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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": [ "# sid10-xlsr: Wav2vec 2.0 with Sidney 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 [Sidney](https://igormq.github.io/datasets/) 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) | | -- | 1.8h |\n", "| SID | 7.2h | -- | 1.0h |\n", "| VoxForge | | -- | 0.1h |\n", "| Total | 7.2h| -- | 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", "| sid\\_10 (demonstration below) |0.186 | 0.327 | 0.207 | 0.505 | 0.124 | 0.835 | 0.472 | 0.379| \n", "| sid\\_10 + 4-gram (demonstration below) |0.096 | 0.223 | 0.115 | 0.432 | 0.101 | 0.791 | 0.348 | 0.301|" ], "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/sid10-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": [ "851cc7d1ac6b4ebd919e22c0953cc345", "2f8441c4ae344acfadd2b425460d3bb6", "03248e6786404fff82e9129e6171b9ac", "2a5cc311026943d591af942fb099dd1c", "d1b5969161e2410291837f9d2dcee50b", "b4315667e48042a2bdf8603c86d26952", "13dde6abcec74f22b1ef0acccfc52f6e", "faa96683bf7940ac8dc283ff6a6439f8", "1cb5a31ae09b415796694624e7c5ed15", "ef31491448ce499aab72d17a2ed0f088", "f7c19ef4271c49028f10d26cc3946448", "6e514cd2bcd849b8aa37d14591f116d1", "f3821511d86d4111ae89061616a3bcf6", "39b0ccec776741f1a6862f48583983cd", "d6e99fb56be44506b050086cfa5d4f9c", "27c247b657ab4806b4aeaaa0f4367bf4", "7e50205354c3451885a090e165856a05", "ba0f1fc46ec74c94a42583b3120bf9b0", "ff0f225ae00646ce9b8259fed0fea4e1", "3cbd5a0bcf8f467bb47066eeac9e7fdb", "a2a18392b7dd4b209c36ad2189d46bdc", "6248c2d0c87b4b56a113c557a8d859c6", "f27b4f98c05842298780c6c37fe83305", "e266c191081f49dab2b5587205358521", "ca2c38931c424e648e283a9b948e3b09", "773f64177b184a8f925257ba639027fb", "b58afe14eb154ec2b1cd8fd83c79bfb4", "e046244bd8db4bbc93b29251660889a2", "d739050242f6471587249bf99ddfff2c", "b96b02fff5444339a7db94a3712cd55a", "302c10ff5072460699daabc326be512b", "c0919ab84e0746e5a1573fb53d86f928", "b691e13c25ec4bd4b91b8c9d8af0da36", "c4085dba7a454ddea78f9730b8ea5818", "8ac1b9e93a3d4701a00af3575836e8ef", "c199f8e4000c45cf88879de069235a3b", "3fbbabe2edb9402993b2c798ee08dece", "e8d9915f0deb4225abb29865f58ec069", "c8334f3018e545b7bc1ac6ef7c01adb1", "36b1035102d54643bdc691ceddb775ce", "f3e63e0a6e454e918a3ffdaebf8663ca", "40a06c6049324f459d8e39b94dc09355", "b666b7b921b74260acae1136bea080a5", "0d00d0cf856c4a3d90e02d429922da41", "92c14dc622834f1987c7466637e3d83d", "5cf125f6c2034457887c5aa6f75de44d", "33bcea53b0c34a29a83ef55ccfe8debf", "de3396399d3e42a98f574258f9500be0", "809c750604a04c2cbed1238e93970934", "efa32f3d55874a63988e6911dc5ed4a0", "a446f8d4fa1a4e6b9b1b5f3fe00fd297", "6bf8fbe431f54ed4ab6d46fd7d9c6e07", "936abd1b050649cbbdb17dd293ce4ead", "aa4e8d8f0768480e97c011e6e93d44a5", "d94fe82096534d5e911571f62a80f4e1", "04e65ff1698b4161afff72277011b943", "15bf16f5af8b4313a2d2698c722c2e88", "01191e60d4694e4db6c8078dc8b8c649", "7359e849842741a38b51a336eb57e60b", "4273ec9869044bed98d6b1e9964a4a88", "fa7255e7737f4a2c9f1e5e9a46eec039", "f3531abe7cda4355931cc4d3c6634fd4", "b9bd363217a74297b18a8eb0e8c0f894", "cd47b6383ad9421790a5f4aaface3b0b", "16d6d4a0d8614289b73b84717ddd4cff", "7046dc8e7b8e4c6e872ada508404ebcc" ] }, "id": "3ToOxo3bI6mq", "outputId": "9d7519ef-a678-4099-bd35-9babe45b7782" }, "source": [ "stt = STT(MODEL_NAME)" ], "id": "3ToOxo3bI6mq", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "851cc7d1ac6b4ebd919e22c0953cc345", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.63k [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": [ "fa757e3ed6be4cfdb709ee304c90ae37", "d1d4748df1ea45d08a4fce466b34bcef", "6ff7cbac1b7f446ab203c17640f96c15", "bbb5a26d28d94d89b07d15c2879cd117", "bc043d5a024a4c53a5006bf508ca1dc2", "482da7b8324c425e95ae43fae1f6c247", "c6422d9020864eb2a18ddd341a0c086e", "9231fd8cd1c94d7b8775724c2b2a7ad4", "64b54915140e4bbfa77be7f96ef9cd53", "03ddd9c221a243e29cf156e20f2bce94", "85803e898003407aa677a60c95f2db97", "2db3b374d043485dad8b56dc4d899630", "e919ad96497447c09b1f234832075c1e", "94c85f571db94af8a3292f2de72c2d79", "18a2f8678b4547bd8a62a31d58406cc3", "ae53fe913a364eb8ab2ef8f32ed7c143", "afc2c548dff24bb1a10e1a10f04ebf53", "206e0f83fa9a42e79a903e00265ae41f", "5c8773ed44fc4618a2f73b1b2518a129", "27e7ec4f9b164a0d9fb7a760a89216ca", "8f83483bfcc744c8a9fd53bf153ed986", "54692ca0a2d94865b2ee2b83f6aaa34a", "8b922d0cb94541d48bd93ea5dfad29ca", "840ffc74f15a448b858822300f34cbe3", "fe0a1c1fce1a46c785d9033e5f110fe5", "83a4df5ee8884dc8ad1e711f843516b6", "f59a3b595e344e139c6543fe9067efcf", "d4d5c0b396374e6f80b8af0e61bcf1ad", "ca628b45d53744bdaa7e2948e4f951e3", "0087397e6758442a8a8665d8b6494aea", "eacd08acd1314055b7ee7dca1b7c9204", "b0c1cefb62994fc08600f8f799895604", "73b11b0f5b5945ddad4ec96888c5444b", "625cb5ef203446da81fd99180b2c5349", "949873bc06a44cde9b8fb5e320a7bbd3", "92574118bd7d42ee98c44af024ba2175", "19ce03c0e81f4d95a446e2973cc056d4", "d5088cf0b30346eca8bdef3e3b3888bf", "4502329407cc4dbdb56123c27521c0b9", "e88952bff5ad429d95fa431895a5fa9a", "c797daf0add94973919b2e37d58a8b3b", "b00964f65cb04b3e9cfe324fc315da29", "2877c43952af4693987dbd6ae06c615d", "27c86d191da04e959bb23e63b6bbc832", "23423df83de34133bc969d86a18cd29d", "247a53fb04c84c0eafc33e3836d26b2a", "5c11a7ae2818477fbbf9c0337c926740", "84f6bcf222344953be681e56d023c62b", "4da1bdc9ff2b4f8d9c69238a17f8f3e9", "8f67eb4411d245629083858c644b45ca", "02142c19d3994a469a967a976b54cc02", "6dff81f868db416b8db119a9cbc3a3b4", "7cc233bb9f334762be99397743ecd6b4", "afb63d2bee634b7bbcd573721bae83a5", "a0efc16693bd4712b9101a6736dc2152" ] }, "outputId": "c49920f9-322b-4938-f2d4-5d122029decf" }, "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": "fa757e3ed6be4cfdb709ee304c90ae37", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00