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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": [ "# voxforge1-xlsr: Wav2vec 2.0 with VoxForge 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 [VoxForge](http://www.voxforge.org/) 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 | | -- | 1.0h |\n", "| VoxForge | 3.9h | -- | 0.1h |\n", "| Total | 3.9h | -- | 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", "| voxforge\\_1 (demonstration below) | 0.468 | 0.608 | 0.503 | 0.505 | 0.717 | 0.731 | 0.561 | 0.584 | \n", "| voxforge\\_1 + 4-gram (demonstration below) | 0.322 | 0.471 | 0.356 | 0.378 | 0.586 | 0.637 | 0.428 | 0.454 |" ], "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/voxforge1-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": [ "24d644c5a4a64694b14b124e7ea19ee2", "4cda92349d184a83907bb266034bb6ef", "c743b254543c445082238d7e951d0863", "e8bb0011193f42c3931d8a83483899fa", "1abefafbb8ed4edab892789f7ba76f13", "5152219eb65148cdb7349665c657c32c", "adf3ea6e6eee41f7ac5709882f81feca", "495fde8be386423586a93eda9a0ffa64", "687d621e29bd4ff9a67831154f6a5b81", "edc4a81f93c848579a753b7c1cd93ca8", "a599a96805994f26b4edf7711b5c7ba7", "245c83aff0af47919c347f44f391db30", "23613f7d2694465bac139191a719c440", "8a3bb6411b5c4d36b255ee4e194fdc57", "6c3ae577341e454e9f507ce48acf0b28", "29b92279a15146da94092b68e89d8df0", "6c14c037aea7497c99a275e2ace0dbc0", "10c8de06d2ab4177b0eb42c18a791aa5", "a8e6e8949d8649d284d6ff227ea2073e", "a1eccf83968545bbaaa00ec796e38478", "e06457b185b84ddbb1590134108204a1", "9a8796de3cc2475c84dba9dbf929a200", "fe127bd9885f407a8b53cf5ec1fa9e88", "1c7a062f7aec44f7bdb97b2c3ed15b3b", "c6593eb61bef493eb8fa2c045ce350a3", "bd8a62f68ff54effa75067d33a2f759d", "f7e111f6cc70484bb4555d221eb2ed8e", "16149abebc2d4164804d51a820c9babb", "7413433bf6f44c5f89a1419c8e1d8da1", "ef97b3bf817f41b496d8bae480c63fe1", "274cc59b31184dad8708395b45bbbba9", "42e549ae2d2a4f968a01cfed034fead2", "93727b3f4cf940a582902accec07124e", "b885e09ab95346bab641a1faf0d1fe5f", "a0455bb60f184a3aa924cecbcc61ce59", "7d28e13459a94dda81d393d8b9d7265b", "27651b5aa6d04d4e87acbc7df692c2de", "8d6fa67be3ac4fda93f465e9ac13b974", "fef31ba0cf744bbbaf9557aafd8210f8", "d4302c7ef1d7404fb2c3fd4c6371d04f", "0f8927a8e22443e8a842716ae62cfa27", "bedc96912e5846749e0b6732f49429df", "7e9be71d6c2d4c46b1399c49628fbd67", "31c83182a90447a7ba8af101ebef9cb0", "95fd2f98a03f49d48f911fe72b46c2d7", "b18cdff255b1460aa693ed44cf0c20f2", "77eec70dd06e4cdab5125e58edcb926b", "972c36c7afd84fbf95356900e452d9e2", "f51fe107f6fc4733b1c582648dbb8967", "b6e0da247d6a40f78c8c085cad61367a", "42277ae6902a448884bc5fa4991e7193", "82c129c73f0647248fe796cfa2c8e76c", "4ae527e0da9b4c74b3eb551799dbbabf", "bd97bb4f59414d448572a8ac020044cd", "0b2919895d1443c89bb666d860a9a3b6", "c27d2744f3ad40b0a9f1f80b4d673848", "e0693931e1314ae5bfb8bd648383d532", "a1301f55ca784c93b1e1f5af330af237", "0a4e2712dff048a79e97ce3fb9b9f132", "757485712d404171921d2bf75079f239", "eca4900e5e6f4ea9b8db5253bb2373bc", "423636d89c2d47a08c426abe84d2c179", "f3ef45cc86f84758960164b2167c6bbf", "0c9295a92a5a4dc3a67c835a0ffe6423", "a47fd635d4a24e068f7a903041a960d2", "d373c4144a484ddebd974c73d9fbfb43" ] }, "id": "3ToOxo3bI6mq", "outputId": "735ba653-707d-4254-b90d-446258cb29f7" }, "source": [ "stt = STT(MODEL_NAME)" ], "id": "3ToOxo3bI6mq", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "24d644c5a4a64694b14b124e7ea19ee2", "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": [ "e63f6f373fb74f30bb95e72ca17b8879", "9ca9d60b70df481f9c9e738c067a7c4c", "f7b7762f6d9042ed9cf5c3e70025ccc9", "529e52fb55bf4d1cb5145b5a979fcfae", "915874d68cbd49269baee93ff3bf2f20", "264a3dee340443c7ab69decff7e0aea2", "6d69c8926bcf4e658a9e687c8241bb5e", "185bdae5ea7a4e3c8d1f47eaddbf1f27", "3b344a0f3d7645bea47a7a7ae411b015", "43923057a5c0412cad38580a5afa9a91", "a4b6db777c6d4764a7403ac871d03f4f", "4807660fba8141d9ab35418dd79385ba", "19064bf2765f4df5b84f6710012503ad", "bc77449759de4584b2a896d33ee8d716", "7e0e980704cf40d09694a65da70f202b", "18ff482520a245c5982f38c0ef5f096f", "e71cce1ab2cd4c1da0369e9476aec433", "1a8acb9f9e754e2794b17d2903ac2958", "8b1f3e47827e44ac8af062d9e84b3b13", "a87c42d8b2974111b5697fc24048a6e9", "73965ab037054b728270706498ef720b", "1e5ecc5db0454eb787e25b836e2c0333", "afb47887f8334c8e951fd04339b7926a", "43f7d2a1138b4393be93005fbecfe9ee", "967242948e3643af88a68e9e2b6b06c9", "06688d627fc248a8a11e1cf8408518ee", "6ff5cd1bbf1d488ea2ecb3aade661ba9", "5e9bd5494e0a47c4aba2f8618364094a", "a931caf2c30d40d4b8b8bdc923f08de4", "c73928a801f1467ea5f12bfddd9d1ea3", "ba2874ff597b47629df0c900db37be12", "42fb34bb42ed400a9858fd9f624b98e0", "b22ca47261d842e5b7735c275750be11", "7882f46de8b04fac9454b3cf144413fb", "d3634fae4819447087255faed38bf52e", "84ada719507544d2a43de1c6d7250014", "024ea6e1ecf647299bdaa756b63bcda8", "38961f0b68904a98b012d48adf805c93", "0897f51267b540d09c302d821537f523", "0be81c70cc2143998afc516dacf1c3f6", "30cf3dfc19bb469f81575a4729b82138", "6f90ca826f014f05b275c59940a5449d", "ed6dc27534994cb8a1a7da62135bde7e", "619d2054d65042889380c5264c165b1d", "2a9288da7e8849ad900562f42edc6c8a", "97f5c1b5dbfe4cb2a585b5231ab01f84", "4ad3e8a7fe9e40f4a8aeaff09bcdbe91", "81e6efcc9df54ed48b96ec81698489fd", "e6f47979b7d54246bab60d9257152946", "4796e809ef0543088172b880be3f882f", "73cef3012dac4763b1b3285c036b606e", "d7f7c80cdd3245bca69688cff38dd57d", "ba7e5c9a4c574be3aeb1fc75ebdde227", "7259099c9e3d4383bdf180a4327b69fa", "9ac3f93cb5f5444e9646e8a2f7a38ba7" ] }, "outputId": "a3134ef1-c611-415e-c19c-1a1db45b0006" }, "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": "e63f6f373fb74f30bb95e72ca17b8879", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00