visualize_cls_attn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pyp/miniconda3/envs/tf2/lib/python3.9/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",
      "2022-11-05 16:52:32 | INFO | models.dual_encoder | Model vitsmall 8x8 built\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DualEncoder(\n",
       "  (audio_encoder): AudioEncoder(\n",
       "    (feature_extractor): ConvFeatureExtractionModel(\n",
       "      (conv_layers): ModuleList(\n",
       "        (0): Sequential(\n",
       "          (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False)\n",
       "          (1): Dropout(p=0.0, inplace=False)\n",
       "          (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True)\n",
       "          (3): GELU()\n",
       "        )\n",
       "        (1): Sequential(\n",
       "          (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False)\n",
       "          (1): Dropout(p=0.0, inplace=False)\n",
       "          (2): GELU()\n",
       "        )\n",
       "        (2): Sequential(\n",
       "          (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False)\n",
       "          (1): Dropout(p=0.0, inplace=False)\n",
       "          (2): GELU()\n",
       "        )\n",
       "        (3): Sequential(\n",
       "          (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False)\n",
       "          (1): Dropout(p=0.0, inplace=False)\n",
       "          (2): GELU()\n",
       "        )\n",
       "        (4): Sequential(\n",
       "          (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False)\n",
       "          (1): Dropout(p=0.0, inplace=False)\n",
       "          (2): GELU()\n",
       "        )\n",
       "        (5): Sequential(\n",
       "          (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False)\n",
       "          (1): Dropout(p=0.0, inplace=False)\n",
       "          (2): GELU()\n",
       "        )\n",
       "        (6): Sequential(\n",
       "          (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False)\n",
       "          (1): Dropout(p=0.0, inplace=False)\n",
       "          (2): GELU()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (post_extract_proj): Linear(in_features=512, out_features=768, bias=True)\n",
       "    (dropout_input): Dropout(p=0.1, inplace=False)\n",
       "    (dropout_features): Dropout(p=0.1, inplace=False)\n",
       "    (pos_conv): Sequential(\n",
       "      (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16)\n",
       "      (1): SamePad()\n",
       "      (2): GELU()\n",
       "    )\n",
       "    (encoder): TransformerEncoder(\n",
       "      (layers): ModuleList(\n",
       "        (0): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (1): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (2): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (3): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (4): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (5): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (6): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (7): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (8): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (9): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (10): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (11): TransformerSentenceEncoderLayer(\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (dropout_module): FairseqDropout()\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.1, inplace=False)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (dropout3): Dropout(p=0.1, inplace=False)\n",
       "          (self_attn_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "      (layer_norm): FusedLayerNorm(torch.Size([768]), eps=1e-05, elementwise_affine=True)\n",
       "    )\n",
       "    (layer_norm): FusedLayerNorm(torch.Size([512]), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (trm): VisionTransformer(\n",
       "    (patch_embed): PatchEmbed(\n",
       "      (proj): Conv2d(3, 384, kernel_size=(8, 8), stride=(8, 8))\n",
       "    )\n",
       "    (pos_drop): Dropout(p=0.0, inplace=False)\n",
       "    (blocks): ModuleList(\n",
       "      (0): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (1): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (2): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (3): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (4): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (5): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (6): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (7): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (8): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (9): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (10): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (11): Block(\n",
       "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
       "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
       "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (drop_path): Identity()\n",
       "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "        (mlp): Mlp(\n",
       "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "          (act): GELU()\n",
       "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "          (drop): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "    (head): Identity()\n",
       "  )\n",
       "  (audio_cls_token_proj): Sequential(\n",
       "    (0): Linear(in_features=768, out_features=1536, bias=True)\n",
       "    (1): GELU()\n",
       "    (2): Linear(in_features=1536, out_features=2048, bias=True)\n",
       "  )\n",
       "  (visual_cls_token_proj): Sequential(\n",
       "    (0): Linear(in_features=384, out_features=1536, bias=True)\n",
       "    (1): GELU()\n",
       "    (2): Linear(in_features=1536, out_features=2048, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"4\"\n",
    "import pickle\n",
    "import argparse\n",
    "import time\n",
    "import torch\n",
    "import soundfile as sf\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "import IPython.display as ipd\n",
    "# import skimage.transform\n",
    "import numpy as np\n",
    "import torchvision.transforms as transforms\n",
    "import seaborn as sns\n",
    "from PIL import Image\n",
    "from models import dual_encoder\n",
    "exp_dir = \"/data/scratch/pyp/exp_pyp/discovery/vg-hubert_3\" # layer=10, threshold=0.8 works well for vghubert_3\n",
    "exp_dir = \"/saltpool0/scratch/pyp/discovery/vg-hubert_3\" # layer=10, threshold=0.8 works well for vghubert_3\n",
    "with open(\"%s/args.pkl\" % exp_dir, \"rb\") as f:\n",
    "    args = pickle.load(f)\n",
    "\n",
    "args = argparse.Namespace(**vars(args))\n",
    "model = dual_encoder.DualEncoder(args)\n",
    "model.load_state_dict(torch.load(os.path.join(exp_dir, \"best_bundle.pth\"))[\"dual_encoder\"])\n",
    "model.eval()\n",
    "model.to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "if \"SpokenCOCO\" in args.train_audio_dataset_json_file:\n",
    "    audio_root = \"/data/scratch/pyp/datasets/coco_pyp/SpokenCOCO/\"\n",
    "    candidates = [{\n",
    "                \"image\": \"val2014/COCO_val2014_000000554625.jpg\",\n",
    "                \"caption\": {\n",
    "                    \"text\": \"A BOY WEARING HEADPHONES USING ONE COMPUTER IN A LONG ROW OF COMPUTERS\",\n",
    "                    \"speaker\": \"myo79vzzpuzjy\",\n",
    "                    \"uttid\": \"myo79vzzpuzjy-35K3O9HUABD0RROUNY1HHLV1P09EFC_554625_28241\",\n",
    "                    \"wav\": \"wavs/train/329/myo79vzzpuzjy-35K3O9HUABD0RROUNY1HHLV1P09EFC_554625_28241.wav\"\n",
    "                },\n",
    "                \"text_alignment\": \"0.40__A__0.51 0.51__BOY__0.89 0.89__WEARING__1.24 1.24__HEADPHONES__1.85 1.85__USING__2.22 2.22__ONE__2.45 2.45__COMPUTER__3.12 3.12__IN__3.30 3.30__A__3.43 3.43__LONG__3.82 3.82__ROW__4.02 4.02__OF__4.15 4.15__COMPUTERS__5.15\"\n",
    "            },\n",
    "            {\n",
    "                \"image\": \"val2014/COCO_val2014_000000374628.jpg\",\n",
    "                \"caption\": {\n",
    "                    \"text\": \"A FULL VIEW OF AN OPEN KITCHEN AND DINING AREA\",\n",
    "                    \"speaker\": \"m15gxvhpg896x2\",\n",
    "                    \"uttid\": \"m15gxvhpg896x2-3XC1O3LBOSMOG2GNZ4TABK0MSAATLP_374628_241613\",\n",
    "                    \"wav\": \"wavs/train/136/m15gxvhpg896x2-3XC1O3LBOSMOG2GNZ4TABK0MSAATLP_374628_241613.wav\"\n",
    "                },\n",
    "                \"text_alignment\": \"0.00__A__0.20 0.20__FULL__0.42 0.42__VIEW__0.58 0.58__OF__0.67 0.67__AN__0.75 0.75__OPEN__1.04 1.04__KITCHEN__1.44 1.44__AND__1.55 1.55__DINING__1.97 1.97__AREA__2.28\"\n",
    "            },\n",
    "            {\n",
    "                \"image\": \"val2014/COCO_val2014_000000051191.jpg\",\n",
    "                \"caption\": {\n",
    "                    \"text\": \"WHITE TILED BATHROOM WITH A VANITY TUB AND WHITE FLOWERS\",\n",
    "                    \"speaker\": \"m2jpqehz49htdm\",\n",
    "                    \"uttid\": \"m2jpqehz49htdm-3OF2M9AATGO9I874KW6RI8OOKDPKZ1_51191_20792\",\n",
    "                    \"wav\": \"wavs/train/47/m2jpqehz49htdm-3OF2M9AATGO9I874KW6RI8OOKDPKZ1_51191_20792.wav\"\n",
    "                },\n",
    "                \"text_alignment\": \"1.43__WHITE__2.05 2.05__TILED__2.66 2.66__BATHROOM__3.21 3.21__WITH__3.51 3.54__A__3.77 3.77__VANITY__4.37 4.37__TUB__4.88 4.88__AND__5.46 5.49__WHITE__6.13 6.16__FLOWERS__7.02\"\n",
    "            },\n",
    "            {\n",
    "                \"image\": \"train2014/COCO_train2014_000000309511.jpg\",\n",
    "                \"caption\": {\n",
    "                    \"text\": \"A YOUNG MAN WHO IS DRINKING A GLASS OF WINE\",\n",
    "                    \"speaker\": \"m2b7l2fwsmok2q\",\n",
    "                    \"uttid\": \"m2b7l2fwsmok2q-3PH3VY7DJLX0D81L9APU6JIJB9HZWD_309511_426660\",\n",
    "                    \"wav\": \"wavs/train/366/m2b7l2fwsmok2q-3PH3VY7DJLX0D81L9APU6JIJB9HZWD_309511_426660.wav\"\n",
    "                },\n",
    "                \"text_alignment\": \"0.25__A__0.33 0.33__YOUNG__0.52 0.52__MAN__0.75 0.75__WHO__0.84 0.84__IS__0.96 0.96__DRINKING__1.33 1.33__A__1.38 1.38__GLASS__1.70 1.70__OF__1.83 1.83__WINE__2.27\"\n",
    "            },\n",
    "            {\n",
    "                \"image\": \"train2014/COCO_train2014_000000348046.jpg\",\n",
    "                \"caption\": {\n",
    "                    \"text\": \"A YELLOW AND WHITE CAT ON TOP OF A BLACK LEATHER CUSHION\",\n",
    "                    \"speaker\": \"m3wglzbhiy8w4l\",\n",
    "                    \"uttid\": \"m3wglzbhiy8w4l-37UQDCYH6XV4E9VA05SFTX66R4D7VL_348046_763501\",\n",
    "                    \"wav\": \"wavs/train/121/m3wglzbhiy8w4l-37UQDCYH6XV4E9VA05SFTX66R4D7VL_348046_763501.wav\"\n",
    "                },\n",
    "                \"text_alignment\": \"1.12__A__1.19 1.19__YELLOW__1.67 1.67__AND__1.86 1.86__WHITE__2.12 2.12__CAT__2.51 2.51__ON__2.67 2.67__TOP__3.10 3.10__OF__3.22 3.22__A__3.28 3.28__BLACK__3.66 3.66__LEATHER__3.91 3.91__CUSHION__4.43\"\n",
    "            },\n",
    "            {\n",
    "                \"image\": \"train2014/COCO_train2014_000000166881.jpg\",\n",
    "                \"caption\": {\n",
    "                    \"text\": \"A PAIR OF PEOPLE STAND ON A DARK BEACH FLYING A KITE\",\n",
    "                    \"speaker\": \"m1jiaccy3qalod\",\n",
    "                    \"uttid\": \"m1jiaccy3qalod-3F1567XTNW5GNKNMT3WP16Y45569QV_166881_761706\",\n",
    "                    \"wav\": \"wavs/train/414/m1jiaccy3qalod-3F1567XTNW5GNKNMT3WP16Y45569QV_166881_761706.wav\"\n",
    "                },\n",
    "                \"text_alignment\": \"1.00__A__1.11 1.11__PAIR__1.36 1.36__OF__1.49 1.49__PEOPLE__1.85 1.85__STAND__2.29 2.29__ON__2.45 2.45__A__2.51 2.51__DARK__2.90 2.90__BEACH__3.21 3.21__FLYING__3.62 3.62__A__3.69 3.69__KITE__4.13\"\n",
    "            },\n",
    "            {\n",
    "                \"image\": \"train2014/COCO_train2014_000000517522.jpg\",\n",
    "                \"caption\": {\n",
    "                    \"text\": \"A MAN RUNNING ON A TENNIS COURT WITH A RACKETT IN HIS HAND\",\n",
    "                    \"speaker\": \"m3ikb9ie32k2x\",\n",
    "                    \"uttid\": \"m3ikb9ie32k2x-3XLBSAQ9Z4CLNT0K12HIE3J2EVW7ZT_517522_509533\",\n",
    "                    \"wav\": \"wavs/train/524/m3ikb9ie32k2x-3XLBSAQ9Z4CLNT0K12HIE3J2EVW7ZT_517522_509533.wav\"\n",
    "                },\n",
    "                \"text_alignment\": \"0.74__A__0.83 0.83__MAN__1.36 1.50__RUNNING__1.91 1.91__ON__2.21 2.21__A__2.35 2.35__TENNIS__2.77 2.77__COURT__3.09 3.09__WITH__3.18 3.18__A__3.31 3.31__RACKETT__3.90 3.90__IN__4.00 4.00__HIS__4.10 4.10__HAND__4.71\"\n",
    "            },\n",
    "            {\n",
    "                \"image\": \"train2014/COCO_train2014_000000158362.jpg\",\n",
    "                \"caption\": {\n",
    "                    \"text\": \"A MAN ON HIS LAP TOP ON A BENCH\",\n",
    "                    \"speaker\": \"m1exzmxuk43d2n\",\n",
    "                    \"uttid\": \"m1exzmxuk43d2n-3X4JMASXCM9BNKXKAPG6D62CI4IB0O_158362_681928\",\n",
    "                    \"wav\": \"wavs/train/385/m1exzmxuk43d2n-3X4JMASXCM9BNKXKAPG6D62CI4IB0O_158362_681928.wav\"\n",
    "                },\n",
    "                \"text_alignment\": \"0.81__A__0.88 0.88__MAN__1.19 1.19__ON__1.34 1.34__HIS__1.59 1.59__LAP__1.81 1.81__TOP__2.12 2.24__ON__2.44 2.44__A__2.50 2.50__BENCH__2.95\"\n",
    "            },\n",
    "            {\n",
    "                \"image\": \"train2014/COCO_train2014_000000158362.jpg\",\n",
    "                \"caption\": {\n",
    "                    \"text\": \"MAN SITTING ON URBAN BENCH USING LAPTOP COMPUTER\",\n",
    "                    \"speaker\": \"m2p98h3lkipyva\",\n",
    "                    \"uttid\": \"m2p98h3lkipyva-3CFJTT4SXTQZS3BA1YTWE8GCIH97I9_158362_684304\",\n",
    "                    \"wav\": \"wavs/train/385/m2p98h3lkipyva-3CFJTT4SXTQZS3BA1YTWE8GCIH97I9_158362_684304.wav\"\n",
    "                },\n",
    "                \"text_alignment\": \"0.62__MAN__0.94 0.94__SITTING__1.32 1.32__ON__1.51 1.51__URBAN__1.85 1.85__BENCH__2.30 2.30__USING__2.58 2.58__LAPTOP__3.21 3.21__COMPUTER__3.76\"\n",
    "            },\n",
    "            {\n",
    "                \"image\": \"train2014/COCO_train2014_000000158362.jpg\",\n",
    "                \"caption\": {\n",
    "                    \"text\": \"A MAN ON A BENCH LOOKING AT HIS LAPTOP\",\n",
    "                    \"speaker\": \"m2zco7272xa5e0\",\n",
    "                    \"uttid\": \"m2zco7272xa5e0-3ZAZR5XV01IRZAVGSJPTUPBGFXMZCV_158362_682780\",\n",
    "                    \"wav\": \"wavs/train/385/m2zco7272xa5e0-3ZAZR5XV01IRZAVGSJPTUPBGFXMZCV_158362_682780.wav\"\n",
    "                },\n",
    "                \"text_alignment\": \"0.21__A__0.31 0.31__MAN__0.62 0.62__ON__0.73 0.73__A__0.81 0.81__BENCH__1.16 1.16__LOOKING__1.47 1.47__AT__1.62 1.62__HIS__1.78 1.78__LAPTOP__2.45\"\n",
    "            }]\n",
    "elif \"places_hdf5_pyp\" in args.train_audio_dataset_json_file:\n",
    "    audio_root = \"/data/scratch/pyp/datasets/PlacesAudio_400k_distro\"\n",
    "    candidates = [\n",
    "{\n",
    "      \"uttid\": \"ALN5N2C8H3AT3-GSUN_5D6AF9119218117725E8342B02830B43\",\n",
    "      \"speaker\": \"ALN5N2C8H3AT3\",\n",
    "      \"asr_text\": \"this is a photo of a girl standing in front of a lighthouse the little girls wear blue print dress she has blonde hair and blue eyes the lighthouse in the background is white with a red roof\",\n",
    "      \"wav\": \"wavs/248/utterance_380795.wav\",\n",
    "      \"image\": \"l/lighthouse/gsun_5d6af9119218117725e8342b02830b43.jpg\",\n",
    "      \"scenelabel\": \"lighthouse\",\n",
    "      \"text_alignment\": \"0.43__THIS__0.62 0.62__IS__0.73 0.73__A__0.82 0.82__PHOTO__1.18 1.18__OF__1.26 1.26__A__1.33 1.33__GIRL__1.62 1.62__STANDING__2.12 2.12__IN__2.23 2.23__FRONT__2.51 2.51__OF__2.62 2.62__A__2.67 2.67__LIGHTHOUSE__3.47 4.12__THE__4.63 4.63__LITTLE__4.89 4.89__GIRL'S__5.22 5.22__WEARING__5.52 5.52__BLUE__5.72 5.72__PRINT__6.00 6.00__DRESS__6.63 6.66__SHE__6.83 6.83__HAS__7.02 7.02__BLONDE__7.34 7.34__HAIR__7.77 7.77__AND__7.99 7.99__BLUE__8.23 8.23__EYES__8.84 9.27__THE__9.69 9.72__LIGHTHOUSE__10.27 10.27__IN__10.34 10.34__THE__10.43 10.43__BACKGROUND__10.94 10.94__IS__11.06 11.06__WHITE__11.41 11.41__WITH__11.59 11.59__A__11.66 11.66__RED__11.95 11.95__ROOF__12.14\"\n",
    "    },\n",
    "    {\n",
    "\"uttid\": \"A8KHWKKGAGOH0-GSUN_3A7F460E9AD86FEEF08B9D52E2AABDC1\",\n",
    "\"speaker\": \"A8KHWKKGAGOH0\",\n",
    "\"asr_text\": \"picture of disney world there's a man of many talents gym there's also a goofy it's brightly colored\",\n",
    "\"wav\": \"wavs/4/utterance_342031.wav\",\n",
    "\"image\": \"a/amusement_park/gsun_3a7f460e9ad86feef08b9d52e2aabdc1.jpg\",\n",
    "\"scenelabel\": \"amusement_park\",\n",
    "\"text_alignment\": \"0.85__PICTURE__1.22 1.22__OF__1.30 1.30__DISNEY__1.61 1.61__WORLD__2.00 2.55__THERE'S__2.72 2.72__A__2.77 2.77__MAN__3.06 3.06__OF__3.14 3.14__MANY__3.36 3.36__TALENTS__3.72 3.72__GYM__4.00 4.26__THERE'S__4.46 4.46__ALSO__4.71 4.71__A__4.78 4.78__GOOFY__5.25 5.77__IT'S__5.97 5.97__BRIGHTLY__6.26 6.26__COLORED__6.66\"\n",
    "},\n",
    "{\n",
    "\"uttid\": \"A2WQT33K6LD9Z5-GSUN_3B1FB5BDCB2390BFB13047C583E9F82A\",\n",
    "\"speaker\": \"A2WQT33K6LD9Z5\",\n",
    "\"asr_text\": \"photograph of a train that is blurry going down railroad tracks on a very overcast day\",\n",
    "\"wav\": \"wavs/5/utterance_224571.wav\",\n",
    "\"image\": \"t/train_railway/gsun_3b1fb5bdcb2390bfb13047c583e9f82a.jpg\",\n",
    "\"scenelabel\": \"train_railway\",\n",
    "\"text_alignment\": \"0.00__PHOTOGRAPH__0.98 0.98__OF__1.30 1.30__A__1.68 1.68__TRAIN__2.17 2.17__THAT__2.35 2.35__IS__2.60 2.60__BLURRY__3.22 3.26__GOING__3.63 3.63__DOWN__3.99 3.99__RAILROAD__4.52 4.52__TRACKS__5.06 5.06__ON__5.20 5.20__A__5.30 5.39__VERY__5.62 5.62__OVERCAST__6.46 6.46__DAY__6.71\"\n",
    "},\n",
    "{\n",
    "\"uttid\": \"A1KH1O9AZ7S642-GSUN_16A6B871CE9F2BAEBAB17247EA185456\",\n",
    "\"speaker\": \"A1KH1O9AZ7S642\",\n",
    "\"asr_text\": \"the large view of the city street there are many people walking around the street\",\n",
    "\"wav\": \"wavs/6/utterance_335654.wav\",\n",
    "\"image\": \"a/alley/gsun_16a6b871ce9f2baebab17247ea185456.jpg\",\n",
    "\"scenelabel\": \"alley\",\n",
    "\"text_alignment\": \"0.16__THE__0.38 0.38__LARGE__0.85 0.85__VIEW__1.04 1.04__OF__1.13 1.13__THE__1.21 1.21__CITY__1.54 1.54__STREET__1.96 2.04__THERE__2.21 2.21__ARE__2.25 2.25__MANY__2.52 2.52__PEOPLE__2.85 2.85__WALKING__3.22 3.22__AROUND__3.51 3.51__THE__3.58 3.58__STREET__3.96\"\n",
    "},\n",
    "{\n",
    "\"uttid\": \"A1KH1O9AZ7S642-GSUN_695A0ABEE3C0C24AA69C2C37A51C2364\",\n",
    "\"speaker\": \"A1KH1O9AZ7S642\",\n",
    "\"asr_text\": \"the train is coming our way we're near a train station\",\n",
    "\"wav\": \"wavs/25/utterance_212666.wav\",\n",
    "\"image\": \"t/train_station/platform/gsun_695a0abee3c0c24aa69c2c37a51c2364.jpg\",\n",
    "\"scenelabel\": \"train_station-platform\",\n",
    "\"text_alignment\": \"0.28__THE__0.37 0.37__TRAIN__0.71 0.71__IS__0.83 0.83__COMING__1.13 1.13__OUR__1.27 1.27__WAY__1.60 1.87__WE'RE__2.05 2.05__NEAR__2.26 2.26__A__2.31 2.31__TRAIN__2.60 2.60__STATION__3.16\"\n",
    "},\n",
    "{\n",
    "\"uttid\": \"A16G6PPH1INQL8-GSUN_3FF681C9F23AF4FAF7A4E7AD348EF508\",\n",
    "\"speaker\": \"A16G6PPH1INQL8\",\n",
    "\"asr_text\": \"there is a red car in front of the building\",\n",
    "\"wav\": \"wavs/46/utterance_191349.wav\",\n",
    "\"image\": \"m/mansion/gsun_3ff681c9f23af4faf7a4e7ad348ef508.jpg\",\n",
    "\"scenelabel\": \"mansion\",\n",
    "\"text_alignment\": \"2.81__THERE__3.06 3.06__IS__3.21 4.84__A__4.95 4.95__RED__5.26 5.26__CAR__5.51 5.51__IN__5.62 5.62__FRONT__5.79 5.79__OF__5.99 5.99__THE__6.22 10.27__BUILDING__10.52\"\n",
    "},\n",
    "{\n",
    "\"uttid\": \"A21EKAOO57Y6KA-GSUN_15E5C5AC4568C1D21936AE5051825E21\",\n",
    "\"speaker\": \"A21EKAOO57Y6KA\",\n",
    "\"asr_text\": \"a woman and what looks like her child is at a playground the woman is wearing a tan or brown shirt with sunglasses the child is in one of those captains porthole type things and he's either looking very sad very happy\",\n",
    "\"wav\": \"wavs/59/utterance_32051.wav\",\n",
    "\"image\": \"p/playground/gsun_15e5c5ac4568c1d21936ae5051825e21.jpg\",\n",
    "\"scenelabel\": \"playground\",\n",
    "\"text_alignment\": \"1.04__A__1.25 1.25__WOMAN__1.76 1.76__AND__2.07 2.07__WHAT__2.28 2.28__LOOKS__2.61 2.61__LIKE__2.84 2.84__HER__3.10 3.10__CHILD__3.70 3.73__IS__3.90 3.90__AT__4.07 4.07__A__4.13 4.13__PLAYGROUND__4.82 4.85__THE__4.95 4.95__WOMAN__5.31 5.31__IS__5.46 5.46__WEARING__5.87 6.01__A__6.17 6.17__TAN__6.71 6.78__OR__7.03 7.03__BROWN__7.34 7.34__SHIRT__7.69 7.69__WITH__7.89 7.89__SUNGLASSES__8.85 9.46__THE__9.59 9.59__CHILD__10.20 10.23__IS__10.62 10.62__IN__10.82 10.82__ONE__10.96 10.96__OF__11.09 11.09__THOSE__11.59 11.63__CAPTAINS__12.19 12.19__<SPOKEN_NOISE>__12.68 12.68__TYPE__12.97 13.04__THINGS__13.59 13.81__AND__14.09 14.09__HE'S__14.39 14.42__EITHER__14.71 14.71__LOOKING__15.07 15.07__VERY__15.33 15.33__SAD__15.80 15.80__VERY__16.09 16.09__HAPPY__16.64\"\n",
    "},\n",
    "{\"uttid\": \"A2BSULOV6QDJ0P-GSUN_758A5EE27625F5D4D640721151C84CC3\",\n",
    "\"speaker\": \"A2BSULOV6QDJ0P\",\n",
    "\"asr_text\": \"picture of a woman standing in front of a large silver globe there are two cartoon characters that look to be made out of bushes goofy and daffy duck behind her\",\n",
    "\"wav\": \"wavs/44/utterance_219124.wav\",\n",
    "\"image\": \"t/topiary_garden/gsun_758a5ee27625f5d4d640721151c84cc3.jpg\",\n",
    "\"scenelabel\": \"topiary_garden\",\n",
    "\"text_alignment\": \"0.51__PICTURE__1.06 1.06__OF__1.22 1.22__A__1.27 1.27__WOMAN__1.67 1.67__STANDING__2.27 2.27__IN__2.46 2.46__FRONT__2.82 2.82__OF__2.93 2.93__A__2.99 2.99__LARGE__3.62 3.62__SILVER__4.06 4.06__GLOBE__4.57 5.15__THERE__5.40 5.40__ARE__5.70 5.70__TWO__6.26 6.29__CARTOON__6.84 6.84__CHARACTERS__7.45 7.45__THAT__7.63 7.63__LOOK__7.84 7.84__TO__7.93 7.93__BE__8.09 8.09__MADE__8.39 8.39__OUT__8.58 8.58__OF__9.02 9.14__BUSHES__9.86 10.38__GOOFY__11.07 11.07__AND__11.30 11.30__DAFFY__11.74 11.74__DUCK__12.05 12.05__BEHIND__12.52 12.52__HER__12.80\"\n",
    "},\n",
    "{\n",
    "\"uttid\": \"ALT6TQMZJKSFQ-GSUN_E0AB805DF5714609ABE149EF8C2AC14F\",\n",
    "\"speaker\": \"ALT6TQMZJKSFQ\",\n",
    "\"asr_text\": \"picture of a highway with their cars and trucks on it the large blue sign and trees on both sides\",\n",
    "\"wav\": \"wavs/45/utterance_292728.wav\",\n",
    "\"image\": \"h/highway/gsun_e0ab805df5714609abe149ef8c2ac14f.jpg\",\n",
    "\"scenelabel\": \"highway\",\n",
    "\"text_alignment\": \"0.57__PICTURE__0.84 0.84__OF__0.90 0.90__A__0.94 0.94__HIGHWAY__1.47 1.47__WITH__1.65 1.65__THEIR__1.94 2.13__CARS__2.48 2.48__AND__2.59 2.59__TRUCKS__2.92 2.92__ON__3.13 3.13__IT__3.42 3.85__THE__4.04 4.04__LARGE__4.36 4.36__BLUE__4.59 4.59__SIGN__5.28 5.64__AND__5.81 5.81__TREES__6.16 6.16__ON__6.30 6.30__BOTH__6.51 6.51__SIDES__7.19\"\n",
    "},\n",
    "{\n",
    "\"uttid\": \"A1LSZRMNFLE4FR-GSUN_AE205FE6D3CF484332F0EC3973BCFB9B\",\n",
    "\"speaker\": \"A1LSZRMNFLE4FR\",\n",
    "\"asr_text\": \"there is a building and the building is near palm trees\",\n",
    "\"wav\": \"wavs/62/utterance_182410.wav\",\n",
    "\"image\": \"m/motel/gsun_ae205fe6d3cf484332f0ec3973bcfb9b.jpg\",\n",
    "\"scenelabel\": \"motel\",\n",
    "\"text_alignment\": \"1.19__THERE__1.43 1.43__IS__1.64 1.64__A__1.77 1.77__BUILDING__2.46 2.49__AND__2.69 2.69__THE__2.79 2.79__BUILDING__3.34 3.34__IS__3.61 3.61__NEAR__3.86 3.86__PALM__4.34 4.34__TREES__4.97\"\n",
    "}\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_word_ali(raw_ali, fps=50):\n",
    "    \"\"\"\n",
    "    raw_ali is a string like 'start1__word1__end1 start2__word2__end2 ...'\n",
    "    \"\"\"\n",
    "    data = []\n",
    "    meta_toks = raw_ali.split()\n",
    "    for meta_tok in meta_toks:\n",
    "        toks = meta_tok.split('__')\n",
    "        if len(toks) == 3:\n",
    "            data.append((float(toks[0])*fps, float(toks[2])*fps, toks[1]))\n",
    "    return data\n",
    "\n",
    "def plot_multi_attention(ax, cls_attn_weights, threshold=0.9,fontsize=12, ybot_r=0, ytop_r=0.1, color='red', valter=False, xleft_r=0, adjust=0.002):\n",
    "    \n",
    "    (ymin, ymax) = ax.get_ylim()\n",
    "    (xmin, xmax) = ax.get_xlim()\n",
    "    ybot = (0 + ybot_r) * ymax\n",
    "    ytop = (0 + ytop_r) * ymax\n",
    "    \n",
    "    # horizontal lines\n",
    "    botline = Line2D([xmin, xmax], [ybot, ybot], color='black')\n",
    "    topline = Line2D([xmin, xmax], [ytop, ytop], color='black')    \n",
    "    botline.set_clip_on(False)\n",
    "    topline.set_clip_on(False)\n",
    "    ax.add_line(botline)\n",
    "    ax.add_line(topline)\n",
    "    \n",
    "    threshold_values = []\n",
    "    step_size = (xmax - xmin) / cls_attn_weights.shape[-1]\n",
    "    colors = ['tab:red', 'green',  'tab:gray', 'slategrey', 'fuchsia','tab:green', 'tab:brown', 'blue', 'tab:olive', 'purple', 'tab:orange', 'm']\n",
    "    for i in range(cls_attn_weights.shape[0]):\n",
    "        if True:\n",
    "            color_used = colors[i]\n",
    "            attn_weight = cls_attn_weights[i].squeeze()\n",
    "            threshold_values.append(torch.quantile(attn_weight, threshold))\n",
    "            threshold_value = torch.quantile(attn_weight, threshold)\n",
    "            attn_weight_thres = torch.nn.functional.threshold(attn_weight,threshold_value,0)\n",
    "            attn_weight_thres = attn_weight_thres/attn_weight_thres.max()\n",
    "            for t in range(cls_attn_weights.shape[-1]):\n",
    "                ax.axvspan(t*step_size-adjust, (t+1)*step_size-adjust, ymin=ybot, ymax=ytop, alpha=attn_weight_thres[t].item(), color=color_used)\n",
    "    return ax\n",
    "\n",
    "def plot_one_ali(ax, ali, fontsize=14, xmax_actual = 607, ybot_r=0, ytop_r=0.1, color='red', valter=False, xleft_r=0, adjust=0):\n",
    "    (ymin, ymax) = ax.get_ylim()\n",
    "    (xmin, xmax) = ax.get_xlim()\n",
    "    ybot = (0 + ybot_r) * ymax\n",
    "    ytop = (0 + ytop_r) * ymax\n",
    "    \n",
    "    # horizontal lines\n",
    "    botline = Line2D([xmin, xmax], [ybot, ybot], color='black')\n",
    "    topline = Line2D([xmin, xmax], [ytop, ytop], color='black')    \n",
    "    botline.set_clip_on(False)\n",
    "    topline.set_clip_on(False)\n",
    "    ax.add_line(botline)\n",
    "    ax.add_line(topline)\n",
    "    \n",
    "    # text location parameters\n",
    "    sign = 1\n",
    "    ydelta = (ytop - ybot)*0.2\n",
    "    ymid = (ybot*0.6 + ytop*0.4)\n",
    "    \n",
    "    for (t_s, t_e, lab) in ali:\n",
    "        t_s = t_s/xmax_actual-adjust\n",
    "        t_e = t_e/xmax_actual-adjust\n",
    "        # add label\n",
    "        text_xs = t_s + (t_e - t_s) * 0.1\n",
    "        text_ys = ymid + sign*ydelta if valter else ymid\n",
    "        sign = sign*-1\n",
    "        ax.text(text_xs, text_ys, lab, fontsize=fontsize)\n",
    "        \n",
    "        # add label boundaries\n",
    "        vline_s = Line2D([t_s, t_s], [ybot, ytop], ls='dashed', color=color, alpha=0.3)\n",
    "        vline_e = Line2D([t_e, t_e], [ybot, ytop], ls='dashed', color=color, alpha=0.3)\n",
    "        vline_s.set_clip_on(False)\n",
    "        vline_e.set_clip_on(False)        \n",
    "        ax.add_line(vline_s)\n",
    "        ax.add_line(vline_e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compact_plot(audio_path, text_alignment, tgt_layer=7, threshold=0.9, pivotal_token=0):\n",
    "    audio, sr = sf.read(audio_path, dtype = 'float32')\n",
    "    if args.normalize:\n",
    "        audio = (audio - np.mean(audio))/np.std(audio)\n",
    "    audio = torch.from_numpy(audio).unsqueeze(0).cuda()\n",
    "    with torch.no_grad():\n",
    "        attn_weights = model.audio_encoder.get_last_selfattention(audio, tgt_layer=tgt_layer, padding_mask = None)\n",
    "        cls_attn_weights = attn_weights[:,:,pivotal_token,1:].transpose(0,1)\n",
    "    word_ali = get_word_ali(text_alignment,fps=cls_attn_weights.shape[-1] / (audio.shape[1]/sr))\n",
    "    plt.figure(figsize=(34,1.5))\n",
    "    ax = plt.gca()\n",
    "    ax.axes.get_xaxis().set_visible(False)\n",
    "    ax.axes.get_yaxis().set_visible(False)\n",
    "    plot_multi_attention(ax, cls_attn_weights.cpu(), threshold=threshold, fontsize=1, ybot_r=0, ytop_r=0.5, color='red', valter=False, xleft_r=0, adjust=0)\n",
    "    plot_one_ali(ax, word_ali, xmax_actual=cls_attn_weights.shape[-1], fontsize=15, ybot_r=0.5, ytop_r=1, color='red', valter=False, xleft_r=0, adjust=0)\n",
    "    # plt.savefig(f\"{os.path.basename(audio_path)}.pdf\", dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for item in candidates:\n",
    "    if not os.path.isdir(audio_root):\n",
    "        audio_root = \"/data1/scratch/coco_pyp/SpokenCOCO/\"\n",
    "    fn = item['caption']['wav'] if 'caption' in item else item['wav']\n",
    "    audio_path = os.path.join(audio_root, fn)\n",
    "    compact_plot(audio_path, item['text_alignment'], tgt_layer=10, pivotal_token=0, threshold=0.8)\n",
    "    # break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AO2nlsfnKK69IksaOHdtg5L8jxY695cuXKxqNxh9rR9HG60Fb26sOh0N33nmnJHW8UWNSpK2TyPOitra2/rGp6YZ69uyp6667jiQhIBlS5LrVVqFQSGPGjNGLL76o008/XW+88YZKS0sTtr7XX39dgUBAZ555psrLyxO2nkSIJYTMnTvXWDI9ALRZnPVPKvZFLrzwQnXr1k1er1dVVVVGy05Zad6OaInCwkKNGjVKkvTGG2+06r2J6ue0tk5ftmyZpLr+YXM/JAGATi0F6zSTuQKx+3mm+8srV67Uzp07NWDAAJWXl+uyyy5Tdna2li1bxg+ugXTTTnlQgEQyJpLomWeeUSAQ0KhRo9S1a1dJ4tckAICUNmHCBEmNT1X+3HPPyev16vzzz9dxxx2XkPU3lowZCoVUVVWliooKSar//6HLxKYxHzBggHJychISGxJn48aNkuqmAz2a0047TRkZGaqurtann36a6NA6vHjaqxdffLEGDBigdevW6dVXX014rJ1NIs+L2DRDvXr1qt/vJvz6179WTk6O5s6dqz179hgrF0DHdumll+rll1/WGWecoddff13dunVL6Po++OADSdJZZ52V0PUkQo8ePTRlyhQdPHhQs2bNSnY4ABCXVO2L9O/fX5K0efNmo+Uiuc444wxJbduviejntLZOj/UPY7McAADSg8lcgVjy5siRI43GGIsjFldBQYF+8IMfKBwOa+nSpUbXBQDoOEjGRNIc2XiRpMsvv1wej0cvv/yyqqurkxUakDYcDkejf5999lmyQwM6pJ/85CdyOBx64YUX5PV6D3vt0Ok0WmPYsGFNnsvTpk07bNnGkjGrqqoUCATqkzDPPfdcuVyu+mnJJaYoT3exX9i2JPnC7XarS5cuktRg9Iirrrqq0eOsw43caFC87dXYtr399tsTFmNnZeq8ONSePXv0+OOP6+abb5YkTZkypcEyn3/+eZPXbIfDUZ/E1Jju3btrypQp8nq9uvvuu48aNwBIdSMBOxwOLVy4UMXFxQlfX2uur01ZvXp1s9fKBQsWNPv+ptosDodDl1xySbPvjSWEPPzww8anpwOA9pSqfZGSkhJJ0v79+42Wi+SKZ78mqp/Tmjo91n6JfY4jXXPNNZo0adJhf62dkh0AYJ6JXIGdO3dq7ty5mjVrlvr06aMZM2Y0ueyCBQua7GsWFRU1WN7r9er555+Xy+XS+PHj659ncCkAwNG4kx0AUpBlSW+9Vfd40KCEDNH7r3/9S2+++aaKior0gx/8oP754uJijR49Wi+++KKefvppTZ482fi6gYQKhaRHH617/NOfJnyo/4kTJzb6fF5eXkLXC6SFBJyPxx57rM4//3ytXr1aL7zwQv3Ut7t27dLKlSuVlZWlsWPHtqrM733ve+rRo0ejr33nO9857N/f+ta3VFpaqt27d2vLli0qLy+vv3k8ePBgSVJ+fr5OO+20w24qxx6TjJki2rmuiKmoqFCfPn0aPB8bAaNDa8M2N9FeveiiizRw4EBVVVXp5ZdfPqyctJWk4zdRrrrqKl111VUNnp84caKmT5/e4Pnc3FyNGTOmyfKONpLmr3/9a82bN09/+MMfdNNNN6l79+6tDxpA66T5dauiokJr167VuHHjtHr16iaTDFJJ9+7dmx2NpLKyUtu2bWvy9abaLNLRR+wsLS3V9ddfr3vuuUd333237rvvvpYFDQCmxVH/pHJfxLZtSXU/EO8U0rwd0VLx7tdE9HNM1ukLFiyQZVmHPTd06ND6e2kA0CmkWJ0WT3tn2LBhDZ4766yz9MYbb6igoKDJdZ500klNXvsbm1Hs+eefl9fr1ciRIw+r20aOHKmSkhKtW7dO//znP5vsvwJIMe2QBwXEkIyJxh04kNDiFy9eLNu2NWbMGGVmZh722pVXXqkXX3xRixYtIhkT6WnXrnZb1fz584+6TEtvonW6m6noHBJwPk6YMEGrV6/WokWL6pMxlyxZIsuydOmll6qwsLBV5U2fPl1Dhw5t8fJDhgzRs88+q8rKSpWXl2vNmjXKzs7WgAED6pepqKjQww8/rK+++kpdunRRVVWVHA5H/eiZSAGtODZjo2C1ZLqvSCRSP5LFkckakydP1qRJk1oeY0fTyuuBqfbqnXfeqdGjR+uOO+7oGMmYUru2dZpi6rw4NOEnKytLxx13nEaNGtVkknJJSUmL2l9N6datm66//nrNmjVLM2fO1Jw5c9pcFoBWSIHrVlu98soruuCCC7RhwwaNHDlSr7/+erNfLsWrNdfXppSXlzd7rZw0aVKzyZjxtlluuukmPfLII/UJIU398AkAEq6N9U8q90ViI80f7UdIHUoatyNaKt79mqh+Tkvr9Fj7pamZECKRSP3j6667Tn/84x+NxAcAaSeF6rR42juxAS4sy9Knn36qt956Sxs2bNDUqVP1+OOPN7nOwYMHt+q+XmMjd0qSx+PRuHHj9PDDD2vRokXMPAWkkwTnQQExTFOOpIg1XlatWqXBgwcf9jdr1ixJ0ptvvqnPP/88mWECHcKhv+by+XxNLhd7LTc3N+ExAelszJgxysrK0ooVK+qnSYpNUT5hwoSErz82uuWaNWtk27bWrl2rgQMHKuOQX7LGki7XrFmjd955R8FgUKeeemrn+rKkA+nfv78k6d133z3qsps2bVIoFFJhYaFOOOGERIfWoZlqr44aNUrnnnuuNmzYoOeffz7hcXcWps6LyZMna/78+Zo/f77mzZunW265JeGjxd50003Ky8vTvHnztHPnzoSuC0D6Kyws1GuvvaZ+/frpvffe0/e///1m+3Xxil0DN2zYkLB1JFq3bt30X//1X/L7/fr973+f7HAAoNVStS9i27Y2btwoSerXr1/c5SF1vP/++5Li26+J6Oe0tE6P9Q9jnwMAkPriae9Mnz5d8+fP18KFC1VZWalVq1YpKytL8+fP1zPPPGMkvp07d2rlypWSpDlz5jSI8W9/+5ukf383BADAoUjGRLt77733tHnzZknSP//5T61du/awv6qqKkl1N3cWL16czFCBDqFr167Kzs6WJH3yySdNLhd77ZhjjmmXuIB0VVhYqIsvvliRSERLlizRli1b9N5776mkpKTZ6RhNiSVjVlZWatOmTTpw4ECDqTViyZiVlZVMUd4BjB49WpL0zDPPKBwON7vsk08+KUkaMWKEnE6a+m1lur1655131v8/NhI14pPO50VJSYl+/vOfKxAIkCQEoEVKSkq0YsUKnXTSSaqsrNSPfvQjhUKhhKzrggsuUFZWlt5//31t2bIlIetoDzfddJPy8/P16KOPaseOHckOBwBaLJX7IitXrtTevXuVn59/2OwcSG/V1dV67bXXJDU+7WtLJaqf05I6fdSoUZKkp59+usF05ACA1GO6vXP++efrN7/5jSTp1ltvNVIXPPnkk/XlvPfeew1i3Lp1qyRp27Zteis27TEAAN9I/jdR6HRivxD51a9+Jdu2G/1btWrVYcsCaDuXy1WfmPXKK680usz27dv1wQcfyOl0Mo0x0AKxaSkWL15cfzNg3Lhx8ng8CV93//79lZ+fr08++URPPfWUJDVIxjz22GN1zDHHqLKyUmvWrJFEMmY6GzVqlMrLy/Xll19q5syZTS730Ucfae7cuXI4HPrlL3/ZjhF2PKbbqyNGjNCgQYO0ceNGPfvss4kMvdNI9/PixhtvrP9C8csvv0x2OADSQM+ePbVixQodc8wxWr58ua644oqEJBt07dpVV199tSTp5z//ebPrsG1b69atMx6DCcXFxfUJIb/73e+SHQ4AtFiq9kV8Pl99e/q6665rMJ0o0teNN94or9ergQMH6rzzzou7LNP9nJbU6aNHj1bfvn31r3/9ix+8AUAaSESuwLRp09SjRw99/PHHWrp0qbEY//KXvzQZY2x68tgonwAAxJCMiXZlWZaWLFkiSRo/fnyTyw0ZMkS9evXS5s2b9d5777VXeECHNXXqVEnSzJkztX79+sNeq66u1tVXX61oNKpLL71UvXv3TkaIQFoZOXKkSkpKVFVVpXnz5klqnynKpboE60GDBkmSHn74YTmdzvp/H6qiokIbN25kZMwOwOl06oknnlBGRoZuv/12/e53v1MkEjlsmbfeekvf/e535ff7NW3aNJ177rlJijb9Jaq9GhuR5pFHHjETaCeX7udFcXGxbrjhBgWDQf3pT39KdjgA0sTxxx+vFStWqLS0VM8//7wmTZqUkBGXZ86cqZNPPlkrVqzQJZdcou3btzdYZuPGjRoxYkR9WzgV3XjjjSooKNBjjz121FGUASAVpGpfpLKyUhUVFfq///s/nXrqqbrtttvaVA5SyyeffKJx48bpT3/6k3Jzc430SxLVzzlane50OrVw4UJlZmbqtttu080336zq6uoGy3399df66KOPjMUFAGi9RLV3srOzNX36dEnS73//+7j6yps2bdIHH3ygrl27asSIEU0uF4v/qaeeStjsFQCA9OROdgDoXJYvX65du3bpW9/6ls4666wml3M6nRo3bpxmz56thQsXMu0JEKeLLrpIN998s2bNmqVBgwbpnHPO0QknnKDq6mpVVlaqurpa3/72t5u9KXvXXXc1+UVbz5499fzzzx/2XHMJD5MnT9bkyZPb9mGAFODxeHTFFVdo7ty52rt3r04++WSdc845bSpr5syZmj9/fpOvP/LII8rJyTnsuSFDhui1117T/v37dfrpp6uwsLDB+yoqKrR06VLV1tbq+OOP1zHHHNOm+JAaBg4cqFdeeUWXX365/vu//1tz5szRoEGDlJ2drS1btmjjxo2S6kawuvfee5McbXpLVHt1+PDhGjJkSP1otYhfMs6LvXv3atKkSU2+ftZZZ+mGG25oUVk33nijHnroIdXU1BiJDUDncMopp2j58uUaNmyYFi1apPz8fOOJ/vn5+Vq9erUuueQS/eUvf9GyZct09tln6/jjj1coFNI//vGP+kSG3/72t0bXLUmPPfZ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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3400x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for item in candidates:\n",
    "    if not os.path.isdir(audio_root):\n",
    "        audio_root = \"/data1/scratch/coco_pyp/SpokenCOCO/\"\n",
    "    fn = item['caption']['wav'] if 'caption' in item else item['wav']\n",
    "    audio_path = os.path.join(audio_root, fn)\n",
    "    compact_plot(audio_path, item['text_alignment'], tgt_layer=11, pivotal_token=0, threshold=0.8)\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "6e4ff504106a9ad7bb1685dd81f14045a7c1eed6a4dae2d438dd7f617b6aea10"
  },
  "kernelspec": {
   "display_name": "Python 3.9.13 64-bit ('tf2': conda)",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}