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+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "M2b-7B0PjEvG",
+ "outputId": "30e8d5a6-10f6-46e6-b231-57972180ee86"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Sun Jan 30 18:06:30 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 40C P0 30W / 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": {
+ "id": "k6Ao2XfngKmI",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "5e3d4c99-5dfe-4e43-ccb5-76e7bdd019b5"
+ },
+ "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 2s (928 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": {
+ "id": "1CCVxM2-hLbE",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 359,
+ "referenced_widgets": [
+ "39e1693dfe364c898f855a0706278262",
+ "93228b298bb74a1f85341d728f9a05e4",
+ "20042b9ba890434ca76d853f95e4ffe7",
+ "4954c5e13785448ea4821396c9f1b466",
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+ "f98fde11c94d4128a1ee24cec5b2c339",
+ "379dccd4e0af4f2ca6971ef7970f20fd",
+ "276b07821d4f4b4594e485237b920c92",
+ "62424b7d68124d94b232eaaab9601ff9",
+ "984d56a0e4cd4f089af687f2769f0f45",
+ "6a913847fd0e4369b2459df029614b23"
+ ]
+ },
+ "outputId": "2441b8a3-6e54-424c-b564-835fef5754e7"
+ },
+ "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"
+ ],
+ "metadata": {
+ "id": "HP3z-kMqhW49"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!sudo rm -r /root/.cache"
+ ],
+ "metadata": {
+ "id": "OvVBJh9C5x_l"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"ha\", split=\"train+validation\", use_auth_token=True)\n",
+ "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"ha\", split=\"test\", use_auth_token=True)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 377,
+ "referenced_widgets": [
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+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c2a6609c04b04e8d827133334ccf81a2",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/10.1k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c24a11878ff14ca8a9ea836fd23f825f",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/2.98k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6ce92181cf4e401fb50c4fabf49c76e3",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/53.1k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Downloading and preparing dataset common_voice/ha to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ha/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8...\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "48d06f2ad64a4983b55411ee7bb5e5d0",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/218M [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8f62c7ccc04843e89060db77a7be4526",
+ "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": "51bd122c6d254fb4af7abb0e538a6fa1",
+ "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": "2402388de6cb458ebf1c3f547f8eb9e9",
+ "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": "1260a8c158284d76870682ffb28e0191",
+ "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": "ad53ad9be8fc4078b5de8ab87d0241a9",
+ "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/ha/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8. 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/ha/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\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",
+ "import unicodedata\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\"] = unicodedata.normalize(\"NFKC\", 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": [
+ "d25da162fed74bdcb79a87150016e64e",
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+ ]
+ },
+ "id": "TwvH3y9DZhvr",
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+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d25da162fed74bdcb79a87150016e64e",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0ex [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "cdda523a818145a3bf23dc32c465fd37",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0ex [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": [
+ "ba9348ff269e4a058ae24f6fc297678f",
+ "adaf37a2e16049eb8b6f4b2c4d3b1463",
+ "18bf244d14d14787acdbe405b7d92720",
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+ "767d92e7a0934a10a7f43ce8e83806ea",
+ "c7d186d9c474401ca21d250f1b0cf3ae",
+ "00ba5e1aa9a446f68ca2f75e34d6de1a",
+ "77929470e6684bc5a2597cb940139fa3",
+ "854faf50c0fd401d90ab3dacf7a47c10",
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+ "3b48ab2e3f5c4ccf89ca20e3e76440af",
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+ "3adc8f8a457a40b983b9398eea4523f0",
+ "08ea281b633b40b58ae36c94b19a85b0"
+ ]
+ },
+ "id": "S9ruLJg6Zscr",
+ "outputId": "169f46ec-cecc-4f75-e4df-7bd7ca8197b0"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ba9348ff269e4a058ae24f6fc297678f",
+ "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": "c7d186d9c474401ca21d250f1b0cf3ae",
+ "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": "77451acd-6c61-41bd-b9dd-4d2b4d18fb2b"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{' ': 0,\n",
+ " \"'\": 1,\n",
+ " 'a': 2,\n",
+ " 'b': 3,\n",
+ " 'c': 4,\n",
+ " 'd': 5,\n",
+ " 'e': 6,\n",
+ " 'f': 7,\n",
+ " 'g': 8,\n",
+ " 'h': 9,\n",
+ " 'i': 10,\n",
+ " 'j': 11,\n",
+ " 'k': 12,\n",
+ " 'l': 13,\n",
+ " 'm': 14,\n",
+ " 'n': 15,\n",
+ " 'o': 16,\n",
+ " 'p': 17,\n",
+ " 'q': 18,\n",
+ " 'r': 19,\n",
+ " 's': 20,\n",
+ " 't': 21,\n",
+ " 'u': 22,\n",
+ " 'v': 23,\n",
+ " 'w': 24,\n",
+ " 'x': 25,\n",
+ " 'y': 26,\n",
+ " 'z': 27,\n",
+ " 'í': 28,\n",
+ " 'ƙ': 29,\n",
+ " 'ƴ': 30,\n",
+ " 'ɓ': 31,\n",
+ " 'ɗ': 32,\n",
+ " 'ʻ': 33}"
+ ]
+ },
+ "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": "3e7ab07c-3b9f-46ef-cd06-d64fd6e47d10"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "36"
+ ]
+ },
+ "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": "c96dc1a7-6f04-45c7-c0c9-cb06ecbe34d1"
+ },
+ "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-ha-cv8\""
+ ],
+ "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": 160
+ },
+ "id": "tPYBu0yZaWqA",
+ "outputId": "43b59f04-1a8e-4434-b3b5-002de1470e07"
+ },
+ "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-ha-cv8 into local empty directory.\n",
+ "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-ha-cv8\n",
+ " ef5c784..64ff0d9 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-ha-cv8/commit/64ff0d9d37d20d2363890c287a646dd76cd7bc50'"
+ ]
+ },
+ "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": [
+ "7df77ec2e4434555bd625c88142624e5",
+ "dc29a68593a147fd9f7f197c6cf031f9",
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+ ]
+ },
+ "id": "Uxt4G7XweZxf",
+ "outputId": "d98d6a8e-4a50-4ba5-9a3e-5f2f00e51d16"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7df77ec2e4434555bd625c88142624e5",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0ex [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e30a930b820f4610acd41d2442b6cbfb",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0ex [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": [
+ "e167261c6b0643c1a9de50f92c0590ef",
+ "64222f57f03140b7b10a9927bb896098",
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+ ]
+ },
+ "id": "tHAXlHO1ez7Z",
+ "outputId": "49b65f90-7ef0-458c-f343-87624c3a8f1b"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e167261c6b0643c1a9de50f92c0590ef",
+ "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=0.1,\n",
+ " layerdrop=0.0,\n",
+ " feat_proj_dropout=0.0,\n",
+ " mask_time_prob=0.75,\n",
+ " mask_time_length=10,\n",
+ " mask_feature_prob=0.25,\n",
+ " mask_feature_length=64,\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": [
+ "2614eaa8a26344ee92bde1764d299bf9",
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+ "7f68b2544ac54127a8755f89038243b9",
+ "c195e4fe65094007b2e6a6b3352665f1"
+ ]
+ },
+ "id": "I5LcwTuNe36t",
+ "outputId": "e8c65e1d-3bef-44ec-eccd-933994f24a1b"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2614eaa8a26344ee92bde1764d299bf9",
+ "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": "eff011f256774283aa53f564e9de1f17",
+ "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_hid.bias', 'project_hid.weight', 'quantizer.codevectors', 'project_q.weight', 'quantizer.weight_proj.bias', 'quantizer.weight_proj.weight', 'project_q.bias']\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",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "9ba3d164-d165-4161-9302-dd727a1da366"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1703: FutureWarning: The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5.Please use the equivalent `freeze_feature_encoder` method instead.\n",
+ " FutureWarning,\n"
+ ]
+ }
+ ]
+ },
+ {
+ "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=100,\n",
+ " gradient_checkpointing=True,\n",
+ " fp16=False,\n",
+ " lr_scheduler_type='cosine_with_restarts',\n",
+ " save_steps=400,\n",
+ " eval_steps=400,\n",
+ " logging_steps=100,\n",
+ " learning_rate=1e-4,\n",
+ " warmup_steps=1000,\n",
+ " save_total_limit=1,\n",
+ " seed=13,\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": "a4a7acb4-db4c-4eb3-ae30-3ebf945a5f21"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/content/wav2vec2-large-xls-r-300m-ha-cv8 is already a clone of https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-ha-cv8. 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": "422b5557-cffe-4d79-e459-a75cf73afec6"
+ },
+ "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",
+ "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+ " FutureWarning,\n",
+ "***** Running training *****\n",
+ " Num examples = 1941\n",
+ " Num Epochs = 100\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 = 6100\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [6100/6100 7:43:11, Epoch 100/100]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ " Wer | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 400 | \n",
+ " 2.959900 | \n",
+ " 2.864991 | \n",
+ " 1.000000 | \n",
+ "
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+ " \n",
+ " 800 | \n",
+ " 2.735700 | \n",
+ " 2.737749 | \n",
+ " 0.995095 | \n",
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+ " \n",
+ " 1200 | \n",
+ " 1.301200 | \n",
+ " 0.668555 | \n",
+ " 0.711050 | \n",
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+ " \n",
+ " 1600 | \n",
+ " 1.045400 | \n",
+ " 0.568642 | \n",
+ " 0.613676 | \n",
+ "
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+ " \n",
+ " 2000 | \n",
+ " 0.906900 | \n",
+ " 0.557609 | \n",
+ " 0.581506 | \n",
+ "
\n",
+ " \n",
+ " 2400 | \n",
+ " 0.820000 | \n",
+ " 0.550226 | \n",
+ " 0.559146 | \n",
+ "
\n",
+ " \n",
+ " 2800 | \n",
+ " 0.741300 | \n",
+ " 0.596990 | \n",
+ " 0.558569 | \n",
+ "
\n",
+ " \n",
+ " 3200 | \n",
+ " 0.687200 | \n",
+ " 0.581653 | \n",
+ " 0.542845 | \n",
+ "
\n",
+ " \n",
+ " 3600 | \n",
+ " 0.634000 | \n",
+ " 0.563644 | \n",
+ " 0.531448 | \n",
+ "
\n",
+ " \n",
+ " 4000 | \n",
+ " 0.602200 | \n",
+ " 0.578025 | \n",
+ " 0.522937 | \n",
+ "
\n",
+ " \n",
+ " 4400 | \n",
+ " 0.570500 | \n",
+ " 0.603629 | \n",
+ " 0.532314 | \n",
+ "
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+ " \n",
+ " 4800 | \n",
+ " 0.540800 | \n",
+ " 0.611867 | \n",
+ " 0.533612 | \n",
+ "
\n",
+ " \n",
+ " 5200 | \n",
+ " 0.522500 | \n",
+ " 0.610544 | \n",
+ " 0.526976 | \n",
+ "
\n",
+ " \n",
+ " 5600 | \n",
+ " 0.526500 | \n",
+ " 0.603407 | \n",
+ " 0.523081 | \n",
+ "
\n",
+ " \n",
+ " 6000 | \n",
+ " 0.515400 | \n",
+ " 0.609443 | \n",
+ " 0.523370 | \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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-400\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-400/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-400/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-400/preprocessor_config.json\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/preprocessor_config.json\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-800\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-800/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-800/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-800/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-400] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1200\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1200/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1200/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1200/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-800] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1600\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1600/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1600/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1600/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1200] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2000\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2000/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1600] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2400\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2400/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2400/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2400/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2000] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2800\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2800/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2800/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2800/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2400] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3200\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3200/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3200/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3200/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2800] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3600\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3600/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3600/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3600/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3200] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4000\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4000/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3600] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4400\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4400/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4400/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4400/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4000] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4800\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4800/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4800/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4800/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4400] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5200\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5200/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5200/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5200/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4800] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5600\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5600/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5600/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5600/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5200] due to args.save_total_limit\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 = 892\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-6000\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-6000/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-6000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-6000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5600] due to args.save_total_limit\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=6100, training_loss=1.261003189086914, metrics={'train_runtime': 27800.0598, 'train_samples_per_second': 6.982, 'train_steps_per_second': 0.219, 'total_flos': 2.5645201559735407e+19, 'train_loss': 1.261003189086914, 'epoch': 100.0})"
+ ]
+ },
+ "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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+ "a57138b30a664ecd97784fe65206c387",
+ "4d3886eb267b48b5b2d888fd19e36e80",
+ "7e1217bc769e47908183e3ad2dc986e9",
+ "9d6447b1a5ba435681b6d0b5152a2324",
+ "6e1f7790f7254218a1eebbb8341bce06"
+ ]
+ },
+ "id": "1DzGjKHifPGK",
+ "outputId": "71a60ef0-ad3a-43dd-902d-e515a5539edf"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/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": "a7b6850a923647c3a1f3e63354b52dfc",
+ "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": "ca2bca9353b246a59b171d33861f8f43",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Upload file runs/Jan30_18-15-30_1cc285606e9d/events.out.tfevents.1643566603.1cc285606e9d.73.0: 18%|#7 …"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-ha-cv8\n",
+ " 64ff0d9..b10ee1b 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': 'ha'}}\n",
+ "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-ha-cv8\n",
+ " b10ee1b..19dfbde 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-ha-cv8/commit/b10ee1b13924b4dcb001e932e35520c79c438059'"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 35
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "93NnxhS_1EIV"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file