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+ } + } + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KX0IBmbjeydD", + "outputId": "da61afd9-8a4e-478a-fcbc-4c7d0466ced9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "!pip --q install datasets" + ] + }, + { + "cell_type": "code", + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "from IPython.display import Audio\n", + "import torch\n", + "import numpy as np\n", + "import pandas as pd\n", + "import soundfile as sf\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use(\"seaborn-whitegrid\")\n" + ], + "metadata": { + "id": "PbGUt9dbnriq" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Zl13ArjoGBq9", + "outputId": "eb811f29-0434-4e0e-a043-017b25d42c3d" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def DownloadDataset(username,key):\n", + " import json\n", + " keys={\"username\":username,\"key\":key}\n", + " ! mkdir ~/.kaggle\n", + " json_object = json.dumps(keys, indent=4)\n", + " with open(r\"/root/.kaggle/kaggle.json\", \"w\") as outfile:\n", + " outfile.write(json_object)\n", + " ! chmod 600 ~/.kaggle/kaggle.json\n", + " ! kaggle datasets download --unzip engmahmoodanaam/cleansada-version-01\n", + "\n", + "#.............................................\n", + "\n", + "DownloadDataset( username = \"engmahmoodanaam\",\n", + " key = \"4a457b4fc3516f9d9b913d770ea64884\"\n", + " )" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wUSLY8BmnrfA", + "outputId": "04ce4306-3c03-4c36-e9c5-4f01ffe9d908" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading cleansada-version-01.zip to /content\n", + " 98% 1.01G/1.03G [00:11<00:00, 109MB/s]\n", + "100% 1.03G/1.03G [00:11<00:00, 96.0MB/s]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def GetDataset(path_csv,path_audio):\n", + " df = pd.read_csv(path_csv)\n", + " audios_data = []\n", + " audios_samplerate = []\n", + " for idx, row in df.iterrows():\n", + " filename = f\"{path_audio}/{row['SegmentID']}.wav\"\n", + " audiodata, samplerate = sf.read(filename)\n", + " audios_data.append(audiodata)\n", + " audios_samplerate.append(samplerate)\n", + "\n", + " df['audio'] = audios_data\n", + " df['samplerate'] = audios_samplerate\n", + " df['text'] = df['ProcessedText']\n", + " df = df[['text','audio','samplerate']]\n", + "\n", + "\n", + " return df\n", + "\n", + "#.............................................\n" + ], + "metadata": { + "id": "l74W1-6ZnrbX" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df = GetDataset(\n", + " path_csv = r'/content/cleanSada-01/data.csv',\n", + " path_audio = r'/content/cleanSada-01/Audios'\n", + " ).iloc[:70]\n", + "\n", + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "mfMp94fvnrXw", + "outputId": "26e03dbe-2713-4651-ad7a-6973ec6ce9cd" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " text \\\n", + "0 يا سلام انتهى الموضوع \n", + "1 يعني يا ابو مسامح \n", + "2 حصة موافقة \n", + "3 والله يا هي فكرة \n", + "4 فكرة تبي تخليك تطير من الفرح \n", + "\n", + " audio samplerate \n", + "0 [3.0517578125e-05, 0.000823974609375, 0.001464... 16000 \n", + "1 [-0.0538330078125, -0.0531005859375, -0.046112... 16000 \n", + "2 [0.001007080078125, -0.004058837890625, -0.003... 16000 \n", + "3 [-0.02789306640625, -0.03045654296875, -0.0335... 16000 \n", + "4 [0.05316162109375, 0.031829833984375, 0.021728... 16000 " + ], + "text/html": [ + "\n", + "
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textaudiosamplerate
0يا سلام انتهى الموضوع[3.0517578125e-05, 0.000823974609375, 0.001464...16000
1يعني يا ابو مسامح[-0.0538330078125, -0.0531005859375, -0.046112...16000
2حصة موافقة[0.001007080078125, -0.004058837890625, -0.003...16000
3والله يا هي فكرة[-0.02789306640625, -0.03045654296875, -0.0335...16000
4فكرة تبي تخليك تطير من الفرح[0.05316162109375, 0.031829833984375, 0.021728...16000
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df", + "summary": "{\n \"name\": \"df\",\n \"rows\": 70,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 69,\n \"samples\": [\n \"\\u0648\\u0634 \\u0633\\u0648\\u064a\\u062a \\u062d\\u062a\\u0649 \\u062a\\u0635\\u064a\\u0631\\u0648\\u0646 \\u0628\\u0647\\u0627\\u0644\\u062c\\u062d\\u0648\\u062f \\u0627\\u064a\\u0647\",\n \"\\u064a\\u0627 \\u0633\\u0644\\u0627\\u0645 \\u0627\\u0646\\u062a\\u0647\\u0649 \\u0627\\u0644\\u0645\\u0648\\u0636\\u0648\\u0639\",\n \"\\u0645\\u0627 \\u0631\\u0627\\u062d \\u0645\\u0631\\u0631 \\u0644\\u0647 \\u0628\\u0633\\u0647\\u0648\\u0644\\u0629\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"audio\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"samplerate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 16000,\n \"max\": 16000,\n \"num_unique_values\": 1,\n \"samples\": [\n 16000\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "code", + "source": [ + "print('df shape: ',df.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dlgCJUn1n4SX", + "outputId": "b773b0f0-6c12-480a-c05e-25317fadfabc" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "df shape: (70, 3)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "---\n", + "\n" + ], + "metadata": { + "id": "T1pF6RY1PKrM" + } + }, + { + "cell_type": "code", + "source": [ + "full_generation_index = 0\n", + "full_generation_df = df.iloc[full_generation_index:full_generation_index+5].reset_index(drop=True)\n", + "\n", + "\n", + "train_df = df.sample(frac=0.8,random_state=42)\n", + "eval_df = df.drop(train_df.index)\n", + "\n", + "\n", + "\n", + "print('train_df shape: ',train_df.shape)\n", + "print('eval_df shape: ',eval_df.shape)\n", + "print('full_generation_df shape: ',full_generation_df.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZFG8CCvdM4hW", + "outputId": "a7e8f7d1-29c4-47d1-ab77-bf2b36b7b393" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "train_df shape: (56, 3)\n", + "eval_df shape: (14, 3)\n", + "full_generation_df shape: (5, 3)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "index = 4\n", + "print(full_generation_df['text'][index])\n", + "Audio(full_generation_df['audio'][index], rate=full_generation_df['samplerate'][index])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 93 + }, + "id": "gbGcXR1qOIcG", + "outputId": "8b57ba29-f453-4d5d-e09a-c580b0f6f82a" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "فكرة تبي تخليك تطير من الفرح\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {}, + "execution_count": 27 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from datasets import DatasetDict,Dataset\n", + "\n", + "ds = {\n", + " \"train\": Dataset.from_pandas(train_df),\n", + " \"eval\": Dataset.from_pandas(eval_df),\n", + " \"full_generation\": Dataset.from_pandas(full_generation_df),\n", + " }\n", + "\n", + "dataset = DatasetDict(ds)\n", + "\n", + "dataset" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hYYJxFCYn5Hv", + "outputId": "fdac8ead-eacc-4b86-f0d7-bb5691973dcb" + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['text', 'audio', 'samplerate', '__index_level_0__'],\n", + " num_rows: 56\n", + " })\n", + " eval: Dataset({\n", + " features: ['text', 'audio', 'samplerate', '__index_level_0__'],\n", + " num_rows: 14\n", + " })\n", + " full_generation: Dataset({\n", + " features: ['text', 'audio', 'samplerate'],\n", + " num_rows: 5\n", + " })\n", + "})" + ] + }, + "metadata": {}, + "execution_count": 28 + } + ] + }, + { + "cell_type": "code", + "source": [ + "dataset.save_to_disk('./dataset')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 113, + "referenced_widgets": [ + "998749ebfcae4b8a87904df802acf7a9", + "2e5c554ee6114f229c5690cad6bf7060", + "ce22b3e69ecf4e21bfe5e22e16341195", + "bcefb41a71474150b6d52ba641bbf5c7", + "5560fbcf95184ec6a45756562eacf4a0", + "0c658108d0414932afd140a1430d2a0d", + "3b70e66d78a74386bdc8d854f237e0ad", + "152a67c06b674f17995767e44f64a679", + "3de5299f28754fbb906272165c95ea57", + "3acb7316c01b4e0fb986bdf739c8b025", + "b6fa7dd5ebba4e6788b0c242530659fb", + "f24acb6723dc47fe9b1f114a95c7ac40", + "0f54060ed4814eb2ad5cd35a92b6c3d4", + "c286b7a0030c4fd1aa570461c099970a", + "65e0bf2886ba420fa28f2511f4c7d824", + "e4aca46b0cb74622a52c42c12de0b6c2", + "a192ba8570464b8bbc6dda1345a2a84f", + "9b0aae7e4ede47459cdaae19571c6c57", + "35479653780e4d1591e4ce1b72b88229", + "a51be96ada234a8b8b46c1de7e90ed92", + "bc77557196504559a8964f37ef89dd9f", + "9c47eb81217f4c248d9984748669a322", + "efa47ac3f3314000b04b3bd2c3ea704a", + "5f81921b0d114db5b860aedcc8074844", + "7851afc2f61a463e858bbe8bf74109b0", + "14b6310fc034427da62a7a7a0614242e", + "a1927a9e8ec1418e9767fa934e4c2023", + "6a52c70f0c744e7a85528f1b3deec4d9", + "56987c5fdd284298b3c66672575a4482", + "64a6d77a5e994c83948a45e491de3c42", + "393232064a2a4af8a1866c6296a610ed", + "7d8f7f5854224704846c343b7d06548a", + "bfa7f944c9d447408803db3ab01692bd" + ] + }, + "id": "JKlxoOjtn5EO", + "outputId": "db5291b1-9727-4e68-bf47-fe550e8a0926" + }, + "execution_count": 29, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Saving the dataset (0/1 shards): 0%| | 0/56 [00:00 of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n", + "WARNING:datasets.fingerprint:Parameter 'function'= of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "preprocess: 0%| | 0/10 [00:00