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MIDI-Audio-Extension
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Steven Zhang commited on
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1 Parent(s): b13c70a

plz work

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  1. .idea/MIDI-Audio-Extension.iml +12 -0
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  8. Poem Generation/.DS_Store +0 -0
  9. Poem Generation/UI_poem_gen.ipynb +0 -1853
  10. Poem Generation/model/config.json +0 -45
  11. Sentiment Analysis/.DS_Store +0 -0
  12. Sentiment Analysis/Dataset_Generation.ipynb +0 -1259
  13. Sentiment Analysis/GUI_SST2.ipynb +0 -1829
  14. Sentiment Analysis/trained_models/.DS_Store +0 -0
  15. Sentiment Analysis/trained_models/bert/config.json +0 -23
  16. app.py +123 -0
  17. music_gen/.DS_Store +0 -0
  18. music_gen/UI_midi_file.ipynb +0 -1
  19. music_gen/mid_file/Comic_Relief.mid +0 -0
  20. music_gen/mid_file/midi_example_ode_to_joy.mid +0 -0
  21. packages.txt +1 -0
  22. requirements.txt +4 -0
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- "execution_count": null,
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- "outputs": [
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- "Mounted at /content/drive\n"
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- "base_uri": "https://localhost:8080/"
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- "id": "AVFoNjUDIxQo",
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- "outputs": [
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- "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
1429
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1529
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1530
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- " Stored in directory: /root/.cache/pip/wheels/13/e4/6c/e8059816e86796a597c6e6b0d4c880630f51a1fcfa0befd5e6\n",
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1536
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1537
- "Installing collected packages: sniffio, mdurl, uc-micro-py, rfc3986, markdown-it-py, h11, anyio, starlette, pynacl, mdit-py-plugins, linkify-it-py, httpcore, cryptography, bcrypt, websockets, uvicorn, python-multipart, pydub, pycryptodome, paramiko, orjson, httpx, ffmpy, fastapi, gradio\n",
1538
- "Successfully installed anyio-3.6.2 bcrypt-4.0.1 cryptography-38.0.3 fastapi-0.86.0 ffmpy-0.3.0 gradio-3.9 h11-0.12.0 httpcore-0.15.0 httpx-0.23.0 linkify-it-py-1.0.3 markdown-it-py-2.1.0 mdit-py-plugins-0.3.1 mdurl-0.1.2 orjson-3.8.1 paramiko-2.12.0 pycryptodome-3.15.0 pydub-0.25.1 pynacl-1.5.0 python-multipart-0.0.5 rfc3986-1.5.0 sniffio-1.3.0 starlette-0.20.4 uc-micro-py-1.0.1 uvicorn-0.19.0 websockets-10.4\n",
1539
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1553
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1554
- " Downloading tokenizers-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
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1564
- "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
1565
- "Successfully installed huggingface-hub-0.10.1 tokenizers-0.13.2 transformers-4.24.0\n"
1566
- ]
1567
- }
1568
- ],
1569
- "source": [
1570
- "!pip install gradio\n",
1571
- "!pip install transformers"
1572
- ]
1573
- },
1574
- {
1575
- "cell_type": "code",
1576
- "source": [
1577
- "import gradio as gr\n",
1578
- "\n",
1579
- "from transformers import AutoTokenizer\n",
1580
- "from transformers import TFAutoModelForCausalLM"
1581
- ],
1582
- "metadata": {
1583
- "id": "1yVHOnYwKZwJ"
1584
- },
1585
- "execution_count": null,
1586
- "outputs": []
1587
- },
1588
- {
1589
- "cell_type": "code",
1590
- "source": [
1591
- "#load model\n",
1592
- "model = TFAutoModelForCausalLM.from_pretrained('drive/MyDrive/FIRE_3rd Sem/peom_gn/')\n",
1593
- "base_model = \"distilgpt2\"\n",
1594
- "tokenizer = AutoTokenizer.from_pretrained(base_model)"
1595
- ],
1596
- "metadata": {
1597
- "colab": {
1598
- "base_uri": "https://localhost:8080/",
1599
- "height": 236,
1600
- "referenced_widgets": [
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- "4b42f65e2e0945fd8f51d39200f29fa8",
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- "246e208413d143949a101914a6216ea8",
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- "0c75250aaa454c638fc8bed7365ce415",
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- "8965848a3d594be188f056ee3644ecb5",
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- "0395259b5c674039b9c51500e33c37e9",
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- "1b65db5804f64ceaaac4cac9c526cd3f",
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- "812aa6f521ce4b798c08bffab3329b67",
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- "4794a81d2ef44fbeb1e88998085a5f43",
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- "6b7df437d08d4c01be16b79e41019e3b",
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- "8c427e0fc2d34a24867fd5d711b080e2",
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- "621717f94333429ab4e47224550771fe",
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- "e58ef4fb5651473c9cf9b889624ccd55",
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- "473c51fb7519486bbe3050c77ab7080c",
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- "e04f3eb8192c4da28816aabe24e7c126",
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1631
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- "153545ef1fc84847bf0d437fc37c0afd",
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- "c761c42270c14cca8818e9e8e5fefaf7",
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- "top": null,
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- "visibility": null,
683
- "width": null
684
- }
685
- },
686
- "5162637d8a9d4fc0824c3cd6dd19cf83": {
687
- "model_module": "@jupyter-widgets/controls",
688
- "model_name": "DescriptionStyleModel",
689
- "model_module_version": "1.5.0",
690
- "state": {
691
- "_model_module": "@jupyter-widgets/controls",
692
- "_model_module_version": "1.5.0",
693
- "_model_name": "DescriptionStyleModel",
694
- "_view_count": null,
695
- "_view_module": "@jupyter-widgets/base",
696
- "_view_module_version": "1.2.0",
697
- "_view_name": "StyleView",
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- "description_width": ""
699
- }
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- }
701
- }
702
- }
703
- },
704
- "cells": [
705
- {
706
- "cell_type": "markdown",
707
- "source": [
708
- "I am planning to work on **SST-2 (Stanford Sentiment Treebank)** dataset. <br>\n",
709
- "https://nlp.stanford.edu/sentiment/index.html <br>\n",
710
- "https://paperswithcode.com/dataset/sst <br>\n",
711
- "https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary"
712
- ],
713
- "metadata": {
714
- "id": "dZXodUszVbwl"
715
- }
716
- },
717
- {
718
- "cell_type": "markdown",
719
- "source": [
720
- "In this dataset each phrase is labelled as either negative or positive. There is a SST-5 dataset as well in which each phrase is labelled as negative, somewhat negative, neutral, somewhat positive or positive. "
721
- ],
722
- "metadata": {
723
- "id": "2XpoSGi03q3v"
724
- }
725
- },
726
- {
727
- "cell_type": "code",
728
- "source": [
729
- "!pip install datasets\n",
730
- "!pip install transformers"
731
- ],
732
- "metadata": {
733
- "colab": {
734
- "base_uri": "https://localhost:8080/"
735
- },
736
- "id": "99CDEisB8zlD",
737
- "outputId": "139321a9-de14-4002-9463-700783940f6c"
738
- },
739
- "execution_count": null,
740
- "outputs": [
741
- {
742
- "output_type": "stream",
743
- "name": "stdout",
744
- "text": [
745
- "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
746
- "Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (2.5.2)\n",
747
- "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (4.64.1)\n",
748
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749
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750
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751
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752
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753
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754
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755
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756
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757
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758
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759
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760
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761
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762
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763
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765
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768
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769
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770
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771
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772
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773
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774
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775
- "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (3.0.4)\n",
776
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777
- "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.8.1)\n",
778
- "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.4)\n",
779
- "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n",
780
- "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
781
- "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
782
- "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.22.2)\n",
783
- "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.12.1)\n",
784
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785
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786
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787
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788
- "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (5.0.0)\n",
789
- "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
790
- "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
791
- "Requirement already satisfied: huggingface-hub<1.0,>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.0)\n",
792
- "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n",
793
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.9.0->transformers) (4.1.1)\n",
794
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795
- "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.1)\n",
796
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797
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798
- "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.25.11)\n",
799
- "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n"
800
- ]
801
- }
802
- ]
803
- },
804
- {
805
- "cell_type": "code",
806
- "source": [
807
- "import pandas as pd\n",
808
- "from datasets import load_dataset\n",
809
- "from transformers import DistilBertTokenizerFast, TFAutoModelForSequenceClassification"
810
- ],
811
- "metadata": {
812
- "id": "9Y_5TS7i8bdN"
813
- },
814
- "execution_count": null,
815
- "outputs": []
816
- },
817
- {
818
- "cell_type": "markdown",
819
- "source": [
820
- "There are many ways to load the dataset, for example using tensorflow_datasets (https://www.tensorflow.org/datasets/api_docs/python/tfds/load), but I am planning to use datasets package"
821
- ],
822
- "metadata": {
823
- "id": "mVILRmQeU7zH"
824
- }
825
- },
826
- {
827
- "cell_type": "code",
828
- "source": [
829
- "data_sst2 = load_dataset(\"glue\", \"sst2\")"
830
- ],
831
- "metadata": {
832
- "colab": {
833
- "base_uri": "https://localhost:8080/",
834
- "height": 87,
835
- "referenced_widgets": [
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- "b470b1d8fb274a6fb933ecab0c18fa12",
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- "3fce668226424a70bd0afcc725d68739",
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- ]
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- },
852
- "execution_count": null,
853
- "outputs": [
854
- {
855
- "output_type": "stream",
856
- "name": "stderr",
857
- "text": [
858
- "WARNING:datasets.builder:Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
859
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860
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861
- {
862
- "output_type": "display_data",
863
- "data": {
864
- "text/plain": [
865
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866
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867
- "application/vnd.jupyter.widget-view+json": {
868
- "version_major": 2,
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870
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871
- }
872
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873
- "metadata": {}
874
- }
875
- ]
876
- },
877
- {
878
- "cell_type": "code",
879
- "source": [
880
- "data_sst2"
881
- ],
882
- "metadata": {
883
- "colab": {
884
- "base_uri": "https://localhost:8080/"
885
- },
886
- "id": "opKFVLJeJqlM",
887
- "outputId": "85fa6285-d54a-4f66-a265-f70e41d95117"
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- },
889
- "execution_count": null,
890
- "outputs": [
891
- {
892
- "output_type": "execute_result",
893
- "data": {
894
- "text/plain": [
895
- "DatasetDict({\n",
896
- " train: Dataset({\n",
897
- " features: ['sentence', 'label', 'idx'],\n",
898
- " num_rows: 67349\n",
899
- " })\n",
900
- " validation: Dataset({\n",
901
- " features: ['sentence', 'label', 'idx'],\n",
902
- " num_rows: 872\n",
903
- " })\n",
904
- " test: Dataset({\n",
905
- " features: ['sentence', 'label', 'idx'],\n",
906
- " num_rows: 1821\n",
907
- " })\n",
908
- "})"
909
- ]
910
- },
911
- "metadata": {},
912
- "execution_count": 19
913
- }
914
- ]
915
- },
916
- {
917
- "cell_type": "code",
918
- "source": [
919
- "data_sst2['train'][0]"
920
- ],
921
- "metadata": {
922
- "colab": {
923
- "base_uri": "https://localhost:8080/"
924
- },
925
- "id": "wgODW_Y9JusF",
926
- "outputId": "fc9e22a0-0a10-468b-a33e-a90629669723"
927
- },
928
- "execution_count": null,
929
- "outputs": [
930
- {
931
- "output_type": "execute_result",
932
- "data": {
933
- "text/plain": [
934
- "{'sentence': 'hide new secretions from the parental units ',\n",
935
- " 'label': 0,\n",
936
- " 'idx': 0}"
937
- ]
938
- },
939
- "metadata": {},
940
- "execution_count": 20
941
- }
942
- ]
943
- },
944
- {
945
- "cell_type": "code",
946
- "source": [
947
- "data_sst2['train'].features"
948
- ],
949
- "metadata": {
950
- "colab": {
951
- "base_uri": "https://localhost:8080/"
952
- },
953
- "id": "oA0i0XHoSYlN",
954
- "outputId": "4fa68594-f67c-43ee-dbdc-35de92056176"
955
- },
956
- "execution_count": null,
957
- "outputs": [
958
- {
959
- "output_type": "execute_result",
960
- "data": {
961
- "text/plain": [
962
- "{'sentence': Value(dtype='string', id=None),\n",
963
- " 'label': ClassLabel(num_classes=2, names=['negative', 'positive'], id=None),\n",
964
- " 'idx': Value(dtype='int32', id=None)}"
965
- ]
966
- },
967
- "metadata": {},
968
- "execution_count": 21
969
- }
970
- ]
971
- },
972
- {
973
- "cell_type": "markdown",
974
- "source": [
975
- "Refrence: https://huggingface.co/docs/transformers/index, \n",
976
- "https://github.com/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb\n",
977
- "\n",
978
- "we need to preprocess our text. Tokenization and preprocessing is generally based on the model architecture you use. \n",
979
- "\n",
980
- "Let's use pretrained distilbert. We can use huggingface transformer library. We can also train our tokenizer from scratch.\n",
981
- "\n",
982
- "we can use AutoTokenizer.from_pretrained as well instead of below function"
983
- ],
984
- "metadata": {
985
- "id": "74nHWqmVMdrj"
986
- }
987
- },
988
- {
989
- "cell_type": "code",
990
- "source": [
991
- "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')"
992
- ],
993
- "metadata": {
994
- "id": "nAIQlwoaLaVn"
995
- },
996
- "execution_count": null,
997
- "outputs": []
998
- },
999
- {
1000
- "cell_type": "code",
1001
- "source": [
1002
- "def preprocess(data):\n",
1003
- " return tokenizer(data['sentence'], truncation=True)"
1004
- ],
1005
- "metadata": {
1006
- "id": "8yFKkFimRe92"
1007
- },
1008
- "execution_count": null,
1009
- "outputs": []
1010
- },
1011
- {
1012
- "cell_type": "markdown",
1013
- "source": [
1014
- "we can use the map method of our dataset object to apply above function on all datapoints of all splits.\n",
1015
- "\n",
1016
- "Note that we passed batched=True to encode the texts by batches together. This is to leverage the full benefit of the fast tokenizer we loaded earlier, which will use multi-threading to treat the texts in a batch concurrently."
1017
- ],
1018
- "metadata": {
1019
- "id": "xCorwRwbS_y_"
1020
- }
1021
- },
1022
- {
1023
- "cell_type": "code",
1024
- "source": [
1025
- "dataset_enc = data_sst2.map(preprocess, batched=True)"
1026
- ],
1027
- "metadata": {
1028
- "colab": {
1029
- "base_uri": "https://localhost:8080/",
1030
- "height": 106,
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- "referenced_widgets": [
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- "id": "CniAzqtmRvV6",
1046
- "outputId": "22a98839-c74e-4697-f87a-0221b976b807"
1047
- },
1048
- "execution_count": null,
1049
- "outputs": [
1050
- {
1051
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1052
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1053
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1056
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1057
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1058
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1060
- }
1061
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1062
- "metadata": {}
1063
- },
1064
- {
1065
- "output_type": "stream",
1066
- "name": "stderr",
1067
- "text": [
1068
- "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-7ab0de81d13d7bdc.arrow\n",
1069
- "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-b7cf8f8990ed6f2f.arrow\n"
1070
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1073
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1074
- {
1075
- "cell_type": "code",
1076
- "source": [
1077
- "dataset_enc[\"train\"].features"
1078
- ],
1079
- "metadata": {
1080
- "colab": {
1081
- "base_uri": "https://localhost:8080/"
1082
- },
1083
- "id": "9ARVOUICRNh2",
1084
- "outputId": "b14ae1a4-7821-4f89-f429-d19aa6e30b08"
1085
- },
1086
- "execution_count": null,
1087
- "outputs": [
1088
- {
1089
- "output_type": "execute_result",
1090
- "data": {
1091
- "text/plain": [
1092
- "{'sentence': Value(dtype='string', id=None),\n",
1093
- " 'label': ClassLabel(num_classes=2, names=['negative', 'positive'], id=None),\n",
1094
- " 'idx': Value(dtype='int32', id=None),\n",
1095
- " 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None),\n",
1096
- " 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)}"
1097
- ]
1098
- },
1099
- "metadata": {},
1100
- "execution_count": 26
1101
- }
1102
- ]
1103
- },
1104
- {
1105
- "cell_type": "code",
1106
- "source": [
1107
- "dataset_enc[\"train\"][0]"
1108
- ],
1109
- "metadata": {
1110
- "colab": {
1111
- "base_uri": "https://localhost:8080/"
1112
- },
1113
- "id": "nAb1QG1QgD7j",
1114
- "outputId": "e7811016-8db1-44a9-f446-3eb49fbbc3c2"
1115
- },
1116
- "execution_count": null,
1117
- "outputs": [
1118
- {
1119
- "output_type": "execute_result",
1120
- "data": {
1121
- "text/plain": [
1122
- "{'sentence': 'hide new secretions from the parental units ',\n",
1123
- " 'label': 0,\n",
1124
- " 'idx': 0,\n",
1125
- " 'input_ids': [101, 5342, 2047, 3595, 8496, 2013, 1996, 18643, 3197, 102],\n",
1126
- " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
1127
- ]
1128
- },
1129
- "metadata": {},
1130
- "execution_count": 27
1131
- }
1132
- ]
1133
- },
1134
- {
1135
- "cell_type": "code",
1136
- "source": [
1137
- "dataset_enc[\"train\"].features[\"label\"]"
1138
- ],
1139
- "metadata": {
1140
- "colab": {
1141
- "base_uri": "https://localhost:8080/"
1142
- },
1143
- "id": "bkSac7FpTWZb",
1144
- "outputId": "905d1a9f-3eec-4940-8702-f9578a6b3256"
1145
- },
1146
- "execution_count": null,
1147
- "outputs": [
1148
- {
1149
- "output_type": "execute_result",
1150
- "data": {
1151
- "text/plain": [
1152
- "ClassLabel(num_classes=2, names=['negative', 'positive'], id=None)"
1153
- ]
1154
- },
1155
- "metadata": {},
1156
- "execution_count": 28
1157
- }
1158
- ]
1159
- },
1160
- {
1161
- "cell_type": "markdown",
1162
- "source": [
1163
- "Convert datasets to tf.data.Dataset, so that Keras can understand it."
1164
- ],
1165
- "metadata": {
1166
- "id": "1gcN67-XObot"
1167
- }
1168
- },
1169
- {
1170
- "cell_type": "code",
1171
- "source": [
1172
- "model = TFAutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=2)"
1173
- ],
1174
- "metadata": {
1175
- "colab": {
1176
- "base_uri": "https://localhost:8080/"
1177
- },
1178
- "id": "3NAAESdaUBNX",
1179
- "outputId": "27be4f23-01a2-423b-a79a-5c7fb7882278"
1180
- },
1181
- "execution_count": null,
1182
- "outputs": [
1183
- {
1184
- "output_type": "stream",
1185
- "name": "stderr",
1186
- "text": [
1187
- "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['vocab_transform', 'vocab_projector', 'vocab_layer_norm', 'activation_13']\n",
1188
- "- This IS expected if you are initializing TFDistilBertForSequenceClassification 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",
1189
- "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
1190
- "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier', 'dropout_39', 'pre_classifier']\n",
1191
- "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
1192
- ]
1193
- }
1194
- ]
1195
- },
1196
- {
1197
- "cell_type": "code",
1198
- "source": [
1199
- "tf_train_dataset = model.prepare_tf_dataset(\n",
1200
- " dataset_enc[\"train\"],\n",
1201
- " shuffle=True,\n",
1202
- " batch_size=64,\n",
1203
- " tokenizer=tokenizer\n",
1204
- ")\n",
1205
- "\n",
1206
- "tf_validation_dataset = model.prepare_tf_dataset(\n",
1207
- " dataset_enc[\"validation\"],\n",
1208
- " shuffle=False,\n",
1209
- " batch_size=64,\n",
1210
- " tokenizer=tokenizer,\n",
1211
- ")\n",
1212
- "\n",
1213
- "tf_validation_test = model.prepare_tf_dataset(\n",
1214
- " dataset_enc[\"test\"],\n",
1215
- " shuffle=False,\n",
1216
- " batch_size=64,\n",
1217
- " tokenizer=tokenizer,\n",
1218
- ")"
1219
- ],
1220
- "metadata": {
1221
- "colab": {
1222
- "base_uri": "https://localhost:8080/"
1223
- },
1224
- "id": "9oW2wFSUOAaF",
1225
- "outputId": "6dedf3f0-5838-4968-a6a6-c6bb25a62a9d"
1226
- },
1227
- "execution_count": null,
1228
- "outputs": [
1229
- {
1230
- "output_type": "stream",
1231
- "name": "stderr",
1232
- "text": [
1233
- "You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
1234
- ]
1235
- }
1236
- ]
1237
- },
1238
- {
1239
- "cell_type": "code",
1240
- "source": [
1241
- "# WE can use tf_train_dataset and tf_validation_dataset in model.fit"
1242
- ],
1243
- "metadata": {
1244
- "id": "VTQQqzGgUp7R"
1245
- },
1246
- "execution_count": null,
1247
- "outputs": []
1248
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- {
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- "cell_type": "code",
1251
- "source": [],
1252
- "metadata": {
1253
- "id": "AHKEdT-KU1yt"
1254
- },
1255
- "execution_count": null,
1256
- "outputs": []
1257
- }
1258
- ]
1259
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- "_model_module": "@jupyter-widgets/base",
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- "_model_module_version": "1.2.0",
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- },
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- "10926435277a4040bdbea6fe457968dc": {
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- "model_module": "@jupyter-widgets/controls",
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- "model_name": "DescriptionStyleModel",
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- "model_module_version": "1.5.0",
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- "state": {
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- "_model_module": "@jupyter-widgets/controls",
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- "_model_module_version": "1.5.0",
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- "_model_name": "DescriptionStyleModel",
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- "_view_count": null,
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- "_view_module": "@jupyter-widgets/base",
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- "_view_module_version": "1.2.0",
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- "_view_name": "StyleView",
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- "description_width": ""
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- }
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- }
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- }
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- }
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- },
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- "id": "Ov0RXE2Zs2at",
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- "outputId": "51eaf6d5-b1db-4f20-a9c9-07adb560da46"
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- },
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- "outputs": [
1400
- {
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- "output_type": "stream",
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- "name": "stdout",
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- "text": [
1404
- "Mounted at /content/drive\n"
1405
- ]
1406
- }
1407
- ],
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- "source": [
1409
- "from google.colab import drive\n",
1410
- "drive.mount('/content/drive')"
1411
- ]
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- },
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- {
1414
- "cell_type": "code",
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- "source": [
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- "!pip install gradio\n",
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- "!pip install transformers"
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- ],
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- "metadata": {
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- },
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- "id": "pQAe4Y4Rs_20",
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- "outputId": "b5ff1523-a3e8-4ab7-b6cc-f60501270564"
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- "outputs": [
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- {
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- "output_type": "stream",
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- "text": [
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- "Building wheels for collected packages: ffmpy, python-multipart\n",
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- " Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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- " Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4712 sha256=fb67edb59b803dc601cf1cfe223761b36790f09ae36d4a2d8f2b387bab48cf92\n",
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- " Stored in directory: /root/.cache/pip/wheels/13/e4/6c/e8059816e86796a597c6e6b0d4c880630f51a1fcfa0befd5e6\n",
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- " Stored in directory: /root/.cache/pip/wheels/2c/41/7c/bfd1c180534ffdcc0972f78c5758f89881602175d48a8bcd2c\n",
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- "Successfully built ffmpy python-multipart\n",
1541
- "Installing collected packages: sniffio, mdurl, uc-micro-py, rfc3986, markdown-it-py, h11, anyio, starlette, pynacl, mdit-py-plugins, linkify-it-py, httpcore, cryptography, bcrypt, websockets, uvicorn, python-multipart, pydub, pycryptodome, paramiko, orjson, httpx, ffmpy, fastapi, gradio\n",
1542
- "Successfully installed anyio-3.6.2 bcrypt-4.0.1 cryptography-38.0.3 fastapi-0.86.0 ffmpy-0.3.0 gradio-3.9 h11-0.12.0 httpcore-0.15.0 httpx-0.23.0 linkify-it-py-1.0.3 markdown-it-py-2.1.0 mdit-py-plugins-0.3.1 mdurl-0.1.2 orjson-3.8.1 paramiko-2.12.0 pycryptodome-3.15.0 pydub-0.25.1 pynacl-1.5.0 python-multipart-0.0.5 rfc3986-1.5.0 sniffio-1.3.0 starlette-0.20.4 uc-micro-py-1.0.1 uvicorn-0.19.0 websockets-10.4\n",
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- "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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1568
- "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
1569
- "Successfully installed huggingface-hub-0.10.1 tokenizers-0.13.2 transformers-4.24.0\n"
1570
- ]
1571
- }
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- ]
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- },
1574
- {
1575
- "cell_type": "code",
1576
- "source": [
1577
- "import gradio as gr\n",
1578
- "\n",
1579
- "from transformers import TFAutoModelForSequenceClassification, AutoTokenizer\n",
1580
- "import numpy as np"
1581
- ],
1582
- "metadata": {
1583
- "id": "HMJoAd5MtFTR"
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- },
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- "execution_count": null,
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- "outputs": []
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- },
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- {
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- "cell_type": "code",
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- "source": [
1591
- "# load model\n",
1592
- "model_v1 = TFAutoModelForSequenceClassification.from_pretrained('drive/MyDrive/FIRE/OOD/train_on_sst2/bert')\n",
1593
- "base_model = \"distilbert-base-uncased\"\n",
1594
- "tokenizer_v1 = AutoTokenizer.from_pretrained(base_model)"
1595
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1653
- "output_type": "stream",
1654
- "name": "stderr",
1655
- "text": [
1656
- "All model checkpoint layers were used when initializing TFDistilBertForSequenceClassification.\n",
1657
- "\n",
1658
- "All the layers of TFDistilBertForSequenceClassification were initialized from the model checkpoint at drive/MyDrive/FIRE/OOD/train_on_sst2/bert.\n",
1659
- "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertForSequenceClassification for predictions without further training.\n"
1660
- ]
1661
- },
1662
- {
1663
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1664
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- ]
1719
- },
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- {
1721
- "cell_type": "code",
1722
- "source": [
1723
- "def model_inference(sen):\n",
1724
- " tokenized_v1 = tokenizer_v1([sen], return_tensors=\"np\", padding=\"longest\")\n",
1725
- " outputs_v1 = model_v1(tokenized_v1).logits\n",
1726
- " classifications_v1 = np.argmax(outputs_v1, axis=1)\n",
1727
- " if classifications_v1[0] == 1:\n",
1728
- " res = \"Positive :)\"\n",
1729
- " else:\n",
1730
- " res = \"Negative :(\"\n",
1731
- " return res"
1732
- ],
1733
- "metadata": {
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- "id": "j8d9tW3StPmz"
1735
- },
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- "execution_count": null,
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- {
1740
- "cell_type": "code",
1741
- "source": [
1742
- "demo = gr.Interface(\n",
1743
- " fn=model_inference,\n",
1744
- " inputs=gr.Textbox(lines=2, placeholder=\"Enter a Sentence\"),\n",
1745
- " outputs=\"text\",\n",
1746
- ")\n",
1747
- "demo.launch()\n"
1748
- ],
1749
- "metadata": {
1750
- "colab": {
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- "base_uri": "https://localhost:8080/",
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- "height": 671
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- },
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- "outputs": [
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- {
1760
- "output_type": "stream",
1761
- "name": "stdout",
1762
- "text": [
1763
- "Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`\n",
1764
- "\n",
1765
- "Using Embedded Colab Mode (NEW). If you have issues, please use share=True and file an issue at https://github.com/gradio-app/gradio/\n",
1766
- "Note: opening the browser inspector may crash Embedded Colab Mode.\n",
1767
- "\n",
1768
- "To create a public link, set `share=True` in `launch()`.\n"
1769
- ]
1770
- },
1771
- {
1772
- "output_type": "display_data",
1773
- "data": {
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1779
- " if (!google.colab.kernel.accessAllowed && !cache) {\n",
1780
- " return;\n",
1781
- " }\n",
1782
- " element.appendChild(document.createTextNode(''));\n",
1783
- " const url = await google.colab.kernel.proxyPort(port, {cache});\n",
1784
- "\n",
1785
- " const external_link = document.createElement('div');\n",
1786
- " external_link.innerHTML = `\n",
1787
- " <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
1788
- " Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
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- " https://localhost:${port}${path}\n",
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- " </a>\n",
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- " `;\n",
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- " element.appendChild(external_link);\n",
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- "\n",
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- " const iframe = document.createElement('iframe');\n",
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- " iframe.src = new URL(path, url).toString();\n",
1797
- " iframe.height = height;\n",
1798
- " iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
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- " iframe.width = width;\n",
1800
- " iframe.style.border = 0;\n",
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- ]
1829
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Sentiment Analysis/trained_models/.DS_Store DELETED
Binary file (6.15 kB)
 
Sentiment Analysis/trained_models/bert/config.json DELETED
@@ -1,23 +0,0 @@
1
- {
2
- "_name_or_path": "distilbert-base-uncased",
3
- "activation": "gelu",
4
- "architectures": [
5
- "DistilBertForSequenceClassification"
6
- ],
7
- "attention_dropout": 0.1,
8
- "dim": 768,
9
- "dropout": 0.1,
10
- "hidden_dim": 3072,
11
- "initializer_range": 0.02,
12
- "max_position_embeddings": 512,
13
- "model_type": "distilbert",
14
- "n_heads": 12,
15
- "n_layers": 6,
16
- "pad_token_id": 0,
17
- "qa_dropout": 0.1,
18
- "seq_classif_dropout": 0.2,
19
- "sinusoidal_pos_embds": false,
20
- "tie_weights_": true,
21
- "transformers_version": "4.24.0",
22
- "vocab_size": 30522
23
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
app.py ADDED
@@ -0,0 +1,123 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+
3
+ """## Imports"""
4
+ import tensorflow as tf
5
+ config = tf.compat.v1.ConfigProto()
6
+ config.gpu_options.allow_growth = True
7
+ session = tf.compat.v1.Session(config=config)
8
+ import gradio as gr
9
+ from pathlib import Path
10
+ import subprocess
11
+ from transformers import AutoTokenizer
12
+ from transformers import TFAutoModelForCausalLM
13
+ from transformers import TFAutoModelForSequenceClassification, AutoTokenizer
14
+ import numpy as np
15
+ from musicautobot.music_transformer.transform import idxenc2stream, midi2idxenc
16
+ from musicautobot.vocab import MusicVocab
17
+ import keras
18
+
19
+ """## Model Loads"""
20
+
21
+ #load poem generation model
22
+ model_poem_gn = TFAutoModelForCausalLM.from_pretrained('merged-ui/models-misc/peom_gn')
23
+ base_model_poem_gn = "distilgpt2"
24
+ tokenizer_poem_gn = AutoTokenizer.from_pretrained(base_model_poem_gn)
25
+
26
+ #load sentiment analysis
27
+ model_sa = TFAutoModelForSequenceClassification.from_pretrained('merged-ui/models-misc/sen_analysis/bert')
28
+ base_model_sa = "distilbert-base-uncased"
29
+ tokenizer_sa = AutoTokenizer.from_pretrained(base_model_sa)
30
+
31
+ #music generation
32
+ """
33
+ base_path = "/content/drive/MyDrive/FIRE_3rd Sem/music_gn/"
34
+ #path_mid_file -> Replace this with model generated file path
35
+ path_mid_file = base_path + "Comic_Relief.mid"
36
+ path_wav_file = base_path + "output_comic.wav"
37
+ subprocess.call(['timidity', path_mid_file, "-Ow", "-o", path_wav_file])"""
38
+ music_gen_base_path = "merged-ui/music_gen/"
39
+ model_music_gen = keras.models.load_model("transformer-final")
40
+
41
+ """## Music Generation"""
42
+
43
+ def predict_music(model, input_vector, num):
44
+ normalized = input_vector / 311
45
+ for i in range(num):
46
+ predict = model.predict(np.reshape(normalized[-100:], (1,100)), verbose = 0)
47
+ normalized = np.append(normalized, predict)
48
+
49
+ result = np.rint(normalized * 311)
50
+ # edits to prediction
51
+ for i in range(100, len(result)):
52
+ if i % 2 == 0:
53
+ if abs(result[i] - 8) < 5 and result[i] != 8:
54
+ result[i] = 8
55
+ else:
56
+ if result[i] < 137:
57
+ result[i] = 137
58
+ return result
59
+
60
+ # this function takes a 100 length encoded song beginning as an input and
61
+ def midi_predict(model, test, num_notes):
62
+ test_midi = idxenc2stream(test.astype("int"), MusicVocab.create())
63
+ test_midi.write('midi',music_gen_base_path+"input_demo.mid")
64
+
65
+ res = predict_music(model, test, num_notes)
66
+ output = idxenc2stream(res.astype("int"), MusicVocab.create())
67
+ output.write('midi',music_gen_base_path+"output_demo.mid")
68
+ path_mid_file = music_gen_base_path + "output_demo.mid"
69
+ path_wav_file = music_gen_base_path + "output_demo.wav"
70
+ # need timidity for this
71
+ subprocess.call(['timidity', path_mid_file, "-Ow", "-o", path_wav_file])
72
+ return
73
+
74
+ def inference_music_gen(audio, num_notes):
75
+ data_e = midi2idxenc(audio.name, MusicVocab.create())
76
+ midi_predict(model_music_gen, data_e[:100], int(num_notes))
77
+ return
78
+
79
+ music_gen_interface = gr.Interface(
80
+ inference_music_gen,
81
+ inputs = [gr.inputs.File(type="file", label="Input"), gr.Textbox(lines = 1, placeholder = "Enter number of notes here")],
82
+ examples=[[music_gen_base_path + "mid_file/Comic_Relief.mid", 300]],
83
+ outputs = gr.outputs.Audio(type="filepath", label="Output")
84
+ )
85
+
86
+ """## Sentiment Analysis"""
87
+
88
+ def inference_sentiment_analysis(sen):
89
+ tokenized_v1 = tokenizer_sa([sen], return_tensors="np", padding="longest")
90
+ outputs_v1 = model_sa(tokenized_v1).logits
91
+ classifications_v1 = np.argmax(outputs_v1, axis=1)
92
+ if classifications_v1[0] == 1:
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+ res = "Positive :)"
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+ else:
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+ res = "Negative :("
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+ return res
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+
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+ sentiment_analysis_interface = gr.Interface(
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+ fn=inference_sentiment_analysis,
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+ inputs=gr.Textbox(lines=2, placeholder="Enter a Sentence"),
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+ outputs="text",
102
+ )
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+
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+ """## Peom Generation"""
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+
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+ def inference_poem_gen(start):
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+ tokenized = tokenizer_poem_gn(start, return_tensors="np")
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+ outputs = model_poem_gn.generate(**tokenized, max_new_tokens=20)
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+ res = tokenizer_poem_gn.decode(outputs[0])
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+ return res.replace("<LINE>", "\n")
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+
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+ poem_gen_interface = gr.Interface(
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+ fn=inference_poem_gen,
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+ inputs=gr.Textbox(lines=2, placeholder="Start Here..."),
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+ outputs="text",
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+ )
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+
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+ """## Combine All"""
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+
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+ demo = gr.TabbedInterface([music_gen_interface, poem_gen_interface, sentiment_analysis_interface],
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+ ["Music Generation", "Poem Generation", "Sentiment Analysis"])
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+ demo.launch(debug=True, share=True)
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+
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filename=python_multipart-0.0.5-py3-none-any.whl size=31678 sha256=e78918c1797ce35e37a94ddbec7bbf53775d69f255e8cd91b36d45f26f1a7fc9\n"," Stored in directory: /root/.cache/pip/wheels/2c/41/7c/bfd1c180534ffdcc0972f78c5758f89881602175d48a8bcd2c\n","Successfully built ffmpy python-multipart\n","Installing collected packages: sniffio, mdurl, uc-micro-py, rfc3986, markdown-it-py, h11, anyio, starlette, pynacl, mdit-py-plugins, linkify-it-py, httpcore, cryptography, bcrypt, websockets, uvicorn, python-multipart, pydub, pycryptodome, paramiko, orjson, httpx, ffmpy, fastapi, gradio\n","Successfully installed anyio-3.6.2 bcrypt-4.0.1 cryptography-38.0.4 fastapi-0.88.0 ffmpy-0.3.0 gradio-3.12.0 h11-0.12.0 httpcore-0.15.0 httpx-0.23.1 linkify-it-py-1.0.3 markdown-it-py-2.1.0 mdit-py-plugins-0.3.1 mdurl-0.1.2 orjson-3.8.2 paramiko-2.12.0 pycryptodome-3.16.0 pydub-0.25.1 pynacl-1.5.0 python-multipart-0.0.5 rfc3986-1.5.0 sniffio-1.3.0 starlette-0.22.0 uc-micro-py-1.0.1 uvicorn-0.20.0 websockets-10.4\n","Reading package lists... Done\n","Building dependency tree \n","Reading state information... Done\n","The following package was automatically installed and is no longer required:\n"," libnvidia-common-460\n","Use 'apt autoremove' to remove it.\n","The following additional packages will be installed:\n"," freepats libaudio2 timidity-daemon\n","Suggested packages:\n"," nas fluid-soundfont-gm fluid-soundfont-gs pmidi\n","The following NEW packages will be installed:\n"," freepats libaudio2 timidity timidity-daemon\n","0 upgraded, 4 newly installed, 0 to remove and 5 not upgraded.\n","Need to get 29.6 MB of archives.\n","After this operation, 35.7 MB of additional disk space will be used.\n","Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 freepats all 20060219-1 [29.0 MB]\n","Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 libaudio2 amd64 1.9.4-6 [50.3 kB]\n","Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 timidity amd64 2.13.2-41 [585 kB]\n","Get:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 timidity-daemon all 2.13.2-41 [5,984 B]\n","Fetched 29.6 MB in 0s (72.0 MB/s)\n","Selecting previously unselected package freepats.\n","(Reading database ... 123991 files and directories currently installed.)\n","Preparing to unpack .../freepats_20060219-1_all.deb ...\n","Unpacking freepats (20060219-1) ...\n","Selecting previously unselected package libaudio2:amd64.\n","Preparing to unpack .../libaudio2_1.9.4-6_amd64.deb ...\n","Unpacking libaudio2:amd64 (1.9.4-6) ...\n","Selecting previously unselected package timidity.\n","Preparing to unpack .../timidity_2.13.2-41_amd64.deb ...\n","Unpacking timidity (2.13.2-41) ...\n","Selecting previously unselected package timidity-daemon.\n","Preparing to unpack .../timidity-daemon_2.13.2-41_all.deb ...\n","Unpacking timidity-daemon (2.13.2-41) ...\n","Setting up freepats (20060219-1) ...\n","Setting up libaudio2:amd64 (1.9.4-6) ...\n","Setting up timidity (2.13.2-41) ...\n","Setting up timidity-daemon (2.13.2-41) ...\n","Adding group timidity....done\n","Adding system user timidity....done\n","Adding user `timidity' to group `audio' ...\n","Adding user timidity to group audio\n","Done.\n","invoke-rc.d: could not determine current runlevel\n","invoke-rc.d: policy-rc.d denied execution of stop.\n","invoke-rc.d: could not determine current runlevel\n","invoke-rc.d: policy-rc.d denied execution of start.\n","Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n","Processing triggers for libc-bin (2.27-3ubuntu1.6) ...\n","Processing triggers for systemd (237-3ubuntu10.56) ...\n"]}],"source":["!pip install gradio\n","!apt-get install timidity"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"wN-kzFUKEgbe","executionInfo":{"status":"ok","timestamp":1669836413098,"user_tz":300,"elapsed":1359,"user":{"displayName":"Rahul Dagar","userId":"06679238259044443515"}}},"outputs":[],"source":["import gradio as gr\n","from pathlib import Path\n","import subprocess"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"IiiTtEdTJtzD","executionInfo":{"status":"ok","timestamp":1669836413099,"user_tz":300,"elapsed":13,"user":{"displayName":"Rahul Dagar","userId":"06679238259044443515"}}},"outputs":[],"source":["def inference():\n"," base_path = \"/content/drive/MyDrive/FIRE_3rd Sem/music_gn/\"\n"," #path_mid_file -> Replace this with model generated file path\n"," path_mid_file = base_path + \"Comic_Relief.mid\"\n"," path_wav_file = base_path + \"output_comic.wav\"\n"," subprocess.call(['timidity', path_mid_file, \"-Ow\", \"-o\", path_wav_file])\n"," return Path(path_wav_file)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":898},"id":"ZSGf7iKuj3Qc","outputId":"c76600ef-3b3d-4dc6-b6af-058503ff96e1"},"outputs":[{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/gradio/outputs.py:81: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n"," \"Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\",\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n","Running on public URL: https://d1c7449a9e2180e2.gradio.app\n","\n","This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"]},{"data":{"text/html":["<div><iframe src=\"https://d1c7449a9e2180e2.gradio.app\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["Exception in thread Thread-21:\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n"," self.run()\n"," File \"/usr/lib/python3.7/threading.py\", line 870, in run\n"," self._target(*self._args, **self._kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/gradio/tunneling.py\", line 39, in handler\n"," chan.send(data)\n"," File \"/usr/local/lib/python3.7/dist-packages/paramiko/channel.py\", line 801, in send\n"," return self._send(s, m)\n"," File \"/usr/local/lib/python3.7/dist-packages/paramiko/channel.py\", line 1198, in _send\n"," raise socket.error(\"Socket is closed\")\n","OSError: Socket is closed\n","\n"]}],"source":["title = \"Music Generation\"\n","description = \"Add Project description\"\n","article = \"<p style='text-align: center'><a href='https://github.com/' target='_blank'>Github Repo</a></p>\"\n","#we can add other project related stuff as well\n","\n","gr.Interface(\n"," inference, \n"," inputs = None, \n"," title=title,\n"," description=description,\n"," article=article,\n"," outputs = gr.outputs.Audio(type=\"filepath\", label=\"Output\")\n"," ).launch(debug=True, share=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eYAysXfiJt8s"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyN94Rbb2hF1lMEWF6piWIaF"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
 
 
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+ musicautobot==0.0.1
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+ gradio==3.12.0
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+ transformers==4.20.1
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+ fastai==1.0.58