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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": [ "# cetuc100-xlsr: Wav2vec 2.0 with CETUC 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 [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz) dataset. This dataset contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus.\n", "\n", "In this notebook the model is tested against other available Brazilian Portuguese datasets. \n", "\n", "| Dataset | Train | Valid | Test |\n", "|--------------------------------|-------:|------:|------:|\n", "| CETUC | 94h | -- | 5.4h |\n", "| Common Voice | | -- | 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", "| cetuc\\_100 (demonstration below)| 0.446 | 0.856 | 0.089 | 0.967 | 1.172 | 0.929 | 0.902 | 0.765 | \n", "| cetuc\\_100 + 4-gram (demonstration below)|0.339 | 0.734 | 0.076 | 0.961 | 1.188 | 1.227 | 0.801 | 0.760 |" ], "id": "FixP81bsISop" }, { "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/cetuc100-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": [ "314dad0b15de4b42a42f27ed4e1dd2f8", "eaa225d9f801485c8012f5d1ac40b8b7", "2c72ef05b3084dcdb2e0bff7d55d78ca", "df9b434c50b2492286eb15f1ff937360", "45e55dfb83934cb9b816c940dbd30755", "3e8c5e71cc6941dbb97684aeef1be33c", "6935e2258372463fb421a07e0c7c72b9", "86d2b295b1314fcf862384a3fb8ce91d", "d40b54029ff84c5a98b1f23c8d729355", "0a586c6205d941c3a41fc3ce1620446e", "c38e6038ca554507b77bdd4dc4006b1d", "e7f3336fec3243f1a639100b9c6a3fa6", "9126d6632d1a47478470456ed37acac1", "b95d8fa93cea4932a17432875022a4f9", "cfbbc1a73215440390fa2eca9b5227eb", "c9422bb601c146238a7cf5ed13d98a32", "299bdf18123748fc84fa17d327ccd3de", "0e4512a39b404e0c877cdb479e4a6b71", "21b7cb9ab485426ba58b1cd3f4a21b8e", "5e78a88287844f48887de708d97e6500", "b677c37c6e4440a883349bea969377dd", "9b34ea28b20c4701b0fa15c3b3f2f481", "4cac1d8fdde940658ac05e40c877d57a", "a6980fdcaafd43dd84915c354b86b9a8", "37af8093520c4d2c86153c24d7b6c95f", "4879d5b750fa4ca2bef3d4e4efa518a5", "f88de9fc94664aad8c24e84466357c6b", "700d0272998246d0b18fd79dbeacf786", "eee8f220ba3d494d89719bc3fb15bec2", "9c199c52732d49fab9f356222bcddc8b", "2240e172e760467bae5427d840e94068", "9e15f4b053d14a6fb7e617380ebe057e", "b61f866b706c406fbff987c9794bda19", "09b1cab5d9044f0892a6b97bdf897a5c", "6be6c72514f14ec3805a6aa7cd17eacf", "09873c3d5bed441f9f2b44daabd40e2d", "2d1bf2bbba05494c8dec81536753aad2", "6924d0a29aac44aa8f1aed8d86ecb05d", "73a59f16b14543fa8bba469bc8377545", "d869387310a64d61988bd822dbba7d7c", "1e6429eeee6e430a8f56af36512909a9", "e9a595ed776c41c3835f3dbd78f543b9", "32805dca37bf4b68b46c2050743f7daa", "c5f0314b313140afb955b53006b64559", "de7842eb1ffd436d94cfc04c4bc1ae04", "63c3817fb2bf4353b7967515b808c250", "f133b63693ac4bbcacabd0e3a25d8a99", "671f21409b144cda854c89c0141b0d7b", "398d3c3b3ff34fbc8f98a463ab35b3c4", "c6c79e54e8c34005b1238c907d53c590", "2798501dc5b14a39ae213d06fc9026d1", "944756c05700444183be46d66dc4a1c4", "1e2aa72bcb0d4c1dbbdf02c5f8794c32", "570a220ee8a44d6ebfb1cdfa927f89f1", "993fc9244625468e97df8ee36941d3f3", "b3824f458f5747bfbae927f98e216cfa", "c590cc40fe2b4991be2ebbfe210ca5d8", "7b553df818ec49bbb134b3b82d2c28c0", "03a5f4d4f3a14848b277f48bc4e89aa0", "128059150b854bc7b31a227b30be6d67", "b751a9d50d434917884ea1dcf5330049", "e29c6cf296624ca1a031cabcdc59f7a2", "ac836d312be146838db096fd97bc2423", "b347502bf3af450c914700a13dd185c2", "95fcc0be2b07464db9a27f456436461c", "76202f3e0ed04f019b11bfaedd1ba7a9" ] }, "id": "3ToOxo3bI6mq", "outputId": "07bf7ac3-7a98-4f68-d60e-3dbbdba94c7b" }, "source": [ "stt = STT(MODEL_NAME)" ], "id": "3ToOxo3bI6mq", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "314dad0b15de4b42a42f27ed4e1dd2f8", "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": [ "7fd4258537734dbfb3ed24d102302bac", "99b9c2b600b24fdd97c345c34b6e1d50", "e925e246a37842df9c9bde06d9ed9058", "767b007d5a35418fa8953a77af45f1e4", "1fdfedde7b7f4532baf5ff33ff0bded0", "2905c62afefc48cfb1e46fda17850ee4", "94a236865fa8421296f23b6580577a49", "99382cec2bdc4e19bddf5eefa451a7e4", "973d63393dfa4cc398d33cc9530f9f00", "34138a3b44674065a7d9e3be9175a8f2", "eabc08c67d0444738740034a5ba95818", "a305130b5dfb49678cefbae9c0fcb92d", "4a928653d19348229f0221d79e7bd08a", "b98a2f328b3e4ea8a77726aab1d2610e", "9d313eca47f3464bafc25d1fd14199ae", "066f944c1553437e825add278976c863", "b39835e2a6b04827a39e7363329fe446", "8e46112e33254ff0bc469747f4c1a7aa", "ca73c7d8ae344cb7b52d3b58d5fb1bfc", "f5e6fde28eb84156b617bba2e51a8f87", "f16c036e8d0d4c8a83808be45f758d51", "741fb1764f064a7aaae852bf2444f783", "faff0b97a5be4fc6b7853b44d420b8a1", "2c40e7e865164ad9a539dc2d9d2cd934", "ebb0c154f0c04259ab331227fe3fc1d6", "c629260a32034c888ae31b67e13f5db0", "889d30fc9c0846fba56d5515b56f9d48", "3bbf7dfef5334d8b96712f9761e90b67", "366c362391184081a9004837c764a8d9", "5eb936fc4a8f4748b13079fdf3699b87", "83bd8a50975b407aa27783b9e0d24dfc", "4111e723128e42c2939a7f2ecbd7fedc", "97ab19a2f6474094b0ceeaf66b5c191f", "dd7ba5f680ec43548386c66327a7f7b3", "b83513433dc54010af81a1ccafbd5aee", "c78e199d97834e77bc00df12663ab51e", "d4cfa8d2f2164b2ebe3d41b2eb1d755c", "0540f3f3c00a48fb939f01da0f815451", "0927d189f68f451c849b45b0a3405b93", "9a4e485b10dd4853933ff1547c51277d", "02f6425d414f4879b799c35589677656", "b0bd901a2340445d9aa0b7ae3230da1f", "69fb658e604b4b41b1a7ffc0459eac1a", "bcaf0279618b40de98afdf47d3cd137c", "b8b40f760b864b1ab75b28644d673c4c", "0bedee4129e54ca5b9e2821bd90b0782", "4d92166fda134cfe80ead961a8e24fd0", "92e3a9c6ee654a01b2081ce862f4f951", "d9c4912af1794d36b5f36c759c6f8907", "2fa2fff554eb48acb4fd390735197b36", "ba7394c7e44b4698a022424616c06b63", "7c5a2e4e28814cb4bfb630f380dc0c72", "a5b66192339d41f3a37ae22bb2258060", "1c6ccd1707ff4671886f3ad4e120cc06", "94c22c6ed55940e7b191a7195a42cbbc" ] }, "outputId": "e6e36fe9-d32d-4b6e-ccd8-f632b4b82c59" }, "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": "7fd4258537734dbfb3ed24d102302bac", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00