1034 lines
28 KiB
Plaintext
1034 lines
28 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"*this notebook requires a working PyTorch GPU environment* "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "v5hvo8QWN-a9"
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},
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"source": [
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"# OpenAI's Whisper\n",
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"\n",
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"...original notebook from https://github.com/fastforwardlabs/whisper-openai/blob/master/WhisperDemo.ipynb\n",
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"\n",
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"Speech to text...\n",
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"\n",
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"more information at\n",
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"- https://openai.com/blog/whisper\n",
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"- https://github.com/openai/whisper\n",
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"\n",
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"\n"
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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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"outputs": [],
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"source": [
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"%%capture\n",
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"# install dependencies\n",
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"\n",
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"! pip install git+https://github.com/openai/whisper.git"
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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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"id": "3CqtR2Fi5-vP"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2023-10-13 09:14:42.948361: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA\n",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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]
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}
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],
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"source": [
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"%%capture\n",
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"# use imports and select cuda\n",
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"import os\n",
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"import numpy as np\n",
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"\n",
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"try:\n",
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" import tensorflow \n",
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"except ImportError:\n",
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" pass\n",
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"\n",
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"import torch\n",
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"import pandas as pd\n",
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"import whisper\n",
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"import torchaudio\n",
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"\n",
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"from ipywebrtc import AudioRecorder, CameraStream\n",
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"from IPython.display import Audio, display\n",
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"import ipywidgets as widgets\n",
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"\n",
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"DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
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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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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 107,
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"referenced_widgets": [
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"09446a03c33742dfa70a9f242f96b3be",
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"8964df95ded44ee28b7ed225c564ed9b",
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"823fe8b97ef94aedaed6889ac580c8eb",
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"5604b41fde3b45dd80b954c6128bccf7",
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"09e1f5b2de9945aea20f6129b7c82ec9",
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"35775e7c3c5846a589410566cbec95fa"
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]
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},
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"id": "-fFdSBBAGjFk",
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"outputId": "5894a254-7fe0-4593-fbee-74491cd72b9f"
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"AudioRecorder(audio=Audio(value=b'', format='webm'), stream=CameraStream(constraints={'audio': True, 'video': …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# manually record using webcam - if u want to use a custom audio file, skip this section\n",
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"camera = CameraStream(constraints={'audio': True,'video':False})\n",
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"recorder = AudioRecorder(stream=camera)\n",
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"recorder"
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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": 6,
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"metadata": {
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"id": "EDDgAohMGrCR"
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},
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"outputs": [],
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"source": [
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"# save recording as file and convert to wav\n",
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"with open('recording.webm', 'wb') as f:\n",
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" f.write(recorder.audio.value)\n",
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"!ffmpeg -i recording.webm -ac 1 -f wav my_recording.wav -y -hide_banner -loglevel panic"
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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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"id": "U7p2AoJItnIM"
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},
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"outputs": [],
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"source": [
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"# Whisper is capable of performing transcriptions for many languages (though it performs better for some languages and worse for others.) Whisper is also capable of detecting the input language. \n",
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"# However, to be on the safe side, we can explicitly tell Whisper which language to expect. \n",
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"language_options = whisper.tokenizer.TO_LANGUAGE_CODE \n",
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"language_list = list(language_options.keys())"
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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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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 49,
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"referenced_widgets": [
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"8c4d89ec973647d1a46aa471311e037c",
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"6c5f2af50210411f801f812dc17c389c",
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"90728ace9c454a3ab05ffb3e0bb664a3",
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"b50eded046cf4d378b1d71a186995d21"
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]
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},
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"id": "dpLnKvlb-vLa",
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"outputId": "bd012b8a-d413-41a9-834a-6674c0e2928a"
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Dropdown(options=('english', 'chinese', 'german', 'spanish', 'russian', 'korean', 'french', 'japanese', 'portu…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Whisper is also capable of several tasks, including English-only transcription, \n",
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"# Any-to-English translation, and non-English transcription. \n",
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"lang_dropdown = widgets.Dropdown(options=language_list, value='english')\n",
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"output = widgets.Output()\n",
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"display(lang_dropdown)"
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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": 9,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 49,
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"referenced_widgets": [
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"43cdc270c46644b6a0308d3c499601fb",
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"5f9cd9708efb486c930c1397be9a566c",
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"20cdb2181c804215b4a1e6006be77734",
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"fb4b05bd474c409288394cd82d6a9179"
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]
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},
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"id": "ilyDW-ALMnke",
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"outputId": "4a34e1f6-519c-46e5-a00b-05fbe3540e18"
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Dropdown(options=('transcribe', 'translate'), value='transcribe')"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"task_dropdown = widgets.Dropdown(options=['transcribe', 'translate'], value='transcribe')\n",
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"output = widgets.Output()\n",
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"display(task_dropdown)"
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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": 11,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "_PokfNJtOYNu",
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"outputId": "227e41ec-b1a5-409d-c3c4-20d4564fb09c"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model is English-only and has 71,825,408 parameters.\n"
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]
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}
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],
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"source": [
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"# load the model (takes some seconds)\n",
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"# hint: Whisper comes in five model sizes, \n",
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"# four of which also have an optimized English-only version. \n",
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"# This notebook loads \"base\"-sized models (bigger than \"tiny\" but smaller than the others), which require about 1GB of RAM.\n",
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"\n",
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"#If you selected English above, the cell below will load the optimized English-only version. Otherwise, it will load the multilingual model.\n",
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"\n",
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"if lang_dropdown.value == \"english\":\n",
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" model = whisper.load_model(\"base.en\")\n",
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"else:\n",
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" model = whisper.load_model(\"base\")\n",
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"print(\n",
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" f\"Model is {'multilingual' if model.is_multilingual else 'English-only'} \"\n",
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" f\"and has {sum(np.prod(p.shape) for p in model.parameters()):,} parameters.\"\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": 12,
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"metadata": {
|
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"colab": {
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|
"base_uri": "https://localhost:8080/"
|
|
},
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|
"id": "DwOUHau-dkUt",
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|
"outputId": "d16eef87-3af5-43e6-b54e-407a2e32f5d8"
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|
},
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|
"outputs": [
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{
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"data": {
|
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"text/plain": [
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"DecodingOptions(task='transcribe', language='english', temperature=0.0, sample_len=None, best_of=None, beam_size=None, patience=None, length_penalty=None, prompt=None, prefix=None, suppress_tokens='-1', suppress_blank=True, without_timestamps=True, max_initial_timestamp=1.0, fp16=True)"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"# set the options\n",
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"options = whisper.DecodingOptions(language=lang_dropdown.value, task=task_dropdown.value, without_timestamps=True)\n",
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"options"
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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": 13,
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|
"metadata": {
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"id": "q6S0VvoK0vfq"
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},
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|
"outputs": [],
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"source": [
|
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"# choose your audio file\n",
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"#audio = whisper.load_audio(\"my_recording.wav\")\n",
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"audio = whisper.load_audio(\"QA-01.mp3\")\n",
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"audio = whisper.pad_or_trim(audio)\n",
|
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"mel = whisper.log_mel_spectrogram(audio).to(model.device)\n",
|
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"result = model.decode(mel, options)"
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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": 14,
|
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"metadata": {
|
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"colab": {
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|
"base_uri": "https://localhost:8080/",
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"height": 53
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},
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|
"id": "Mg64_MWW1uMb",
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"outputId": "444cfab3-f2bd-4519-9779-3f4aba72d1cc"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'How many people are there in your family? There are five people in my family. My father, mother, brother, sister, and me. Does your family live in a house or an apartment? We live in a house in the countryside. What does your father do? My father is a doctor. He works at the local hospital. How old is your mother? She is 40 years old, one year younger than my father.'"
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]
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},
|
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"execution_count": 14,
|
|
"metadata": {},
|
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"# print the text\n",
|
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"result.text"
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
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"metadata": {},
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|
"outputs": [],
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"source": [
|
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"# or write it into a text file\n",
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"\n",
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"text_file = open(\"output.txt\", \"w\")\n",
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"text_file.write(result.text)\n",
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" \n",
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"#close file\n",
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"text_file.close()"
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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": 17,
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|
"metadata": {},
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"outputs": [],
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"source": [
|
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"# close all widgets\n",
|
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"from ipywidgets import Widget\n",
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"Widget.close_all()"
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]
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},
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{
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"cell_type": "code",
|
|
"execution_count": 18,
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"metadata": {},
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|
"outputs": [],
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"source": [
|
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"# it takes around 2GB memory on GPU, so please clear it\n",
|
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"from numba import cuda\n",
|
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"device = cuda.get_current_device()\n",
|
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"device.reset()"
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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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"outputs": [],
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"source": []
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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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"collapsed_sections": [],
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"include_colab_link": true,
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"provenance": []
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},
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"gpuClass": "standard",
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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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": "3.10.12"
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},
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"widgets": {
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"_data_src": "blob:https://nfrp4p17vqk-496ff2e9c6d22116-0-colab.googleusercontent.com/d984436f-3337-406a-97dd-76a1145ef36f",
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"_dom_classes": [],
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"_view_module_version": "~0.6.0",
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"_view_name": "AudioRecorderView",
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"audio": "IPY_MODEL_8964df95ded44ee28b7ed225c564ed9b",
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"autosave": false,
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"codecs": "",
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"filename": "record",
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"format": "webm",
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"layout": "IPY_MODEL_823fe8b97ef94aedaed6889ac580c8eb",
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"recording": false,
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}
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