Repository: LAION-AI/CLAP (language: python)

Issue:
add comments labelling different branches of parameters.



Discussion / hints:

Fix patch:
diff --git a/.gitignore b/.gitignore
index 5e43e01..2da684c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -151,3 +151,5 @@ core.*
 src/test.py
 src/dev.ipynb
 src/sizes.json
+*.pt
+dev.ipynb
diff --git a/dev.ipynb b/dev.ipynb
new file mode 100644
index 0000000..feeb037
--- /dev/null
+++ b/dev.ipynb
@@ -0,0 +1,514 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "27bf8dca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.11.0\n",
+      "0.11.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torchaudio\n",
+    "import torchaudio.functional as F\n",
+    "import torchaudio.transforms as T\n",
+    "\n",
+    "print(torch.__version__)\n",
+    "print(torchaudio.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcafb812",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aab08868",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import time\n",
+    "\n",
+    "import librosa\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e81f3909",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DEFAULT_OFFSET = 201\n",
+    "SWEEP_MAX_SAMPLE_RATE = 48000\n",
+    "DEFAULT_LOWPASS_FILTER_WIDTH = 6\n",
+    "DEFAULT_ROLLOFF = 0.99\n",
+    "DEFAULT_RESAMPLING_METHOD = \"sinc_interpolation\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "db6315e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_sine_sweep(sample_rate, offset=DEFAULT_OFFSET):\n",
+    "    max_sweep_rate = sample_rate\n",
+    "    freq = _get_log_freq(sample_rate, max_sweep_rate, offset)\n",
+    "    delta = 2 * math.pi * freq / sample_rate\n",
+    "    cummulative = torch.cumsum(delta, dim=0)\n",
+    "    signal = torch.sin(cummulative).unsqueeze(dim=0)\n",
+    "    return signal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e472e123",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "configs = {\n",
+    "    \"downsample (48 -> 44.1 kHz)\": [48000, 44100],\n",
+    "    \"downsample (16 -> 8 kHz)\": [16000, 8000],\n",
+    "    \"upsample (44.1 -> 48 kHz)\": [44100, 48000],\n",
+    "    \"upsample (8 -> 16 kHz)\": [8000, 16000],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0b70c069",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _get_log_freq(sample_rate, max_sweep_rate, offset):\n",
+    "    \"\"\"Get freqs evenly spaced out in log-scale, between [0, max_sweep_rate // 2]\n",
+    "\n",
+    "    offset is used to avoid negative infinity `log(offset + x)`.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    start, stop = math.log(offset), math.log(offset + max_sweep_rate // 2)\n",
+    "    return torch.exp(torch.linspace(start, stop, sample_rate, dtype=torch.double)) - offset\n",
+    "\n",
+    "\n",
+    "def _get_inverse_log_freq(freq, sample_rate, offset):\n",
+    "    \"\"\"Find the time where the given frequency is given by _get_log_freq\"\"\"\n",
+    "    half = sample_rate // 2\n",
+    "    return sample_rate * (math.log(1 + freq / offset) / math.log(1 + half / offset))\n",
+    "\n",
+    "\n",
+    "def _get_freq_ticks(sample_rate, offset, f_max):\n",
+    "    # Given the original sample rate used for generating the sweep,\n",
+    "    # find the x-axis value where the log-scale major frequency values fall in\n",
+    "    time, freq = [], []\n",
+    "    for exp in range(2, 5):\n",
+    "        for v in range(1, 10):\n",
+    "            f = v * 10 ** exp\n",
+    "            if f < sample_rate // 2:\n",
+    "                t = _get_inverse_log_freq(f, sample_rate, offset) / sample_rate\n",
+    "                time.append(t)\n",
+    "                freq.append(f)\n",
+    "    t_max = _get_inverse_log_freq(f_max, sample_rate, offset) / sample_rate\n",
+    "    time.append(t_max)\n",
+    "    freq.append(f_max)\n",
+    "    return time, freq\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a0326ce1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label = \"downsample (48 -> 44.1 kHz)\"\n",
+    "times, rows = [], []\n",
+    "sample_rate = configs[label][0]\n",
+    "resample_rate = configs[label][1]\n",
+    "waveform = get_sine_sweep(sample_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "13f8fe7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 48000])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "waveform.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c48544ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reformat_to_tensor(nparray):\n",
+    "    tensor = torch.tensor(nparray)\n",
+    "    return tensor.unsqueeze(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "692e3a40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lowpass_filter_width=64,\n",
+    "rolloff=0.9475937167399596,\n",
+    "resampling_method=\"kaiser_window\",\n",
+    "beta=14.769656459379492,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "4d179dc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([10, 48000])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "waveform.repeat(10,1).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "f2fd051a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([32000])"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "waveform[0][:32000].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "8fb0ecbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = F.resample(\n",
+    "    waveform.repeat(32,1),\n",
+    "    sample_rate,\n",
+    "    resample_rate,\n",
+    "    lowpass_filter_width=64,\n",
+    "    rolloff=0.9475937167399596,\n",
+    "    resampling_method=\"kaiser_window\",\n",
+    ").squeeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "ca95f77f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 4.3050e-08,  2.9573e-06,  9.1035e-06,  ..., -1.9602e-01,\n",
+       "         2.4939e-01, -3.2606e-01], dtype=torch.float64)"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "4927e8eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 4.3050e-08,  2.9573e-06,  9.1035e-06,  ..., -1.9602e-01,\n",
+       "         2.4939e-01, -3.2606e-01], dtype=torch.float64)"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "3488c570",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 23s, sys: 334 ms, total: 1min 24s\n",
+      "Wall time: 18.8 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "for i in range(1):\n",
+    "    resampler = T.Resample(\n",
+    "        sample_rate,\n",
+    "        resample_rate,\n",
+    "        lowpass_filter_width=64,\n",
+    "        rolloff=0.9475937167399596,\n",
+    "        resampling_method=\"kaiser_window\",\n",
+    "        dtype=waveform.dtype,\n",
+    "    )\n",
+    "    resampler(waveform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "7f73bd62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(44100,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(librosa.resample(waveform_np, orig_sr=sample_rate, target_sr=resample_rate, res_type=\"kaiser_best\").shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d6c5dd5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 48000])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reformat_to_tensor(waveform_np).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "23d6828d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "waveform_np = waveform.squeeze().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "01216d45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(48000,)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "waveform_np.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3439c69d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def benchmark_resample(\n",
+    "    method,\n",
+    "    waveform,\n",
+    "    sample_rate,\n",
+    "    resample_rate,\n",
+    "    lowpass_filter_width=DEFAULT_LOWPASS_FILTER_WIDTH,\n",
+    "    rolloff=DEFAULT_ROLLOFF,\n",
+    "    resampling_method=DEFAULT_RESAMPLING_METHOD,\n",
+    "    beta=None,\n",
+    "    librosa_type=None,\n",
+    "    iters=5,\n",
+    "):\n",
+    "    if method == \"functional\":\n",
+    "        begin = time.time()\n",
+    "        for _ in range(iters):\n",
+    "            F.resample(\n",
+    "                waveform,\n",
+    "                sample_rate,\n",
+    "                resample_rate,\n",
+    "                lowpass_filter_width=lowpass_filter_width,\n",
+    "                rolloff=rolloff,\n",
+    "                resampling_method=resampling_method,\n",
+    "            )\n",
+    "        elapsed = time.time() - begin\n",
+    "        return elapsed / iters\n",
+    "    elif method == \"transforms\":\n",
+    "        resampler = T.Resample(\n",
+    "            sample_rate,\n",
+    "            resample_rate,\n",
+    "            lowpass_filter_width=lowpass_filter_width,\n",
+    "            rolloff=rolloff,\n",
+    "            resampling_method=resampling_method,\n",
+    "            dtype=waveform.dtype,\n",
+    "        )\n",
+    "        begin = time.time()\n",
+    "        for _ in range(iters):\n",
+    "            resampler(waveform)\n",
+    "        elapsed = time.time() - begin\n",
+    "        return elapsed / iters\n",
+    "    elif method == \"librosa\":\n",
+    "        waveform_np = waveform.squeeze().numpy()\n",
+    "        begin = time.time()\n",
+    "        for _ in range(iters):\n",
+    "            librosa.resample(waveform_np, orig_sr=sample_rate, target_sr=resample_rate, res_type=librosa_type)\n",
+    "        elapsed = time.time() - begin\n",
+    "        return elapsed / iters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "99ed16e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.02197718620300293"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "benchmark_resample(\n",
+    "        \"functional\",\n",
+    "        waveform,\n",
+    "        sample_rate,\n",
+    "        resample_rate,\n",
+    "        lowpass_filter_width=64,\n",
+    "        rolloff=0.9475937167399596,\n",
+    "        resampling_method=\"kaiser_window\",\n",
+    "        beta=14.769656459379492,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "215a89d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python3.10",
+   "language": "python",
+   "name": "py310"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/run_code.txt b/run_code.txt
index 619b231..c08f60a 100644
--- a/run_code.txt
+++ b/run_code.txt
@@ -1,4 +1,4 @@
-# Single Card
+# Single Card toy dataset
 
 CUDA_VISIBLE_DEVICES=0 python -m training.main \
     --save-frequency 1 \
@@ -15,10 +15,11 @@ CUDA_VISIBLE_DEVICES=0 python -m training.main \
     --lr=1e-3 \
     --wd=0.1 \
     --epochs=30 \
-    --workers=8 \
-    --model PANN-14
+    --workers=1 \
+    --model PANN-14 \
+    --resample-method="TorchAudio"
 
-# Multiple Cards
+# Multiple Cards toy dataset
 
 torchrun --nproc_per_node 4 -m training.main \
     --save-frequency 1 \
@@ -35,6 +36,90 @@ torchrun --nproc_per_node 4 -m training.main \
     --lr=1e-3 \
     --wd=0.1 \
     --epochs=30 \
-    --workers=8 \
+    --workers=1 \
     --use-bn-sync \
-    --model PANN-14
\ No newline at end of file
+    --model PANN-14 \
+    --resample-method="TorchAudio"
+
+
+CUDA_VISIBLE_DEVICES=0 python -m training.main \
+    --save-frequency 100 \
+    --save-top-performance 3 \
+    --save-most-recent \
+    --zeroshot-frequency 1 \
+    --dataset-type="webdataset" \
+    --datasetnames "Clotho" "audioset" "audiocaps" "BBCSoundEffects"\
+    --datasetinfos "train" "unbalanced_train" "balanced_train"\
+    --precision="fp32" \
+    --pretrained="openai" \
+    --warmup 10000 \
+    --batch-size=32 \
+    --lr=1e-3 \
+    --wd=0.1 \
+    --epochs=30 \
+    --workers=1 \
+    --use-bn-sync \
+    --model PANN-14 \
+    --resample-method="None" \
+   # --remotedata # if not remote dataset, please uncomment
+
+# Muitiple Card WDS train on "Clotho" "audioset" "audiocaps" "BBCSoundEffects" with types: "train" "unbalanced_train" "balanced_train"
+torchrun --nproc_per_node 8 -m training.main \
+    --save-frequency 100 \
+    --save-top-performance 3 \
+    --save-most-recent \
+    --dataset-type="webdataset" \
+    --precision="fp32" \
+    --pretrained="openai" \
+    --warmup 10000 \
+    --batch-size=96 \
+    --lr=1e-3 \
+    --wd=0.1 \
+    --epochs=200 \
+    --workers=1 \
+    --use-bn-sync \
+    --model PANN-14 \
+    --resample-method="None" \
+    --datasetnames "Clotho" "audioset" "audiocaps" "BBCSoundEffects" \
+    --datasetinfos "train" "unbalanced_train" "balanced_train"\
+
+
+
+CUDA_VISIBLE_DEVICES=0 python -m training.main \
+    --save-frequency 100 \
+    --save-top-performance 3  \
+    --save-most-recent \
+    --dataset-type="webdataset" \
+    --precision="fp32" \
+    --pretrained="openai" \
+    --warmup 10000  \
+    --batch-size=96 \
+    --lr=1e-3 \
+    --wd=0.1 \
+    --epochs=200  \
+    --workers=4 \
+    --use-bn-sync  \
+    --model PANN-14 \
+    --resample-method="None" \
+    --datasetnames "Clotho" "audiocaps" "BBCSoundEffects" \
+    --datasetinfos "train" "unbalanced_train" "balanced_train"
+
+
+torchrun --nproc_per_node 8 -m training.main \
+    --save-frequency 100 \
+    --save-top-performance 3  \
+    --save-most-recent \
+    --dataset-type="webdataset" \
+    --precision="fp32" \
+    --pretrained="openai" \
+    --warmup 10000  \
+    --batch-size=96 \
+    --lr=1e-3 \
+    --wd=0.1 \
+    --epochs=200  \
+    --workers=4 \
+    --use-bn-sync  \
+    --model PANN-14 \
+    --resample-method="None" \
+    --datasetnames "Clotho" "audiocaps" "BBCSoundEffects" \
+    --datasetinfos "train" "unbalanced_train" "balanced_train"
diff --git a/src/data/BBCSoundEffects/train.txt b/src/data/BBCSoundEffects/train.txt
index 60c8ae5..13a075e 100644
--- a/src/data/BBCSoundEffects/train.txt
+++ b/src/data/BBCSoundEffects/train.txt
@@ -1,29 +1,29 @@
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/0.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/1.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/2.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/3.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/4.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/5.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/6.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/7.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/8.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/9.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/10.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/11.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/12.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/13.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/14.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/15.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/16.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/17.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/18.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/19.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/20.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/21.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/22.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/23.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/24.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/25.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/26.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/27.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/train/28.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/0.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/1.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/2.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/3.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/4.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/5.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/6.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/7.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/8.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/9.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/10.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/11.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/12.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/13.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/14.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/15.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/16.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/17.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/18.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/19.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/20.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/21.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/22.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/23.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/24.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/25.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/26.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/27.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/train/28.tar
\ No newline at end of file
diff --git a/src/data/Clotho/train.txt b/src/data/Clotho/train.txt
index c090f18..ba57de3 100644
--- a/src/data/Clotho/train.txt
+++ b/src/data/Clotho/train.txt
@@ -1,8 +1,8 @@
-s3://laion-audio/webdataset_tar/Clotho/train/0.tar
-s3://laion-audio/webdataset_tar/Clotho/train/1.tar
-s3://laion-audio/webdataset_tar/Clotho/train/2.tar
-s3://laion-audio/webdataset_tar/Clotho/train/3.tar
-s3://laion-audio/webdataset_tar/Clotho/train/4.tar
-s3://laion-audio/webdataset_tar/Clotho/train/5.tar
-s3://laion-audio/webdataset_tar/Clotho/train/6.tar
-s3://laion-audio/webdataset_tar/Clotho/train/7.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/train/0.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/train/1.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/train/2.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/train/3.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/train/4.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/train/5.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/train/6.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/train/7.tar
\ No newline at end of file
diff --git a/src/data/Clotho/valid.txt b/src/data/Clotho/valid.txt
index 47909a2..049cb05 100644
--- a/src/data/Clotho/valid.txt
+++ b/src/data/Clotho/valid.txt
@@ -1,3 +1,3 @@
-s3://laion-audio/webdataset_tar/Clotho/valid/0.tar
-s3://laion-audio/webdataset_tar/Clotho/valid/1.tar
-s3://laion-audio/webdataset_tar/Clotho/valid/2.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/valid/0.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/valid/1.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/valid/2.tar
\ No newline at end of file
diff --git a/src/data/audiocaps/train.txt b/src/data/audiocaps/train.txt
index 101fcfb..fce7b94 100644
--- a/src/data/audiocaps/train.txt
+++ b/src/data/audiocaps/train.txt
@@ -1,91 +1,90 @@
-s3://laion-audio/webdataset_tar/audiocaps/train/0.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/1.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/2.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/3.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/4.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/5.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/6.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/7.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/8.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/9.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/10.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/11.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/12.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/13.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/14.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/15.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/16.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/17.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/18.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/19.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/20.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/21.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/22.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/23.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/24.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/25.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/26.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/27.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/28.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/29.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/30.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/31.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/32.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/33.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/34.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/35.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/36.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/37.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/38.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/39.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/40.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/41.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/42.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/43.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/44.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/45.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/46.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/47.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/48.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/49.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/50.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/51.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/52.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/53.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/54.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/55.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/56.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/57.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/58.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/59.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/60.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/61.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/62.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/63.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/64.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/65.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/66.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/67.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/68.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/69.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/70.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/71.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/72.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/73.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/74.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/75.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/76.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/77.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/78.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/79.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/80.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/81.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/82.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/83.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/84.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/85.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/86.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/87.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/88.tar
-s3://laion-audio/webdataset_tar/audiocaps/train/89.tar
-
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/0.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/1.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/2.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/3.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/4.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/5.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/6.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/7.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/8.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/9.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/10.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/11.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/12.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/13.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/14.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/15.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/16.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/17.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/18.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/19.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/20.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/21.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/22.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/23.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/24.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/25.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/26.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/27.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/28.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/29.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/30.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/31.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/32.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/33.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/34.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/35.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/36.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/37.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/38.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/39.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/40.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/41.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/42.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/43.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/44.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/45.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/46.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/47.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/48.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/49.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/50.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/51.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/52.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/53.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/54.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/55.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/56.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/57.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/58.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/59.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/60.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/61.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/62.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/63.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/64.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/65.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/66.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/67.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/68.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/69.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/70.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/71.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/72.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/73.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/74.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/75.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/76.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/77.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/78.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/79.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/80.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/81.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/82.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/83.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/84.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/85.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/86.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/87.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/88.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/train/89.tar
\ No newline at end of file
diff --git a/src/data/audiocaps/valid.txt b/src/data/audiocaps/valid.txt
index 36ab3eb..4e23b9b 100644
--- a/src/data/audiocaps/valid.txt
+++ b/src/data/audiocaps/valid.txt
@@ -1,5 +1,5 @@
-s3://laion-audio/webdataset_tar/audiocaps/valid/0.tar
-s3://laion-audio/webdataset_tar/audiocaps/valid/1.tar
-s3://laion-audio/webdataset_tar/audiocaps/valid/2.tar
-s3://laion-audio/webdataset_tar/audiocaps/valid/3.tar
-s3://laion-audio/webdataset_tar/audiocaps/valid/4.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/valid/0.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/valid/1.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/valid/2.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/valid/3.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/valid/4.tar
\ No newline at end of file
diff --git a/src/data/audioset/balanced_train.txt b/src/data/audioset/balanced_train.txt
index 9da7b74..7dcee76 100644
--- a/src/data/audioset/balanced_train.txt
+++ b/src/data/audioset/balanced_train.txt
@@ -1,41 +1,41 @@
-s3://laion-audio/webdataset_tar/audioset/balanced_train/0.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/1.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/2.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/3.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/4.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/5.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/6.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/7.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/8.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/9.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/10.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/11.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/12.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/13.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/14.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/15.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/16.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/17.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/18.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/19.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/20.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/21.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/22.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/23.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/24.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/25.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/26.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/27.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/28.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/29.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/30.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/31.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/32.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/33.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/34.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/35.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/36.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/37.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/38.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/39.tar
-s3://laion-audio/webdataset_tar/audioset/balanced_train/40.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/0.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/1.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/2.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/3.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/4.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/5.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/6.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/7.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/8.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/9.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/10.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/11.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/12.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/13.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/14.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/15.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/16.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/17.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/18.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/19.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/20.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/21.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/22.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/23.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/24.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/25.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/26.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/27.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/28.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/29.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/30.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/31.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/32.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/33.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/34.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/35.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/36.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/37.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/38.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/39.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/balanced_train/40.tar
\ No newline at end of file
diff --git a/src/data/audioset/eval.txt b/src/data/audioset/eval.txt
index 3db340f..35e57bc 100644
--- a/src/data/audioset/eval.txt
+++ b/src/data/audioset/eval.txt
@@ -1,37 +1,37 @@
-s3://laion-audio/webdataset_tar/audioset/eval/0.tar
-s3://laion-audio/webdataset_tar/audioset/eval/1.tar
-s3://laion-audio/webdataset_tar/audioset/eval/2.tar
-s3://laion-audio/webdataset_tar/audioset/eval/3.tar
-s3://laion-audio/webdataset_tar/audioset/eval/4.tar
-s3://laion-audio/webdataset_tar/audioset/eval/5.tar
-s3://laion-audio/webdataset_tar/audioset/eval/6.tar
-s3://laion-audio/webdataset_tar/audioset/eval/7.tar
-s3://laion-audio/webdataset_tar/audioset/eval/8.tar
-s3://laion-audio/webdataset_tar/audioset/eval/9.tar
-s3://laion-audio/webdataset_tar/audioset/eval/10.tar
-s3://laion-audio/webdataset_tar/audioset/eval/11.tar
-s3://laion-audio/webdataset_tar/audioset/eval/12.tar
-s3://laion-audio/webdataset_tar/audioset/eval/13.tar
-s3://laion-audio/webdataset_tar/audioset/eval/14.tar
-s3://laion-audio/webdataset_tar/audioset/eval/15.tar
-s3://laion-audio/webdataset_tar/audioset/eval/16.tar
-s3://laion-audio/webdataset_tar/audioset/eval/17.tar
-s3://laion-audio/webdataset_tar/audioset/eval/18.tar
-s3://laion-audio/webdataset_tar/audioset/eval/19.tar
-s3://laion-audio/webdataset_tar/audioset/eval/20.tar
-s3://laion-audio/webdataset_tar/audioset/eval/21.tar
-s3://laion-audio/webdataset_tar/audioset/eval/22.tar
-s3://laion-audio/webdataset_tar/audioset/eval/23.tar
-s3://laion-audio/webdataset_tar/audioset/eval/24.tar
-s3://laion-audio/webdataset_tar/audioset/eval/25.tar
-s3://laion-audio/webdataset_tar/audioset/eval/26.tar
-s3://laion-audio/webdataset_tar/audioset/eval/27.tar
-s3://laion-audio/webdataset_tar/audioset/eval/28.tar
-s3://laion-audio/webdataset_tar/audioset/eval/29.tar
-s3://laion-audio/webdataset_tar/audioset/eval/30.tar
-s3://laion-audio/webdataset_tar/audioset/eval/31.tar
-s3://laion-audio/webdataset_tar/audioset/eval/32.tar
-s3://laion-audio/webdataset_tar/audioset/eval/33.tar
-s3://laion-audio/webdataset_tar/audioset/eval/34.tar
-s3://laion-audio/webdataset_tar/audioset/eval/35.tar
-s3://laion-audio/webdataset_tar/audioset/eval/36.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/0.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/1.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/2.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/3.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/4.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/5.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/6.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/7.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/8.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/9.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/10.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/11.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/12.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/13.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/14.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/15.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/16.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/17.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/18.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/19.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/20.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/21.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/22.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/23.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/24.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/25.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/26.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/27.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/28.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/29.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/30.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/31.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/32.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/33.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/34.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/35.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/eval/36.tar
\ No newline at end of file
diff --git a/src/data/audioset/unbalanced_train.txt b/src/data/audioset/unbalanced_train.txt
index 32c695d..8f5b8d4 100644
--- a/src/data/audioset/unbalanced_train.txt
+++ b/src/data/audioset/unbalanced_train.txt
@@ -1,3735 +1,3735 @@
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/0.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/1.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/2.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/4.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/5.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/6.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/7.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/8.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/9.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/10.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/11.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/12.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/13.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/14.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/15.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/16.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/17.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/18.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/19.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/20.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/21.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/22.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/23.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/24.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/25.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/26.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/27.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/28.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/29.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/30.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/31.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/32.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/33.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/34.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/35.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/36.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/37.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/38.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/39.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/40.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/41.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/42.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/43.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/44.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/45.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/46.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/47.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/48.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/49.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/50.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/51.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/52.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/53.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/54.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/55.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/56.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/57.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/58.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/59.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/60.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/61.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/62.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/63.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/64.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/65.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/66.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/67.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/68.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/69.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/70.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/71.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/72.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/73.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/74.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/75.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/76.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/77.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/78.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/79.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/80.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/81.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/82.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/83.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/84.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/85.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/86.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/87.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/88.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/89.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/90.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/91.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/92.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/93.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/94.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/95.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/96.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/97.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/98.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/99.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/100.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/101.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/102.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/103.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/104.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/105.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/106.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/107.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/108.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/109.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/110.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/111.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/112.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/113.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/114.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/115.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/116.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/117.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/118.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/119.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/120.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/121.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/122.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/123.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/124.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/125.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/126.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/127.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/128.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/129.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/130.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/131.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/132.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/133.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/134.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/135.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/136.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/137.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/138.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/139.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/140.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/141.tar
-s3://laion-audio/webdataset_tar/audioset/unbalanced_train/142.tar
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+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3567.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3568.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3569.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3570.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3571.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3572.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3573.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3574.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3575.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3576.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3577.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3578.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3579.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3580.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3581.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3582.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3583.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3584.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3585.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3586.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3587.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3588.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3589.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3590.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3591.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3592.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3593.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3594.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3595.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3596.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3597.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3598.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3599.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3600.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3601.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3602.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3603.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3604.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3605.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3606.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3607.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3608.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3609.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3610.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3611.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3612.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3613.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3614.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3615.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3616.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3617.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3618.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3619.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3620.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3621.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3622.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3623.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3624.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3625.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3626.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3627.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3628.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3629.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3630.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3631.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3632.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3633.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3634.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3635.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3636.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3637.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3638.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3639.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3640.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3641.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3642.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3643.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3644.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3645.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3646.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3647.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3648.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3649.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3650.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3651.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3652.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3653.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3654.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3655.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3656.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3657.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3658.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3659.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3660.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3661.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3662.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3663.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3664.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3665.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3666.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3667.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3668.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3669.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3670.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3671.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3672.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3673.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3674.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3675.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3676.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3677.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3678.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3679.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3680.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3681.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3682.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3683.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3684.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3685.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3686.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3687.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3688.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3689.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3690.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3691.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3692.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3693.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3694.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3695.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3696.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3697.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3698.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3699.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3700.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3701.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3702.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3703.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3704.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3705.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3706.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3707.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3708.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3709.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3710.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3711.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3712.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3713.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3714.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3715.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3716.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3717.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3718.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3719.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3720.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3721.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3722.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3723.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3724.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3725.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3726.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3727.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3728.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3729.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3730.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3731.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3732.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3733.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audioset/unbalanced_train/3734.tar
\ No newline at end of file
diff --git a/src/dev.py b/src/dev.py
index 5d07f6a..8e9aa28 100644
--- a/src/dev.py
+++ b/src/dev.py
@@ -1,52 +1,164 @@
-from training.data import *
-from easydict import EasyDict as edict
-from open_clip import create_model_and_transforms, trace_model, create_model
-from training.distributed import is_master, init_distributed_device, world_info_from_env
-from training.params import parse_args
-
-#     --save-frequency 1 \
-#     --zeroshot-frequency 1 \
-#     --dataset-type="toy" \
-#     --train-data="/mnt/audio_clip/test/data/indexes/balanced_train.h5"  \
-#     --val-data="/mnt/audio_clip/test/data/indexes/eval.h5"  \
-#     --train-ipc="/mnt/audio_clip/test/data/train_ipc.npy" \
-#     --val-ipc="/mnt/audio_clip/test/data/val_ipc.npy" \
-#     --csv-img-key filepath \
-#     --csv-caption-key title \
-#     --precision="fp32" \
-#     --warmup 10000 \
-#     --batch-size=32 \
-#     --lr=1e-3 \
-#     --wd=0.1 \
-#     --epochs=30 \
-#     --workers=8 \
-#     --model PANN-14
-args = parse_args()
-device = init_distributed_device(args)
-
-model, model_cfg = create_model(
-    args.model,
-    args.pretrained,
-    precision=args.precision,
-    device=device,
-    jit=args.torchscript,
-    force_quick_gelu=args.force_quick_gelu
-)
+# from training.data import *
+# from easydict import EasyDict as edict
+# from open_clip import create_model_and_transforms, trace_model, create_model
+# from training.distributed import is_master, init_distributed_device, world_info_from_env
+# from training.params import parse_args
+
+# #     --save-frequency 1 \
+# #     --zeroshot-frequency 1 \
+# #     --dataset-type="toy" \
+# #     --train-data="/mnt/audio_clip/test/data/indexes/balanced_train.h5"  \
+# #     --val-data="/mnt/audio_clip/test/data/indexes/eval.h5"  \
+# #     --train-ipc="/mnt/audio_clip/test/data/train_ipc.npy" \
+# #     --val-ipc="/mnt/audio_clip/test/data/val_ipc.npy" \
+# #     --csv-img-key filepath \
+# #     --csv-caption-key title \
+# #     --precision="fp32" \
+# #     --warmup 10000 \
+# #     --batch-size=32 \
+# #     --lr=1e-3 \
+# #     --wd=0.1 \
+# #     --epochs=30 \
+# #     --workers=8 \
+# #     --model PANN-14
+# args = parse_args()
+# device = init_distributed_device(args)
+# args.train_data = ["/mnt/audio_clip/webdataset_tar/audioset/unbalanced_train/0.tar", "/mnt/audio_clip/webdataset_tar/audioset/unbalanced_train/1.tar"]
+# model, model_cfg = create_model(
+#     args.model,
+#     args.pretrained,
+#     precision=args.precision,
+#     device=device,
+#     jit=args.torchscript,
+#     force_quick_gelu=args.force_quick_gelu
+# )
     
-data = get_toy_dataset(args, model_cfg, is_train=True)
-
-dl = data.dataloader
-for i, batch in enumerate(dl):
-    # print(i, batch)
-    print("batch.keys", batch.keys())
-    # 'hdf5_path', 'index_in_hdf5', 'audio_name', 'waveform', 'target', 'text'
-    print("hdf5_path", batch["hdf5_path"])
-    print("index_in_hdf5", batch["index_in_hdf5"])
-    print("audio_name", batch["audio_name"])
-    print("waveform", batch["waveform"])
-    print("waveform.shape", batch["waveform"].shape)
-    print("target", batch["target"])
-    print("target.shape", batch["target"].shape)
-    print("text", batch["text"])
-    print("text.shape", batch["text"].shape)
-    break 
\ No newline at end of file
+# data = get_wds_dataset(args, model_cfg, is_train=True)
+
+# dl = data.dataloader
+# for i, batch in enumerate(dl):
+#     # print(i, batch)
+#     # print("batch.keys", batch.keys())
+#     # # 'hdf5_path', 'index_in_hdf5', 'audio_name', 'waveform', 'target', 'text'
+#     # print("hdf5_path", batch["hdf5_path"])
+#     # print("index_in_hdf5", batch["index_in_hdf5"])
+#     # print("audio_name", batch["audio_name"])
+#     # print("waveform", batch["waveform"])
+#     # print("waveform.shape", batch["waveform"].shape)
+#     # print("target", batch["target"])
+#     # print("target.shape", batch["target"].shape)
+#     # print("text", batch["text"])
+#     # print("text.shape", batch["text"].shape)
+#     print("osr", batch[-1])
+#     print()
+#     break
+
+import webdataset as wds
+import soundfile as sf
+import io
+import os
+import random
+import copy
+from tqdm import tqdm
+
+def log_and_continue(exn):
+    """Call in an exception handler to ignore any exception, isssue a warning, and continue."""
+    logging.warning(f"Handling webdataset error ({repr(exn)}). Ignoring.")
+    return True
+
+def preprocess(
+    sample,
+):
+    """
+    Preprocess a single sample for wdsdataloader.
+    """
+    keys = list(sample.keys())
+    audio_ext = "flac"
+    audio_data, orig_sr = sf.read(io.BytesIO(sample[audio_ext]))
+    sample["waveform"] = audio_data
+    return sample
+
+
+
+# def check(j):
+#     input_shards = ["/mnt/audio_clip/webdataset_tar/audioset/unbalanced_train/"+str(i)+".tar" for i in range(j,3735)]
+#     # input_shards = ["/mnt/audio_clip/webdataset_tar/audioset/unbalanced_train/"+str(i)+".tar" for i in range(38,56)]
+#     # input_shards = ["/mnt/audio_clip/webdataset_tar/audioset/eval/28.tar"]
+#     pipeline = [wds.SimpleShardList(input_shards)]
+#     _SHARD_SHUFFLE_SIZE = 2000
+#     _SHARD_SHUFFLE_INITIAL = 500
+#     _SAMPLE_SHUFFLE_SIZE = 5000
+#     _SAMPLE_SHUFFLE_INITIAL = 1000
+#     pipeline.extend([
+#         # wds.detshuffle(bufsize=_SHARD_SHUFFLE_SIZE, initial=_SHARD_SHUFFLE_INITIAL),
+#         wds.split_by_node,
+#         wds.split_by_worker,
+#         # at this point, we have an iterator over the shards assigned to each worker at each node
+#         wds.tarfile_to_samples(handler=log_and_continue),
+#         # wds.shuffle(
+#         #     bufsize=_SAMPLE_SHUFFLE_SIZE,
+#         #     initial=_SAMPLE_SHUFFLE_INITIAL,
+#         #     rng=random.Random(1)),
+#         #wds.repeatedly,  # FIXME determine if this is beneficial
+#     ])
+#     pipeline.extend([wds.map(preprocess),wds.to_tuple("__url__", "__key__", "waveform"),wds.batched(1)])
+#     dataset = wds.DataPipeline(*pipeline)
+#     dataloader = wds.WebLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
+#     for i, batch in enumerate(dataloader):
+#         previous_batch = copy.deepcopy(batch)
+#     return previous_batch
+# errors = []
+# j = 279
+# while True:
+#     try:
+#         previous_batch = check(j)
+#     except:
+#         print(int(previous_batch[0][0][0].split("/")[-1].split(".tar")[0])+1)
+#         check(int(previous_batch[0][0][0].split("/")[-1].split(".tar")[0])+1)
+#         errors.append(int(previous_batch[0][0][0].split("/")[-1].split(".tar")[0]))
+#         pass
+# print(errors)
+
+for i in tqdm(reversed(range(1226*2,1226*3+1))):
+    try:
+        input_shards = ["/mnt/audio_clip/webdataset_tar/audioset/unbalanced_train/"+str(i)+".tar"]
+        # input_shards = ["/mnt/audio_clip/webdataset_tar/audioset/eval/28.tar"]
+        pipeline = [wds.SimpleShardList(input_shards)]
+        _SHARD_SHUFFLE_SIZE = 2000
+        _SHARD_SHUFFLE_INITIAL = 500
+        _SAMPLE_SHUFFLE_SIZE = 5000
+        _SAMPLE_SHUFFLE_INITIAL = 1000
+        pipeline.extend([
+            # wds.detshuffle(bufsize=_SHARD_SHUFFLE_SIZE, initial=_SHARD_SHUFFLE_INITIAL),
+            wds.split_by_node,
+            wds.split_by_worker,
+            # at this point, we have an iterator over the shards assigned to each worker at each node
+            wds.tarfile_to_samples(handler=log_and_continue),
+            # wds.shuffle(
+            #     bufsize=_SAMPLE_SHUFFLE_SIZE,
+            #     initial=_SAMPLE_SHUFFLE_INITIAL,
+            #     rng=random.Random(1)),
+            #wds.repeatedly,  # FIXME determine if this is beneficial
+        ])
+        pipeline.extend([wds.map(preprocess),wds.to_tuple("__url__", "__key__", "waveform"),wds.batched(1)])
+        dataset = wds.DataPipeline(*pipeline)
+        dataloader = wds.WebLoader(dataset, batch_size=1, shuffle=False, num_workers=0)
+        for k, batch in enumerate(dataloader):
+            previous_batch = copy.deepcopy(batch)
+    except:
+        print(i)
+        print(previous_batch)
+        pass
+# main training loop
+# generator = iter(dataloader)
+# for i in range(9999999999999):
+#     try:
+#         # Samples the batch
+#         if i>0:
+#             previous = copy.deepcopy(batch)
+#         batch = next(generator)
+#         print(batch)
+#     except:
+#         errors.append(previous)
+#         pass
+# print(errors)
\ No newline at end of file
diff --git a/src/open_clip/model.py b/src/open_clip/model.py
index 394ce6e..6085703 100644
--- a/src/open_clip/model.py
+++ b/src/open_clip/model.py
@@ -363,7 +363,7 @@ def __init__(
         act_layer = QuickGELU if quick_gelu else nn.GELU
 
         # audio branch
-
+        # audio branch parameters
         if audio_cfg.model_type == "PANN":
             self.audio_branch = create_pann_model(audio_cfg)
         elif audio_cfg.model_type == "HTSAT":
@@ -373,7 +373,7 @@ def __init__(
             raise RuntimeError(f'Model config for {audio_cfg.model_type} not found.')
             
         # text branch
-        
+        # text branch parameters
         self.transformer = Transformer(
             width=text_cfg.width,
             layers=text_cfg.layers,
@@ -381,19 +381,23 @@ def __init__(
             act_layer=act_layer,
         )
 
+        # text branch parameters
         self.text_transform = MLPLayers(
-            units=[embed_dim,embed_dim,embed_dim], dropout=0.1
+            units=[512,512,512], dropout=0.1
         )
+        # audio branch parameters
         self.audio_transform = MLPLayers(
-            units=[embed_dim,embed_dim,embed_dim], dropout=0.1
+            units=[512,512,512], dropout=0.1
         )
-        
+
+        # below here is text branch parameters
         self.vocab_size = text_cfg.vocab_size
         self.token_embedding = nn.Embedding(text_cfg.vocab_size, text_cfg.width)
         self.positional_embedding = nn.Parameter(torch.empty(self.context_length, text_cfg.width))
         self.ln_final = LayerNorm(text_cfg.width)
 
-        self.text_projection = nn.Parameter(torch.empty(text_cfg.width, embed_dim))
+        self.audio_projection = MLPLayers(units=[embed_dim, 512, 512], dropout=0.1)
+        self.text_projection = nn.Parameter(torch.empty(text_cfg.width, 512)) # HARDCORE as 512
         self.logit_scale_a = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
         self.logit_scale_t = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
         self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)
@@ -468,10 +472,9 @@ def forward(self, audio, text):
         if audio is None:
             return self.encode_text(text)
         elif text is None:
-            return self.encode_audio(audio)
-        audio_features = self.encode_audio(audio)["embedding"]
+            return self.audio_projection(self.encode_audio(audio)["embedding"])
+        audio_features = self.audio_projection(self.encode_audio(audio)["embedding"])
         audio_features = F.normalize(audio_features, dim=-1)
-
         text_features = self.encode_text(text)
         text_features = F.normalize(text_features, dim=-1)
 
@@ -559,7 +562,7 @@ def build_model_from_openai_state_dict(state_dict: dict, model_cfg):
         if key.startswith("visual."):
             state_dict.pop(key, None)
 
-    for key in ["text_projection", "logit_scale","input_resolution", "context_length", "vocab_size"]:
+    for key in ["logit_scale","input_resolution", "context_length", "vocab_size"]:
         state_dict.pop(key, None)
 
     # not use fp16
diff --git a/src/open_clip/model_configs/HTSAT-base.json b/src/open_clip/model_configs/HTSAT-base.json
index 32593c7..6cef625 100644
--- a/src/open_clip/model_configs/HTSAT-base.json
+++ b/src/open_clip/model_configs/HTSAT-base.json
@@ -2,11 +2,11 @@
     "embed_dim": 1024,
     "audio_cfg": {
         "audio_length": 1024,
-        "clip_samples": 320000,
+        "clip_samples": 480000,
         "mel_bins": 64,
-        "sample_rate": 32000,
+        "sample_rate": 48000,
         "window_size": 1024,
-        "hop_size": 320,
+        "hop_size": 480,
         "fmin": 50,
         "fmax": 14000,
         "class_num": 527,
diff --git a/src/open_clip/model_configs/HTSAT-large.json b/src/open_clip/model_configs/HTSAT-large.json
index d23e66d..699cdb1 100644
--- a/src/open_clip/model_configs/HTSAT-large.json
+++ b/src/open_clip/model_configs/HTSAT-large.json
@@ -2,11 +2,11 @@
     "embed_dim": 2048,
     "audio_cfg": {
         "audio_length": 1024,
-        "clip_samples": 320000,
+        "clip_samples": 480000,
         "mel_bins": 64,
-        "sample_rate": 32000,
+        "sample_rate": 48000,
         "window_size": 1024,
-        "hop_size": 320,
+        "hop_size": 480,
         "fmin": 50,
         "fmax": 14000,
         "class_num": 527,
diff --git a/src/open_clip/model_configs/HTSAT-tiny.json b/src/open_clip/model_configs/HTSAT-tiny.json
index d7b8218..a6e7821 100644
--- a/src/open_clip/model_configs/HTSAT-tiny.json
+++ b/src/open_clip/model_configs/HTSAT-tiny.json
@@ -2,11 +2,11 @@
     "embed_dim": 768,
     "audio_cfg": {
         "audio_length": 1024,
-        "clip_samples": 320000,
+        "clip_samples": 480000,
         "mel_bins": 64,
-        "sample_rate": 32000,
+        "sample_rate": 48000,
         "window_size": 1024,
-        "hop_size": 320,
+        "hop_size": 480,
         "fmin": 50,
         "fmax": 14000,
         "class_num": 527,
diff --git a/src/open_clip/model_configs/PANN-10.json b/src/open_clip/model_configs/PANN-10.json
index 2ae9238..954ddf6 100644
--- a/src/open_clip/model_configs/PANN-10.json
+++ b/src/open_clip/model_configs/PANN-10.json
@@ -2,11 +2,11 @@
     "embed_dim": 1024,
     "audio_cfg": {
         "audio_length": 1024,
-        "clip_samples": 320000,
+        "clip_samples": 480000,
         "mel_bins": 64,
-        "sample_rate": 32000,
+        "sample_rate": 48000,
         "window_size": 1024,
-        "hop_size": 320,
+        "hop_size": 480,
         "fmin": 50,
         "fmax": 14000,
         "class_num": 527,
diff --git a/src/open_clip/model_configs/PANN-14.json b/src/open_clip/model_configs/PANN-14.json
index 03d9d98..39a5134 100644
--- a/src/open_clip/model_configs/PANN-14.json
+++ b/src/open_clip/model_configs/PANN-14.json
@@ -2,11 +2,11 @@
     "embed_dim": 2048,
     "audio_cfg": {
         "audio_length": 1024,
-        "clip_samples": 320000,
+        "clip_samples": 480000,
         "mel_bins": 64,
-        "sample_rate": 32000,
+        "sample_rate": 48000,
         "window_size": 1024,
-        "hop_size": 320,
+        "hop_size": 480,
         "fmin": 50,
         "fmax": 14000,
         "class_num": 527,
diff --git a/src/open_clip/model_configs/PANN-6.json b/src/open_clip/model_configs/PANN-6.json
index 652f447..21ebc34 100644
--- a/src/open_clip/model_configs/PANN-6.json
+++ b/src/open_clip/model_configs/PANN-6.json
@@ -2,11 +2,11 @@
     "embed_dim": 512,
     "audio_cfg": {
         "audio_length": 1024,
-        "clip_samples": 320000,
+        "clip_samples": 480000,
         "mel_bins": 64,
-        "sample_rate": 32000,
+        "sample_rate": 48000,
         "window_size": 1024,
-        "hop_size": 320,
+        "hop_size": 480,
         "fmin": 50,
         "fmax": 14000,
         "class_num": 527,
diff --git a/src/open_clip/utils.py b/src/open_clip/utils.py
index 627e5b6..3440e09 100644
--- a/src/open_clip/utils.py
+++ b/src/open_clip/utils.py
@@ -44,6 +44,49 @@ def freeze_batch_norm_2d(module, module_match={}, name=''):
                 res.add_module(child_name, new_child)
     return res
 
+def exist(dataset_name, dataset_type):
+    """
+    Check if dataset exists
+    """
+    if dataset_name in ["Clotho", "audiocaps"] and dataset_type in ["train", "test", "valid"]:
+        return True
+    elif dataset_name == "BBCSoundEffects" and dataset_type in ["train", "test"]:
+        return True
+    elif dataset_name == "audioset" and dataset_type in ["balanced_train", "unbalanced_train", "eval"]:
+        return True
+    else:
+        return False
+        
+
+def get_tar_path_from_dataset_name(dataset_names, dataset_types, islocal, template='/mnt/audio_clip/code/CLAP/src/data/datasetname/datasettype.txt'):
+    """
+    Get tar path from dataset name and type
+    """
+    txt_paths = []
+    for i in range(len(dataset_names)):
+        for j in range(len(dataset_types)):
+            if exist(dataset_names[i],dataset_types[j]):
+                txt_loc = template.replace("datasetname",dataset_names[i]).replace("datasettype",dataset_types[j])
+                txt_paths.append(txt_loc)
+            else:
+                print("Skipping dataset " + dataset_names[i] + " with " + dataset_types[j])
+                continue
+    return get_tar_path_from_txts(txt_paths, islocal=islocal)
+    
+def get_tar_path_from_txts(txt_path, islocal):
+    """
+    Get tar path from txt path
+    """
+    if isinstance(txt_path, (list,tuple)):
+        return sum([get_tar_path_from_txts(txt_path[i], islocal=islocal) for i in range(len(txt_path))], [])
+    if isinstance(txt_path, str):
+        with open(txt_path) as f:
+            lines = f.readlines()
+        if islocal:
+            lines = [lines[i].split("\n")[0].replace("pipe:s3cmd get s3://laion-audio/", "/mnt/audio_clip/") for i in range(len(lines))]
+        else:
+            lines = [lines[i].split("\n")[0] for i in range(len(lines))]
+        return lines
 
 def get_mix_lambda(mixup_alpha, batch_size):
     mixup_lambdas = [np.random.beta(mixup_alpha, mixup_alpha, 1)[0] for _ in range(batch_size)]
diff --git a/src/training/data.py b/src/training/data.py
index eb3e010..c89601f 100644
--- a/src/training/data.py
+++ b/src/training/data.py
@@ -20,7 +20,8 @@
 import soundfile as sf
 import librosa
 import io
-
+import torchaudio
+import torchaudio.functional as F
 from pathlib import Path
 import wget
 import random
@@ -310,6 +311,9 @@ def log_and_continue(exn):
 _SAMPLE_SHUFFLE_INITIAL = 1000
 
 def sample_prop(sizefile, inputs, proportion, is_local=True):
+    """
+    Sample a proportion of the data.
+    """
     file_path_dict = {os.path.split(inputs[i])[1]:os.path.split(inputs[i])[0] for i in range(len(inputs))}
     sampled_filepath_dict = {}
     sampled_size_dict = {}
@@ -337,34 +341,51 @@ def preprocess(
     max_len,
     dtype,
     res_type,
+    resample_method="TorchAudio",
 ):
     """
     Preprocess a single sample for wdsdataloader.
     """
-    keys = list(sample.keys())
-    for k in keys:
-        if (audio_ext in k) and (audio_ext!=k): # if the key is not extention of audio, something like 'xxxxx.flac'
-            sample[audio_ext] = sample[k]
-            del sample[k]
+    #keys = list(sample.keys())
+    #for k in keys:
+    #    if (audio_ext in k) and (audio_ext!=k): # if the key is not extention of audio, something like 'xxxxx.flac'
+    #        sample[audio_ext] = sample[k]
+    #        del sample[k]
+
+    #    if (text_ext in k) and (text_ext!=k): # if the key is not extention of audio, something like 'xxxxx.json'
+    #        sample[text_ext] = sample[k]
+    #        del sample[k]
+    audio_data, orig_sr = sf.read(io.BytesIO(sample[audio_ext]))
 
-        if (text_ext in k) and (text_ext!=k): # if the key is not extention of audio, something like 'xxxxx.json'
-            sample[text_ext] = sample[k]
-            del sample[k]
+   # if samplerate is not None:
+   #     if resample_method == "TorchAudio_":
+   #         audio_data = F.resample(
+   #             torch.tensor(audio_data).unsqueeze(0),
+   #             orig_sr,
+   #             32000,
+   #             lowpass_filter_width=64,
+   #             rolloff=0.9475937167399596,
+   #             resampling_method="kaiser_window",
+   #         ).squeeze(0)
+   #     elif resample_method == "librosa":
+   #         audio_data = librosa.resample(
+   #             audio_data, orig_sr=orig_sr, target_sr=samplerate, res_type=res_type
+   #         )
+   #     elif resample_method is None or resample_method == "None" or resample_method == "TorchAudio":
+   #         pass
+   #     else:
+   #         raise ValueError(f"Unknown resample method: {resample_method}")
 
-    audio_data, orig_sr = sf.read(io.BytesIO(sample[audio_ext]))
-    if samplerate is not None:
-        audio_data = librosa.resample(
-            audio_data, orig_sr=orig_sr, target_sr=samplerate, res_type=res_type
-        )
     if len(audio_data) > max_len:  # random clip if too long
         overflow = len(audio_data) - max_len
         idx = np.random.randint(0, overflow + 1)
-        if np.random.rand() > 0.5:
-            audio_data = audio_data[idx : idx + max_len]
-        else:
-            audio_data = audio_data[
-                len(audio_data) + 1 - idx - max_len : len(audio_data) + 1 - idx
-            ]
+        audio_data = audio_data[idx : idx + max_len]
+        #if np.random.rand() > 0.5:
+        #    audio_data = audio_data[idx : idx + max_len]
+        #else:
+        #    audio_data = audio_data[
+        #        len(audio_data) + 1 - idx - max_len : len(audio_data) + 1 - idx
+        #    ]
     else:  # padding if too short
         audio_data = np.pad(
             audio_data,
@@ -372,10 +393,10 @@ def preprocess(
             mode="constant",
             constant_values=0,
         )
-    if mono:  # convert to mono
-        audio_data = librosa.to_mono(audio_data)
+    #if mono:  # convert to mono
+    #    audio_data = librosa.to_mono(audio_data)
 
-    sample["waveform"] = audio_data
+    sample["waveform"] = torch.tensor(audio_data).float()
     del sample[audio_ext]
     texts = json.loads(sample[text_ext].decode('utf-8'))["text"]
     if isinstance(texts, list) and isinstance(texts[0], str) and len(texts) > 1:
@@ -385,6 +406,7 @@ def preprocess(
     del sample[text_ext]
     sample["audio_name"] = sample["__key__"].split("/")[-1]+"."+audio_ext
     sample["text_name"] = sample["__key__"].split("/")[-1]+"."+text_ext
+    sample["audio_orig_sr"] = orig_sr
     return sample
 
 
@@ -402,11 +424,15 @@ def get_wds_dataset(
     res_type="kaiser_best",
     proportion=1.0,
     sizefilepath_=None,
-    is_local=True
+    is_local=None,
+    resample_method=None,
 ):
     """
     Get a dataset for wdsdataloader.
     """
+    if is_local is None and (not args.remotedata is None):
+        is_local = not args.remotedata
+        
     input_shards = args.train_data if is_train else args.val_data
     assert input_shards is not None
 
@@ -462,9 +488,10 @@ def get_wds_dataset(
                 max_len=max_len,
                 dtype=dtype,
                 res_type=res_type,
+                resample_method=args.resample_method,
             )
         ),
-        wds.to_tuple("__url__", "__key__", "waveform", "text", "raw_text", "audio_name", "text_name"),
+        wds.to_tuple("__url__", "__key__", "waveform", "text", "raw_text", "audio_name", "text_name", "audio_orig_sr"),
         wds.batched(args.batch_size, partial=not is_train),
     ])
 
@@ -504,7 +531,7 @@ def get_wds_dataset(
 
     return DataInfo(dataloader, None)
 
-def wds_batch_list2dict(batch, keys=["__url__", "__key__", "waveform", "text", "raw_text", "audio_name", "text_name"]):
+def wds_batch_list2dict(batch, keys=["__url__", "__key__", "waveform", "text", "raw_text", "audio_name", "text_name", "audio_orig_sr"]):
     """
     Return a dictionary of the batch, with keys as the names of the fields.
     """
diff --git a/src/training/main.py b/src/training/main.py
index cae95ac..45d1826 100644
--- a/src/training/main.py
+++ b/src/training/main.py
@@ -2,8 +2,8 @@
 import os
 import random
 from datetime import datetime
-
-
+import bisect
+import copy
 import numpy as np
 import torch
 import torch.backends.cudnn as cudnn
@@ -32,6 +32,51 @@
 from training.params import parse_args
 from training.scheduler import cosine_lr
 from training.train import train_one_epoch, evaluate
+from open_clip.utils import get_tar_path_from_dataset_name
+
+
+def maintain_ckpts(args, startidx, all_idx_len):
+    for i in reversed(range(startidx, all_idx_len)):
+        if os.path.exists(os.path.join(args.checkpoint_path,f"epoch_top_{i}.pt")):
+            os.rename(os.path.join(args.checkpoint_path,f"epoch_top_{i}.pt"), os.path.join(args.checkpoint_path,f"epoch_top_{i+1}.pt"))
+    if os.path.exists(os.path.join(args.checkpoint_path,f"epoch_top_{all_idx_len}.pt")):
+        os.remove(os.path.join(args.checkpoint_path,f"epoch_top_{all_idx_len}.pt"))
+    return
+
+
+def update_top_k_performance(new_metrics_inputs, current_top_k_ckpt_metrics, args, ckpt, bignumbetter=True):
+    """
+    Record the top-k performance of the current epoch.
+    current_top_k_metrics is a dictionary of the form: {1: top_1_ckpt_measure, 2: top_2_ckpt_measure, ...}
+    """
+    if isinstance(new_metrics_inputs, (list, tuple)): 
+        new_metrics_inputs = np.mean(new_metrics_inputs)
+        return update_top_k_performance(new_metrics_inputs, current_top_k_ckpt_metrics, args=args, ckpt=ckpt, bignumbetter=bignumbetter)
+    elif isinstance(new_metrics_inputs, dict):
+        new_metrics_inputs = np.mean(list(new_metrics_inputs.values()))
+        return update_top_k_performance(new_metrics_inputs, current_top_k_ckpt_metrics, args=args, ckpt=ckpt, bignumbetter=bignumbetter)
+    elif isinstance(new_metrics_inputs, (float, int)):
+        update_flag = {k: False for k in current_top_k_ckpt_metrics.keys()}
+        sorted_keys = sorted(current_top_k_ckpt_metrics.keys())
+        sorted_values = sorted(current_top_k_ckpt_metrics.values(), reverse=bignumbetter)
+        sorted_values_ = copy.deepcopy(sorted_values)
+        sorted_values.append(new_metrics_inputs)
+        sorted_values = sorted(sorted_values, reverse=bignumbetter)
+        sorted_values = sorted_values[:-1]
+
+        if sorted_values == sorted_values_:
+            return current_top_k_ckpt_metrics, new_metrics_inputs
+        else:
+            for i in range(len(sorted_keys)):
+                if current_top_k_ckpt_metrics[sorted_keys[i]] != sorted_values[i]:
+                    current_top_k_ckpt_metrics[sorted_keys[i]] = sorted_values[i]
+                    update_flag[sorted_keys[i]] = True
+            for i in range(len(update_flag)):
+                if update_flag[i]:
+                    maintain_ckpts(args, i, len(sorted_keys))
+                    torch.save(ckpt, os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"),)
+                    break
+            return current_top_k_ckpt_metrics, new_metrics_inputs
 
 
 def random_seed(seed=42, rank=0):
@@ -45,7 +90,12 @@ def main():
 
     # sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule?
     args.model = args.model.replace('/', '-')
-
+    if args.datasetinfos is None:
+        args.datasetinfos = ["train", "unbalanced_train", "balanced_train"]
+    if args.dataset_type == "webdataset":
+        args.train_data = get_tar_path_from_dataset_name(args.datasetnames, args.datasetinfos, islocal=not args.remotedata, template=args.data_txt_example)
+        args.val_data = get_tar_path_from_dataset_name(args.datasetnames, ["valid", "test"], islocal=not args.remotedata, template=args.data_txt_example)
+        # args.val_data = get_tar_path_from_dataset_name(args.datasetnames, ["valid"], islocal=not args.remotedata, template=args.data_txt_example)
     # get the name of the experiments
     if args.name is None:
         args.name = '-'.join([
@@ -257,6 +307,8 @@ def main():
     elif start_epoch == 0 and 'val' in data:
         evaluate(model, data, 0, args, writer)
         # pass
+    if args.save_top_performance:
+        current_top_k_ckpt_metrics = {i:0 for i in range(args.save_top_performance)} # initialize the top-k metric for ckpts to 0
 
     for epoch in range(start_epoch, args.epochs):
         if is_master(args):
@@ -266,8 +318,9 @@ def main():
         completed_epoch = epoch + 1
 
         if any(v in data for v in ('val', 'imagenet-val', 'imagenet-v2')):
-            evaluate(model, data, completed_epoch, args, writer)
-
+            metrics = evaluate(model, data, completed_epoch, args, writer)
+            if args.save_top_performance:
+                filtered_metrics = [v for k,v in metrics.items() if "_R@10" in k] # check all R@10 metrics and use it to update the ckpt
         # Saving checkpoints.
         if args.save_logs:
             checkpoint_dict = {
@@ -291,6 +344,8 @@ def main():
                     checkpoint_dict,
                     os.path.join(args.checkpoint_path, f"epoch_latest.pt"),
                 )
+            if args.save_top_performance:
+                update_top_k_performance(filtered_metrics, current_top_k_ckpt_metrics, args, checkpoint_dict, bignumbetter=True)
 
     if args.wandb and is_master(args):
         wandb.finish()
diff --git a/src/training/params.py b/src/training/params.py
index 9bc6a89..0b8f31f 100644
--- a/src/training/params.py
+++ b/src/training/params.py
@@ -84,6 +84,30 @@ def parse_args():
         default=None,
         help="Path to imagenet v2 for conducting zero shot evaluation.",
     )
+    parser.add_argument(
+        "--datasetnames",
+        nargs='+',
+        default=None,
+        help="If loading webdataset, spedify the dataset names to load. Can be some of these: Clotho, audioset, audiocaps, BBCSoundEffects",
+    )
+    parser.add_argument(
+        "--datasetinfos",
+        nargs='+',
+        default=None,
+        help="If loading webdataset, spedify the dataset types to load. Can be some of these: train, test, valid, unbalanced_train, balanced_train, eval",
+    )
+    parser.add_argument(
+        "--remotedata",
+        default=False,
+        action='store_true',
+        help="if the dataset is remote, set this flag",
+    )
+    parser.add_argument(
+        "--data-txt-example",
+        type=str,
+        default='/mnt/audio_clip/code/CLAP/src/data/datasetname/datasettype.txt',
+        help="An example of the path to the txt file for the dataset",
+    )
     parser.add_argument(
         "--logs",
         type=str,
@@ -133,6 +157,9 @@ def parse_args():
     parser.add_argument(
         "--save-frequency", type=int, default=1, help="How often to save checkpoints."
     )
+    parser.add_argument(
+        "--save-top-performance", type=int, default=0, help="Save the top x performance weights if the value >0"
+    )
     parser.add_argument(
         "--save-most-recent",
         action="store_true",
@@ -145,6 +172,13 @@ def parse_args():
     parser.add_argument(
         "--val-frequency", type=int, default=1, help="How often to run evaluation with val data."
     )
+    parser.add_argument(
+        "--resample-method",
+        default=None,
+        type=str,
+        required=True,
+        help="Set None to disable resampling. Set TorchAudio to use batchwise torchaudio.resample. Set Librosa to use librosa.resample in the dataloader. Set TorchAudio_ to use torchaudio.resample in the dataloader.",
+    )
     parser.add_argument(
         "--resume",
         default=None,
diff --git a/src/training/train.py b/src/training/train.py
index 53897ed..694d9ab 100644
--- a/src/training/train.py
+++ b/src/training/train.py
@@ -8,7 +8,8 @@
 import numpy as np
 import torch
 import torch.nn.functional as F
-
+import torchaudio
+import torchaudio.functional as audioF
 try:
     import wandb
 except ImportError:
@@ -64,7 +65,8 @@ def train_one_epoch(model, data, epoch, optimizer, scaler, scheduler, args, tb_w
     sample_digits = math.ceil(math.log(dataloader.num_samples + 1, 10))
 
     # for toy dataset
-    dataloader.dataset.generate_queue()
+    if args.dataset_type == 'toy':
+        dataloader.dataset.generate_queue()
 
     loss_m = AverageMeter()
     batch_time_m = AverageMeter()
@@ -72,13 +74,23 @@ def train_one_epoch(model, data, epoch, optimizer, scaler, scheduler, args, tb_w
     end = time.time()
 
     for i, batch in enumerate(dataloader):
-        if args.dataset_type == 'webdataset':
-            batch = wds_batch_list2dict(batch)
-
+        # if args.dataset_type == 'webdataset':
+        #     batch = wds_batch_list2dict(batch)
+        #     batch["text"] = batch["text"][:,0,:]
         step = num_batches_per_epoch * epoch + i
         scheduler(step)
-        audios = batch["waveform"]
-        texts = batch["text"]
+        audios = batch[2]  # (yusong) todo:  change to retrieve from index for now.
+        #if args.resample_method=="TorchAudio":
+            # kaiser_best
+        #    audios = audioF.resample(
+        #        audios,
+        #        batch["audio_orig_sr"][0],
+        #        32000,
+        #        lowpass_filter_width=64,
+        #        rolloff=0.9475937167399596,
+        #        resampling_method="kaiser_window",
+        #    )
+        texts = batch[3][:,0,:]
         audios = audios.to(device=device, non_blocking=True)
         texts = texts.to(device=device, non_blocking=True)
 
@@ -177,10 +189,24 @@ def evaluate(model, data, epoch, args, tb_writer=None):
         all_audio_features, all_text_features, all_audio_features_mlp, all_text_features_mlp = [], [], [], []
         with torch.no_grad():
             for i, batch in enumerate(dataloader):
-                if args.dataset_type == 'webdataset':
-                    batch = wds_batch_list2dict(batch)
-                audios = batch["waveform"]
-                texts = batch["text"]
+                # if args.dataset_type == 'webdataset':
+                #     batch = wds_batch_list2dict(batch)
+                #     batch["text"] = batch["text"][:,0,:]
+                #
+                # audios = batch["waveform"].float()
+                # if args.resample_method=="TorchAudio":
+                #     # kaiser_best
+                #     audios = audioF.resample(
+                #         audios,
+                #         batch["audio_orig_sr"][0],
+                #         32000,
+                #         lowpass_filter_width=64,
+                #         rolloff=0.9475937167399596,
+                #         resampling_method="kaiser_window",
+                #     )
+                # texts = batch["text"].long()
+                audios = batch[2]  # (yusong) todo:  change to retrieve from index for now.
+                texts = batch[3][:, 0, :]
                 audios = audios.to(device=device, non_blocking=True)
                 texts = texts.to(device=device, non_blocking=True)
 
@@ -266,7 +292,7 @@ def get_metrics(audio_features, text_features, audio_features_mlp, text_features
 
     for name, logit in logits.items():
         ranking = torch.argsort(logit, descending=True)
-        preds = torch.where(ranking == ground_truth)[1]
+        preds = torch.where(ranking == ground_truth)[1]  # (yusong) todo: this line is slow as uses single thread
         preds = preds.detach().cpu().numpy()
         metrics[f"{name}_mean_rank"] = preds.mean() + 1
         metrics[f"{name}_median_rank"] = np.floor(np.median(preds)) + 1


Test patch:
diff --git a/src/data/BBCSoundEffects/test.txt b/src/data/BBCSoundEffects/test.txt
index 03650ce..79d4c4a 100644
--- a/src/data/BBCSoundEffects/test.txt
+++ b/src/data/BBCSoundEffects/test.txt
@@ -1,4 +1,4 @@
-s3://laion-audio/webdataset_tar/BBCSoundEffects/test/0.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/test/1.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/test/2.tar
-s3://laion-audio/webdataset_tar/BBCSoundEffects/test/3.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/test/0.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/test/1.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/test/2.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/BBCSoundEffects/test/3.tar
\ No newline at end of file
diff --git a/src/data/Clotho/test.txt b/src/data/Clotho/test.txt
index eefdfb5..ba7ce64 100644
--- a/src/data/Clotho/test.txt
+++ b/src/data/Clotho/test.txt
@@ -1,3 +1,3 @@
-s3://laion-audio/webdataset_tar/Clotho/test/0.tar
-s3://laion-audio/webdataset_tar/Clotho/test/1.tar
-s3://laion-audio/webdataset_tar/Clotho/test/2.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/test/0.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/test/1.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/Clotho/test/2.tar
\ No newline at end of file
diff --git a/src/data/audiocaps/test.txt b/src/data/audiocaps/test.txt
index b81f6b9..2b5f13d 100644
--- a/src/data/audiocaps/test.txt
+++ b/src/data/audiocaps/test.txt
@@ -1,9 +1,9 @@
-s3://laion-audio/webdataset_tar/audiocaps/test/0.tar
-s3://laion-audio/webdataset_tar/audiocaps/test/1.tar
-s3://laion-audio/webdataset_tar/audiocaps/test/2.tar
-s3://laion-audio/webdataset_tar/audiocaps/test/3.tar
-s3://laion-audio/webdataset_tar/audiocaps/test/4.tar
-s3://laion-audio/webdataset_tar/audiocaps/test/5.tar
-s3://laion-audio/webdataset_tar/audiocaps/test/6.tar
-s3://laion-audio/webdataset_tar/audiocaps/test/7.tar
-s3://laion-audio/webdataset_tar/audiocaps/test/8.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/test/0.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/test/1.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/test/2.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/test/3.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/test/4.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/test/5.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/test/6.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/test/7.tar
+pipe:s3cmd get s3://laion-audio/webdataset_tar/audiocaps/test/8.tar
\ No newline at end of file