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datasets\n", " Attempting uninstall: fsspec\n", " Found existing installation: fsspec 2024.10.0\n", " Uninstalling fsspec-2024.10.0:\n", " Successfully uninstalled fsspec-2024.10.0\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n", "torch 2.5.1+cu124 requires nvidia-cublas-cu12==12.4.5.8; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cublas-cu12 12.5.3.2 which is incompatible.\n", "torch 2.5.1+cu124 requires nvidia-cuda-cupti-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-cupti-cu12 12.5.82 which is incompatible.\n", "torch 2.5.1+cu124 requires nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-nvrtc-cu12 12.5.82 which is incompatible.\n", "torch 2.5.1+cu124 requires nvidia-cuda-runtime-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-runtime-cu12 12.5.82 which is incompatible.\n", "torch 2.5.1+cu124 requires nvidia-cudnn-cu12==9.1.0.70; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cudnn-cu12 9.3.0.75 which is incompatible.\n", "torch 2.5.1+cu124 requires nvidia-cufft-cu12==11.2.1.3; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cufft-cu12 11.2.3.61 which is incompatible.\n", "torch 2.5.1+cu124 requires nvidia-curand-cu12==10.3.5.147; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-curand-cu12 10.3.6.82 which is incompatible.\n", "torch 2.5.1+cu124 requires nvidia-cusolver-cu12==11.6.1.9; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusolver-cu12 11.6.3.83 which is incompatible.\n", "torch 2.5.1+cu124 requires nvidia-cusparse-cu12==12.3.1.170; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusparse-cu12 12.5.1.3 which is incompatible.\n", "torch 2.5.1+cu124 requires nvidia-nvjitlink-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-nvjitlink-cu12 12.5.82 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n" ] } ] }, { "cell_type": "markdown", "source": [ "To train our model, we’ll use the GTZAN dataset, which is a popular dataset of 1,000 songs for music genre classification. Each song is a 30-second clip from one of 10 genres of music, spanning disco to metal. We can get the audio files and their corresponding labels from the Hugging Face Hub with the load_dataset() function from 🤗 Datasets:" ], "metadata": { "id": "EY9JJTGYJJJx" } }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "gtzan = load_dataset(\"marsyas/gtzan\", \"all\")\n", "gtzan" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 451, "referenced_widgets": [ "124f2b9105c3498cb3ab44e13ebcde8e", "660e326b4ade4990a10037166e32112a", "96396bf5df894e3d8a059e6964dd4c4c", "4053c0fafa6f45e4ac7e7d938c6c43d5", "c138b71108e742c686d36960227f8e43", "3decdcf925eb44189a45dba6ab82fb8d", "b7a40cc0795f4d119241c645639bf86b", "08e009ebe65a4f8d923268ac2b4ea9f8", "a1ee8e481ab64f518cc146f6de1bef73", "fbc1ed1c95a24daebf9d561b9b3989c0", "b1b89810a9e14e5590dd65d12259699f", "4a8bc73026fe41e9afd0487cb63b6d61", "6b870e345a4c474dad958b94d4cd20d0", "c9510c4108f246f7bad5bac24ad8ce0a", "c7f5c4536dd94116b1f2a78939ec4995", "9f3083f1888c429db29a8292269120e6", "43687ea9a6e94bfeadfc1d8973e2c069", "d8025fe423d344fd95b68eaac1729666", "ba31de357ee34990bf0d6f0f30aad598", "e17d2b26fcc34fdb8ab4697740e6cf1f", "a39513cabbc94d8f83aae61a1407af97", "f6efcbc4c4bf430daec641073f1383dc", "4ae5fa48f4a34e21a24a39d37bce1c73", "1cd09eeafb9f4453a113bd4dd4658b04", "d0e38e26dd374137b26514ec04c9ccbb", "6e4865cfce994963ab7623789aaf3db5", "62e29d955cd94042bdaf367cea1beb6e", "f78a36138ad946b89eaff1f5c0bd935b", "b111ba901a7644bf966e13cf045d8844", "933eb73dd315409599c2aa093c81cf2e", "e9db215ca8c6487e9141288c3fe56e03", "bdb2e2b6d4c7404e8f018d74322feee7", "9cbe47620208462cb8ef7b46eac8e223", "51ee0c942e864fcf9ecee4e328cf1753", "159cb23f1f0b47df8d8f84ecd062806c", "d78961852bb64a5cbde9a2a474b7db0d", "471adeb3c06b4be289f5f75f92ae08cb", "2d1d8babcbc348519e53a7df912ac446", "e399ce22be014ba5aa38424b7b119301", "ba9f0c4865c54753afba8d51c24180bd", "9f6da4993b2f4ff18c8a81316d6026fb", "a67757f16a4e400a8f409a46ae144002", "7d994e370cf047078c263b689cc17986", "0bd36c663d564e41be55c623c5b1fd04" ] }, "id": "NIlEvqTry_Ea", "outputId": "8ff3e82e-48dc-479f-8650-b8d029a83c8e" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/4.42k [00:00" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Keyboard interruption in main thread... closing server.\n", "Killing tunnel 127.0.0.1:7860 <> https://7d04398922fbd750f0.gradio.live\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "markdown", "source": [ "Similar to tokenization in NLP, audio and speech models require the input to be encoded in a format that the model can process. In 🤗 Transformers, the conversion from audio to the input format is handled by the feature extractor of the model. Similar to tokenizers, 🤗 Transformers provides a convenient AutoFeatureExtractor class that can automatically select the correct feature extractor for a given model. To see how we can process our audio files, let’s begin by instantiating the feature extractor for DistilHuBERT from the pre-trained checkpoint:" ], "metadata": { "id": "3dPP5vZrKGxO" } }, { "cell_type": "code", "source": [ "from transformers import AutoFeatureExtractor\n", "\n", "model_id = \"ntu-spml/distilhubert\"\n", "feature_extractor = AutoFeatureExtractor.from_pretrained(\n", " model_id, do_normalize=True, return_attention_mask=True\n", ")" ], "metadata": { "id": "gHYkoj3A0O5w", "colab": { "base_uri": "https://localhost:8080/", "height": 119, "referenced_widgets": [ "43cfc8082a754bd8987bd8f1e02fe01c", "d102a585f96140c6a56873a1c61104b3", "335e06a1feca49acbc24d04c676b1259", "b072f12d505d4f3bb62b89f6a0f5e7b6", "84b73c6c974e4d818a9d8d4a50e95f8c", "551d740bd5794a43bc6672790cbd78b4", "3660fa206a3747b7805d1771eeaa221c", "cbadcec3a0c344ef80c75c01ac5e2f24", "3cd7aa5cceb840eebea71fa7720052bb", "3f5c0387e4994de68f7f4e3c46637a6c", "41d8c7a6103540abb584a8436b308b4a", "43a44338a7fa468d85b3e47ff28ada3d", "49e766d1adb94be4b900bb127f186bec", "e857a5629bd54810a26096e18dd49a4f", "1f9ced6b4ed8421e9a96c8b47851c272", "803322fe86bd4b8295e544c697217740", "7f3477744c23456fbed403833d8bd664", "7cd6d4af619a4444a0b4b117a68ac378", "1c0f17da5c464a8e8a6a4a507ab33eb7", "4b3d5aa0a5854af398737585d5793a42", "084ad1f811d8477fa9c1368b60cd4922", "0acafb894fb8477aab21db2e98aea481" ] }, "outputId": "68bfe2ea-a448-47f5-a26b-eacca3b794c7" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "0it [00:00, ?it/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "43cfc8082a754bd8987bd8f1e02fe01c" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/214 [00:00 =2.0.0 in /usr/local/lib/python3.11/dist-packages (from evaluate) (3.2.0)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from evaluate) (1.26.4)\n", "Requirement already satisfied: dill in /usr/local/lib/python3.11/dist-packages (from evaluate) (0.3.8)\n", "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from evaluate) (2.2.2)\n", "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.11/dist-packages (from evaluate) (2.32.3)\n", 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evaluate.load(\"accuracy\")\n", "\n", "\n", "def compute_metrics(eval_pred):\n", " \"\"\"Computes accuracy on a batch of predictions\"\"\"\n", " predictions = np.argmax(eval_pred.predictions, axis=1)\n", " return metric.compute(predictions=predictions, references=eval_pred.label_ids)" ], "metadata": { "id": "UTKa7ZMMLc6-", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "1638993a6d1945b691b9dfe5324e86b9", "7b008f1eb72f4e87949b11dadd61381e", "f5b53f7b59ef45bf9306dec096ddfd80", "693791fd7cfa4d4da4145b1b1c4b9272", "805f62ab1b434516ac8db9b4cb9dbf87", "ceab3be91e4e4aeeb1914361302d5265", "662c8ec3235c4ceb9af4a07b54bf2fde", "7189cbfc03d44f82a1de3025d2e54d54", "6fa4f9d2792245dcb5ee56486f642e36", "ad6592ac841644188ef0286366db00f5", "cbbf2de08cff46b4bd3215eaea3f8685" ] }, "outputId": "65269ee4-aeed-4ac6-8509-0ef2b653d02f" }, "execution_count": 21, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 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(Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n", "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n", "wandb: Paste an API key from your profile and hit enter:" ] }, { "name": "stdout", "output_type": "stream", "text": [ " ··········\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\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[33mgyaneshhere\u001b[0m to \u001b[32mhttps://api.wandb.ai\u001b[0m. 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EpochTraining LossValidation LossAccuracy
12.0014001.8735160.560000
21.2705001.2513550.630000
31.0219001.1399320.650000
40.7034000.8793410.690000
50.5411000.6779570.810000
60.4426000.5764450.840000
70.2074000.5894850.810000
80.1131000.5772080.800000
90.1932000.5551830.820000
100.0699000.5500360.840000

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=1130, training_loss=0.750134692914718, metrics={'train_runtime': 5982.3779, 'train_samples_per_second': 1.503, 'train_steps_per_second': 0.189, 'total_flos': 6.133988274624e+17, 'train_loss': 0.750134692914718, 'epoch': 10.0})" ] }, "metadata": {}, "execution_count": 22 } ] }, { "cell_type": "code", "source": [ "kwargs = {\n", " \"dataset_tags\": \"marsyas/gtzan\",\n", " \"dataset\": \"GTZAN\",\n", " \"model_name\": f\"{model_name}-finetuned-gtzan\",\n", " \"finetuned_from\": model_id,\n", " \"tasks\": \"audio-classification\",\n", "}" ], "metadata": { "id": "HicTvUR7u9kL" }, "execution_count": 23, "outputs": [] }, { "cell_type": "code", "source": [ "trainer.push_to_hub(**kwargs)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 88 }, "id": "YR7D8MJ2vDPs", "outputId": "0ed6d21d-f19e-47c2-9dee-39cbd04b2827" }, "execution_count": 24, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "CommitInfo(commit_url='https://huggingface.co/Gyaneshere/distilhubert-finetuned-gtzan/commit/0f11e20d0d094f5854a30187a2e2ff4361481ecc', commit_message='End of training', commit_description='', oid='0f11e20d0d094f5854a30187a2e2ff4361481ecc', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Gyaneshere/distilhubert-finetuned-gtzan', endpoint='https://huggingface.co', repo_type='model', repo_id='Gyaneshere/distilhubert-finetuned-gtzan'), pr_revision=None, pr_num=None)" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 24 } ] }, { "cell_type": "code", "source": [ "from transformers import pipeline\n", "\n", "pipe = pipeline(\n", " \"audio-classification\", model=\"Gyaneshere/distilhubert-finetuned-gtzan\"\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 131, "referenced_widgets": [ "bb53ae09fb3f417aa7a777042de69df8", "68eac0e850a24b0a9b5ba102aff06c89", "6a69e89d741b4f54b7cfa720a5c921c9", "78a22c0990ab4d0ea156ecc847020649", "49d7449cd8ba4e15ba3cf163b14f5a73", "0d739b2f4c3043cf9370946412e5c3af", "684d35fab09d41c28d735c3ca5adb881", "85b9bdaf15b54e219648904af8122eb4", "ecc100fe96574ddfb122330b421f62ea", "256836a46ce6435989305158f8134f04", "89555a9a15a0403897635010d20b4286", "99e8e6c2b67144748e676c5c9dee4f90", "0e83f914ca9a4e64b6bccf8dbb91ed5f", "49d9e4a1ec1f4d57b476482ed894cd52", "9a4a03ab90454f469d24e3d34265ecc6", "fde48f0d4c904d15bc77217cf1df8f6d", "c09220143456448db16faca65c469cf1", "4d71ca70253543e7a90e8f9f601afce8", "e12ea719369e4e86a4241d64dc58b66c", "14f3aa47f5d04bcd9fdc608b4fdf68e0", "becee08c68704875b165f3df9a9487ee", "ff9a58969e46492aac30e286c016af55", "7c733af3a86b4cc6a8ad1139ee15b40f", "0e942687f0d44da0b5efd0a4161fc1c1", "c0bfcaea9cba406facab3ffd160c8694", "c401c5e51ba7426c8463850f6acde331", "b1431c5cc9594a7fbce88c01ea8bed46", "32d819d93aac4cb7ae44a2b5fbe584d6", "72a5e6ba5e7b4d83a6ccb4b1436cb1b8", "285e2716dfaf4bc18553d43ff09527d5", "2d092e687d8d4beea9e9a90697b89be9", "06afdae102e441d0b69e62c13fcd3f61", "9db5add5a4664d7fb6b95497150ee55c" ] }, "id": "4P60q9B3vKNy", "outputId": "59b3e429-29dc-43b7-a13f-5ea20223d352" }, "execution_count": 25, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.88k [00:00