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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": [ "# tedx100-xlsr: Wav2vec 2.0 with TEDx 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 [TEDx multilingual in Portuguese](http://www.openslr.org/100) 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 | | -- | 0.1h |\n", "| MLS | | -- | 3.7h |\n", "| Multilingual TEDx (Portuguese) | 148.8h| -- | 1.8h |\n", "| SID | | -- | 1.0h |\n", "| VoxForge | | -- | 0.1h |\n", "| Total |148.8h | -- | 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", "| tedx\\_100 (demonstration below) |0.138 | 0.369 | 0.169 | 0.165 | 0.794 | 0.222 | 0.395 | 0.321| \n", "| tedx\\_100 + 4-gram (demonstration below) |0.123 | 0.414 | 0.171 | 0.152 | 0.982 | 0.215 | 0.395 | 0.350|" ], "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/tedx100-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": [ "26c8b8f67bcb4804bad3fc0bb3be54ee", "462be365c05341878da3914893d67311", "c8997d9069f14102967f835f4ecd7770", "3b879d767bdc4aa3a937100cbc90cc62", "5148be181efe4efe97ef09083eceedd3", "af8f0b3b9b01484389e923a4d00d8c69", "3925c452920240c0a3c308824b64e039", "edbd8af7c65c4918a2225956fc16b9b4", "78552dd1fd1a461f890c9de71a328c6c", "f22d4f0654924db7b7e0e98e90cd187b", "ef8b867edf4e4e8899ca1eaba9e78724", "79509f30e2b243178260e1f3a2883d76", "01aac6437c8f4535a9ae5f628c8110b1", "d60b60cec442414d96abb18ee9013ab6", "3ebf21cf0e7340619003d04df4b3a6c1", "bae4bea3cd634b64a26f98f34e9d42c8", "5b741c147b28442f9490856b207d0585", "d65a0a45bec44df1a5d708d9a2e68bff", "95ee5d2942a44cd0a39fdaee8e84eb62", "ec6b71edd9104ae1a6ca3cb14b1d75e9", "f6791b073734448eb3b8c4e688a979bf", "bd6358f7f374413eb393e64632e96edd", "41bda048d26f4cc6ac2d2f3b8d4c5097", "a84c7ca7d68744518c363d1090d4e219", "37a3cc08bcc04ded8556b3c4116e9b16", "155955b50de849fa812bdf16e7743314", "948e196ca9934800bddc8963e5422cc1", "a0c67a8333974160ab4a44bac055df76", "3ac3eb9767374b31a554ef5a9e2531e9", "fd389b9fbb8d48c78b40a09c5288f73b", "23206107f46347e0b6adf1b8b6d6b68f", "f809179a99364ea1a8eed7f110e5c878", "375ac796b85141a3a02c25b271efbd1e", "d7c1c44d180244e7911a06f25fb70cd6", "1472ec9bf0974e50af0cb6d68b20fd30", "db8746d9b49049989328d638454af35a", "1358f2a2b3c3419f81e32870da4f9b7d", "9a231efb2be4405d9f43dda1a3edf9b0", "63c11a5e81b542ae9c78fef0e960693c", "c32bd7c52ff047f2a6041bb625b27eb4", "ca8cfdb378b242ada2a72d5cbde091fc", "b7ebfabf9fac41d1ac5e1c5f23968570", "cd77c3c4563b47c49cfeb1b8123c141e", "daf7bd839937404681afe0884d1b09c9", "7faa2b0ab84b498090a40d7dabfc7ed8", "6b0d4562438e41329cc8ea93c166e7e8", "c1c83dc04286485aa47d93c6e9ea3b4e", "75a49db0681a4922b9d6a294be3c3a2f", "2de1284407304fe48ece339205b2d566", "316c0c536adb4982813c45ca3a5660c3", "c18e43c4f1a74454ad8cc36a9a402d79", "6aea5794087a41ebb886456e49286a7f", "2c461ce15e294182aee87660aa6159d3", "b4cf03767ae0474fb5cc3b1efaefe23a", "23c8eb085aa549d59307a14a7f1a07a0", "0eb7880fa90e4f51b466b48e6fbb4299", "e0682b94d8e748a5a1b2f6a814999caf", "714612d3fe174364ae805b6526e243fb", "2096450a9cb247da86f68392d1d5eb99", "ba557a31ad664c869da14f6b6d98e813", "2468878dba56432da1b98ace0fa8cc55", "c9da2a288d42432dbc8d3391943991d9", "6b6ac40c2df943a8855cc5757111753c", "36520c4559b649f589a6747adf5ab52f", "4a1e0f151d864396a6ae0f16e5f755aa", "16b9ae54f08f4c8fa88367a1cb8e196b" ] }, "id": "3ToOxo3bI6mq", "outputId": "bd2b2b5c-9268-44ae-df80-ee78a75f6697" }, "source": [ "stt = STT(MODEL_NAME)" ], "id": "3ToOxo3bI6mq", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "26c8b8f67bcb4804bad3fc0bb3be54ee", "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": 266, "referenced_widgets": [ "e6193bbe8db34c94923c251e8765ab47", "f4a5fab2c31540b591973ac8f729f4a7", "11cc54d111174a9484baef995a08051b", "82f101314f8e40d897cd1abc41e5dbc4", "add8423c401e4629b239baccf7909727", "c50cdfc5e1e74c6582ab5cb1d8c5e804", "ef80695c01f94aaa9396554a5f440990", "7ab11995e65f4048a59eea0bd48b1a5c", "ff3cec5273f44e38b14fa69ce9b1dec1", "40177adfe7264470a765d124ee0c2692", "583b7bb759c344b3ab24eb43f0b091dd", "4d51a22ff16d465381d7fb5949693c94", "6356f9e57d7c434890b38dfe2075afdb", "fc3b5e965211453e9e2911d8946b35b6", "7f42c88895e34e82a22e1e243133309a", "38c97520bc144dc6b556f4be84712721", "08065249811a41daa90933387f1ef56b", "6e65ad19c0d5438f9fc78cfd03325ef5", "c5cbf1ae30c744fcb897164a17aa7821", "2be4782fcc22408aafbdc7ea38b002ae", "a4f3c44c715c4202bff3fd1c5e2b2934", "56564a3fd847457a9fff0d5e9568a297", "8ea4c2ff8e5d43b488ace38f7a391081", "7caebfcdea784e32937fd037c84521c1", "e23ba79ecbeb4dcc9f9a71200408bc28", "1e10a656b7774f169f10cd424fe39b1c", "3f14cb8861054c2d8a9a5022d04d8021", "8d604fbc8b2545f1bb3a06c85e01dad4", "1c46f66c2f544a5587d0cab6b210b1ff", "5ee3c1562a4b4d50a0baba9a49ee5b17", "32dd12eb8be84ad8a18e495e668954c5", "dbd2c781c47648d0adf4d3cfedd29209", "71fc6033dab9453380aa3ae22e9475c7", "ddf288a757cc4678865593398f7b6b34", "831df470fc1c4940816c33de407868ac", "55ef7186e4b04b16a62d23c2664a926e", "1d8df641d6cd46aa9eea7bba3c736e0a", "dc8a7151594643b9a4b41d5cef4f7128", "6bad57c62e37441daea1d1e7888b15c2", "04c3f8c441054f81adbe747db80b221f", "4ba0b61bcdac4d9daf3464faf4fc8c30", "e413bc2ca96046089ffea92f1aa4b55f", "ad5e49969d974ee09ce8fc0216695b3a", "955a8c30077b49d39a2c08ad5b5b96ac", "7cd309efcdfb488c8fb21f1a31c1dedb", "1c92e4f097674fc7a9cee948f9cc57cd", "201a5cdf2beb4b118eb313653ee63c01", "db8d45ed65694d7a8ffb5724b3b07f18", "2c20d01fd249461fb09a5ffa8d1eb303", "2b4e0583b4d54994a0c93f4dde9e7e74", "0420fd55f59b48608da578a4ea52ed53", "4294c3fbb7a24c419512fa565a820f2d", "e5657b5a03e44a5d8246a22e9f34d198", "88291b7eac994a99b4665a570d324d31", "30650c7db19745deb5bf9435fb352287" ] }, "outputId": "e9fbf6dc-7b6a-4628-e661-247a493136c4" }, "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": "e6193bbe8db34c94923c251e8765ab47", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00