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+ "| 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": "25e24d57-5818-4850-85dd-a6b9c91bcd1e"
+ },
+ "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 (1,669 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": 415,
+ "referenced_widgets": [
+ "21aca65c133040c2856f187fe640763b",
+ "6b943f96e09b41468710919f47af8e4b",
+ "70305cdcc0df40fe859f0a58dcb1d5e7",
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+ "bf1c397ab5934920a73d22965309c3e5",
+ "937b29e1b1044c2e836db961affe18ef",
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+ ]
+ },
+ "id": "1CCVxM2-hLbE",
+ "outputId": "cf91d49d-ec85-414a-a616-cbf3707c74d2"
+ },
+ "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\", \"as\", split=\"train+validation\", use_auth_token=True)\n",
+ "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"as\", split=\"test\", use_auth_token=True)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 377,
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+ "execution_count": null,
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+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7c2d0d747a7f45b4bfb9faf9dabfed22",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/9.79k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "03e2ae13d5ad49f58e8b3d6aca0155f7",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/2.92k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "67578b8e12c14c6c91ad27fa456e5b67",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/49.7k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Downloading and preparing dataset common_voice/as to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/as/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba...\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ef011176117746e6872beff0abbdfdeb",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/34.9M [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "cb962d0e454f4265931848486266986b",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "90df4dd410dd4e379e6140a2f0706457",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "17742327cdaf4f15b5ca5987613175e6",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "af9d96aaf1fc4d339a7b59364d74f746",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "676fedef8e1d4497b4419749ffeaffb8",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Dataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/as/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba. Subsequent calls will reuse this data.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/as/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",
+ " batch[\"sentence\"] = re.sub('’ ',' ',batch[\"sentence\"])\n",
+ " batch[\"sentence\"] = re.sub(' ‘',' ',batch[\"sentence\"])\n",
+ " batch[\"sentence\"] = re.sub('’|‘','\\'',batch[\"sentence\"])\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": [
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+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "68df622468b7431f908561c073dc4abc",
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+ },
+ "text/plain": [
+ " 0%| | 0/624 [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "version_minor": 0,
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+ },
+ "text/plain": [
+ " 0%| | 0/281 [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def extract_all_chars(batch):\n",
+ " all_text = \" \".join(batch[\"sentence\"])\n",
+ " vocab = list(set(all_text))\n",
+ " return {\"vocab\": [vocab], \"all_text\": [all_text]}"
+ ],
+ "metadata": {
+ "id": "0KhG6IzQZkPh"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n",
+ "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 81,
+ "referenced_widgets": [
+ "36a4aba90d5c448bbeb2fd95bff7fc91",
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+ "outputId": "bde3f8b1-4b53-4369-e25d-0f21b399fd35"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "36a4aba90d5c448bbeb2fd95bff7fc91",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ " 0%| | 0/1 [00:00, ?ba/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a5f8bae38f304123b2bd4fd85287df5c",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ " 0%| | 0/1 [00:00, ?ba/s]"
+ ]
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))"
+ ],
+ "metadata": {
+ "id": "BseCaBF-Z1J9"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))}\n",
+ "vocab_dict"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "H8aNXGLcZ7aq",
+ "outputId": "2aa16be2-62fd-4de3-f7f8-4c5584d0f026"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{' ': 0,\n",
+ " \"'\": 1,\n",
+ " 'ঁ': 2,\n",
+ " 'ং': 3,\n",
+ " 'ঃ': 4,\n",
+ " 'অ': 5,\n",
+ " 'আ': 6,\n",
+ " 'ই': 7,\n",
+ " 'ঈ': 8,\n",
+ " 'উ': 9,\n",
+ " 'এ': 10,\n",
+ " 'ঐ': 11,\n",
+ " 'ও': 12,\n",
+ " 'ঔ': 13,\n",
+ " 'ক': 14,\n",
+ " 'খ': 15,\n",
+ " 'গ': 16,\n",
+ " 'ঘ': 17,\n",
+ " 'ঙ': 18,\n",
+ " 'চ': 19,\n",
+ " 'ছ': 20,\n",
+ " 'জ': 21,\n",
+ " 'ঝ': 22,\n",
+ " 'ঞ': 23,\n",
+ " 'ট': 24,\n",
+ " 'ঠ': 25,\n",
+ " 'ড': 26,\n",
+ " 'ঢ': 27,\n",
+ " 'ণ': 28,\n",
+ " 'ত': 29,\n",
+ " 'থ': 30,\n",
+ " 'দ': 31,\n",
+ " 'ধ': 32,\n",
+ " 'ন': 33,\n",
+ " 'প': 34,\n",
+ " 'ফ': 35,\n",
+ " 'ব': 36,\n",
+ " 'ভ': 37,\n",
+ " 'ম': 38,\n",
+ " 'য': 39,\n",
+ " 'র': 40,\n",
+ " 'ল': 41,\n",
+ " 'শ': 42,\n",
+ " 'ষ': 43,\n",
+ " 'স': 44,\n",
+ " 'হ': 45,\n",
+ " '়': 46,\n",
+ " 'া': 47,\n",
+ " 'ি': 48,\n",
+ " 'ী': 49,\n",
+ " 'ু': 50,\n",
+ " 'ূ': 51,\n",
+ " 'ৃ': 52,\n",
+ " 'ে': 53,\n",
+ " 'ৈ': 54,\n",
+ " 'ো': 55,\n",
+ " 'ৌ': 56,\n",
+ " '্': 57,\n",
+ " 'ৎ': 58,\n",
+ " 'ড়': 59,\n",
+ " 'ঢ়': 60,\n",
+ " 'য়': 61,\n",
+ " 'ৰ': 62,\n",
+ " 'ৱ': 63}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
+ "del vocab_dict[\" \"]"
+ ],
+ "metadata": {
+ "id": "CXO9uuQ5Z9ig"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n",
+ "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n",
+ "len(vocab_dict)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6ZzvK62RaFXk",
+ "outputId": "4796fbbc-73db-4b5d-a0a5-5e8c492064a2"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "66"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import json\n",
+ "with open('vocab.json', 'w') as vocab_file:\n",
+ " json.dump(vocab_dict, vocab_file)"
+ ],
+ "metadata": {
+ "id": "1iC1L9fLaAjr"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import Wav2Vec2CTCTokenizer\n",
+ "\n",
+ "tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\"./\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "KQQNKodMaGxH",
+ "outputId": "b2cc555b-0442-45f5-ee71-63b84d38b8dc"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "file ./config.json not found\n",
+ "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "repo_name = \"wav2vec2-large-xls-r-300m-as\""
+ ],
+ "metadata": {
+ "id": "P7l3pLQpaSxB"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "tokenizer.push_to_hub(repo_name)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "tPYBu0yZaWqA",
+ "outputId": "09e3d61d-05fb-4701-b921-24e4fa116f97"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:1004: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n",
+ " FutureWarning,\n",
+ "Cloning https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-as into local empty directory.\n",
+ "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-as\n",
+ " 35c510e..46fe0a4 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-as/commit/46fe0a4ed7ef2244e91dfee3a9153f0ce7c7da47'"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 19
+ }
+ ]
+ },
+ {
+ "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": [
+ "b7d43dfcf210417187184f0d42b34167",
+ "51f4c100f25e4981ad248b61b45bede2",
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+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "b7d43dfcf210417187184f0d42b34167",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ " 0%| | 0/624 [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "aa593380281b405eab58917dad8ede17",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ " 0%| | 0/281 [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import torch\n",
+ "\n",
+ "from dataclasses import dataclass, field\n",
+ "from typing import Any, Dict, List, Optional, Union\n",
+ "\n",
+ "@dataclass\n",
+ "class DataCollatorCTCWithPadding:\n",
+ " \"\"\"\n",
+ " Data collator that will dynamically pad the inputs received.\n",
+ " Args:\n",
+ " processor (:class:`~transformers.Wav2Vec2Processor`)\n",
+ " The processor used for proccessing the data.\n",
+ " padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
+ " Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
+ " among:\n",
+ " * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
+ " sequence if provided).\n",
+ " * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
+ " maximum acceptable input length for the model if that argument is not provided.\n",
+ " * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
+ " different lengths).\n",
+ " \"\"\"\n",
+ "\n",
+ " processor: Wav2Vec2Processor\n",
+ " padding: Union[bool, str] = True\n",
+ " \n",
+ " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> 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": [
+ "4e57d172ec1744dd92ec9db65e19cd8b",
+ "de60c992211448dfa5e2d1d793cf020e",
+ "7ce662a81a164e168d62baad50fb6dea",
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+ "ce34e2c2c25840d782d35ba411dbfa40",
+ "4d1d3d8dbaa14d99834be53d96f50dfa"
+ ]
+ },
+ "id": "tHAXlHO1ez7Z",
+ "outputId": "c213f39c-fad8-4bfe-f062-136f0956cdf3"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "4e57d172ec1744dd92ec9db65e19cd8b",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.91k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import numpy as np"
+ ],
+ "metadata": {
+ "id": "vNC_sB-KmI6a"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def compute_metrics(pred):\n",
+ " pred_logits = pred.predictions\n",
+ " pred_ids = np.argmax(pred_logits, axis=-1)\n",
+ "\n",
+ " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n",
+ "\n",
+ " pred_str = processor.batch_decode(pred_ids)\n",
+ " # we do not want to group tokens when computing the metrics\n",
+ " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n",
+ "\n",
+ " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
+ "\n",
+ " return {\"wer\": wer}"
+ ],
+ "metadata": {
+ "id": "daxfLXFee1gb"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import Wav2Vec2ForCTC\n",
+ "\n",
+ "model = Wav2Vec2ForCTC.from_pretrained(\n",
+ " \"facebook/wav2vec2-xls-r-300m\", \n",
+ " attention_dropout=0.2,\n",
+ " hidden_dropout=0.05,\n",
+ " feat_proj_dropout=0.05,\n",
+ " mask_time_prob=0.05,\n",
+ " layerdrop=0.04,\n",
+ " ctc_loss_reduction=\"mean\", \n",
+ " pad_token_id=processor.tokenizer.pad_token_id,\n",
+ " vocab_size=len(processor.tokenizer),\n",
+ ")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 188,
+ "referenced_widgets": [
+ "6269e756bd6d49a394e9dc4120997a0a",
+ "f8dc86d571e9474bb94d6e4bab7bfd30",
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+ "ae55c385e72848a39f328acb3e1bac66",
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+ ]
+ },
+ "id": "I5LcwTuNe36t",
+ "outputId": "1e484035-3af8-42af-d49a-e4d3b86fae5d"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6269e756bd6d49a394e9dc4120997a0a",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.53k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "79ccaf488d7d437caeb0a216ef828198",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.18G [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['project_q.bias', 'quantizer.weight_proj.bias', 'project_hid.bias', 'quantizer.weight_proj.weight', 'project_hid.weight', 'quantizer.codevectors', 'project_q.weight']\n",
+ "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.bias', 'lm_head.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model.freeze_feature_extractor()"
+ ],
+ "metadata": {
+ "id": "PDNPanGpe63p"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import TrainingArguments\n",
+ "\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=repo_name,\n",
+ " group_by_length=True,\n",
+ " per_device_train_batch_size=16,\n",
+ " gradient_accumulation_steps=2,\n",
+ " evaluation_strategy=\"steps\",\n",
+ " num_train_epochs=240,\n",
+ " gradient_checkpointing=True,\n",
+ " fp16=False,\n",
+ " save_steps=400,\n",
+ " eval_steps=400,\n",
+ " logging_steps=400,\n",
+ " learning_rate=3e-4,\n",
+ " warmup_ratio=0.12,\n",
+ " save_total_limit=1,\n",
+ " push_to_hub=True,\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "g5JmKDsCfFIe"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import Trainer\n",
+ "\n",
+ "trainer = Trainer(\n",
+ " model=model,\n",
+ " data_collator=data_collator,\n",
+ " args=training_args,\n",
+ " compute_metrics=compute_metrics,\n",
+ " train_dataset=common_voice_train,\n",
+ " eval_dataset=common_voice_test,\n",
+ " tokenizer=processor.feature_extractor,\n",
+ ")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "sAAtz7MhfIjU",
+ "outputId": "e825f34e-4bb2-4c7a-edb2-b1cad544f57f"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/content/wav2vec2-large-xls-r-300m-as is already a clone of https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-as. Make sure you pull the latest changes with `repo.git_pull()`.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "trainer.train()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "vvVB8vl9fOZh",
+ "outputId": "aa547cc5-510e-40ad-e39d-2c980cafcf96"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running training *****\n",
+ " Num examples = 624\n",
+ " Num Epochs = 240\n",
+ " Instantaneous batch size per device = 16\n",
+ " Total train batch size (w. parallel, distributed & accumulation) = 32\n",
+ " Gradient Accumulation steps = 2\n",
+ " Total optimization steps = 4560\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"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [4560/4560 5:57:58, Epoch 239/240]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ " Wer | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 400 | \n",
+ " 5.702700 | \n",
+ " 3.415665 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 800 | \n",
+ " 1.163800 | \n",
+ " 1.349787 | \n",
+ " 0.746089 | \n",
+ "
\n",
+ " \n",
+ " 1200 | \n",
+ " 0.226600 | \n",
+ " 1.614738 | \n",
+ " 0.727313 | \n",
+ "
\n",
+ " \n",
+ " 1600 | \n",
+ " 0.147300 | \n",
+ " 1.664912 | \n",
+ " 0.710773 | \n",
+ "
\n",
+ " \n",
+ " 2000 | \n",
+ " 0.104300 | \n",
+ " 1.769063 | \n",
+ " 0.708985 | \n",
+ "
\n",
+ " \n",
+ " 2400 | \n",
+ " 0.077900 | \n",
+ " 1.830036 | \n",
+ " 0.700939 | \n",
+ "
\n",
+ " \n",
+ " 2800 | \n",
+ " 0.061300 | \n",
+ " 1.868072 | \n",
+ " 0.691551 | \n",
+ "
\n",
+ " \n",
+ " 3200 | \n",
+ " 0.047100 | \n",
+ " 1.856712 | \n",
+ " 0.687528 | \n",
+ "
\n",
+ " \n",
+ " 3600 | \n",
+ " 0.034300 | \n",
+ " 1.905392 | \n",
+ " 0.683952 | \n",
+ "
\n",
+ " \n",
+ " 4000 | \n",
+ " 0.026500 | \n",
+ " 1.901989 | \n",
+ " 0.678587 | \n",
+ "
\n",
+ " \n",
+ " 4400 | \n",
+ " 0.021900 | \n",
+ " 1.906781 | \n",
+ " 0.667859 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ],
+ "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 = 281\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-400\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-400/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-400/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-400/preprocessor_config.json\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/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 = 281\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-800\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-800/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-800/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-800/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/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 = 281\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-1200\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-1200/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-1200/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-1200/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/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 = 281\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-1600\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-1600/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-1600/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-1600/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/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 = 281\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-2000\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2000/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-2000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/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 = 281\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-2400\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2400/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-2400/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2400/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/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 = 281\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-2800\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2800/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-2800/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2800/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/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 = 281\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-3200\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-3200/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-3200/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-3200/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/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 = 281\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-3600\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-3600/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-3600/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-3600/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/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 = 281\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-4000\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-4000/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-4000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-4000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/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",
+ "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 = 281\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-4400\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-4400/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-4400/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-4400/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/checkpoint-4000] 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=4560, training_loss=0.668572286451072, metrics={'train_runtime': 21486.0103, 'train_samples_per_second': 6.97, 'train_steps_per_second': 0.212, 'total_flos': 2.2650880010566533e+19, 'train_loss': 0.668572286451072, 'epoch': 239.97})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 34
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "trainer.push_to_hub()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 370,
+ "referenced_widgets": [
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+ "32f1d153753a49f8b5c1003910c998ce",
+ "32a812df48524cf1b1724513f2445aa1",
+ "2f80a8b66c384572a1b08b8bd81cef85",
+ "94352f05dbdd44298046f92b6edadede",
+ "3fe3f8a724f9480596d7c5c0d05c008d",
+ "bc6378868a194e6989548b3d4f8decbf",
+ "321c6979d8b54a3aab07e2b7b40779ae",
+ "cccb9f89f25043f2a305120ce4000eac",
+ "474a5f28f8a6410a8febf0ee0da60716"
+ ]
+ },
+ "id": "1DzGjKHifPGK",
+ "outputId": "6a7809a4-b5ce-4116-a557-f99332e2af28"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-as/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-as/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": "7bb658e851f14ba8b34c287a03ce2aad",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Upload file pytorch_model.bin: 0%| | 3.37k/1.18G [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "0ff572a0c3a0463dadc46ee97e1ac546",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Upload file runs/Jan21_06-00-30_78c0e8b6174a/events.out.tfevents.1642744880.78c0e8b6174a.74.0: 34%|###3 …"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-as\n",
+ " 46fe0a4..87eccaa main -> 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': 'as'}}\n",
+ "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-as\n",
+ " 87eccaa..d297d9f 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-as/commit/87eccaaad11b7893d86f1dfcf0cd64ef7483b868'"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 35
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "93NnxhS_1EIV"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file