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"align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "source": [ "!nvidia-smi" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "M2b-7B0PjEvG", "outputId": "d9502785-125f-4f4c-96c6-0a610aa19aae" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fri Jan 21 23:28:23 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 495.46 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 35C P0 26W / 250W | 0MiB / 16280MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YZXTLpRzg8gp" }, "outputs": [], "source": [ "%%capture\n", "!pip install datasets\n", "!pip install transformers\n", "!pip install huggingface_hub\n", "!pip install torchaudio\n", "!pip install librosa\n", "!pip install jiwer" ] }, { "cell_type": "code", "source": [ "!apt install git-lfs" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "k6Ao2XfngKmI", "outputId": "49874e26-4f4b-42c5-a1c7-3957c0045b99" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Reading package lists... Done\n", "Building dependency tree \n", "Reading state information... Done\n", "The following NEW packages will be installed:\n", " git-lfs\n", "0 upgraded, 1 newly installed, 0 to remove and 37 not upgraded.\n", "Need to get 2,129 kB of archives.\n", "After this operation, 7,662 kB of additional disk space will be used.\n", "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 git-lfs amd64 2.3.4-1 [2,129 kB]\n", "Fetched 2,129 kB in 1s (1,878 kB/s)\n", "Selecting previously unselected package git-lfs.\n", "(Reading database ... 155229 files and directories currently installed.)\n", "Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n", "Unpacking git-lfs (2.3.4-1) ...\n", "Setting up git-lfs (2.3.4-1) ...\n", "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n" ] } ] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359, "referenced_widgets": [ "4304086a2b9c440fb8d0023623b7a05c", "0dd1e64d5eff409f8314614165d80b9d", "596a7a303b344b218a486f1f05475b5c", "d2e04d910237444fa3ab81c7e3add0fe", "fa45a714388f4347be87b2ede73b07e8", "09906643b8d849d596fd5dc880c518d8", "b659ee2c2e164c308f4fdb475f9aab5c", "f119033dcb1e4e8cb992d5f6765a1d9c", "3b51dd44e0c5457f818b232864174428", "1826b962f0764b1187672d875e21beee", "98c1a99919a74c52ae3d4c225f22df1d", "19fb7c9b85fc44ecbadca8260199d173", "064f5b8dfcd444ce9809500aa1ae1cf4", "fcd247d57db44a2eaaee4774037bba3a", "512ec4070bbf42ffbadc28f2d6381b70", "0008c146c73c44e0b4632ff50f8f5432", "03d6264c6b104e1cb5013b3762bba159" ] }, "id": "1CCVxM2-hLbE", "outputId": "bfe36be0-7cbb-4cfe-c8e8-3df2caf63657" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", "\n", "git config --global credential.helper store\u001b[0m\n" ] } ] }, { "cell_type": "code", "source": [ "from datasets import load_dataset, load_metric, Audio, concatenate_datasets" ], "metadata": { "id": "HP3z-kMqhW49" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"or\", split=\"train+validation\", use_auth_token=True)\n", "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"or\", split=\"test\", use_auth_token=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hnwqYEFnhji5", "outputId": "8eb3f4f7-4803-4bec-964a-23ca4e5338c0" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/or/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n", "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/or/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n" ] } ] }, { "cell_type": "code", "source": [ "common_voice_train = common_voice_train.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])\n", "common_voice_test = common_voice_test.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])" ], "metadata": { "id": "e_Km6s8AJgWz" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import re\n", "chars_to_remove_regex = '''[\\।\\!\\\"\\,\\-\\.\\?\\:\\|\\“\\”\\–\\;\\'\\’]'''\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n", " return batch" ], "metadata": { "id": "DcXT9xueZfMX" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "15bac754283448f5a4f217b5d68a9e32", "235ce8ff0d1d465ea0f635588e3113bc", "178a2b86b6874c2fa6fdcd8325cd3e05", "5ba6ce957a7449e1943f4b50015c7bac", "a89fa7f53ea6477ab1b793e85372b386", "4d4b686146b44e6692e6170fab424f0d", "4625e2a4440e4ae7a5989fd57a5a32a3", "e901642b08c040ae81fd27456e9ae07f", "f7c1af45cf9f4e38a031c244830f1a38", "2a649091a8e948a883aadf5aedf27d56", "1bb64d1986f4424c9356b00e81da8c74", "04d5929262444ae088bec02452f0b196", "9fca65b824b249a28c6683a91efa5149", "8ad89cf4900247659f443e7cc8006f61", "993c0ff65d424975bee72839acd7150f", "28c042ae5c1d4ce5a91a44c3f688c227", "160134a6d96e451181d9a8009531a09c", "6eb86a965e174c159e082f00f949e52a", "30d7eaa6072748589cf179ea4fb22301", "54cde0e31fb94359b518f3d4c4a46388", "da3616fed108470399fb9b5dcb1bdae2", "0621e673a09c4836a186111c8837e9eb" ] }, "id": "TwvH3y9DZhvr", "outputId": "7d1838db-994d-44b7-b9ec-21540983a4b0" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "15bac754283448f5a4f217b5d68a9e32", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/537 [00:00 main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-or/commit/8de2057248265218330a8cca85d229f88b76fb18'" ] }, "metadata": {}, "execution_count": 27 } ] }, { "cell_type": "code", "source": [ "from transformers import Wav2Vec2FeatureExtractor\n", "\n", "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)" ], "metadata": { "id": "1RseFfmleI3H" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ], "metadata": { "id": "NGDrT6saeOqy" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "common_voice_train = common_voice_train.cast_column(\"audio\", Audio(sampling_rate=16_000))\n", "common_voice_test = common_voice_test.cast_column(\"audio\", Audio(sampling_rate=16_000))" ], "metadata": { "id": "nOpdod1weRfa" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def prepare_dataset(batch):\n", " audio = batch[\"audio\"]\n", "\n", " # batched output is \"un-batched\"\n", " batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n", " batch[\"input_length\"] = len(batch[\"input_values\"])\n", " \n", " with processor.as_target_processor():\n", " batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n", " return batch" ], "metadata": { "id": "KimgJI22eVYa" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)\n", "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "9ac066ecd54b4fb9aa00a28242f0ec9a", "e8e8e7c5e934478cb7b9fceacebd3dd4", "7a0433e277d24b88a86ccbc6bcda2d41", "02af1dde76ce454cb9632216ebdf5c29", "f99f9571d05f4fe59b16899aefa2f537", "0c9c7705477c4959be3915b3c85ddb1b", "777b284e0a024060a150b1a0194b5805", "f9a40b00fd224f9baf9d959c20c8cdbc", "b223b3f129414d9180fbcedeeddf86ac", "348d15831c4b4f4b994762da5d1cc9fc", "a37767c0fecd48828efccf6ed2468e33", "8451c92b0de14f91a2ba5ba12ef8fe2d", "ca5b0d482a054e54b2d0d4275658f0aa", "08fed23a861e412b95945fc364447ff6", "b9ca3ef14c6c4f4296d4de8a23bada50", "d8015e53cb51468e9d5d85128854a4c1", "85cbd69458e148af925324d1149f2d0f", "63410210d49145f6ab3298c1c99d6e35", "4196f4a17be74564ae4ec642827678fc", "2f72124c09c24994b74a219a1c9a2248", "0be9b09ce9fe4cc2b017dc3909d5eb41", "9689d09f69ca4cadad37a556effd24be" ] }, "id": "Uxt4G7XweZxf", "outputId": "ca232410-2dda-40f0-ea7d-d41d0f41e836" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9ac066ecd54b4fb9aa00a28242f0ec9a", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/537 [00:00 Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lenghts and need\n", " # different padding methods\n", " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", "\n", " batch = self.processor.pad(\n", " input_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", " with self.processor.as_target_processor():\n", " labels_batch = self.processor.pad(\n", " label_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " # replace padding with -100 to ignore loss correctly\n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ], "metadata": { "id": "k1hUL21-ecSE" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ], "metadata": { "id": "3WErNElzezOc" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "wer_metric = load_metric(\"wer\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "2d7f2dfa59bc4298ab14d89dab9323d5", "56ab060fdf064fcdb4c946f99d12bf13", "019f97b52b6c40988557c11071101124", "b481a4e2a4d84d2595958cacbc421ffb", "2f6caf1171d04ad680d52b7ab28aed9a", "e7b0b3e6d8d742428e3b9b03cdc22451", "cd4c2dce2ebc46048a062be59f0eb0bf", "fe4686c64a224789b08bedf5fba6a436", "bbfe18b6d49b42f6986f9e21bd65722a", "ec7392a3347a4c80a1cc62c9bd1dbf6d", "a9cb9dad1736446eab7493e0f1c80838" ] }, "id": "tHAXlHO1ez7Z", "outputId": "0cd3facc-9d80-4294-8177-5577c7233c22" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2d7f2dfa59bc4298ab14d89dab9323d5", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.91k [00:00\n", " \n", " \n", " [4080/4080 5:48:13, Epoch 240/240]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining LossValidation LossWer
4006.0493002.9727861.000000
8000.5306001.2895200.613811
12000.1253001.6854220.570332
16000.0763001.9432840.586957
20000.0552001.4393460.557545
24000.0382001.4665350.553708
28000.0286001.5441280.531969
32000.0212001.6501560.511509
36000.0168001.6411030.533248
40000.0129001.6618080.516624

" ], "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 112\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or/checkpoint-400\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or/checkpoint-400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-400/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/preprocessor_config.json\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 112\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or/checkpoint-800\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or/checkpoint-400] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 112\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or/checkpoint-800] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 112\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or/checkpoint-1600\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-1600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or/checkpoint-1200] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 112\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or/checkpoint-2000\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or/checkpoint-1600] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 112\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or/checkpoint-2000] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 112\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or/checkpoint-2800\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-2800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or/checkpoint-2800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-2800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or/checkpoint-2400] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 112\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or/checkpoint-3200\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-3200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or/checkpoint-3200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-3200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or/checkpoint-2800] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 112\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or/checkpoint-3600\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-3600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or/checkpoint-3600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-3600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or/checkpoint-3200] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 112\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or/checkpoint-4000\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-4000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or/checkpoint-4000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/checkpoint-4000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or/checkpoint-3600] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=4080, training_loss=0.6819741196638229, metrics={'train_runtime': 20901.6388, 'train_samples_per_second': 6.166, 'train_steps_per_second': 0.195, 'total_flos': 2.21195815354744e+19, 'train_loss': 0.6819741196638229, 'epoch': 240.0})" ] }, "metadata": {}, "execution_count": 42 } ] }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 342, "referenced_widgets": [ "833ad4d97b524838a36e91e8317f2529", "60ff0be3b1be41769337303c51f846b5", "e4440f422ae242e39522a24078a6752e", "20e8df37f7cb403bb14d55c269602389", "29fde6da8462420099361ab005505a00", "0464fde65cf24c90a13f8812000d5845", "9625b3b0f02a4022a71ee915060a2508", "18d548e454934f40b1076c164787e142", "52c1f33ff2a54bc3a4c2d97c91b27994", "4acc88a482944692b38f74d871623400", "223e615d7af4477781d1c00a081e7ce8", "71f6ad0d05c94887b0890ee8818241f0", "0ddc20eddfd94bea9496fe3fcfa8d5fe", "516c0bb64a864f49b012a40b468677d3", "edb5e248559f428395225e1e42fc4843", "c77c178a7b744979aece53ea5d3502c1", "c53250ddc6724c1b9245054c14ef5989", "1ea5ac5ff88d40a7ab2b26a530b36faf", "c7a0e5c30e034db294024ac75fb27861", "2aa293cecc0f430296d32750a62b0337", "1a67f291c82c4786808dad9fc2079f10", "ff11bd3c10c4413eab190bdbcfa36fd9" ] }, "id": "1DzGjKHifPGK", "outputId": "595af0b6-2457-4c8e-9f65-a9ee2ea150bd" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or/preprocessor_config.json\n", "Several commits (2) will be pushed upstream.\n", "The progress bars may be unreliable.\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "833ad4d97b524838a36e91e8317f2529", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.37k/1.18G [00:00 main\n", "\n", "Dropping the following result as it does not have all the necessary fields:\n", "{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'or'}}\n", "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-or\n", " 2e30dba..ac42681 main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-or/commit/2e30dbad96b5318caff7ca93a288410ec8dfda0d'" ] }, "metadata": {}, "execution_count": 43 } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "93NnxhS_1EIV" }, "execution_count": null, "outputs": [] } ] }