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"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