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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": [ "# commonvoice100-xlsr: Wav2vec 2.0 with Common Voice 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 [Common Voice 7.0](https://commonvoice.mozilla.org/pt) 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 | 37.8h | -- | 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", "| commonvoice\\_100 (demonstration below) |0.088 | 0.126 | 0.121 | 0.173 | 0.177 | 0.424 | 0.145 | 0.179 |\n", "| commonvoice\\_100 + 4-gram (demonstration below) |0.057 | 0.095 | 0.076 | 0.138 | 0.146 | 0.382 | 0.130 | 0.146|" ], "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/commonvoice100-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": [ "430e51bd35254d89b8867b91e2526520", "c3e17e8e269542319e0832be39e4b907", "26c13081c81e4d0e988f0a763b0748e4", "22ccc174d33c41d8ad4167a0e3dc3a53", "a52c05bd24044b61b7eddecc218d8793", "0265b5e51442470cb27ed77224a9e71e", "54c9f1dba51642a6af53cec8f20b573a", "82ef1d74841443228181bbe6786867ee", "1e25bdb6379843c2971fbc2361420f7c", "2342f93127b34bbeb0b36e25da448515", "67f92bb07e954a768a6d04efb8ee646e", "d6da00ef19f44729a03471dbbd5550f5", "58fefef1b15b4f4c9f21b492f728f37a", "13e953cff66d49cebcebd05285a22474", "d64e5eea69b3480593b2b13a819692ef", "da62a1b80aff4559836e1cd4c7bc9fb6", "d6b3f2104c4e4ff4addf69dd31f17f8d", "6902094f259147988705e10a0ecb44d0", "552191b750994bbd9e7c1a0efc356077", "6dc20c71acf44cedba0a57b541bcf664", "3673d4beb575457299e9ca69076983ec", "05f1d74ff7aa46d2a00ae3e78cee7f58", "9d32a753681b4c58b96585c6e766859a", "84d12170811c4110b7a4cde52466d98e", "e403fd4180d94d0a944490fa3e322f9d", "2fc80e33aa6145d9ab6a44ae42a8bf57", "38d7d588e2e648faa192abae64949810", "02306d0875a540d99a0ba5ff949bf468", "8d9c1d371a58449595bb14e27b4d7c99", "9d7cd83dedba4ccfa62efea1da2bb789", "38d9f39ff53e4952a2a694cbee27e297", "ac85475c15604e80a642c801b15ec2fc", "08c04e06dd26430a966de102528a4c06", "02dafb66a81d41d1af592ec1405cfd1c", "f3f266dea16e4b5b86280b2974c43c49", "c6895016daa04601ad108fa99f096999", "f4cb45741f9e4f1c89c0a297e7a67f9d", "fb535e75484f47bebd96d4b4225d537b", "711e2e2f72ef498887c475c2387b1d58", "8ed047a0ff2448a2a8fef0f357c62164", "f56647f1840d408c82ffaa0bf8523de8", "d271e061093d4f51852e773ee4d28e27", "e3170696ab6b4b04ab3db9335327f0d4", "827f0dd2548944538145255a1c8fc448", "95ba9917df3f43b191907aa4f5353714", "b336ab05cdfa47f38a0c753e23c54558", "996434a7cb964bf39a3562e7f4abfe2b", "45510f4361104b6cbb7c83d490e4f5c1", "38e698ea9906445c9d6925b834cfae13", "18d7d645ab2e47208ae70f6f0d4bc36a", "d42dd80fa214452ab392c0d0d32693f2", "d9630cfdff3f4352959cdbbafbf172f9", "fc528a3d1a0446d4b386d96ef04efda3", "c50f7d02dbec4d0e8ddb95cc29fcdcde", "426839b2174f45abbc579bac9d176305", "080c6f897c564c6bab591a965f191dba", "5419043e677d4e55a58584fefe6311e8", "b4173edff62f467d94554f732480d188", "e67cd194131648d88c8291c1c2b47b76", "166b6eddc8da4a28ae41018d933ce768", "585444881d71444aa41e3f2ba3060cd7", "359803a0b65c4022a178098bba2b3fbf", "ad72d7d580b64e76a70fb859deded4af", "f82c1046b4784516b0da568a74a92f76", "f23c8bed58cf4a6da8350ccbefbae0b8", "366ad92b8c854108aa24edf19d832e97" ] }, "id": "3ToOxo3bI6mq", "outputId": "c306d2a7-3000-42b4-cd87-a40cb427a796" }, "source": [ "stt = STT(MODEL_NAME)" ], "id": "3ToOxo3bI6mq", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "430e51bd35254d89b8867b91e2526520", "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": 264, "referenced_widgets": [ "fd30fba7e459444194e7cac71e1ed657", "994b0d32f81d493cb98154faebd468a6", "dec5acd947c44685a60a2b367fbe9af6", "394e4c883d694c968d3ca224814e5269", "2a5f9820751d4741a1551a25b3380ea1", "0454762ec77a45c7be44f08c1832134b", "13de79b6429b450990226c3def14e842", "1a8b07d3a3664756bd06a8e94e83cdef", "b07dd189462e4050995eb0af3e37c578", "2b3045aa40cd4245a4afa3e979a5ca8d", "7ac707faff064e0d878b12d97d160509", "b88c558316334aa7b549ff6253494934", "54f0d70cff4341099cc84f37ba1b2c8f", "090fb56a9b1b408ca9bc8f0acd0cd5d1", "dcb661c7eaf14dd984cef93103a0d28c", "6dd805bc457248cd8cf785f6869b879d", "a32d1f5f380944dfbf5f84749fd934fc", "f404a72fbae94ef7baeefc081cf29f16", "d90278f316f24c6b898ff0312c7ea4ef", "5719e308ea334541916fc3ca500312d0", "ab3f301f4564447f80dced40cabcf5b6", "764761d3cdd944e7a178bd46fb302eab", "6f63bff2b1db4aff9ad69b0156fdbe7c", "e51e86cd14824fd1b00f52e751897be5", "fbf87014c74549f5b505959335c059ff", "dadcc4b7079446c087041db91edc6b36", "f919bfed922840c88345eeb45e7c4219", "512de2b036c94f1ab2dde8c783d8c923", "be0782b4f4a24a479090c9320f5c9c52", "3b9d96632fd64d2ea9c59cbfc3c0dc70", "cd00407fc16a4681a8d5f6fa6f25605c", "bd8b789bd68049e39bda6bed67b62ed8", "feccba435f664c1db0e7d0fd662a41d4", "dcac61ebe38f44f4b56e937a92967665", "f07aeda78fab494396b8ba4b298f5bae", "4755cb6edbd648b8a291218afb84a398", "c85849823c92445196a53122ec67e79c", "be64899abe644b98b6a8f9d4dd5d230b", "48c53ea3c7534cd7b145cc452700cb1f", "7c40ff3e858d4153a7ccf0f1ff4198aa", "fd62b603e3fa4afd8837863df196976d", "b3549b5216984a0b8e7fbd8ab50017a4", "375d1ce9932a40e0a1c89772e4a12faf", "fa51844e503a47809fec07f6a36f8042", "ac977a42071247e790b2e209ce50b43b", "cf8eaac2280d413897652441dfaca318", "ab4576f2b49b4560acd17ada07fa87ae", "905a979f0a334775aefaf0920a1e23cc", "86aa0209144047269204f586dc2690dc", "3aff19fbb1b04515944e859dae1224c8", "73a53aff63404358813fa4cd9faa5211", "4e2edee3545541e49b4a06c6257850db", "bc516bda4a954306a7d5d7134459c0c0", "f6dfb7f3f3db45a787c3d2ea7d9a70cb", "634a0cd1f3c74d02aafdb56636630e5c" ] }, "outputId": "835be842-3af1-4156-f6b4-7f44d1d1ffeb" }, "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": "fd30fba7e459444194e7cac71e1ed657", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00