notebooks/2.1_wav2vec2_colab.ipynb

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    "# TRANSCRIBING AUDIO FILES WITH WAV2VEC2\n",
    "\n",
    "Transcribing audio files into text is among the most tedious and time consuming tasks in business and research. As such I was particularly keen to try [Hugging Face's implementation of Facebook's Wav2Vec2 model](https://huggingface.co/transformers/model_doc/wav2vec2.html) when it was released for transformers 4.3.\n",
    "\n",
    "I was curious to see how well it would perform for short and long speeches, different accents and different \"delivery formats\" - be it formal speeches or a poetry recital. The three notebooks in this repo cover the results from my trials, ranging from a short audio snippet (62s) to a poetry recital (5minutes 34s) and a 12-minute-plus political speech.\n",
    "\n",
    "The accents in these audio clips involve: White American, African American and Singaporean Chinese.\n",
    "\n",
    "I find the results from Wav2Vec2 to be really impressive, and think this can help open up new ways to \"chain link\" NLP tasks directly from audio to textual analysis.\n",
    "\n",
    "Long audio clips are very memory-intensive, however, and efforts to process audio files longer than 90s tend to crash normal work machines and even Colab Pro notebooks. I have a minor workaround in notebooks2.0/2.1 that are somewhat clumsy, but they get the job done within a reasonable period of time. Will figure out a more efficient way to do this at some point.\n",
    "\n",
    "\n",
    "## REFERENCES\n",
    "\n",
    "- Documentation of [Hugging Face's implementation of Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html)\n",
    "\n",
    "- Hosted inference [API on Hugging Face](https://huggingface.co/facebook/wav2vec2-base-960h)\n",
    "\n",
    "- Paper on [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477)\n",
    "\n",
    "\n",
    "## RESULTS\n",
    "\n",
    "- The output text files of the two longer trials can be downloaded [here](https://www.dropbox.com/s/zx4bfct1zhl18az/amanda_gorman.txt) and [here](https://www.dropbox.com/s/gu3e6ns4x4tty61/lhl_wef.txt)\n",
    "\n",
    "\n",
    "## REQUIREMENTS\n",
    "\n",
    "- [transformers](https://pypi.org/project/transformers/) >= 4.3\n",
    "- [librosa](https://pypi.org/project/librosa/)\n",
    "- if you want to use your own audio clips, make sure to downsample them to 16kHz as the Wav2Vec2 model used here was pretrained and fine-tuned on 16kHz sampled speech audio. I used [Audacity](https://www.audacityteam.org/) to split up the audio files in this repo.\n",
    "\n",
    "\n",
    "## MODELS\n",
    "\n",
    "- There are several versions of the Wav2Vec2 model on Hugging Face's model hub. I haven't tried them out to see what the difference in output quality is like. Check them out [here](https://huggingface.co/models?search=wav2ve).\n",
    "\n",
    "- This repo uses the [wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) model throughout."
   ]
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   "source": [
    "# 1. TRANSCRIBE LONGER AUDIO CLIPS ON COLAB (PRO)\n",
    "\n",
    "This is the second of two notebooks on transcribing longer audio clips with Wav2Vec2. With clips beyond 10 minutes (I've tried up to audio clips around 21 minutes), I find it better to run the notebook on Colab. \n",
    "\n",
    "If you have a Colab Pro account, switch to the \"TPU\" option so that you'll be allocated 35Gb of RAM, the max avail under the Pro settings (\"GPU\" setting gets you about 25Gb RAM).\n",
    "\n",
    "For this trial, I picked a 12.5 minutes speech by [Singapore Prime Minister Lee Hsien Loong](https://www.youtube.com/watch?v=izrdoAm4_Gw) to see how the Wav2Vec2 model deals with an Asian accent, on top of the longer audio clips.\n",
    "\n",
    "The original audio clip was split into 13 parts, with the first 12 all being a minute long. You can split the original speech into fewer but longer clips if you have access to better compute.\n",
    "\n",
    "This took about 2 minutes 4s to run on Colab Pro, a fraction of what a human user would need. The model didn't struggle with the change in accent, though it ought to be noted that the Prime Minister is a very experienced public speaker. Results could vary if the trial was conducted with an Asian speaker who is less proficient in English and/or public speaking.\n",
    "\n",
    "To run this, you'll have to upload the \"lhl_wef\" sub-folder in the \"audio\" folder to your G-drive and make any necessary changes to path etc. "
   ]
  },
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    },
    "id": "JPHnyUBviH0c",
    "outputId": "aa64ca9d-c6d7-4767-b46a-cb0182c4952f"
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     "text": [
      "\u001b[K     |████████████████████████████████| 1.8MB 7.3MB/s \n",
      "\u001b[K     |████████████████████████████████| 3.2MB 23.0MB/s \n",
      "\u001b[K     |████████████████████████████████| 890kB 54.7MB/s \n",
      "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
     ]
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   ],
   "source": [
    "! pip install -q transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "KtL848T3hKIg",
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     "name": "stdout",
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     "text": [
      "Mounted at /content/drive/\n"
     ]
    }
   ],
   "source": [
    "# load access to your G-drive where the audio files have to be stored\n",
    "\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "b1dL-TWaiL2_"
   },
   "outputs": [],
   "source": [
    "import librosa\n",
    "import pandas as pd\n",
    "import os\n",
    "import torch\n",
    "from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "92s-I_7Kg6Fi"
   },
   "outputs": [],
   "source": [
    "os.chdir(\"/content/drive/My Drive/Colab Notebooks\")"
   ]
  },
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   "source": [
    "#load tokenizer and pre-trained model\n",
    "tokenizer = Wav2Vec2Tokenizer.from_pretrained(\"facebook/wav2vec2-base-960h\")\n",
    "model = Wav2Vec2ForCTC.from_pretrained(\"facebook/wav2vec2-base-960h\")"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 DEFINE FUNCTIONS TO TRANSCRIBE SHORTER AUDIO CLIPS ONE AT A TIME\n",
    "\n",
    "The functions return a dataframe for ease of \"transfer\" to other NLP tasks.\n",
    "\n",
    "## NOTE:\n",
    "\n",
    "- Make sure the numbering of your split audio files start at \"1\", and not \"01\", or \"001\".\n",
    "\n",
    "- Change the file path/names for the audio files as necessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "084YJhhjlnfo"
   },
   "outputs": [],
   "source": [
    "def split(x, y):\n",
    "    speech = {}\n",
    "    input_values = {}\n",
    "    logits = {}\n",
    "    predicted_ids = {}\n",
    "    transcribe = {}\n",
    "    for i in range(x, y+1):\n",
    "        speech[i], rate = librosa.load(\n",
    "            \"lhl_wef/lhl_wef-%d.flac\" % i, sr=16000\n",
    "        )\n",
    "        input_values[i] = tokenizer(speech[i], return_tensors=\"pt\").input_values\n",
    "        logits[i] = model(input_values[i]).logits\n",
    "        predicted_ids[i] = torch.argmax(logits[i], dim=-1)\n",
    "        transcribe[i] = tokenizer.decode(predicted_ids[i][0])\n",
    "    return transcribe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "x9YgcAR4XG8D"
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   "source": [
    "def transcript(num_clips):\n",
    "    trans = {}\n",
    "    for j in range(1, num_clips):\n",
    "        if num_clips - j > 0:\n",
    "            trans[j] = pd.DataFrame.from_dict(\n",
    "                split(j, j + 1), orient=\"index\"\n",
    "            ).rename(columns={0: \"Transcribed_Text\"})\n",
    "        else:\n",
    "            pass\n",
    "    return (\n",
    "        pd.concat(trans)\n",
    "        .drop_duplicates(subset=[\"Transcribed_Text\"])\n",
    "        .reset_index(drop=True)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 TRANSCRIBE SHORTER CLIPS\n",
    "\n",
    "Even on Colab Pro, the notebook crashes with individual clips longer than 90s. So manage the length of each clip as necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "scP9E_yPrGpq",
    "outputId": "7062b8a8-72d9-4cd0-cbb4-b4143a421282"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20min 16s, sys: 2min 55s, total: 23min 11s\n",
      "Wall time: 2min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = transcript(num_clips = 13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 CHECK RESULTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "9EASlZmLXffy",
    "outputId": "1248c9ac-2930-4871-ae0a-01520d355394"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transcribed_Text</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IAM VERY HONOR TO SPEAK AT DISCLOSING ADDRESS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CRITICAL THAT VAXINES ARE RULED OUT QUICKLY AC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UTIONS AND RULES AND NORMES WAS ERODING POPULO...</td>\n",
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       "      <th>3</th>\n",
       "      <td>SO THAT ALL COUNTRIES AV SPECIALLY THE LEAST D...</td>\n",
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       "      <th>4</th>\n",
       "      <td>NORMOUS STRESS ONLY UNPRECEDENTED LEVELS OF EM...</td>\n",
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       "                                    Transcribed_Text\n",
       "0  IAM VERY HONOR TO SPEAK AT DISCLOSING ADDRESS ...\n",
       "1  CRITICAL THAT VAXINES ARE RULED OUT QUICKLY AC...\n",
       "2  UTIONS AND RULES AND NORMES WAS ERODING POPULO...\n",
       "3  SO THAT ALL COUNTRIES AV SPECIALLY THE LEAST D...\n",
       "4  NORMOUS STRESS ONLY UNPRECEDENTED LEVELS OF EM..."
      ]
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     "execution_count": 9,
     "metadata": {
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   "source": [
    "#checking that all 13 clips were transcribed\n",
    "df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "Gq5WNQLLLXoT",
    "outputId": "90e8a620-a083-4005-b611-5ab7114e7a7d"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 1)"
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     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
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    }
   ],
   "source": [
    "df.shape"
   ]
  },
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       "       \"CRITICAL THAT VAXINES ARE RULED OUT QUICKLY ACROSS THE WORLD BUT EVEN WITH VAXINES THE PANDAMIC IS FAR FROM BEING QUELLED THE NEW VARIANCE DISCOVERED AND THE U K IN SOUTH AFRICA AND BRASIL ARE WARRYING AND FURTHER MUTATIONS WILL SURELY EMERGE UNTIL A LARGE PART OF THE WORLD'S POPULATION IS VACCINATED WE STILL NEEDS STRONG PUBLIC HEALTH MEASURES EVERYWHERE TO SUPPRESS THE SPREAD OF THE VIOLUS AND KEEP POPULATION SAFE WHAT WILL THE POSTCOVE NINETEEN WOLL LOOK LIKE WILL COUNTRIES EMERGE MORE RESOLVE TO BUILD A MORE RESILIANT BUT STILL GLOBALIZED WELL OR ARE WE HEADED TOWARDS A LESS INTEGRATED GLOBAL ECONOMY A LESS STABLE INTERNATIONAL ORDER THE ANSWER DEPENDS ON THE DECISIONS THAT COUNTRIES TAKE NOW EVEN BEFORE COVER NINETEEN GLOBILIZATION WAS ALREADY UNDER PRESSURE CONFIDENCE AND MALTILACERAL INSTIT\",\n",
       "       \"UTIONS AND RULES AND NORMES WAS ERODING POPULOUS POLITICS NATIVISM NATIONALISM PROTECTIONISM WERE ON THE RISE COUNTRYS INITIAL REACTIONS TO THE PENDEMIC SEEMED TO HERL GRUBILIZATIONS DEMIES BORDERS WERE CLOSE SUPPLY CHANGE WERE BADLY DISRUPTED EACH COUNTRY SCRAMBLE TO SECURE ITS OWN SUPPLIES OF ESSENTIAL GOODS ESPECIALLY IN MEDICINES FACE MASKS AND VENTILATORS IT WAS EACH MAN FOR HIMSELF BUT AS THEIR SITUATION UNFOLDED WE WERE FORCEFULLY REMINDED THAT OUR FATES WERE INTERTWINED AND THAT WE HAD TO WORK TOGETHER AND SO WE DID IN MANY AREAS WE RESTORED SUPPLY CHANGE WE REPATRATED EACH OTHER'S CITIZENS STUCK OVER SEAS WE SHARED TESTS AND MEDICAL SUPPLIES WE SUPPORTED VAXYNG MALTILATERALISM INITIATIVES LIKE THE KOVACS\",\n",
       "       'SO THAT ALL COUNTRIES AV SPECIALLY THE LEAST DEVELOP ONES WOULD HAVE ACCESS TO VACSCENES AND AS WE GRADUALLY REBUILT CONFIDENCE IN ONE ANOTHER WE OPENED UP CONTROL CORRIDORS FOR TRAVEL AND TRADE BETWEEN COUNTRIES CRUCIALLY INTERNATIONAL SCIENTIFIC CO OPERATION IN THE FIGHT AGAINST COVERNINETEEN CONTINUE DOCTORS AND SCIENTISTS SHARED INFORMATION ABOUT THE DISEASE AND THE VIRUS STUDYING THEM DEVELOPING TREATMENTS AND TESTING VAKSCENES THIS ENABLE US TO IMPROVE PATIENT CARE AND TO PRODUCE EFFECTIVE ACTENES IN RECORD TIME SOME USING NEW ECKNOWLEDGIES SUCH INTERNATIONAL CO OPERATION AND MALTILATERAL EFFORTS REMAIN ESSENTIAL TO TACKLE THE GLOBAL PANDEMIC COHERENTLY WITH BORDER CLOSURES AND LOCK DOWNS ECONOMIES HAVE ALL TAKEN A DEEP PLUNGE THE LIBLIHOODS OF MILLIONS CAME UNDER IN',\n",
       "       'NORMOUS STRESS ONLY UNPRECEDENTED LEVELS OF EMERGENCY SPENDING AND MAGETARY STIMULUS HAVE KEPT US AFLOAD PROVIDING A LIFE LINE TO COMPANIES WORKERS AND FAMILIES CENTRAL BANKS HAVE PLAYED THEIR PART TO PREVENT FINANCIAL SYSTEMS AND GLOBAL CAPITAL MARKETS FROM SEIZING UP UNLIKEN PREVIOUS CRISES THESE EXTRAORDINARY MEASURES CANNOT BE SUSTAINED INDEFINITELY IN FACT SPENDING PACKAGES ARE ALREADY TAPERING OFF BUT HOPEFULLY AS FASCINATION BECOMES MORE WIDESPREAD AND WE MAKE HEADWAYS SUPPRESSING THE VIOLUS COVET NINETEEN RESTRICTIONS CAN BE PROGRESSIVELY EASE AND ECONOMIES WILL REBOUN THE WORLD BANK AND I MY FORECAST GLOBAL GROWTH TO RECOVER THIS YEAR IT WILL NOT RESTORE OUTPUT TO PRICOVENINETEEN LEVELS BUT TI SOMETHING STILL TO BE THANKFUL FOR NOW WE ARE ENTERING A NEW PHASE',\n",
       "       'THE PANDEMIC HAS EXPOSED BUSINESSES AND JOBS WHICH ARE NOT GOING TO REMAIN VIABLE THEY HAVE TO BE LET GO TO ALLOW NEW GROWTH AND BETTER JOBS TO BE CREATED IN THEIR PLACE HARD DECISIONS HAVE TO BE MADE AND THIS WILL EXASCEBATE EXISTING STRESSES GOVERNMENTS WILL COME UNDER MORE PRESSURE TO ADOPT PROTECTIONISTS AND NATIVEST POSITIONS TO RESUME GROWTH WE MUST LOOK BEYOND RETURNING TO THE STATEST QUA ANTI WE MUST LOOK AHEAD WILEN COUNTRIES GOVERNMENTS AND BUSINESSES MUST COLLABORATE TO TACT NEW MARKETS AND DEVELOP NOVELT ACKNOWLEDGES EXTERNALLY COUNTRIES NEED TO STRENGTHEN THE FRAMEWORK FOR INTERNATIONAL CO OPERATION AS AN IMMEDIATE TASK COUNTRIES SHOULD COLLABORATE TO DEVELOP A STANDARDIZE ROBUST SYSTEM TO VERIFY THE AUTHENTICITY OF TESTS AND VACCINATIONS THIS IS ESSENTIAL TO REOPEN B',\n",
       "       'ORDERS AND RESUME INTERNATIONAL TRAVEL IN THE LONGER TERM COUNTRIES SHOULD WORK TOGETHER TO UPDATE AND STRENGTHENINTS TERNATIONAL INSTITUTIONS LIKE THE W TEOL AND CREATE NEW RULES TO GOVERN AND FOSTER NOVEL FORMS OF ECONOMIC ACTIVITY FOR EXAMPLE TO SUSTAIN THE GROWTH OF THE DIGITAL ECONOMY AND FACILITATE SAFE SECURE AND EFFICIENT CROSS BORDER E PAYMENTS AND DATA FLOWS WE HAVE TO DEVELOP NEW E TRADE REGULATIONS SINGAPOLE HAS CONCLUDED DIGITAL ECONOMY AGREEMENTS WITH LIKE MINDED COUNTRIES LIKE AUSTRALIA CHILLE AND NEW ZEALAN WE HOPE THAT THIS IS ONLY THE BEGINNING WE ENCOURAGE ALL COUNTRIES TO COME TOGETHER TO SHAPE AND GROW THE DIGITAL ECONOMY GLOBLY THUSSIGNING OF THE REGINAL COMPREHENSIVE ECONOMIC PARTNERSHIP OR THE ARLC E P LAST',\n",
       "       \"TIA BY FIFTEEN COUNTRIES IN ASIA WAS ALSO A MAJOR COLLECTIVE COMMITMENT TO TRADE AN ECONOMIC INTEGRATION AMIDST THE PANDEMIC THE ARC P WILL BROADEN TRAN OPEN AT MARKETS IN EAST AND SOUTH EASTATIA AND AUSTRALASIA AND HOPEFULLY PREVENT THE PUSH FOR RASILIANTS AND SELF RELIANCE FROM GOING TOO FAR WHILE DEALING WITH THE AFTERMATH OF COVET NINETEEN WE MUST NOT LOSE SIGHT OF OTHER LONG TERM CHALLENGES THAT AFFECT ALL OF US ONE MAJOR PROBLEM IS CLIMATE CHANGE TWENTY TWENTY WAS THE WORLD'S HOTTESEER ON RECORD EXTREME WEATHER EVENTS HAVE BECOME MUCH MORE FREQUENT LAST YIAR CARBONEMISSIONS WENT DOWN BUT ONLY BECAUSE OF COVET NINETEEN OTHERWISE THE TREN HAS BEEN INEXORABLY UPWARDS CLIMATE CHANGE IS CLEARLY EX\",\n",
       "       \"CELERATING DANGEROUSLY AND IT IS LATE IN THE DAY BUT IF COUNTRIES ACT NOW AND IN CONCERT HUMAN KIND CAN STILL HOPE TO AVERT A CATASTROPHE WE ALL KNOW WHAT WE NEED TO DO WITHIN INDIVIDUAL COUNTRIES TOMUSTER SUPPORT FOR POLICIES AND MEASURES THAT WILL SLOW THE CHANGES AND LIMIT GLOBLE WARNING COLLECTIVELY TO SET HIGHER COMMON STANDARDS AND HOLD ONE ANOTHER TO OUR MUTUAL COMMITMENTS WHETHER IT'S TIGHTENING EMISSION RULES FASING OUT FOSSILFIELD SUBSIDIES OR PROMOTING RENEWABLE ENERGY WE CAN TAKE SOME COMFORT THAT COUNTRIES ARE NOW TAKING CLIMATE CHANGE MORE SERIOUSLY THE US HAS REJOINED THE PARIS AGREEMENT CHINA HAS ANNOUNCED A ZERO EMISSIONS TARGET BY TWENTY SIXLY BUT MUCH MORE STILL NEEDS TO BE DONE GOING BEYOND OUR PARIS COMMITMENTS OTHERWISE\",\n",
       "       'WE RISK GRAVE CONSEQUENCES IN THE NOT TOO DISTANT FUTURE EVEN WITHIN OUR OWN LIFETIMES TO TACKLE THESE CHALLENGES COVET NINETEEN ECONOMIC RECOVERY AND CLIMATE CHANGE GLOBA CO OPERATION IS ESSENTIAL BUT GETTING COUNTRIES TO WORK TOGETHER IS NOT SIMPLY A MATTER OF NURTURING AND SHOWING GOODWILL THE INTERNATIONAL ORDER MUST BE UNDERPINNED BY STABLE GREAT POWER RELATIONS BIG COUNTRIES NATURALLY JOSTLE AND COMPETE WITH ONE ANOTHER FOR INFLUENCE AND POWER BUT THEY ALSO NEED TO WORK WITH ONE ANOTHER THROUGH ESTABLISH AN ACCEPTED RULES AND NORMS ON ISSUES WHICH AFFECT US ALL BE IT PANDAMICS ECONOMIC CO OPERATION OR CLIMATE CHANGE RECENT YEARS HAVE WITNESSED GROWING FRICTION AND DISTRUST RATHER THAN CO OPERATION AND CONFIDENCE BUILDING AMONG MAJOR POWERS THE MOST WORRYING TREND I',\n",
       "       'S THE W S CHINA RELATIONS THIS REMAINS THE MOST IMPORTANT BYLATURAL RELATIONSHIP FOR THE WORLD IN THE YEARS AHEAD OVER THE LAST FOUR YEARS TENSIONS BETWEEN THE U S AND CHINA HAVE INTENSIFIED SHARPLY BOTH POWERS HAVE ADOPTED MORE SERTIVE AND UNCOMPROMISING POSTURES THE U S NOW SEES CHINAS AS STRATEGIC RIVAL AND CHALLENGER TO ITS PRE EMINENT POSITION AND CHINA IS VIGOROUSLY ASSERTING WHAT IT CONSIDERS ITS RIGHTFUL PLACE IN THE WORLD ON BOTH SIDES DOMESTIC PRESSURES TO HARDEN THEIR EXTERNAL POSITIONS ARE CONSIDERABLE AND MODERATE VOICES HAVE BEEN MARGINALIZE GIVEN THE ENORMOUS STAKES DIFFICULT AS IT WILL BE IT CANNOT POSSIBLY BE TOO LATE FOR THE U S AND CHINA TO RESET THE TONE OF THEIR INTERACTIONS AND AVERT A CLASH',\n",
       "       \"BETWEEN THEM WHICH WILL BECOME A GENERATIONAL TWILIGHT STRUGGLE THE NEW US ADMINISTRATION IS AN OPPORTUNITY TO STEER THE RELATIONSHIP TOWARDS SAFER WATERS AMID PRESIDENT BIDONS MANY URGENT PREOCCUPATIONS THE U S CHINA RELATIONSHIP SHOULD BECOME A KEY STRATEGIC PRORITY TO BILD A STABLE INTERNATIONAL ORDER REGULAR CONSTRUCTIVE DIALOGUE IS CRITICAL IAM THUS HAPPY TO SEE MANY DISTINGUISHED PARTICIPANTS TAKING PART IN THE DAVOSA GENDER WEEK THE WORLD ECONOMIC FORUM PLAYS AN IMPORTANT RULE PROMOTING DIALOGUE BRINGING TOGETHER LEADERS IN GOVERNMENT INDUSTRY AND CIVIL SOCIETY IT'S A FORUM WHERE LEADERS FROM COUNTRIES LARGE AND SMALLER LIKE CAN SPEAK AND BE HEARD AND THIS IS WHY WHEN PROFESSOR SHROB ASK ME WHETHER SINGO POET HOSTESS SPECIAL ANNUAL MEETING OF THE W E F I AGREED IT WAS NOT\",\n",
       "       'A DECISION LIGHTLY TAKEN BUT WE ARE HAPPY TO MAKE A MODEST CONTRIBUTION TO THE GLOBAL DISCUSSION AS THE HOST COUNTRY WE WILL WORK WITH A W E F TO ENSURE THE HEALTH AND SAFETY OF ALL I WELCOME ALL OF YOU TO SING A POR IN MAY SO THAT WE CAN TAKE THESE DISCUSSIONS FORWARD AND FORGE A NEW PARTH AHEAD TOGETHER THANK YOU'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Transcribed_Text\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "74h9YwVcdH2A"
   },
   "outputs": [],
   "source": [
    "#lhl_wef = df[\"Transcribed_Text\"].apply(''.join)\n",
    "\n",
    "#lhl_wef.to_csv(\"lhl_wef.txt\", sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g5KGZ_BnMhs9"
   },
   "source": [
    "## NOTE:\n",
    "\n",
    "You'll have to clean up some of the transcribed text, but the amount of time saved here is pretty obvious."
   ]
  }
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