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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": "fb01bc434d964db08fde7f9f2c90ea3c",
"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": "d4adc62013644ed0b16056aa217448a9",
"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": 5,
"id": "cffd49ca-3524-4ac4-8ba5-bc4fcc9e0f53",
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "attempted relative import with no known parent package",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mRepCodec\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RepCodec\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01myaml\u001b[39;00m\n",
"\u001b[0;31mImportError\u001b[0m: attempted relative import with no known parent package"
]
}
],
"source": [
"from .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()\n",
"model.to(\"cuda:0\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a9a1731e-052c-4af0-a29c-b171a988b300",
"metadata": {},
"outputs": [],
"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": 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",
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