293 lines
7.5 KiB
Plaintext
293 lines
7.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "kayiMLgsBnVt"
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},
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"outputs": [],
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"source": [
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"!pip install -q requests torch bitsandbytes transformers sentencepiece accelerate openai gradio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"executionInfo": {
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"elapsed": 15255,
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"status": "ok",
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"timestamp": 1744678358807,
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"user": {
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"displayName": "Kenneth Andales",
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"userId": "04047926009324958530"
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},
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"user_tz": -480
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},
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"id": "ByKEQHyhiLl7"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import requests\n",
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"from IPython.display import Markdown, display, update_display\n",
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"from openai import OpenAI\n",
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"from google.colab import drive, userdata\n",
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"from huggingface_hub import login\n",
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"from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TextStreamer\n",
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"import torch\n",
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"import gradio as gr"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"executionInfo": {
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"elapsed": 2,
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"status": "ok",
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"timestamp": 1744678358815,
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"user": {
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"displayName": "Kenneth Andales",
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"userId": "04047926009324958530"
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},
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"user_tz": -480
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},
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"id": "9tzK_t3jiOo1"
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},
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"outputs": [],
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"source": [
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"AUDIO_MODEL = 'whisper-1'\n",
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"LLAMA = \"meta-llama/Meta-Llama-3.1-8B-Instruct\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"executionInfo": {
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"elapsed": 737,
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"status": "ok",
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"timestamp": 1744678360474,
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"user": {
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"displayName": "Kenneth Andales",
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"userId": "04047926009324958530"
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},
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"user_tz": -480
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},
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"id": "PYNmGaQniW73"
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},
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"outputs": [],
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"source": [
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"hf_token = userdata.get('HF_TOKEN')\n",
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"login(hf_token, add_to_git_credential=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"executionInfo": {
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"elapsed": 555,
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"status": "ok",
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"timestamp": 1744678362522,
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"user": {
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"displayName": "Kenneth Andales",
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"userId": "04047926009324958530"
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},
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"user_tz": -480
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},
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"id": "yGjVTeMEig-b"
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},
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"outputs": [],
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"source": [
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"openai_api_key = userdata.get(\"OPENAI_API_KEY\")\n",
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"openai = OpenAI(api_key=openai_api_key)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {
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"executionInfo": {
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"elapsed": 9,
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"status": "ok",
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"timestamp": 1744679561600,
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"user": {
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"displayName": "Kenneth Andales",
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"userId": "04047926009324958530"
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},
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"user_tz": -480
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},
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"id": "6jboyASHilLz"
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},
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"outputs": [],
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"source": [
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"def message_prompt(transciption):\n",
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" system_message = \"\"\"\n",
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" You are an assistant that translate japanese text into two different languages like 'English' and 'Filipino',\n",
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" please display the translated text into markdown and include the original text from japanese using 'Romaji',\n",
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" sample format would be - original text (converted to romaji): orignal_translated_text_here \\n\\n translated to english: translated_english_text_here \\n\\n translated to filipino: translated_filipino_text_here\"\n",
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" \"\"\"\n",
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"\n",
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" user_propmpt = f\"Here is the transcripted japanese audio and translate it into two languages: '{transciption}'. No explaination just the translated languages only.\"\n",
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"\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": system_message},\n",
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" {\"role\": \"user\", \"content\": user_propmpt}\n",
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" ]\n",
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"\n",
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" return messages"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"executionInfo": {
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"elapsed": 7,
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"status": "ok",
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"timestamp": 1744678366113,
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"user": {
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"displayName": "Kenneth Andales",
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"userId": "04047926009324958530"
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},
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"user_tz": -480
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},
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"id": "nYrf_wKmmoUs"
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},
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"outputs": [],
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"source": [
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"quant_config = BitsAndBytesConfig(\n",
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" load_in_4bit=True,\n",
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" bnb_4bit_use_double_quant=True,\n",
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" bnb_4bit_quant_type=\"nf4\",\n",
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" bnb_4bit_compute_dtype=torch.bfloat16\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"executionInfo": {
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"elapsed": 7,
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"status": "ok",
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"timestamp": 1744678367778,
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"user": {
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"displayName": "Kenneth Andales",
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"userId": "04047926009324958530"
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},
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"user_tz": -480
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},
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"id": "ESlOaRGioqUQ"
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},
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"outputs": [],
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"source": [
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"def translation(messages):\n",
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" tokenizer = AutoTokenizer.from_pretrained(LLAMA)\n",
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" tokenizer.pad_token = tokenizer.eos_token\n",
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" inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\").to(\"cuda\")\n",
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" streamer = TextStreamer(tokenizer)\n",
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" model = AutoModelForCausalLM.from_pretrained(LLAMA, device_map=\"auto\", quantization_config=quant_config)\n",
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" outputs = model.generate(inputs, max_new_tokens=2000, streamer=streamer)\n",
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"\n",
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" return tokenizer.decode(outputs[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {
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"executionInfo": {
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"elapsed": 6,
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"status": "ok",
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"timestamp": 1744679567326,
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"user": {
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"displayName": "Kenneth Andales",
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"userId": "04047926009324958530"
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},
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"user_tz": -480
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},
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"id": "FSGFTvIEys0j"
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},
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"outputs": [],
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"source": [
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"def translate_text(file):\n",
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" try:\n",
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" audio_file = open(file, \"rb\")\n",
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"\n",
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" transciption = openai.audio.transcriptions.create(\n",
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" model=AUDIO_MODEL,\n",
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" file=audio_file,\n",
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" response_format=\"text\",\n",
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" language=\"ja\"\n",
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" )\n",
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"\n",
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" messages = message_prompt(transciption)\n",
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" response = translation(messages)\n",
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"\n",
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" return response\n",
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" except Exception as e:\n",
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" return f\"Unexpected error: {str(e)}\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "bexgSsWuvUmU"
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},
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"outputs": [],
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"source": [
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"with gr.Blocks() as demo:\n",
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" gr.Markdown(\"# 🎙️ Anime Audio Translator\")\n",
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" with gr.Row():\n",
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" with gr.Column():\n",
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" audio_file = gr.Audio(type=\"filepath\", label=\"Upload Audio\")\n",
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" button = gr.Button(\"Translate\", variant=\"primary\")\n",
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"\n",
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" with gr.Column():\n",
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" gr.Label(value=\"Result of translated text to 'English' and 'Filipino'\", label=\"Character\")\n",
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" output_text = gr.Markdown()\n",
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"\n",
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" button.click(\n",
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" fn=translate_text,\n",
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" inputs=audio_file,\n",
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" outputs=output_text,\n",
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" trigger_mode=\"once\"\n",
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" )\n",
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"demo.launch()"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"authorship_tag": "ABX9TyO+HrhlkaVchpoGIfmYAHdf",
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"gpuType": "T4",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python [conda env:base] *",
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"language": "python",
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"name": "conda-base-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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