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"_view_count": null, "_view_module_version": "1.2.0", "_model_module": "@jupyter-widgets/controls" } }, "5e46679ee690404b94b2c7c75750b752": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_view_name": "LayoutView", "grid_template_rows": null, "right": null, "justify_content": null, "_view_module": "@jupyter-widgets/base", "overflow": null, "_model_module_version": "1.2.0", "_view_count": null, "flex_flow": null, "width": null, "min_width": null, "border": null, "align_items": null, "bottom": null, "_model_module": "@jupyter-widgets/base", "top": null, "grid_column": null, "overflow_y": null, "overflow_x": null, "grid_auto_flow": null, "grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "cf966af3-94bf-46eb-93af-1995c326bc25" }, "source": [ "# commonvoice10-xlsr: Wav2vec 2.0 with Common Voice Dataset" ], "id": "cf966af3-94bf-46eb-93af-1995c326bc25" }, { "cell_type": "markdown", "metadata": { "id": "78e2333a-f973-4483-8790-6cbd52ee7b9e" }, "source": [ "This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [Common Voice 7.0](https://commonvoice.mozilla.org/pt) dataset.\n", "\n", "In this notebook the model is tested against other available Brazilian Portuguese datasets. \n", "\n", "| Dataset | Train | Valid | Test |\n", "|--------------------------------|-------:|------:|------:|\n", "| CETUC | | -- | 5.4h |\n", "| Common Voice | 37.8h | -- | 9.5h |\n", "| LaPS BM | | -- | 0.1h |\n", "| MLS | | -- | 3.7h |\n", "| Multilingual TEDx (Portuguese) | | -- | 1.8h |\n", "| SID | | -- | 1.0h |\n", "| VoxForge | | -- | 0.1h |\n", "| Total | | -- | 21.6h |\n" ], "id": "78e2333a-f973-4483-8790-6cbd52ee7b9e" }, { "cell_type": "markdown", "metadata": { "id": "FixP81bsISop" }, "source": [ "#### Summary\n", "\n", "| | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG |\n", "|----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|\n", "| commonvoice10 (demonstration below) | 0.133 | 0.189 | 0.165 | 0.189 | 0.247 | 0.474 | 0.251 | 0.235 | \n", "| commonvoice10 + 4-gram (demonstration below) | 0.060 | 0.117 | 0.088 | 0.136 | 0.181 | 0.394 | 0.227 | 0.171 |" ], "id": "FixP81bsISop" }, { "cell_type": "markdown", "metadata": { "id": "wl0IljpZEdic" }, "source": [ "#### Transcription examples\n", "\n", "| Text | Transcription |\n", "|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|\n", "| ||\n" ], "id": "wl0IljpZEdic" }, { "cell_type": "markdown", "metadata": { "id": "ad5dea33-ccd9-44b6-b0fc-76853e672d63" }, "source": [ "## Demonstration" ], "id": "ad5dea33-ccd9-44b6-b0fc-76853e672d63" }, { "cell_type": "code", "metadata": { "id": "sV6KKZTILStX" }, "source": [ "MODEL_NAME = \"lgris/commonvoice10-xlsr\" " ], "id": "sV6KKZTILStX", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "iZvFEIPUIvSp" }, "source": [ "### Imports and dependencies" ], "id": "iZvFEIPUIvSp" }, { "cell_type": "code", "metadata": { "id": "XGYRppRdXNBg" }, "source": [ "%%capture\n", "!pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html\n", "!pip install datasets\n", "!pip install jiwer\n", "!pip install transformers\n", "!pip install soundfile\n", "!pip install pyctcdecode\n", "!pip install https://github.com/kpu/kenlm/archive/master.zip" ], "id": "XGYRppRdXNBg", "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "19ef3f04-a137-461a-bcdb-b1e9cd39639f" }, "source": [ "import jiwer\n", "import torchaudio\n", "from datasets import load_dataset, load_metric\n", "from transformers import (\n", " Wav2Vec2ForCTC,\n", " Wav2Vec2Processor,\n", ")\n", "from pyctcdecode import build_ctcdecoder\n", "import torch\n", "import re\n", "import sys" ], "id": "19ef3f04-a137-461a-bcdb-b1e9cd39639f", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "PeoG2z5dIA1u" }, "source": [ "### Helpers" ], "id": "PeoG2z5dIA1u" }, { "cell_type": "code", "metadata": { "id": "4EbwTazyHzD8" }, "source": [ "chars_to_ignore_regex = '[\\,\\?\\.\\!\\;\\:\\\"]' # noqa: W605\n", "\n", "def map_to_array(batch):\n", " speech, _ = torchaudio.load(batch[\"path\"])\n", " batch[\"speech\"] = speech.squeeze(0).numpy() \n", " batch[\"sampling_rate\"] = 16_000 \n", " batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower().replace(\"’\", \"'\")\n", " batch[\"target\"] = batch[\"sentence\"]\n", " return batch" ], "id": "4EbwTazyHzD8", "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "W6Np1z9oH5hy" }, "source": [ "def calc_metrics(truths, hypos):\n", " wers = []\n", " mers = []\n", " wils = []\n", " for t, h in zip(truths, hypos):\n", " try:\n", " wers.append(jiwer.wer(t, h))\n", " mers.append(jiwer.mer(t, h))\n", " wils.append(jiwer.wil(t, h))\n", " except: # Empty string?\n", " pass\n", " wer = sum(wers)/len(wers)\n", " mer = sum(mers)/len(mers)\n", " wil = sum(wils)/len(wils)\n", " return wer, mer, wil" ], "id": "W6Np1z9oH5hy", "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "LYxa8t0TH5kT" }, "source": [ "def load_data(dataset):\n", " data_files = {'test': f'{dataset}/test.csv'}\n", " dataset = load_dataset('csv', data_files=data_files)[\"test\"]\n", " return dataset.map(map_to_array)" ], "id": "LYxa8t0TH5kT", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "z4lqZOc3IzYY" }, "source": [ "### Model" ], "id": "z4lqZOc3IzYY" }, { "cell_type": "code", "metadata": { "id": "vTubqxQmE1yE" }, "source": [ "class STT:\n", "\n", " def __init__(self, \n", " model_name, \n", " device='cuda' if torch.cuda.is_available() else 'cpu', \n", " lm=None):\n", " self.model_name = model_name\n", " self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)\n", " self.processor = Wav2Vec2Processor.from_pretrained(model_name)\n", " self.vocab_dict = self.processor.tokenizer.get_vocab()\n", " self.sorted_dict = {\n", " k.lower(): v for k, v in sorted(self.vocab_dict.items(), \n", " key=lambda item: item[1])\n", " }\n", " self.device = device\n", " self.lm = lm\n", " if self.lm: \n", " self.lm_decoder = build_ctcdecoder(\n", " list(self.sorted_dict.keys()),\n", " self.lm\n", " )\n", "\n", " def batch_predict(self, batch):\n", " features = self.processor(batch[\"speech\"], \n", " sampling_rate=batch[\"sampling_rate\"][0], \n", " padding=True, \n", " return_tensors=\"pt\")\n", " input_values = features.input_values.to(self.device)\n", " attention_mask = features.attention_mask.to(self.device)\n", " with torch.no_grad():\n", " logits = self.model(input_values, attention_mask=attention_mask).logits\n", " if self.lm:\n", " logits = logits.cpu().numpy()\n", " batch[\"predicted\"] = []\n", " for sample_logits in logits:\n", " batch[\"predicted\"].append(self.lm_decoder.decode(sample_logits))\n", " else:\n", " pred_ids = torch.argmax(logits, dim=-1)\n", " batch[\"predicted\"] = self.processor.batch_decode(pred_ids)\n", " return batch" ], "id": "vTubqxQmE1yE", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "WjhisE7QKkMK" }, "source": [ "### Download datasets" ], "id": "WjhisE7QKkMK" }, { "cell_type": "code", "metadata": { "id": "hXeb8QpBKnS2" }, "source": [ "%%capture\n", "!gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI\n", "!mkdir bp_dataset\n", "!unzip bp_dataset -d bp_dataset/" ], "id": "hXeb8QpBKnS2", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "vlWQnHKDKz4Q" }, "source": [ "### Tests " ], "id": "vlWQnHKDKz4Q" }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 209, "referenced_widgets": [ "68037c37b18d4c80bbb3997e67ebf61c", "d5852851166b458b9335eeccc49f2e3c", "fcf3f29f29e8497ab7e10e5aa17d10d8", "4ad19c3cb85c476f8d28ce9211b5e708", "e6646961ea6442a59cb5f618982598b9", "13f1b70a236f4aa9988d34493146a741", "e5fbc6e8669d44d69a0e8177dd479e52", "7dccb30626884f1cba7919c68e8a50e0", "7a26c63b23414af2815577bd2cb90643", "fe9934a17eb54598974d8cb3e85d36a8", "1a593510bd9d4becb8c9e87c5ee39137", "3e6858c4773046119e8d426f2c7fdbd5", "2ee966926a1447d68b35b8e05fa7bb4f", "9e49bf97081648adb99a83cbf8ba625e", "264a5059c6344888b41ce949eefd5893", "440362e580ee4901b13799a6c71e3748", "25e24d0d590447efa27ae32015472bee", "2c5b38b154e942a2b1ce6f5a8fb3a873", "58de10d38a6d45ebbd6a389536b0c90f", "19998415fd464ee79122a909ab4a0f37", "4480c436add14c99a1b9dfc255336e59", "35745842350147d98ad0c18b118c39e5", "4798b48f82f748dd93ff88ee526440fc", "8ac548d168b3430e9ac2097e7353b03e", "43875987e906427ca8e82be7b017c856", "fe7a6911a5bc4d1b8e62610d3a62d430", "59419754a10f4f698be7c3a33a865d7f", "8e1ca5b3bd964987a650a06aeed86e07", "0e651d70a700471ca3ee7fa3a82bc03e", "451a7762b7154c50906a36dd2bebaaa0", "dc663a9983e1473aab76acfc4272e60d", "d4b8dd8ad56b481698d27d4868b3b859", "e10144c98869444586b09768f05aa18a", "91abbd5a86c94aabad631dcd9cb093e0", "28fd93c6d9b24fea93ce9bc340ad3c6a", "66a4081e2f1747afbcd10845bf05450f", "34a2556cf8e941d0919ecaa08de6a2bd", "3d869c395c0f41daa7e4e0b60e6ab103", "92dbba79dc2840f8943bf7c9f3a6e5b4", "988248f4fea74ff086bf95c9b5ee5a09", "f05a8c3a08bb40f5859f5f5445a9aaaa", "800d93aa3de24740bbb9cee027fb415c", "dfbe881b57ca4e6885467d246b814ca2", "9e1eb6b12fc64571805fb079694cffe9", "ea903ef3278747d48f2d051a37231f59", "b2657080b33a41669ad47ca44f3acbd5", "16ef86557fcd4b2f94e6cbfb97c99542", "b3777330d8e24d88a051edb362910bd7", "259f0619b9fb488f9770c74f5c0f7576", "9b2fef5cd5814bf7bd5d4f286efa2373", "e193c4b952e3471d9e62d85c35d9e292", "1bef9c78a5b24106b90584cd048fe9df", "0e397221c69f43d09a1feb7228ff77e9", "17a614d9f34e4e46ae35a19c02da4834", "776e3b020b7e4196a7fbc2a960d51b15", "1ed00294b7924f23996e368aed6c5a8d", "a6a8a29763184b0787faa506c6964a72", "5aaec8dbdaf44c36a3a19e285375d086", "d6361aa7ac814c4ba6d8aaac64fb0198", "4f8701a8832d452b92f5fa3b5c3f8433", "5489363a0fab48ca8863d6dad4803cb3", "13643c90698a408fa8b441e6fd29195d", "6296d9a98b4b43a9ba929cc8c8c1d792", "05d56a2c625b43ae993160f0d64d2b09", "2ced041cc93342cfa9bd2fa01e482706", "41d13ec3d8394bd9a480e94e88445749" ] }, "id": "3ToOxo3bI6mq", "outputId": "745f9fe2-0399-4677-95f0-539b5db18495" }, "source": [ "stt = STT(MODEL_NAME)" ], "id": "3ToOxo3bI6mq", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "68037c37b18d4c80bbb3997e67ebf61c", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.64k [00:00 1 in alphabet. This is unusual unless style is BPE, but the alphabet was not recognized as BPE type. Is this correct?\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "MB8scRnIVu1O" }, "source": [ "#### CETUC" ], "id": "MB8scRnIVu1O" }, { "cell_type": "code", "metadata": { "id": "iaaBB9-3VpSH", "colab": { "base_uri": "https://localhost:8080/", "height": 264, "referenced_widgets": [ "31a4390e1e804c46bf4bb5cc209957cc", "cf722ec3c7c443ed98c6b4c4719ff3ea", "707be7a4081f40ae93f64b9986f2d007", "77ef73f557fe494e995c170ec4232342", "1b73fd454a044db08cf5bf214a63ab5e", "6f9744dc9a064b0183fa1c3aa91c427f", "7de45a69656b45a6a1501ea936350161", "174be6b99d9f4b8c9d5c5e98972cc90f", "3606bd8a579749a7b3696adb8c17edad", "909f60c288bc405db2c9bd9bc5d3cb57", "707b80e3d2d743f49d052de5c2fbb361", "5d40e102c0a74ba899740d1617d6e327", "9a0fa9709a104197b8251644ced74a3b", "b303cc4a353841a4868eba7716fd7796", "1d79b2cbb45040edb848b16994a72f54", "eddad9e696b0493dade7cea5007f0dad", "dd86d76067aa4ca8858ca9322e01dd32", "10d69038195548fbbca2a020588fd5a0", "87c2eb4498d7463494c64cecaeba8984", "d7cd2f4127244075acad7d06362b6b9d", "b0bd88d9cf8c46f3970726afe921daa9", "3401c69d18b248049e209772c7232780", "4f9d0069f3af42a2a94ed68f80a215a5", "acceb485fdd74e55b25c45c7e04dcded", "98396d0a37a84ef08fd95996484f8789", "749629be23f94fc3a3d089aae9e1b875", "6f6ca549cf124f8e954b37cfc0eeb312", "b1705234e48f46d78241c0c1ea3f2d0f", "205e774cc8f34e84b3d6f3aa64d72d22", "60b16cc6aec941faa37818f478057893", "3c5663cc072d40a3ac3c488dcd00063f", "470683c95ae445cbbfaff8ff1e0097af", "ae4e948491d64109ba178369bf213e70", "9efedf267fba4ec7bda57da085605725", "7f650391c27e48cab7b7cbeee61fc7d0", "c52de1afa85947f8b7b877ae85e6d998", "43d2c9924cbb4c79a277caea846ed98c", "c3e50953413a4e0197e861f6af080136", "f8926031ebd646e7be256e9058650da8", "f51a8739167d4aa0bede78041768b876", "3119c0ae483c41aa8280f982f930a565", "8937a8e4d1ff4de190b759d7f7d27b6a", "fe7a7eaa686f471dae298ab7736cd917", "587c7b8434274738a009d648d84ddb18", "c85accabd8794623b557ef7d103d003d", "8e34de8e84614ceab5369464b5ef73dc", "6fb83899f08c4d83b7ee1f4ba4299829", "1a8d809ddb2e47f58f3ae13b0a038f95", "68a3ce9dba2c4d76916231c9cb612952", "5ff60c87bb66477aa90cbea95404a60e", "d77d6750c21143feba7d580d912b83b1", "32823fc1a3dc44b99b90c73f97a5794e", "42c0d75465a343d2a8e65b4245c46d0d", "7024221c75a845d8b83b0d45aabac6aa", "6257a303052d4c54b42c23bf6b3d2406" ] }, "outputId": "3beb8889-64df-4ccc-bd82-dadddf316c23" }, "source": [ "ds = load_data('cetuc_dataset')\n", "result = ds.map(stt.batch_predict, batched=True, batch_size=8) \n", "wer, mer, wil = calc_metrics(result[\"sentence\"], result[\"predicted\"])\n", "print(\"CETUC WER:\", wer)" ], "id": "iaaBB9-3VpSH", "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Using custom data configuration default-042b60e033d80878\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-042b60e033d80878/0.0.0/bf68a4c4aefa545d0712b2fcbb1b327f905bbe2f6425fbc5e8c25234acb9e14a...\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "31a4390e1e804c46bf4bb5cc209957cc", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00