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null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "46e0ed9fd82b45e2afdb8185655b8b8b": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jRiIYO86FZwj", "outputId": "b1f47a80-f5c6-4520-d047-c4f9206e4e8b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.4/297.4 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K 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datasets import DatasetDict,Dataset\n", "import soundfile as sf\n", "import os\n", "import matplotlib.pyplot as plt\n", "plt.style.use(\"seaborn-whitegrid\")\n" ], "metadata": { "id": "L0mwyL-EFh2F" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "W769XOa5Fhyl", "outputId": "716d49d7-0f1a-47fb-e27f-68dbf1c41478" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "!git clone https://github.com/Mahmood-Anaam/VitsModelSplit.git" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5ZS8kVTJFhve", "outputId": "9274f79e-a480-4d8e-a8fa-927947333f6f" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'VitsModelSplit'...\n", "remote: Enumerating objects: 181, done.\u001b[K\n", "remote: Counting objects: 100% (181/181), done.\u001b[K\n", "remote: Compressing objects: 100% (115/115), done.\u001b[K\n", "remote: Total 181 (delta 109), reused 132 (delta 63), pack-reused 0\u001b[K\n", "Receiving objects: 100% (181/181), 21.22 MiB | 40.46 MiB/s, done.\n", "Resolving deltas: 100% (109/109), done.\n" ] } ] }, { "cell_type": "markdown", "source": [ "\n", "\n", "---\n", "\n" ], "metadata": { "id": "Ed6KQi3bg4TS" } }, { "cell_type": "code", "source": [ "from VitsModelSplit.vits_model import VitsModel\n", "from VitsModelSplit.PosteriorDecoderModel import PosteriorDecoderModel\n", "from VitsModelSplit.feature_extraction import VitsFeatureExtractor\n", "\n", "from transformers import AutoTokenizer, HfArgumentParser, set_seed\n", "from VitsModelSplit.Arguments import DataTrainingArguments, ModelArguments, VITSTrainingArguments\n" ], "metadata": { "id": "-4aZETCsa3C4" }, "execution_count": 5, "outputs": [] }, { "cell_type": "code", "source": [ "model = VitsModel.from_pretrained(\"facebook/mms-tts-ara\",cache_dir=\"./\")\n", "tokenizer = AutoTokenizer.from_pretrained(\"facebook/mms-tts-ara\",cache_dir=\"./\")\n", "feature_extractor = VitsFeatureExtractor()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 284, "referenced_widgets": [ "26eba4b916a14c09ba9349e5113991cf", "1ba80f3aaff44ee1a65026a3bed54deb", "4ed0475a547c4584a1794a4a233d33a0", "c3356a69e8884755ab162cffc9537864", "3f4d6f767c584b419e32259c2a3c7507", "eb66729fbb9c4e50893b61db2e850f5a", "fcc817f28bf64c7ab45fc18c0f453169", "083d10c2662645e789ca0c0b094bad4e", "b0196e91e0b24273852fd20191ba2c0a", "625cade43fff4db1893845bd22f19b48", "bcb552d885374d778f7101dc1739a873", "7e86843a0afe44a78210760a92411117", "30c50b7d79eb45ebb5b82b0ec1209b60", "b9af9002372344fab8e91ee79ba90d81", "8ab1eb38be1b4ee29975de30a6323ab2", "843bd27bdfc4439cb2e455f6f44eb8c1", "404a3166f68e441ca60c057b75db0246", "729309cfde0847849a4c83f2efb9789a", "48409186bd234169a5fe43d207402b9a", "dd9e1951fc3742d19389dd59694fe2c6", "84029ee47a594362bf0b2cdbc938e92d", "234e7bbe055f4d208008bcf6af1bb948", "49f8d3d8cfe24b3a864cb57053db6788", "724b19b41d414405a957674fe757bc73", "bba1f9ce5b3a4a94ad7a90954c6fcdb2", "b241ad516869421eb60047215535d3a6", "5a07b1c4448e4bc39bfefa900287e5c4", "cf984c7ad36b47ca9758cd06c8f043f6", "ba3ee841d8f641cb8bc891da306d1ca7", "f24e65ce18f24159934d073d09a9414c", "f621895cecff4820a55445a0e9ccf0c7", "4bc32474f2194649b21b5d5c9cb340c4", "eaab7d543ed049f1b1efec7a4ef392ab", "86db1b1af2bc4643a505cad4d7135264", "dd02bed5420f455a973f8367ff58cb33", "d51ed119dded4580af08c7d0ef325bb3", "7059bac41eff4acba94f86accd2e321b", "6f80573093ce453f85117198766c0a38", "5fba9b22d79e4d0ab2d7cebbe15ac706", "4bf367cea86045b8bf23652308247077", "f2c545f473bc4c33a54f94dd547ba998", "850f4bd076384ff995e7060f86bc5b77", "848415b52eef472b91410eba506ab37b", "1ef0e6f48df2488e96c5fca0a7be4286", "7b5037c1732441528d396fef9cced232", "d7e73c62b5bc410aaacc037ba3fa435c", "4b737eee51a1497d977148d30dfe9bad", "8fa45d6a7cc14abb872ab180c5cb23f8", "3ffee1d2c4a849a9b8df33039d86e945", "cf17edab22cc49efa594b41d41d1947f", "169baa2ee62d4f9f96727fee72055d1d", "488409d44d024596adb7f6c8cc2261ab", "fa370520efe54f8cb5622817c4b59569", "9477a2ca9fe34b2495a9f4df9bfddcf9", "46e0ed9fd82b45e2afdb8185655b8b8b" ] }, "id": "W5cH1Yvyb6It", "outputId": "82837eb5-2753-4c8c-954d-96fb26e6abb3" }, "execution_count": 6, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.64k [00:00" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "markdown", "source": [ "\n", "\n", "---\n", "\n" ], "metadata": { "id": "Fx1HrDAdtOfX" } }, { "cell_type": "code", "source": [ "dataset_dir = '/content/drive/MyDrive/FeaturesCollectionDataset'\n", "\n", "posterior_decoder_model.train(True)\n", "\n", "posterior_decoder_model.trainer(\n", " train_dataset_dir = os.path.join(dataset_dir,'train'),\n", " eval_dataset_dir = os.path.join(dataset_dir,'eval'),\n", " full_generation_dir = os.path.join(dataset_dir,'full_generation'),\n", " feature_extractor = VitsFeatureExtractor(),\n", " training_args = training_args,\n", " full_generation_sample_index= 0,\n", " project_name = \"Posterior_Decoder_Finetuning\",\n", " wandbKey = \"782b6a6e82bbb5a5348de0d3c7d40d1e76351e79\",\n", " )\n", "\n", "posterior_decoder_model.train(False)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "GmD2CSxbb5_G", "outputId": "aecc974a-5c93-4e16-da21-af4452d1f2e4" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmodelasg\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Tracking run with wandb version 0.16.6" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Run data is saved locally in /content/wandb/run-20240412_020441-twr08lud" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Syncing run dainty-brook-37 to Weights & Biases (docs)
" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ " View project at https://wandb.ai/modelasg/Posterior_Decoder_Finetuning" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ " View run at https://wandb.ai/modelasg/Posterior_Decoder_Finetuning/runs/twr08lud" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "TRAINIG - batch 0, waveform torch.Size([10, 83040, 1]), step_loss_mel 0.6283440589904785, lr 1.99975e-05... \n", "TRAINIG - batch 1, waveform torch.Size([10, 135680, 1]), step_loss_mel 0.6494752764701843, lr 1.99975e-05... \n", "TRAINIG - batch 2, waveform torch.Size([10, 52036, 1]), step_loss_mel 0.6461055874824524, lr 1.99975e-05... \n", "TRAINIG - batch 3, waveform torch.Size([10, 62720, 1]), step_loss_mel 0.5772804021835327, lr 1.99975e-05... \n", "TRAINIG - batch 4, waveform torch.Size([6, 46560, 1]), step_loss_mel 0.6477117538452148, lr 1.99975e-05... \n", "VALIDATION - batch 0, waveform torch.Size([10, 67200, 1]), step_loss_mel 0.6477117538452148 ... \n", "VALIDATION - batch 1, waveform torch.Size([4, 67520, 1]), step_loss_mel 0.5747188329696655 ... \n", "TRAINIG - batch 5, waveform torch.Size([10, 138560, 1]), step_loss_mel 0.5918751358985901, lr 1.99975e-05... \n", "TRAINIG - batch 0, waveform torch.Size([10, 83040, 1]), step_loss_mel 0.5546226501464844, lr 1.99950003125e-05... \n", "TRAINIG - batch 1, waveform torch.Size([10, 135680, 1]), step_loss_mel 0.5856809020042419, lr 1.99950003125e-05... \n", "TRAINIG - batch 2, waveform torch.Size([10, 52036, 1]), step_loss_mel 0.5916953086853027, lr 1.99950003125e-05... \n", "TRAINIG - batch 3, waveform torch.Size([10, 62720, 1]), step_loss_mel 0.5707828998565674, lr 1.99950003125e-05... \n", "VALIDATION - batch 0, waveform torch.Size([10, 67200, 1]), step_loss_mel 0.5707828998565674 ... \n", "VALIDATION - batch 1, waveform torch.Size([4, 67520, 1]), step_loss_mel 0.5912371277809143 ... \n", "TRAINIG - batch 4, waveform torch.Size([6, 46560, 1]), step_loss_mel 0.5765572786331177, lr 1.99950003125e-05... \n", "TRAINIG - batch 5, waveform torch.Size([10, 138560, 1]), step_loss_mel 0.5534473061561584, lr 1.99950003125e-05... \n", "TRAINIG - batch 0, waveform torch.Size([10, 83040, 1]), step_loss_mel 0.5429945588111877, lr 1.9992500937460937e-05... \n", "TRAINIG - batch 1, waveform torch.Size([10, 135680, 1]), step_loss_mel 0.5512351989746094, lr 1.9992500937460937e-05... \n", "TRAINIG - batch 2, waveform torch.Size([10, 52036, 1]), step_loss_mel 0.5699723958969116, lr 1.9992500937460937e-05... \n", "VALIDATION - batch 0, waveform torch.Size([10, 67200, 1]), step_loss_mel 0.5699723958969116 ... \n", "VALIDATION - batch 1, waveform torch.Size([4, 67520, 1]), step_loss_mel 0.5864882469177246 ... \n", "TRAINIG - batch 3, waveform torch.Size([10, 62720, 1]), step_loss_mel 0.5551315546035767, lr 1.9992500937460937e-05... \n", "TRAINIG - batch 4, waveform torch.Size([6, 46560, 1]), step_loss_mel 0.5773655772209167, lr 1.9992500937460937e-05... \n", "TRAINIG - batch 5, waveform torch.Size([10, 138560, 1]), step_loss_mel 0.5770468711853027, lr 1.9992500937460937e-05... \n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "PosteriorDecoderModel(\n", " (posterior_encoder): VitsPosteriorEncoder(\n", " (conv_pre): Conv1d(513, 192, kernel_size=(1,), stride=(1,))\n", " (wavenet): VitsWaveNet(\n", " (in_layers): ModuleList(\n", " (0-15): 16 x ParametrizedConv1d(\n", " 192, 384, kernel_size=(5,), stride=(1,), padding=(2,)\n", " (parametrizations): ModuleDict(\n", " (weight): ParametrizationList(\n", " (0): _WeightNorm()\n", " )\n", " )\n", " )\n", " )\n", " (res_skip_layers): ModuleList(\n", " (0-14): 15 x ParametrizedConv1d(\n", " 192, 384, kernel_size=(1,), stride=(1,)\n", " (parametrizations): ModuleDict(\n", " (weight): ParametrizationList(\n", " (0): _WeightNorm()\n", " )\n", " )\n", " )\n", " (15): ParametrizedConv1d(\n", " 192, 192, kernel_size=(1,), stride=(1,)\n", " (parametrizations): ModuleDict(\n", " (weight): ParametrizationList(\n", " (0): _WeightNorm()\n", " )\n", " )\n", " )\n", " )\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (conv_proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,))\n", " )\n", " (decoder): VitsHifiGan(\n", " (conv_pre): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,))\n", " (upsampler): ModuleList(\n", " (0): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,))\n", " (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,))\n", " (2): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,))\n", " (3): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,))\n", " )\n", " (resblocks): ModuleList(\n", " (0): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n", " (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))\n", " (2): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n", " )\n", " )\n", " (1): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))\n", " (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))\n", " (2): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))\n", " )\n", " )\n", " (2): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))\n", " (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))\n", " (2): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))\n", " )\n", " )\n", " (3): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n", " (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))\n", " (2): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n", " )\n", " )\n", " (4): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))\n", " (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))\n", " (2): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))\n", " )\n", " )\n", " (5): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))\n", " (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))\n", " (2): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))\n", " )\n", " )\n", " (6): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n", " (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))\n", " (2): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n", " )\n", " )\n", " (7): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))\n", " (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))\n", " (2): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))\n", " )\n", " )\n", " (8): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))\n", " (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))\n", " (2): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))\n", " )\n", " )\n", " (9): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n", " (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))\n", " (2): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n", " )\n", " )\n", " (10): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))\n", " (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))\n", " (2): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))\n", " )\n", " )\n", " (11): HifiGanResidualBlock(\n", " (convs1): ModuleList(\n", " (0): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))\n", " (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))\n", " (2): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))\n", " )\n", " (convs2): ModuleList(\n", " (0-2): 3 x Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))\n", " )\n", " )\n", " )\n", " (conv_post): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)\n", " )\n", ")" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "markdown", "source": [ "\n", "\n", "---\n", "\n" ], "metadata": { "id": "Ei01IXslBLEQ" } }, { "cell_type": "code", "source": [ "model_new = VitsModel.from_pretrained(\"facebook/mms-tts-ara\",cache_dir=\"./\")\n", "model_new.posterior_encoder = posterior_decoder_model.posterior_encoder.to(model_new.device)\n", "model_new.decoder = posterior_decoder_model.decoder.to(model_new.device)" ], "metadata": { "id": "XfKRl6eLBKCU", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "814f01e1-06aa-411b-9d1d-1814cd7e5ef4" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Some weights of the model checkpoint at facebook/mms-tts-ara were not used when initializing VitsModel: ['flow.flows.0.wavenet.in_layers.0.weight_g', 'flow.flows.0.wavenet.in_layers.0.weight_v', 'flow.flows.0.wavenet.in_layers.1.weight_g', 'flow.flows.0.wavenet.in_layers.1.weight_v', 'flow.flows.0.wavenet.in_layers.2.weight_g', 'flow.flows.0.wavenet.in_layers.2.weight_v', 'flow.flows.0.wavenet.in_layers.3.weight_g', 'flow.flows.0.wavenet.in_layers.3.weight_v', 'flow.flows.0.wavenet.res_skip_layers.0.weight_g', 'flow.flows.0.wavenet.res_skip_layers.0.weight_v', 'flow.flows.0.wavenet.res_skip_layers.1.weight_g', 'flow.flows.0.wavenet.res_skip_layers.1.weight_v', 'flow.flows.0.wavenet.res_skip_layers.2.weight_g', 'flow.flows.0.wavenet.res_skip_layers.2.weight_v', 'flow.flows.0.wavenet.res_skip_layers.3.weight_g', 'flow.flows.0.wavenet.res_skip_layers.3.weight_v', 'flow.flows.1.wavenet.in_layers.0.weight_g', 'flow.flows.1.wavenet.in_layers.0.weight_v', 'flow.flows.1.wavenet.in_layers.1.weight_g', 'flow.flows.1.wavenet.in_layers.1.weight_v', 'flow.flows.1.wavenet.in_layers.2.weight_g', 'flow.flows.1.wavenet.in_layers.2.weight_v', 'flow.flows.1.wavenet.in_layers.3.weight_g', 'flow.flows.1.wavenet.in_layers.3.weight_v', 'flow.flows.1.wavenet.res_skip_layers.0.weight_g', 'flow.flows.1.wavenet.res_skip_layers.0.weight_v', 'flow.flows.1.wavenet.res_skip_layers.1.weight_g', 'flow.flows.1.wavenet.res_skip_layers.1.weight_v', 'flow.flows.1.wavenet.res_skip_layers.2.weight_g', 'flow.flows.1.wavenet.res_skip_layers.2.weight_v', 'flow.flows.1.wavenet.res_skip_layers.3.weight_g', 'flow.flows.1.wavenet.res_skip_layers.3.weight_v', 'flow.flows.2.wavenet.in_layers.0.weight_g', 'flow.flows.2.wavenet.in_layers.0.weight_v', 'flow.flows.2.wavenet.in_layers.1.weight_g', 'flow.flows.2.wavenet.in_layers.1.weight_v', 'flow.flows.2.wavenet.in_layers.2.weight_g', 'flow.flows.2.wavenet.in_layers.2.weight_v', 'flow.flows.2.wavenet.in_layers.3.weight_g', 'flow.flows.2.wavenet.in_layers.3.weight_v', 'flow.flows.2.wavenet.res_skip_layers.0.weight_g', 'flow.flows.2.wavenet.res_skip_layers.0.weight_v', 'flow.flows.2.wavenet.res_skip_layers.1.weight_g', 'flow.flows.2.wavenet.res_skip_layers.1.weight_v', 'flow.flows.2.wavenet.res_skip_layers.2.weight_g', 'flow.flows.2.wavenet.res_skip_layers.2.weight_v', 'flow.flows.2.wavenet.res_skip_layers.3.weight_g', 'flow.flows.2.wavenet.res_skip_layers.3.weight_v', 'flow.flows.3.wavenet.in_layers.0.weight_g', 'flow.flows.3.wavenet.in_layers.0.weight_v', 'flow.flows.3.wavenet.in_layers.1.weight_g', 'flow.flows.3.wavenet.in_layers.1.weight_v', 'flow.flows.3.wavenet.in_layers.2.weight_g', 'flow.flows.3.wavenet.in_layers.2.weight_v', 'flow.flows.3.wavenet.in_layers.3.weight_g', 'flow.flows.3.wavenet.in_layers.3.weight_v', 'flow.flows.3.wavenet.res_skip_layers.0.weight_g', 'flow.flows.3.wavenet.res_skip_layers.0.weight_v', 'flow.flows.3.wavenet.res_skip_layers.1.weight_g', 'flow.flows.3.wavenet.res_skip_layers.1.weight_v', 'flow.flows.3.wavenet.res_skip_layers.2.weight_g', 'flow.flows.3.wavenet.res_skip_layers.2.weight_v', 'flow.flows.3.wavenet.res_skip_layers.3.weight_g', 'flow.flows.3.wavenet.res_skip_layers.3.weight_v', 'posterior_encoder.wavenet.in_layers.0.weight_g', 'posterior_encoder.wavenet.in_layers.0.weight_v', 'posterior_encoder.wavenet.in_layers.1.weight_g', 'posterior_encoder.wavenet.in_layers.1.weight_v', 'posterior_encoder.wavenet.in_layers.10.weight_g', 'posterior_encoder.wavenet.in_layers.10.weight_v', 'posterior_encoder.wavenet.in_layers.11.weight_g', 'posterior_encoder.wavenet.in_layers.11.weight_v', 'posterior_encoder.wavenet.in_layers.12.weight_g', 'posterior_encoder.wavenet.in_layers.12.weight_v', 'posterior_encoder.wavenet.in_layers.13.weight_g', 'posterior_encoder.wavenet.in_layers.13.weight_v', 'posterior_encoder.wavenet.in_layers.14.weight_g', 'posterior_encoder.wavenet.in_layers.14.weight_v', 'posterior_encoder.wavenet.in_layers.15.weight_g', 'posterior_encoder.wavenet.in_layers.15.weight_v', 'posterior_encoder.wavenet.in_layers.2.weight_g', 'posterior_encoder.wavenet.in_layers.2.weight_v', 'posterior_encoder.wavenet.in_layers.3.weight_g', 'posterior_encoder.wavenet.in_layers.3.weight_v', 'posterior_encoder.wavenet.in_layers.4.weight_g', 'posterior_encoder.wavenet.in_layers.4.weight_v', 'posterior_encoder.wavenet.in_layers.5.weight_g', 'posterior_encoder.wavenet.in_layers.5.weight_v', 'posterior_encoder.wavenet.in_layers.6.weight_g', 'posterior_encoder.wavenet.in_layers.6.weight_v', 'posterior_encoder.wavenet.in_layers.7.weight_g', 'posterior_encoder.wavenet.in_layers.7.weight_v', 'posterior_encoder.wavenet.in_layers.8.weight_g', 'posterior_encoder.wavenet.in_layers.8.weight_v', 'posterior_encoder.wavenet.in_layers.9.weight_g', 'posterior_encoder.wavenet.in_layers.9.weight_v', 'posterior_encoder.wavenet.res_skip_layers.0.weight_g', 'posterior_encoder.wavenet.res_skip_layers.0.weight_v', 'posterior_encoder.wavenet.res_skip_layers.1.weight_g', 'posterior_encoder.wavenet.res_skip_layers.1.weight_v', 'posterior_encoder.wavenet.res_skip_layers.10.weight_g', 'posterior_encoder.wavenet.res_skip_layers.10.weight_v', 'posterior_encoder.wavenet.res_skip_layers.11.weight_g', 'posterior_encoder.wavenet.res_skip_layers.11.weight_v', 'posterior_encoder.wavenet.res_skip_layers.12.weight_g', 'posterior_encoder.wavenet.res_skip_layers.12.weight_v', 'posterior_encoder.wavenet.res_skip_layers.13.weight_g', 'posterior_encoder.wavenet.res_skip_layers.13.weight_v', 'posterior_encoder.wavenet.res_skip_layers.14.weight_g', 'posterior_encoder.wavenet.res_skip_layers.14.weight_v', 'posterior_encoder.wavenet.res_skip_layers.15.weight_g', 'posterior_encoder.wavenet.res_skip_layers.15.weight_v', 'posterior_encoder.wavenet.res_skip_layers.2.weight_g', 'posterior_encoder.wavenet.res_skip_layers.2.weight_v', 'posterior_encoder.wavenet.res_skip_layers.3.weight_g', 'posterior_encoder.wavenet.res_skip_layers.3.weight_v', 'posterior_encoder.wavenet.res_skip_layers.4.weight_g', 'posterior_encoder.wavenet.res_skip_layers.4.weight_v', 'posterior_encoder.wavenet.res_skip_layers.5.weight_g', 'posterior_encoder.wavenet.res_skip_layers.5.weight_v', 'posterior_encoder.wavenet.res_skip_layers.6.weight_g', 'posterior_encoder.wavenet.res_skip_layers.6.weight_v', 'posterior_encoder.wavenet.res_skip_layers.7.weight_g', 'posterior_encoder.wavenet.res_skip_layers.7.weight_v', 'posterior_encoder.wavenet.res_skip_layers.8.weight_g', 'posterior_encoder.wavenet.res_skip_layers.8.weight_v', 'posterior_encoder.wavenet.res_skip_layers.9.weight_g', 'posterior_encoder.wavenet.res_skip_layers.9.weight_v']\n", "- This IS expected if you are initializing VitsModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing VitsModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of VitsModel were not initialized from the model checkpoint at facebook/mms-tts-ara and are newly initialized: ['discriminator.discriminators.0.convs.0.bias', 'discriminator.discriminators.0.convs.0.weight', 'discriminator.discriminators.0.convs.1.bias', 'discriminator.discriminators.0.convs.1.weight', 'discriminator.discriminators.0.convs.2.bias', 'discriminator.discriminators.0.convs.2.weight', 'discriminator.discriminators.0.convs.3.bias', 'discriminator.discriminators.0.convs.3.weight', 'discriminator.discriminators.0.convs.4.bias', 'discriminator.discriminators.0.convs.4.weight', 'discriminator.discriminators.0.convs.5.bias', 'discriminator.discriminators.0.convs.5.weight', 'discriminator.discriminators.0.final_conv.bias', 'discriminator.discriminators.0.final_conv.weight', 'discriminator.discriminators.1.convs.0.bias', 'discriminator.discriminators.1.convs.0.weight', 'discriminator.discriminators.1.convs.1.bias', 'discriminator.discriminators.1.convs.1.weight', 'discriminator.discriminators.1.convs.2.bias', 'discriminator.discriminators.1.convs.2.weight', 'discriminator.discriminators.1.convs.3.bias', 'discriminator.discriminators.1.convs.3.weight', 'discriminator.discriminators.1.convs.4.bias', 'discriminator.discriminators.1.convs.4.weight', 'discriminator.discriminators.1.final_conv.bias', 'discriminator.discriminators.1.final_conv.weight', 'discriminator.discriminators.2.convs.0.bias', 'discriminator.discriminators.2.convs.0.weight', 'discriminator.discriminators.2.convs.1.bias', 'discriminator.discriminators.2.convs.1.weight', 'discriminator.discriminators.2.convs.2.bias', 'discriminator.discriminators.2.convs.2.weight', 'discriminator.discriminators.2.convs.3.bias', 'discriminator.discriminators.2.convs.3.weight', 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'posterior_encoder.wavenet.res_skip_layers.3.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.4.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.4.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.5.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.5.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.6.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.6.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.7.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.7.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.8.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.8.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.9.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.9.parametrizations.weight.original1']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ] }, { "cell_type": "code", "source": [ "from VitsModelSplit.dataset_features_collector import FeaturesCollectionDataset\n", "\n", "dataset_dir = '/content/drive/MyDrive/FeaturesCollectionDataset'\n", "full_generation_sample_index = 0\n", "full_generation_dataset = FeaturesCollectionDataset(dataset_dir = os.path.join(dataset_dir,'full_generation'),\n", " device = model_new.device\n", " )\n", "full_generation_sample = full_generation_dataset[full_generation_sample_index]\n", "\n", "set_seed(42)\n", "with torch.no_grad():\n", " full_generation =model_new(\n", " input_ids =full_generation_sample[\"input_ids\"],\n", " attention_mask=full_generation_sample[\"attention_mask\"],\n", " speaker_id=full_generation_sample[\"speaker_id\"]\n", " )\n", " full_generation_waveform = full_generation.waveform.cpu().numpy().reshape(-1)\n", "\n", "\n", "Audio(full_generation_waveform, rate=model_new.config.sampling_rate)" ], "metadata": { "id": "I9YkPdvXCOnr", "colab": { "base_uri": "https://localhost:8080/", "height": 76 }, "outputId": "469f1ec9-4159-44ad-b13e-98ce019d41b4" }, "execution_count": 12, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {}, "execution_count": 12 } ] } ] }