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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": [ "# lapsbm1-xlsr: Wav2vec 2.0 with LaPSBM 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 [LaPS BM](https://github.com/falabrasil/gitlab-resources) 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 | | -- | 9.5h |\n", "| LaPS BM | 0.8h | -- | 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", "| lapsbm1\\_100 (demonstration below) | 0.111 | 0.418 | 0.145 | 0.299 | 0.562 | 0.580 | 0.469 | 0.369 | \n", "| lapsbm1\\_100 + 4-gram (demonstration below) | 0.061 | 0.305 | 0.089 | 0.201 | 0.452 | 0.525 | 0.381 | 0.287 |" ], "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/lapsbm1-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": [ "3fda6aa5cc8a4a33b7af653922246008", "43c84991fbd14a93be95b9bde3b00a39", "059a8b826db043c5a6df033f4cb9213b", "a1ae5356a08c419d8f9a868329bcfb4f", "2c45831822ec48b5aac1b088e86c8fc1", "214b99c11dd04f8a99d50a38c5a4c98b", "95669089f6b74830a189ac692cac75dc", "ea77c07c338441bf8d213ede920462a3", "1182035008b6471a98a045debac98b14", "74ea04401f2b41309c1dc29249746e29", "eb35ad59b6bd4239ba5ea14a798cc93f", "bbe4669c530e48de8e72ad005bf55671", "55ad1e8a74404da0a16b63c28c405a20", "ab345b69569e4396a2b634a89a6c725f", "9bb424ad5d474e65a64305556996b4cc", "409c171aba82428da0632c8a0cf895b0", "b6c50e2aa21d456a929786e66a0ca45e", "e9c4eabff902436f99436f8436b62280", "686e11da0401459aa08f0bfa52ba907a", "1ea31549d4114e958b9da59da9c1b3cb", "84e5527dab224b32a9c5d01ddc39a05a", "b8ff71b84ef6473ebd0fb55111c29c11", "f116a139680c49dba14079a8918e8da6", "8a1b33b7bb01473ca83f27a532f4246f", "424058a2cb9247869278ab0c28b7ec05", "991e98666bfe46559dddba1c85940321", "23eb624ed53d4121a2a0f207495faf9b", "8d9690dc2b994f5580cce57c02abc64c", "21af0e36548445dda232fa48e799e63b", "ad9b6e6ead97409ab74c7928b741f771", "e093876d5607431d86a104a90cb0c7f2", "db1b5bc367214db0b3de56b1d02c47ed", "7d32a41e8137476f8dccae0a8a728017", "e072e8db028947c8b7f6206fdf807783", "464809d170f3433fa02b07a9b635bc22", "e9c28e944b414600907c2a1c249b1543", "d97f350289ad44dba513023357516668", "c23b59060bda4079bd683c178018edfd", "a35310a35cfe45f7acd91ac1d87ea43b", "c9fca30641174bf7b836c5a2982e5f00", "03ea458646954f22b8273efbbd7b43aa", "649e1cad6e3e4b72ae8fa0d6a0c82314", "bd5b3e4b85a14ae1a61d450917002473", "54d49459b61d4c219228bfa4d8801f3d", "800922d874294be188473379b7c2b058", "8bea7aa21a2c4ca58529fe9814b9dbb7", "00fd2440318b42738dc47f808062901f", "04f4fa84147b49829b552f6c258aabcd", "a483455ff707472dbcfacae442bc1449", "f0f10573497a4173a015557ea2212bab", "3f35e32a36fd4aa58c0c73d7d09617cc", "42a58bb58058445bb949cc08fdb6a254", "f5091174cac94a6fa25de800d9a5e863", "31e43833303e459eb5ce4ecc6f811f82", "128ce499c79c4e6ebb6aed237e6cda3c", "8a432f75dbac4cd09ecb278970c5cbe3", "06c1a92c9ec54a98a97afdb061411edf", "060d96f551e44b0e9cda920220df179f", "56807bafd90f421d9c23a7999f2045a8", "2358591a02964c6ca81d73fb93eb5ec4", "a9bd126c5b1d4847a75d3748d5cc5598", "54b86f797d564895b458e7267dc6efc8", "77b7bfe9a4144edbad070486def4f915", "49e6812c89a14649a0f0df569e054998", "9a8691556dd54e10ae292edae1530546", "96867ba3f013484587e862c1a2e1ebf7" ] }, "id": "3ToOxo3bI6mq", "outputId": "1d195925-a642-44cf-f22b-c8dae738878e" }, "source": [ "stt = STT(MODEL_NAME)" ], "id": "3ToOxo3bI6mq", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3fda6aa5cc8a4a33b7af653922246008", "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", "Unigrams and labels don't seem to agree.\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": [ "f256a5bbfd5f486293405011b35020c4", "664caacf5aa04f79b1dd8b59ffc30831", "3e2b5b7baea0457e9ab4992be73fee69", "650b1744a20d48ee9c12e0f6354e76e3", "cbfb7acc3afa4251aedbbc1a39a775d1", "2f32a985463f4e0ca92b94b3555ed4b5", "2a4b549b4e6d42449dbeb904fd5eec7b", "5bb5d045dcbb4ca5b6a4250689021b05", "97286ce377d2498eac0a23c77b5e69da", "71afee7f7cfc40d890b857c9cd91a512", "d9562a8dba5a42189bd58666dcc44218", "1cd6244d816d4f2bb3086fa32079039d", "3a1da9f94dcd4b7197d127280581936d", "40d88bc1c50142a9bdd30355fa0cccf3", "ccca5f51a3494af88375c2807f0ae0d3", "9b86648992e049c3a89d5ff38af2a59c", "92d644e84d4b4c6c84dfbbb7afc85425", "350c7d98c40443d1ab9d1305a325cbb7", "3a68ceabfad343b3bb5aa759123ea5a4", "499008f97adf42c18821f81855ce01d4", "5d7a3733ed134b459e31bab2e4c69f0b", "710a04be6d5f456c9b296555b8b7b9a6", "dc3749a7519a42da914dafa0ca3c549a", "7b8770294607410d82fd1875ca4e03a8", "cc5fc76239c140da97dfec19dd893c06", "170bdf62fed848c4b28c487745c30579", "0a002c4320bf47178857884e395a6f34", "659714ca0b1e410abef5b1ee34057557", "5e71a27f7dfd428e91ee045b871ffcd4", "dbcf83cfe5d646578f781f11670211d7", "0ecdc22b41384d6eb12df71a3e558647", "5566fb52e4154a0186f82f7d3b451c87", "d54fe8c6516c4489b24c969ecb87dc8f", "9a5ec086db8e45cbacf43a579942c326", "8834f63fd70b415c825877977dcb7509", "b97d375f74414133bdead2d41c7abf2f", "d940911bf71a41cebae953e149f8d5a5", "0e444f841f754dad9b9fccd2adc3669f", "162a5bc751504f67bf4bcf5be51feb8b", "ee5b0bf78283442fad11f19f5d339de4", "2dcf71dc59004ef9a5c9dd50a54e9ca3", "06346ad2416b42a6bb7112a7e7cb38f1", "74da4cdad4b64c97b9686c08495475f6", "d9b4aba9a6cf4044b85fdba179d0ce92", "ebb19905bb014cdf837df448e6cdb4e5", "4497b29ef6044dc3af954440bb94204d", "b7d776b1d7b847fbbc9eec02575c99f7", "c5a6afcf05f643fd82757e72f4acb8d7", "8efc31026beb4d3cbe95ba44c51a7816", "6286cb7bae664d2fa1b4a6ebacfa5399", "c35add40db2344eb86bdc562d7321bdb", "0bc2358ca7654bab8a0f6235adf6a5a7", "b310416c9eb249a3ba3c829cc5f1ee4c", "33e5e47b01314fe396392f985ff2871b", "18472230a4744b82856c0231278eac85" ] }, "outputId": "344dbf8b-5e20-4986-cd46-3f57ccdfbb7d" }, "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": "f256a5bbfd5f486293405011b35020c4", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00