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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "72bf1b45-66fd-450d-8d5c-bec9e0b3d08f",
"metadata": {},
"outputs": [],
"source": [
"from data2vec_feature_reader import Data2vecFeatureReader\n",
"\n",
"reader = Data2vecFeatureReader(\"./../../models/vox_pretrained.pt\", 18, device=\"cuda:0\", max_chunk=1600000)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "84a9d238-048a-4772-a47b-5aadc50f36df",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "490421d1c2f54cca9855f1a5397185f8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading dataset shards: 0%| | 0/45 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "be44942581b34d5388b0264e7b40d472",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading dataset shards: 0%| | 0/60 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from datasets import load_dataset\n",
"from tqdm import tqdm\n",
"import pandas as pd\n",
"\n",
"cache_dir = \"./../../../cache\"\n",
"\n",
"dataset = load_dataset(\"openslr/librispeech_asr\", cache_dir=cache_dir, trust_remote_code=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "cffd49ca-3524-4ac4-8ba5-bc4fcc9e0f53",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RepCodec(\n",
" (encoder): Encoder(\n",
" (conv): Conv1d(\n",
" (conv): Conv1d(1024, 1024, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
" )\n",
" (conv_blocks): ModuleList(\n",
" (0-1): 2 x EncoderBlock(\n",
" (res_units): ModuleList(\n",
" (0-1): 2 x ResidualUnit(\n",
" (activation): ELU(alpha=1.0)\n",
" (conv1): Conv1d(\n",
" (conv): Conv1d(1024, 1024, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
" )\n",
" (conv2): Conv1d1x1(1024, 1024, kernel_size=(1,), stride=(1,), bias=False)\n",
" )\n",
" )\n",
" (conv): Conv1d(\n",
" (conv): Conv1d(1024, 1024, kernel_size=(3,), stride=(1,), padding=(1,))\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (decoder): Decoder(\n",
" (conv1): Conv1d(\n",
" (conv): Conv1d(1024, 1024, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
" )\n",
" (conv_blocks): ModuleList(\n",
" (0-1): 2 x DecoderBlock(\n",
" (conv): Conv1d(\n",
" (conv): Conv1d(1024, 1024, kernel_size=(3,), stride=(1,), padding=(1,))\n",
" )\n",
" (res_units): ModuleList(\n",
" (0-1): 2 x ResidualUnit(\n",
" (activation): ELU(alpha=1.0)\n",
" (conv1): Conv1d(\n",
" (conv): Conv1d(1024, 1024, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
" )\n",
" (conv2): Conv1d1x1(1024, 1024, kernel_size=(1,), stride=(1,), bias=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (conv2): Conv1d(\n",
" (conv): Conv1d(1024, 1024, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
" )\n",
" )\n",
" (projector): Projector(\n",
" (project): Conv1d(\n",
" (conv): Conv1d(1024, 1024, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
" )\n",
" )\n",
" (quantizer): Quantizer(\n",
" (codebook): ResidualVQ(\n",
" (layers): ModuleList(\n",
" (0): VectorQuantize()\n",
" )\n",
" )\n",
" )\n",
")"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from repcodec.RepCodec import RepCodec\n",
"import torch\n",
"import yaml\n",
"\n",
"config = \"./../repcodec/configs/repcodec_dim1024.yaml\"\n",
"with open(config) as fp:\n",
" conf = yaml.load(fp, Loader=yaml.FullLoader)\n",
"\n",
"model = RepCodec(**conf)\n",
"model.load_state_dict(torch.load(\"./../../models/data2vec_large_l18.pkl\", map_location=\"cuda:0\")[\"model\"][\"repcodec\"])\n",
"model.quantizer.initial()\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "a9a1731e-052c-4af0-a29c-b171a988b300",
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[22], line 27\u001b[0m\n\u001b[1;32m 23\u001b[0m feat\u001b[38;5;241m.\u001b[39mappend(feat_chunk)\n\u001b[1;32m 25\u001b[0m features \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat(feat, \u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m---> 27\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m z \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mprojector(x)\n\u001b[1;32m 29\u001b[0m _, idx \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mquantizer\u001b[38;5;241m.\u001b[39mcodebook\u001b[38;5;241m.\u001b[39mforward_index(z\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m))\n",
"File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m/jupyter_workspace/users/Darshan/RepCodec/repcodec/modules/encoder.py:86\u001b[0m, in \u001b[0;36mEncoder.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 86\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 87\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_blocks):\n\u001b[1;32m 88\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv_blocks[i](x)\n",
"File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m/jupyter_workspace/users/Darshan/RepCodec/repcodec/layers/conv_layer.py:55\u001b[0m, in \u001b[0;36mConv1d.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m 49\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m x (Tensor): Float tensor variable with the shape (B, C, T).\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124;03m Returns:\u001b[39;00m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;124;03m Tensor: Float tensor variable with the shape (B, C, T).\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 55\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
"File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:310\u001b[0m, in \u001b[0;36mConv1d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:306\u001b[0m, in \u001b[0;36mConv1d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv1d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m 304\u001b[0m weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m 305\u001b[0m _single(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 307\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mRuntimeError\u001b[0m: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same"
]
}
],
"source": [
"import torch.nn.functional as F\n",
"\n",
"sample = dataset[\"train.clean.100\"][1]\n",
"\n",
"x = sample[\"audio\"][\"array\"]\n",
"\n",
"with torch.no_grad():\n",
" x = torch.from_numpy(x).float().to(reader.device)\n",
" if reader.task.cfg.normalize:\n",
" x = F.layer_norm(x, x.shape)\n",
" x = x.view(1, -1)\n",
"\n",
" feat = []\n",
" for start in range(0, x.size(1), reader.max_chunk):\n",
" x_chunk = x[:, start: start + reader.max_chunk]\n",
" res = reader.model.extract_features(\n",
" source=x_chunk,\n",
" padding_mask=None,\n",
" mask=False,\n",
" layer=reader.layer,\n",
" )\n",
" feat_chunk = res[\"x\"]\n",
" feat.append(feat_chunk)\n",
" \n",
" features = torch.cat(feat, 1).permute(0, 2, 1)\n",
"\n",
" x = model.encoder(features)\n",
" z = model.projector(x)\n",
" _, idx = model.quantizer.codebook.forward_index(z.transpose(2, 1))\n",
" tokens = idx.cpu().data.numpy().tolist()[0]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d51709a9-6fb3-450b-a517-005367095663",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 804, 1024])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "dfc977d7-f27c-40d7-b545-fbdf26728cbe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([726, 1024])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feat.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1810e6dc-2ece-4aca-a29a-e1933b8ce82a",
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"import os\n",
"import sys\n",
"\n",
"import tqdm\n",
"from npy_append_array import NpyAppendArray\n",
"\n",
"def get_shard_range(tot, nshard, rank):\n",
" assert rank < nshard and rank >= 0, f\"invaid rank/nshard {rank}/{nshard}\"\n",
" start = round(tot / nshard * rank)\n",
" end = round(tot / nshard * (rank + 1))\n",
" assert start < end, f\"start={start}, end={end}\"\n",
" logger.info(\n",
" f\"rank {rank} of {nshard}, process {end-start} \"\n",
" f\"({start}-{end}) out of {tot}\"\n",
" )\n",
" return start, end\n",
"\n",
"def get_path_iterator(tsv, nshard, rank):\n",
" with open(tsv, \"r\") as f:\n",
" root = f.readline().rstrip()\n",
" lines = [line.rstrip() for line in f]\n",
" start, end = get_shard_range(len(lines), nshard, rank)\n",
" lines = lines[start:end]\n",
" def iterate():\n",
" for line in lines:\n",
" subpath, nsample = line.split(\"\\t\")\n",
" yield f\"{root}/{subpath}\", int(nsample)\n",
" return iterate, len(lines)\n",
"\n",
"def dump_feature(reader, generator, num, nshard, rank, feat_dir):\n",
" iterator = generator()\n",
"\n",
" feat_path = f\"{feat_dir}/{rank}_{nshard}.npy\"\n",
" leng_path = f\"{feat_dir}/{rank}_{nshard}.len\"\n",
"\n",
" os.makedirs(feat_dir, exist_ok=True)\n",
" if os.path.exists(feat_path):\n",
" os.remove(feat_path)\n",
"\n",
" feat_f = NpyAppendArray(feat_path)\n",
" with open(leng_path, \"w\") as leng_f:\n",
" for path, nsample in tqdm.tqdm(iterator, total=num):\n",
" feat = reader.get_feats(path, nsample)\n",
" feat_f.append(feat.cpu().numpy())\n",
" leng_f.write(f\"{len(feat)}\\n\")\n",
" logger.info(\"finished successfully\")\n",
"\n",
"generator, num = get_path_iterator(tsv_path, nshard, rank)\n",
"dump_feature(reader, generator, num, nshard, rank, feat_dir)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"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": "3.8.10"
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"nbformat": 4,
"nbformat_minor": 5
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|