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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"source": [
"!pip install gtts\n",
"!pip install gradio\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ufQmdSNceI4n",
"outputId": "2c7a47dc-d12e-4913-fe64-4d227fd71a5b"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting gtts\n",
" Downloading gTTS-2.5.3-py3-none-any.whl.metadata (4.1 kB)\n",
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"Installing collected packages: gtts\n",
"Successfully installed gtts-2.5.3\n",
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" Downloading gradio-4.44.0-py3-none-any.whl.metadata (15 kB)\n",
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]
}
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 626
},
"id": "s-QPCL4neAAp",
"outputId": "dea5531e-c00a-455b-fab0-3639b94b39e6"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
"\n",
"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
"Running on public URL: https://051fe2f8995655b4cd.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"<div><iframe src=\"https://051fe2f8995655b4cd.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": []
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"# Import necessary libraries and modules\n",
"from transformers import BlipProcessor, BlipForConditionalGeneration, MBartForConditionalGeneration, MBart50Tokenizer\n",
"from gtts import gTTS\n",
"from PIL import Image\n",
"import gradio as gr\n",
"\n",
"# Pipeline Component 1: Image Captioning Model\n",
"class ImageToText:\n",
" def __init__(self):\n",
" \"\"\"Initializes the BLIP model for image captioning.\"\"\"\n",
" self.processor = BlipProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n",
" self.model = BlipForConditionalGeneration.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n",
" print(\"BLIP Image Captioning Model Loaded\")\n",
"\n",
" def generate_caption(self, img):\n",
" \"\"\"Generates a caption for the given image.\"\"\"\n",
" inputs = self.processor(images=img, return_tensors=\"pt\")\n",
" generated_ids = self.model.generate(**inputs)\n",
" caption = self.processor.decode(generated_ids[0], skip_special_tokens=True)\n",
" return caption\n",
"\n",
"# Pipeline Component 2: Arabic Translation Model (mBART)\n",
"class ArabicTranslator:\n",
" def __init__(self):\n",
" \"\"\"Initializes the mBART model for English to Arabic translation.\"\"\"\n",
" self.tokenizer = MBart50Tokenizer.from_pretrained(\"facebook/mbart-large-50-many-to-many-mmt\")\n",
" self.model = MBartForConditionalGeneration.from_pretrained(\"facebook/mbart-large-50-many-to-many-mmt\")\n",
" print(\"mBART Arabic Translation Model Loaded\")\n",
"\n",
" def translate(self, text):\n",
" \"\"\"Translates the given English text to Arabic.\"\"\"\n",
" inputs = self.tokenizer(text, return_tensors=\"pt\", src_lang=\"en_XX\")\n",
" translated = self.model.generate(inputs[\"input_ids\"], forced_bos_token_id=self.tokenizer.lang_code_to_id[\"ar_AR\"])\n",
" translated_text = self.tokenizer.batch_decode(translated, skip_special_tokens=True)[0]\n",
" return translated_text\n",
"\n",
"# Pipeline Component 3: Text-to-Speech Model (gTTS)\n",
"class TextToSpeech:\n",
" def __init__(self, lang='ar'):\n",
" \"\"\"Initializes the Text-to-Speech system for Arabic.\"\"\"\n",
" self.lang = lang\n",
"\n",
" def generate_audio(self, text):\n",
" \"\"\"Generates audio from the given Arabic text.\"\"\"\n",
" tts = gTTS(text=text, lang=self.lang, slow=False)\n",
" audio_file_path = 'output.mp3'\n",
" tts.save(audio_file_path)\n",
" return audio_file_path\n",
"\n",
"# Main Pipeline Integration\n",
"class ImageToArabicSpeechPipeline:\n",
" def __init__(self):\n",
" \"\"\"Initializes all pipeline components.\"\"\"\n",
" self.caption_model = ImageToText()\n",
" self.translation_model = ArabicTranslator()\n",
" self.tts_model = TextToSpeech()\n",
"\n",
" def process_image(self, img):\n",
" \"\"\"Processes the image, generates a caption, translates it to Arabic, and converts it to speech.\"\"\"\n",
" caption = self.caption_model.generate_caption(img)\n",
" translated_text = self.translation_model.translate(caption)\n",
" audio_file = self.tts_model.generate_audio(translated_text)\n",
" return caption, translated_text, audio_file\n",
"\n",
"# Gradio Interface Setup\n",
"def demo(image):\n",
" \"\"\"Function to be used in Gradio for processing the image and returning caption, translation, and audio.\"\"\"\n",
" img = Image.open(image)\n",
" pipeline = ImageToArabicSpeechPipeline()\n",
" caption, translated_text, audio_file = pipeline.process_image(img)\n",
" return caption, translated_text, audio_file\n",
"\n",
"# Define Gradio Interface\n",
"iface = gr.Interface(\n",
" fn=demo,\n",
" inputs=gr.Image(type=\"filepath\"),\n",
" outputs=[gr.Textbox(label=\"Caption\"), gr.Textbox(label=\"Translated Text\"), gr.Audio(label=\"Generated Speech\")]\n",
")\n",
"\n",
"# Launch the Gradio Interface\n",
"iface.launch()\n"
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "BqT55AmHeBCv"
},
"execution_count": null,
"outputs": []
}
]
} |