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httpx, fastapi, aiohttp, gradio-client, gradio\n","Successfully installed SpeechRecognition-3.10.0 aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 fastapi-0.95.2 ffmpy-0.3.0 frozenlist-1.3.3 gradio-3.32.0 gradio-client-0.2.5 h11-0.14.0 httpcore-0.17.2 httpx-0.24.1 huggingface-hub-0.14.1 linkify-it-py-2.0.2 mdit-py-plugins-0.3.3 multidict-6.0.4 orjson-3.8.13 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.27.0 tokenizers-0.13.3 transformers-4.29.2 uc-micro-py-1.0.2 uvicorn-0.22.0 websockets-11.0.3 yarl-1.9.2\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3e536a1aafbe42c7b16c55a97c702522"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/443 [00:00<?, 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warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:324: UserWarning: `optional` parameter is deprecated, and it has no effect\n","  super().__init__(source=source, type=type, label=label, optional=optional)\n","/usr/local/lib/python3.10/dist-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n","Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n","\n","To create a public link, set `share=True` in `launch()`.\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["(async (port, path, width, height, cache, element) => {\n","                        if (!google.colab.kernel.accessAllowed && !cache) {\n","                            return;\n","                        }\n","                        element.appendChild(document.createTextNode(''));\n","                        const url = await google.colab.kernel.proxyPort(port, {cache});\n","\n","                        const external_link = document.createElement('div');\n","                        external_link.innerHTML = `\n","                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n","                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n","                                    https://localhost:${port}${path}\n","                                </a>\n","                            </div>\n","                        `;\n","                        element.appendChild(external_link);\n","\n","                        const iframe = document.createElement('iframe');\n","                        iframe.src = new URL(path, url).toString();\n","                        iframe.height = height;\n","                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n","                        iframe.width = width;\n","                        iframe.style.border = 0;\n","                        element.appendChild(iframe);\n","                    })(7860, \"/\", \"100%\", 500, false, window.element)"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":1}],"source":["!pip install torch transformers gradio SpeechRecognition\n","\n","import torch\n","from transformers import AutoTokenizer, AutoModelForQuestionAnswering\n","import gradio as gr\n","import speech_recognition as sr\n","\n","# Step 1: Define the question-answering model\n","model_name = \"bert-large-uncased-whole-word-masking-finetuned-squad\"\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","model = AutoModelForQuestionAnswering.from_pretrained(model_name)\n","\n","# Step 2: Create a function to perform speech-to-text conversion\n","def speech_to_text(audio_path):\n","    recognizer = sr.Recognizer()\n","    with sr.AudioFile(audio_path) as source:\n","        audio = recognizer.record(source)\n","    text = recognizer.recognize_google(audio)\n","    return text\n","\n","# Step 3: Create a function to analyze the accuracy in answering questions\n","def analyze_accuracy(question, audio_path):\n","    try:\n","        context = speech_to_text(audio_path)\n","        encoding = tokenizer.encode_plus(question, context, return_tensors=\"pt\", max_length=512, truncation=True)\n","        input_ids = encoding[\"input_ids\"]\n","        attention_mask = encoding[\"attention_mask\"]\n","\n","        with torch.no_grad():\n","            outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n","\n","        start_scores = outputs.start_logits\n","        end_scores = outputs.end_logits\n","\n","        all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])\n","        answer = tokenizer.convert_tokens_to_string(all_tokens[torch.argmax(start_scores): torch.argmax(end_scores)+1])\n","\n","        return answer\n","    except Exception as e:\n","        recommendation = \"The data has high Dimension of Noice. Enhance your Preprocessing techniques and Try again.\"\n","        return recommendation\n","\n","# Step 4: Create a Gradio interface to interact with the model\n","question_input = gr.inputs.Textbox(label=\"Question\")\n","audio_input = gr.inputs.Audio(label=\"Audio file\")\n","answer_output = gr.outputs.Textbox(label=\"Answer\")\n","\n","iface = gr.Interface(fn=analyze_accuracy, inputs=[question_input, audio_input], outputs=answer_output)\n","iface.launch()\n"]}]}
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