vocalsspeechseparation.ipynb

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      "Collecting musdb\r\n",
      "  Downloading musdb-0.4.0-py2.py3-none-any.whl (29 kB)\r\n",
      "Requirement already satisfied: numpy>=1.7 in /opt/conda/lib/python3.10/site-packages (from musdb) (1.23.5)\r\n",
      "Requirement already satisfied: pyaml in /opt/conda/lib/python3.10/site-packages (from musdb) (21.10.1)\r\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from musdb) (4.64.1)\r\n",
      "Collecting stempeg>=0.2.3\r\n",
      "  Downloading stempeg-0.2.3-py3-none-any.whl (963 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m963.5/963.5 kB\u001b[0m \u001b[31m36.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hCollecting ffmpeg-python>=0.2.0\r\n",
      "  Downloading ffmpeg_python-0.2.0-py3-none-any.whl (25 kB)\r\n",
      "Requirement already satisfied: PyYAML in /opt/conda/lib/python3.10/site-packages (from pyaml->musdb) (6.0)\r\n",
      "Requirement already satisfied: future in /opt/conda/lib/python3.10/site-packages (from ffmpeg-python>=0.2.0->stempeg>=0.2.3->musdb) (0.18.3)\r\n",
      "Installing collected packages: ffmpeg-python, stempeg, musdb\r\n",
      "Successfully installed ffmpeg-python-0.2.0 musdb-0.4.0 stempeg-0.2.3\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install musdb\n",
    "# !pip install librosa\n",
    "# !pip install museval"
   ]
  },
  {
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   "source": [
    "import os\n",
    "import random\n",
    "import math\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import musdb\n",
    "import warnings\n",
    "# import librosa\n",
    "from tqdm.auto import tqdm\n",
    "from torchaudio.transforms import Spectrogram, MelSpectrogram, InverseSpectrogram"
   ]
  },
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   "source": [
    "class MUSDB18Dataset(Dataset):\n",
    "    def __init__(self, musdb18_root, subset=\"train\", split = \"train\", sr=44100, duration=2.0, seed = 42):\n",
    "        self.sr = sr\n",
    "        self.duration = duration\n",
    "        self.seed = seed\n",
    "        if(subset == 'test'):\n",
    "            self.musdb18 = musdb.DB(root = musdb18_root, subsets=subset)\n",
    "            print(f\"Number of Testing Samples: {len(self.musdb18)}\")\n",
    "        else:\n",
    "            self.musdb18 = musdb.DB(root = musdb18_root, subsets = subset, split = split)\n",
    "            if split == \"train\":\n",
    "                print(f\"Number of Training Samples: {len(self.musdb18)}\")\n",
    "            else:\n",
    "                print(f\"Number of Validation Samples: {len(self.musdb18)}\")\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        random.seed(self.seed)\n",
    "        track = self.musdb18.tracks[index]\n",
    "        track.chunk_duration = self.duration\n",
    "        track.chunk_start = random.uniform(0, track.duration - track.chunk_duration)\n",
    "        \n",
    "        # Load the mixture waveform\n",
    "        audio = track.audio.T\n",
    "        audio = audio.mean(axis = 0, keepdims = True)\n",
    "        audio_tensor = torch.from_numpy(audio).float()\n",
    "        \n",
    "        # Load the target waveform (vocals)\n",
    "        vocals = track.targets[\"vocals\"].audio.T\n",
    "        vocals = vocals.mean(axis = 0, keepdims = True)\n",
    "        others = track.targets[\"accompaniment\"].audio.T\n",
    "        others = others.mean(axis = 0, keepdims = True)\n",
    "        \n",
    "        vocals_tensor = torch.from_numpy(np.concatenate((vocals, others), axis = 0)).float()\n",
    "        \n",
    "        return audio_tensor, vocals_tensor\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.musdb18.tracks)"
   ]
  },
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   "source": [
    "def get_musdb18_dataloaders(root, batch_size=8, num_workers=0, seed = 42):\n",
    "    train_set = MUSDB18Dataset(root, subset=\"train\")\n",
    "    val_set = MUSDB18Dataset(root, subset=\"train\", split = 'valid')\n",
    "    test_set = MUSDB18Dataset(root, subset=\"test\")\n",
    "\n",
    "    train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
    "    val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
    "    test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
    "\n",
    "    return train_loader, val_loader, test_loader"
   ]
  },
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   "source": [
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, d_model = 512, num_heads = 4, num_layers = 4, dropout = 0.1):\n",
    "        super().__init__()\n",
    "        \n",
    "        # input embedding layer\n",
    "        self.input_embed = nn.Linear(101, d_model)\n",
    "\n",
    "        # transformer encoder layers\n",
    "        encoder_layers = nn.TransformerEncoderLayer(d_model=d_model, nhead=num_heads, dropout=dropout)\n",
    "        self.encoder = nn.TransformerEncoder(encoder_layers, num_layers=num_layers)\n",
    "        \n",
    "        decoder_layers = nn.TransformerDecoderLayer(d_model=d_model, nhead = num_heads, dropout=dropout)\n",
    "        self.decoder = nn.TransformerDecoder(decoder_layers, num_layers = num_layers//2)\n",
    "                \n",
    "        # output embedding layer\n",
    "        self.output_embed = nn.Linear(d_model, 202)\n",
    "        \n",
    "        self.transform = Spectrogram(n_fft = 200)\n",
    "        self.invtransform = InverseSpectrogram(n_fft = 200)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        length = x.size(-1)\n",
    "        \n",
    "        x = self.transform(x)\n",
    "        x = x.permute(3, 0, 1, 2)\n",
    "        x = x.reshape(x.size(0), -1, x.size(-1))\n",
    "        \n",
    "        # apply input embedding layer\n",
    "        x = self.input_embed(x)\n",
    "        \n",
    "        # apply transformer encoder layers\n",
    "        x = self.encoder(x)\n",
    "        memory = x\n",
    "        \n",
    "        # apply transformer decoder layers\n",
    "        x = self.decoder(x, memory)\n",
    "        \n",
    "        # apply output embedding layer\n",
    "        x = self.output_embed(x)\n",
    "        \n",
    "        # reshape x back to (batch_size, num_channels, num_frames, num_bins)\n",
    "        x = x.reshape(x.size(1), 2, -1, x.size(0))\n",
    "        \n",
    "        y = self.invtransform(x.type(torch.complex64), length)\n",
    "        \n",
    "        return y"
   ]
  },
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   "source": [
    "def train(model, dataloader, optimizer, loss_fn):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "\n",
    "    for i, (audio, vocals) in enumerate(dataloader):\n",
    "        # move data to GPU if available\n",
    "        if torch.cuda.is_available():\n",
    "            audio = audio.cuda()\n",
    "            vocals = vocals.cuda()\n",
    "\n",
    "        # forward pass\n",
    "        prediction = model(audio)\n",
    "        loss = loss_fn(prediction, vocals)\n",
    "\n",
    "        # backward pass and optimization\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # update total loss\n",
    "        total_loss += loss.item()\n",
    "\n",
    "    # compute average loss\n",
    "    avg_loss = total_loss / len(dataloader)\n",
    "\n",
    "    return avg_loss"
   ]
  },
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   "source": [
    "def validate(model, dataloader, loss_fn):\n",
    "    model.eval()\n",
    "    total_loss = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (audio, vocals) in enumerate(dataloader):\n",
    "            # move data to GPU if available\n",
    "            if torch.cuda.is_available():\n",
    "                audio = audio.cuda()\n",
    "                vocals = vocals.cuda()\n",
    "\n",
    "            # forward pass\n",
    "            prediction = model(audio)\n",
    "            loss = loss_fn(prediction, vocals)\n",
    "\n",
    "            # update total loss\n",
    "            total_loss += loss.item()\n",
    "\n",
    "    # compute average loss\n",
    "    avg_loss = total_loss / len(dataloader)\n",
    "\n",
    "    return avg_loss"
   ]
  },
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   "source": [
    "def calculate_sdr(predicted_output, ground_truth):\n",
    "    \"\"\"\n",
    "    Calculates the Signal-to-Distortion Ratio (SDR) metric between a predicted output and its corresponding ground truth.\n",
    "\n",
    "    Args:\n",
    "    predicted_output: A numpy array of shape (number of channels, number of frames, number of bins).\n",
    "    ground_truth: A numpy array of shape (number of channels, number of frames, number of bins).\n",
    "\n",
    "    Returns:\n",
    "    sdr: A scalar representing the SDR value between predicted_output and ground_truth.\n",
    "    \"\"\"\n",
    "    eps = np.finfo(np.float32).eps  # To avoid division by zero errors\n",
    "    num_channels = predicted_output.shape[0]\n",
    "    sdr_sum = 0\n",
    "    \n",
    "#     print(predicted_output.shape, ground_truth.shape)\n",
    "    \n",
    "    for c in range(num_channels):\n",
    "        # Compute the power of the true source signal\n",
    "        true_source_power = np.sum(ground_truth[c]**2)\n",
    "\n",
    "        # Compute the scalar product between true source signal and predicted signal\n",
    "        true_pred_scalar = np.sum(ground_truth[c] * predicted_output[c])\n",
    "\n",
    "        # Compute the SDR for this channel\n",
    "        sdr = 10 * np.log10(true_source_power / (np.sum(ground_truth[c]**2) - true_pred_scalar + eps) + eps)\n",
    "        if not math.isnan(sdr):\n",
    "            sdr_sum += sdr\n",
    "\n",
    "    # Compute the average SDR across all channels\n",
    "    sdr = sdr_sum / num_channels\n",
    "\n",
    "    return -sdr"
   ]
  },
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   "source": [
    "def test(model, dataloader):\n",
    "    model.eval()\n",
    "    all_targets = []\n",
    "    all_predictions = []\n",
    "    sdr = 0\n",
    "    count = 0\n",
    "    with torch.no_grad():\n",
    "        for i, (audio, vocals) in tqdm(enumerate(dataloader)):\n",
    "            # move data to GPU if available\n",
    "            if torch.cuda.is_available():\n",
    "                audio = audio.cuda()\n",
    "                vocals = vocals.cuda()\n",
    "\n",
    "            # forward pass\n",
    "            prediction = model(audio)\n",
    "            \n",
    "            # convert predictions and ground truth to numpy arrays\n",
    "            prediction = prediction.cpu().numpy()\n",
    "            vocals = vocals.cpu().numpy()\n",
    "            \n",
    "            for i in range(prediction.shape[0]):\n",
    "                pred = prediction[i,:,:]\n",
    "                targ = vocals[i,:,:]\n",
    "                sdr += calculate_sdr(pred, targ)\n",
    "                count += 1\n",
    "    mean_sdr = sdr/count\n",
    "    return mean_sdr"
   ]
  },
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   "source": [
    "def fit(model, optimizer, loss_fn, num_epochs, train_dataloader, val_dataloader, test_loader):\n",
    "    for epoch in tqdm(range(num_epochs)):\n",
    "        # train model for one epoch\n",
    "        train_loss = train(model, train_dataloader, optimizer, loss_fn)\n",
    "        print(f'Epoch {epoch + 1} - Train loss: {train_loss:.4f}')\n",
    "\n",
    "        # evaluate model on validation data\n",
    "        val_loss = validate(model, val_dataloader, loss_fn)\n",
    "        print(f'Epoch {epoch + 1} - Val loss: {val_loss:.4f}')\n",
    "        print(\"---------------------------------------------\")\n",
    "        \n",
    "        if ((epoch+1)%20 == 0):\n",
    "            with warnings.catch_warnings():\n",
    "                warnings.filterwarnings(\"ignore\", category=RuntimeWarning)    \n",
    "                print(f\"Epoch: {epoch+1} | Training SDR: {test(model, train_loader)}\")\n",
    "                print(f\"Epoch: {epoch+1} | Validation SDR: {test(model, val_loader)}\")\n",
    "                print(f\"Epoch: {epoch+1} | Testing SDR: {test(model, test_loader)}\")"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Training Samples: 87\n",
      "Number of Validation Samples: 13\n",
      "Number of Testing Samples: 50\n"
     ]
    }
   ],
   "source": [
    "root = \"/kaggle/input/musdb18/musdb18\"\n",
    "train_loader, val_loader, test_loader = get_musdb18_dataloaders(root, batch_size = 2)\n",
    "# for x, y in val_loader:\n",
    "#     print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "61882525",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-02T15:44:58.000158Z",
     "iopub.status.busy": "2023-05-02T15:44:57.999879Z",
     "iopub.status.idle": "2023-05-02T23:20:14.762141Z",
     "shell.execute_reply": "2023-05-02T23:20:14.761228Z"
    },
    "papermill": {
     "duration": 27316.769064,
     "end_time": "2023-05-02T23:20:14.764115",
     "exception": false,
     "start_time": "2023-05-02T15:44:57.995051",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "version_major": 2,
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      "text/plain": [
       "  0%|          | 0/150 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 - Train loss: 0.0087\n",
      "Epoch 1 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 2 - Train loss: 0.0085\n",
      "Epoch 2 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 3 - Train loss: 0.0085\n",
      "Epoch 3 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 4 - Train loss: 0.0086\n",
      "Epoch 4 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 5 - Train loss: 0.0085\n",
      "Epoch 5 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 6 - Train loss: 0.0086\n",
      "Epoch 6 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 7 - Train loss: 0.0084\n",
      "Epoch 7 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 8 - Train loss: 0.0085\n",
      "Epoch 8 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 9 - Train loss: 0.0084\n",
      "Epoch 9 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 10 - Train loss: 0.0084\n",
      "Epoch 10 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 11 - Train loss: 0.0085\n",
      "Epoch 11 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 12 - Train loss: 0.0086\n",
      "Epoch 12 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 13 - Train loss: 0.0084\n",
      "Epoch 13 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 14 - Train loss: 0.0085\n",
      "Epoch 14 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 15 - Train loss: 0.0086\n",
      "Epoch 15 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 16 - Train loss: 0.0084\n",
      "Epoch 16 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 17 - Train loss: 0.0084\n",
      "Epoch 17 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 18 - Train loss: 0.0085\n",
      "Epoch 18 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 19 - Train loss: 0.0085\n",
      "Epoch 19 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 20 - Train loss: 0.0085\n",
      "Epoch 20 - Val loss: 0.0081\n",
      "---------------------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20 | Training SDR: 4.381951302589786\n"
     ]
    },
    {
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      "application/vnd.jupyter.widget-view+json": {
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       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20 | Validation SDR: 2.8559357632980644\n"
     ]
    },
    {
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       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20 | Testing SDR: 3.4786836161328436\n",
      "Epoch 21 - Train loss: 0.0084\n",
      "Epoch 21 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 22 - Train loss: 0.0084\n",
      "Epoch 22 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 23 - Train loss: 0.0084\n",
      "Epoch 23 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 24 - Train loss: 0.0085\n",
      "Epoch 24 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 25 - Train loss: 0.0084\n",
      "Epoch 25 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 26 - Train loss: 0.0084\n",
      "Epoch 26 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 27 - Train loss: 0.0085\n",
      "Epoch 27 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 28 - Train loss: 0.0085\n",
      "Epoch 28 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 29 - Train loss: 0.0084\n",
      "Epoch 29 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 30 - Train loss: 0.0084\n",
      "Epoch 30 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 31 - Train loss: 0.0084\n",
      "Epoch 31 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 32 - Train loss: 0.0085\n",
      "Epoch 32 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 33 - Train loss: 0.0084\n",
      "Epoch 33 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 34 - Train loss: 0.0085\n",
      "Epoch 34 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 35 - Train loss: 0.0085\n",
      "Epoch 35 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 36 - Train loss: 0.0084\n",
      "Epoch 36 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 37 - Train loss: 0.0085\n",
      "Epoch 37 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 38 - Train loss: 0.0084\n",
      "Epoch 38 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 39 - Train loss: 0.0084\n",
      "Epoch 39 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 40 - Train loss: 0.0084\n",
      "Epoch 40 - Val loss: 0.0081\n",
      "---------------------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7fecdf35d4bd449cbab28926c3dfb3dc",
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       "0it [00:00, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 40 | Training SDR: 4.37362010303388\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 40 | Validation SDR: 2.604438680902009\n"
     ]
    },
    {
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       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 40 | Testing SDR: 3.3811675321711108\n",
      "Epoch 41 - Train loss: 0.0085\n",
      "Epoch 41 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 42 - Train loss: 0.0084\n",
      "Epoch 42 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 43 - Train loss: 0.0085\n",
      "Epoch 43 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 44 - Train loss: 0.0085\n",
      "Epoch 44 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 45 - Train loss: 0.0085\n",
      "Epoch 45 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 46 - Train loss: 0.0085\n",
      "Epoch 46 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 47 - Train loss: 0.0084\n",
      "Epoch 47 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 48 - Train loss: 0.0084\n",
      "Epoch 48 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 49 - Train loss: 0.0084\n",
      "Epoch 49 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 50 - Train loss: 0.0085\n",
      "Epoch 50 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 51 - Train loss: 0.0084\n",
      "Epoch 51 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 52 - Train loss: 0.0085\n",
      "Epoch 52 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 53 - Train loss: 0.0085\n",
      "Epoch 53 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 54 - Train loss: 0.0085\n",
      "Epoch 54 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 55 - Train loss: 0.0085\n",
      "Epoch 55 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 56 - Train loss: 0.0085\n",
      "Epoch 56 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 57 - Train loss: 0.0084\n",
      "Epoch 57 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 58 - Train loss: 0.0084\n",
      "Epoch 58 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 59 - Train loss: 0.0084\n",
      "Epoch 59 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 60 - Train loss: 0.0084\n",
      "Epoch 60 - Val loss: 0.0081\n",
      "---------------------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f21ec6ebf4ff4806ba46ab226fdf7a70",
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      "text/plain": [
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     },
     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 60 | Training SDR: 4.380830574204235\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 60 | Validation SDR: 2.7118411976705548\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 60 | Testing SDR: 3.501611276765067\n",
      "Epoch 61 - Train loss: 0.0085\n",
      "Epoch 61 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 62 - Train loss: 0.0084\n",
      "Epoch 62 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 63 - Train loss: 0.0085\n",
      "Epoch 63 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 64 - Train loss: 0.0084\n",
      "Epoch 64 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 65 - Train loss: 0.0084\n",
      "Epoch 65 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 66 - Train loss: 0.0084\n",
      "Epoch 66 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 67 - Train loss: 0.0084\n",
      "Epoch 67 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 68 - Train loss: 0.0085\n",
      "Epoch 68 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 69 - Train loss: 0.0085\n",
      "Epoch 69 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 70 - Train loss: 0.0085\n",
      "Epoch 70 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 71 - Train loss: 0.0084\n",
      "Epoch 71 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 72 - Train loss: 0.0084\n",
      "Epoch 72 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 73 - Train loss: 0.0084\n",
      "Epoch 73 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 74 - Train loss: 0.0085\n",
      "Epoch 74 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 75 - Train loss: 0.0084\n",
      "Epoch 75 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 76 - Train loss: 0.0084\n",
      "Epoch 76 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 77 - Train loss: 0.0085\n",
      "Epoch 77 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 78 - Train loss: 0.0085\n",
      "Epoch 78 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 79 - Train loss: 0.0084\n",
      "Epoch 79 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 80 - Train loss: 0.0084\n",
      "Epoch 80 - Val loss: 0.0081\n",
      "---------------------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 80 | Training SDR: 4.37803028384051\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1535991d04764e6babcc6333fb982bd6",
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       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 80 | Validation SDR: 2.8329063120176774\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 80 | Testing SDR: 3.4517191176068422\n",
      "Epoch 81 - Train loss: 0.0084\n",
      "Epoch 81 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 82 - Train loss: 0.0084\n",
      "Epoch 82 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 83 - Train loss: 0.0084\n",
      "Epoch 83 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 84 - Train loss: 0.0085\n",
      "Epoch 84 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 85 - Train loss: 0.0084\n",
      "Epoch 85 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 86 - Train loss: 0.0085\n",
      "Epoch 86 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 87 - Train loss: 0.0084\n",
      "Epoch 87 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 88 - Train loss: 0.0085\n",
      "Epoch 88 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 89 - Train loss: 0.0085\n",
      "Epoch 89 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 90 - Train loss: 0.0085\n",
      "Epoch 90 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 91 - Train loss: 0.0085\n",
      "Epoch 91 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 92 - Train loss: 0.0084\n",
      "Epoch 92 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 93 - Train loss: 0.0084\n",
      "Epoch 93 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 94 - Train loss: 0.0084\n",
      "Epoch 94 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 95 - Train loss: 0.0085\n",
      "Epoch 95 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 96 - Train loss: 0.0084\n",
      "Epoch 96 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 97 - Train loss: 0.0084\n",
      "Epoch 97 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 98 - Train loss: 0.0084\n",
      "Epoch 98 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 99 - Train loss: 0.0085\n",
      "Epoch 99 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 100 - Train loss: 0.0084\n",
      "Epoch 100 - Val loss: 0.0081\n",
      "---------------------------------------------\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 100 | Training SDR: 4.382703280385917\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 100 | Validation SDR: 2.851630327315451\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 100 | Testing SDR: 3.5017789044590972\n",
      "Epoch 101 - Train loss: 0.0084\n",
      "Epoch 101 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 102 - Train loss: 0.0084\n",
      "Epoch 102 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 103 - Train loss: 0.0085\n",
      "Epoch 103 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 104 - Train loss: 0.0084\n",
      "Epoch 104 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 105 - Train loss: 0.0085\n",
      "Epoch 105 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 106 - Train loss: 0.0084\n",
      "Epoch 106 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 107 - Train loss: 0.0084\n",
      "Epoch 107 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 108 - Train loss: 0.0084\n",
      "Epoch 108 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 109 - Train loss: 0.0084\n",
      "Epoch 109 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 110 - Train loss: 0.0085\n",
      "Epoch 110 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 111 - Train loss: 0.0085\n",
      "Epoch 111 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 112 - Train loss: 0.0085\n",
      "Epoch 112 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 113 - Train loss: 0.0085\n",
      "Epoch 113 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 114 - Train loss: 0.0084\n",
      "Epoch 114 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 115 - Train loss: 0.0084\n",
      "Epoch 115 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 116 - Train loss: 0.0085\n",
      "Epoch 116 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 117 - Train loss: 0.0084\n",
      "Epoch 117 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 118 - Train loss: 0.0084\n",
      "Epoch 118 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 119 - Train loss: 0.0084\n",
      "Epoch 119 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 120 - Train loss: 0.0084\n",
      "Epoch 120 - Val loss: 0.0081\n",
      "---------------------------------------------\n"
     ]
    },
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      "Epoch: 120 | Training SDR: 4.368817907445449\n"
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      "Epoch: 120 | Validation SDR: 2.658878710767567\n"
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      "Epoch: 120 | Testing SDR: 3.4365972452315305\n",
      "Epoch 121 - Train loss: 0.0085\n",
      "Epoch 121 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 122 - Train loss: 0.0085\n",
      "Epoch 122 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 123 - Train loss: 0.0084\n",
      "Epoch 123 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 124 - Train loss: 0.0084\n",
      "Epoch 124 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 125 - Train loss: 0.0085\n",
      "Epoch 125 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 126 - Train loss: 0.0085\n",
      "Epoch 126 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 127 - Train loss: 0.0084\n",
      "Epoch 127 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 128 - Train loss: 0.0085\n",
      "Epoch 128 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 129 - Train loss: 0.0085\n",
      "Epoch 129 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 130 - Train loss: 0.0085\n",
      "Epoch 130 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 131 - Train loss: 0.0085\n",
      "Epoch 131 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 132 - Train loss: 0.0085\n",
      "Epoch 132 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 133 - Train loss: 0.0085\n",
      "Epoch 133 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 134 - Train loss: 0.0085\n",
      "Epoch 134 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 135 - Train loss: 0.0084\n",
      "Epoch 135 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 136 - Train loss: 0.0085\n",
      "Epoch 136 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 137 - Train loss: 0.0085\n",
      "Epoch 137 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 138 - Train loss: 0.0084\n",
      "Epoch 138 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 139 - Train loss: 0.0084\n",
      "Epoch 139 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 140 - Train loss: 0.0084\n",
      "Epoch 140 - Val loss: 0.0081\n",
      "---------------------------------------------\n"
     ]
    },
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     "text": [
      "Epoch: 140 | Training SDR: 4.371554973340649\n"
     ]
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     "text": [
      "Epoch: 140 | Validation SDR: 2.928474809066214\n"
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     "text": [
      "Epoch: 140 | Testing SDR: 3.4561650745970267\n",
      "Epoch 141 - Train loss: 0.0085\n",
      "Epoch 141 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 142 - Train loss: 0.0085\n",
      "Epoch 142 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 143 - Train loss: 0.0085\n",
      "Epoch 143 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 144 - Train loss: 0.0084\n",
      "Epoch 144 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 145 - Train loss: 0.0085\n",
      "Epoch 145 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 146 - Train loss: 0.0085\n",
      "Epoch 146 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 147 - Train loss: 0.0085\n",
      "Epoch 147 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 148 - Train loss: 0.0084\n",
      "Epoch 148 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 149 - Train loss: 0.0085\n",
      "Epoch 149 - Val loss: 0.0081\n",
      "---------------------------------------------\n",
      "Epoch 150 - Train loss: 0.0084\n",
      "Epoch 150 - Val loss: 0.0081\n",
      "---------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# define model and optimizer\n",
    "model = TransformerModel(num_heads = 8, num_layers = 6)\n",
    "# model = PretrainedTransformer(output_size=257, num_heads = 8, num_layers = 6)\n",
    "if torch.cuda.is_available():\n",
    "    model = model.cuda()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "\n",
    "# define loss function\n",
    "loss_fn = nn.MSELoss()\n",
    "\n",
    "fit(model, optimizer, loss_fn, 150, train_loader, val_loader, test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "830c696d",
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     "iopub.execute_input": "2023-05-02T23:20:14.809588Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE: 0.008412279839500447\n",
      "Validation MSE: 0.008107607452464955\n",
      "Testing MSE: 0.0066387111600488425\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training MSE: {validate(model, train_loader, loss_fn)}\")\n",
    "print(f\"Validation MSE: {validate(model, val_loader, loss_fn)}\")\n",
    "print(f\"Testing MSE: {validate(model, test_loader, loss_fn)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52658269",
   "metadata": {
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     "iopub.execute_input": "2023-05-02T23:24:16.927294Z",
     "iopub.status.busy": "2023-05-02T23:24:16.926912Z",
     "iopub.status.idle": "2023-05-02T23:28:20.062128Z",
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    },
    "papermill": {
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     "exception": false,
     "start_time": "2023-05-02T23:24:16.827108",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training SDR: 4.369476915549454\n"
     ]
    },
    {
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       "0it [00:00, ?it/s]"
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation SDR: 2.8677127313681163\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a98a56f30e2b4f23ac669b6055acc301",
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       "0it [00:00, ?it/s]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing SDR: 3.4226250170195756\n"
     ]
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   ],
   "source": [
    "with warnings.catch_warnings():\n",
    "    warnings.filterwarnings(\"ignore\", category=RuntimeWarning)    \n",
    "    print(f\"Training SDR: {test(model, train_loader)}\")\n",
    "    print(f\"Validation SDR: {test(model, val_loader)}\")\n",
    "    print(f\"Testing SDR: {test(model, test_loader)}\")"
   ]
  }
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