 'f3lVkdM3fGQ321',\n", " 'a_oqcg0hvpo1259',\n", " '-dUhzZayxiQ087',\n", " 'VWny8Gf3BwQ274',\n", " 'ynLpZGegiJE069',\n", " 'zP-7VtZYKF4009',\n", " 'f3lVkdM3fGQ434',\n", " '1s6loyqVezs041',\n", " 'rWAOb5iliBM154',\n", " 'Rm9GMZBiX_M117',\n", " 'UBMHK4IQGHE146',\n", " 's6_XKC9XKu4074',\n", " 'Ij-8Ilis4Ic372',\n", " '478TeAxm12g878',\n", " '478TeAxm12g071',\n", " '478TeAxm12g261',\n", " 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