Audio2Text_Translate_Text2Speech.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AAwM-ISMRMYS"
      },
      "outputs": [],
      "source": [
        "!pip install openai-whisper"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9CcZX6aeRnPd"
      },
      "outputs": [],
      "source": [
        "!pip install pytube"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentencepiece"
      ],
      "metadata": {
        "id": "rC9925JMS8AQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DogUrR1FSRyI"
      },
      "outputs": [],
      "source": [
        "import whisper\n",
        "import pytube\n",
        "import re\n",
        "from tqdm import tqdm\n",
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Transcribe**:"
      ],
      "metadata": {
        "id": "TKkuPdXdVVN1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class TranscribeVideo:\n",
        "    def __init__(self):\n",
        "        self.model = whisper.load_model(\"base\")\n",
        "\n",
        "    def _get_audio_from_yt(self, video_url: str):\n",
        "        # Create a YouTube object\n",
        "        yt = pytube.YouTube(video_url)\n",
        "\n",
        "        # Download the audio stream\n",
        "        audio_stream = yt.streams.filter(only_audio=True).first()\n",
        "        self.audio_path = f\"{video_url.split('watch?v=')[-1]}.m4a\"\n",
        "        audio_stream.download(filename=self.audio_path)\n",
        "\n",
        "    def _write_text(self, text: str):\n",
        "        self.text_path = f\"transcribe_{self.audio_path.split('.')[0]}.txt\"\n",
        "        with open(self.text_path, \"w\") as fp:\n",
        "            fp.write(text)\n",
        "\n",
        "    def get_audio(self):\n",
        "        # Extract the audio from the file\n",
        "        with open(self.audio_path, \"rb\") as f:\n",
        "            return f.read()\n",
        "\n",
        "    def get_text(self):\n",
        "        # Extract the audio from the file\n",
        "        with open(self.text_path, \"r\") as f:\n",
        "            return f.read()\n",
        "\n",
        "    def to_text(self, video_url: str):\n",
        "        print(f\"Downloading audio from: {video_url}\")\n",
        "\n",
        "        # Download audio stream from YouTube\n",
        "        self._get_audio_from_yt(video_url)\n",
        "\n",
        "        print(f\"Audio saved to: {self.audio_path}\")\n",
        "        print(\"Transcription started, please wait...\")\n",
        "\n",
        "        # Transcribe audio\n",
        "        result = self.model.transcribe(self.audio_path, fp16=False)\n",
        "\n",
        "        # Save text in a file\n",
        "        self._write_text(result['text'])\n",
        "        print(f\"Text saved to: {self.text_path}\")\n",
        "\n",
        "        return result['text']\n",
        "\n",
        ""
      ],
      "metadata": {
        "id": "toEUkZRNHn8D"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the video URL\n",
        "video_url = \"https://www.youtube.com/watch?v=TRjq7t2Ms5I\"\n",
        "tv = TranscribeVideo()"
      ],
      "metadata": {
        "id": "jSaKBYiyLpIX"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tv.to_text(video_url)"
      ],
      "metadata": {
        "id": "WxJNOYNQL9ye"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Translate**:"
      ],
      "metadata": {
        "id": "fsoAxQHRVa45"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class TranslateText:\n",
        "    def __init__(self):\n",
        "        model_name = 'Helsinki-NLP/opus-mt-en-de'\n",
        "        self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n",
        "\n",
        "    def split_text_into_sentences(self, text):\n",
        "        \"\"\"\n",
        "        Split a text into sentences using regular expressions.\n",
        "\n",
        "        Parameters:\n",
        "        - text: The input text.\n",
        "\n",
        "        Returns:\n",
        "        A list of sentences.\n",
        "        \"\"\"\n",
        "        sentences = re.split(r'(?<!\\w\\.\\w.)(?<![A-Z][a-z]\\.)(?<=\\.|\\?)\\s', text)\n",
        "        return sentences\n",
        "\n",
        "    def split_text_into_chunks(self, text, chunk_size=512):\n",
        "        \"\"\"\n",
        "        Split a large text into fixed-size chunks based on characters.\n",
        "\n",
        "        Parameters:\n",
        "        - text: The input text to be split.\n",
        "        - chunk_size: The desired size of each chunk.\n",
        "\n",
        "        Returns:\n",
        "        A list of text chunks.\n",
        "        \"\"\"\n",
        "        sentences = self.split_text_into_sentences(text)\n",
        "        chunks = []\n",
        "        current_chunk = \"\"\n",
        "\n",
        "        for sentence in sentences:\n",
        "            if len(current_chunk) + len(sentence) <= chunk_size:\n",
        "                current_chunk += sentence + \" \"\n",
        "            else:\n",
        "                chunks.append(current_chunk.strip())\n",
        "                current_chunk = sentence + \" \"\n",
        "\n",
        "        # Add the last chunk if it's not empty\n",
        "        if current_chunk:\n",
        "            chunks.append(current_chunk.strip())\n",
        "\n",
        "        return chunks\n",
        "\n",
        "    def translate_chunk(self, chunk_text: str):\n",
        "        input_ids = self.tokenizer.encode(chunk_text, return_tensors=\"pt\")\n",
        "        outputs = self.model.generate(input_ids)\n",
        "        decoded = self.tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "\n",
        "        return decoded\n",
        "\n",
        "    def en2de(self, text: str, chunk_size: int):\n",
        "        # Split text into chunks\n",
        "        chunks = self.split_text_into_chunks(text, chunk_size)\n",
        "\n",
        "        # translate texts\n",
        "        translated = []\n",
        "        for chunk in tqdm(chunks):\n",
        "            translated.append(self.translate_chunk(chunk))\n",
        "\n",
        "        # return translated text\n",
        "        return \" \".join(translated)"
      ],
      "metadata": {
        "id": "5RribWYpTdQr"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tt = TranslateText()\n",
        "translated_text = tt.en2de(tv.get_text(), chunk_size=512)\n",
        "translated_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "id": "VohTH3vtUU_H",
        "outputId": "6d56a6d1-13a7-458b-df43-157dad8f0d47"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 48/48 [04:27<00:00,  5.57s/it]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Hey, jeder. Mein Name ist Jerry, Mitgründer und CEO von Womendex, und heute werden wir darüber reden, wie man Produktion-ready, Rag-Anwendungen bauen. Ich denke, es gibt noch Zeit für eine Verlosung für den Eimer Hut, so wenn Sie Jungs an unserem Stand stoppen, füllen Sie bitte das Google-Formular. Okay. Lassen Sie uns zurück zum Thema. Jeder weiß, dass es eine Tonne von erstaunlichen Anwendungsfällen in Genai in letzter Zeit. Wissenssuche in QA, Gesprächsagenten, Workflow-Automatisierung, Dokumentenverarbeitung. Dies sind alles Dinge, die Sie bauen können, vor allem mit Hilfe der Argumentationsfähigkeiten von LMs, über Ihre Daten. Also, wenn wir nur eine schnelle Auffrischung in Bezug auf wie Paradigmen, wie Sie tatsächlich Sprachmodelle, um Daten zu verstehen, die nicht über trainiert wurde, gibt es wirklich wie zwei Paradigmen. Eines ist das Abrufen Augmentation, wo Sie das Modell reparieren mögen und Sie im Grunde erstellen eine Datenpipeline, um Kontext in die Eingabeaufforderung von einigen Datenquelle in die Eingabeaufforderung des Sprachmodells zu setzen. So wie eine Vektordatenbank, wissen Sie, unstrukturierter Text, SQL-Datenbank, et cetera. Das nächste Paradigma hier ist Feinabstimmung. Wie können wir Wissen in die Gewichte des Netzwerks, indem wir tatsächlich die Gewichte des Modells selbst aktualisieren einige Adapter auf dem Modell, aber im Grunde eine Art Trainingsprozess über einige neue Daten, um tatsächlich Wissen zu integrieren? Wir werden wahrscheinlich ein wenig mehr über Retrieval Augmentation sprechen, aber dies ist nur, um Ihnen zu helfen, loszulegen und wirklich das Leitbild des Unternehmens zu verstehen. Okay, lassen Sie uns über Rags reden, Retrieval Augmented Generation. Es ist eine Art Schlagwort geworden, aber wir werden zuerst durch den aktuellen Rags Stack gehen, um ein QA-System zu bauen. Dies besteht wirklich aus zwei Hauptkomponenten, Datenaufnahme, sowie Datenabfrage, die Retrieval und Synthese enthält. Wenn du gerade erst mit dem Lama-Index angefangen hast, kannst du das im Grunde wie fünf-Scheiben-Zeilen Code machen, also musst du nicht wirklich darüber nachdenken. Aber wenn Sie einige der unteren Ebenen Komponenten lernen wollen und ich wie jeder Ingenieur, AI-Ingenieur, im Grunde wie lernen, wie diese Komponenten unter der Haube arbeiten, würde ich Sie ermutigen, einige unserer Docs zu überprüfen, um wirklich zu verstehen, wie Sie tatsächlich Datenaufnahme und Datenabfrage, wie Sie tatsächlich aus einer Vektordatenbank abrufen, und wie synthetisieren Sie das mit einem L on? Das ist also im Grunde der Schlüssel-Stack, der heutzutage entsteht, wie für jede Art von Chatbot, wie Chat über Ihr PDF, wie über Ihre unstrukturierten Daten, viele dieser Dinge verwenden im Grunde diese gleichen Prinzipien, wie, wie Sie tatsächlich laden Daten von einer Datenquelle und tatsächlich abrufen und Abfrage darüber. Aber ich denke, als Entwickler tatsächlich entwickeln diese Anwendungen, sie erkennen, dass dies nicht ganz genug ist. Als ob es bestimmte Probleme gibt, auf die Sie sich einlassen, sind Blocker für die Produktion dieser Anwendungen. Und was sind diese Herausforderungen mit NIEVRAG? Ein Aspekt hier ist genau wie die Antwort, und das ist das Wichtigste, worauf wir uns konzentrieren, als wäre die Antwortqualität nicht sehr gut. Wenn Sie z.B. auf schlechte Retrieval-Probleme stoßen, wie z.B. während des Retrieval-Stadiums von Ihrer Vektordatenbank, wenn Sie nicht wirklich die entsprechenden Brocken aus Ihrer Vektordatenbank zurückgeben, werden Sie nicht in der Lage sein, den korrekten Kontext tatsächlich in die L on zu setzen. Dies beinhaltet also bestimmte Probleme wie geringe Präzision, nicht alle Brocken im abgerufenen Satz sind relevant. Dies führt zu einer Halluzination, wie verloren in den mittleren Problemen, Sie haben eine Menge Fluff in der Return-Antwort. Dies kann bedeuten, dass ein geringer Rückruf, wie Ihr Top-K nicht hoch genug ist, oder im Grunde wie der Satz von Informationen wie, die Sie brauchen, um die Frage wirklich zu beantworten, ist einfach nicht da. Und natürlich gibt es andere Probleme zu wie veraltete Informationen. Und viele von Ihnen, die heutzutage Apps bauen, könnten mit einigen wie Schlüsselbegriffe wie vertraut sein, nur warum die LLM ist nicht immer Sie wissen, garantiert, Ihnen eine richtige Antwort. Es gibt Halluzination, Relevanz, wie Toxizität Bias. Es gibt eine Menge Probleme auf der L auf der Seite. Also, was können wir tun? Was können wir wirklich tun, um die Leistung einer Retrieval Augmented Generation Anwendung zu verbessern? Und für viele von Ihnen wie Sie könnten in bestimmte Probleme laufen, und es läuft wirklich die Gamut über wie die gesamte Pipeline. Es gibt Dinge, die Sie auf den Daten tun können, wie können wir zusätzliche Informationen über genau wie die rohen Textblöcke speichern, nicht wahr? Dass Sie in die Vektor-Datenbank setzen. Können Sie diese Daten-Pipeline irgendwie optimieren, spielen herum mit Brockengrößen, diese Art von Sache? Können Sie die Einbettungsdarstellung selbst optimieren? Viele Male, wenn Sie ein vortrainiertes Einbettungsmodell verwenden, ist es nicht wirklich optimal, um Ihnen die beste Leistung zu geben. Es gibt den Retrieval-Algorithmus, wissen Sie, die Standardsache, die Sie tun, ist nur die Top-Case die meisten ähnlichen Elemente aus Ihrer Vektor-Datenbank, um zur LLM zurückzukehren. Und was sind beides einfache Dinge, die man so gut wie harte Dinge tun kann? Und es gibt auch Synthese. Wie, warum gibt es, ja, es gibt wie ein V in der, sowieso, so können wir LLM's für mehr als Generation verwenden? Und so im Grunde, wie Sie die LLM verwenden können, um tatsächlich zu helfen, wie die Argumentation im Gegensatz zu reiner, nur reine Generation, richtig? Sie können es tatsächlich verwenden, um zu versuchen, zu überlegen, angesichts einer Frage, können Sie es in einfachere Fragen, Weg zu verschiedenen Datenquellen, und Art und Weise haben eine anspruchsvollere Art und Weise wie das Tragen Ihrer Daten? Natürlich, wie wenn Sie irgendwie um einige meiner letzten Gespräche gewesen, wie ich immer sagen, bevor Sie wirklich versuchen, eine dieser Techniken, Sie müssen ziemlich effektiv sein, stellen Sie sicher, dass Sie einen Weg, um, Sie haben tatsächlich einen Weg, um die Leistung zu messen. So werde ich wahrscheinlich zwei Minuten damit verbringen, über Evaluation zu sprechen. Simon, mein Mitbegründer, hat gestern einen Workshop über wirklich genau wie Sie bewerten, bauen einen Datensatz, bewerten, ziehen Systeme und helfen iterate darauf. Wenn Sie den Workshop verpasst haben, keine Sorge. Ich werde die Folien und Materialien online zur Verfügung, so dass Sie einen Blick zu nehmen. Auf einem sehr hohen Niveau in Bezug auf die Evaluation, ist es wichtig, weil Sie im Grunde müssen Benchmark für Ihr System zu verstehen, wie Sie auf iterate und verbessern Sie es. Und es gibt wie ein paar verschiedene Möglichkeiten, die Sie versuchen können, Bewertung zu tun, richtig? Ich denke, Anton aus Kroma war nur einige davon sagen, aber wie Sie im Grunde brauchen einen Weg, um sowohl das Ende zu End-Lösung zu bewerten, wie Sie Ihre Eingabe-Abfrage sowie die Ausgabe-Antwort haben. Sie wollen auch wahrscheinlich in der Lage sein, wie bestimmte Komponenten zu bewerten, wie wenn Sie festgestellt haben, dass der Abruf wie der Teil ist, der verbessert werden muss, benötigen Sie wie Retrieval Metriken, um wirklich zu verstehen, wie Sie Ihr Retrieval-System verbessern können. So gibt es Retrieval und es gibt Synthese. Lassen Sie uns ein wenig wie 30 Sekunden auf jedem sprechen. So können Sie sehen, dass Sie Auswertung auf Retrieval durchgeführt haben. Wie sieht das aus? Im Grunde wollen Sie sicherstellen, dass das Zeug, das zurückgegeben wird, tatsächlich die Abfrage beantwortet und dass Sie eine Reihe von Flausch nicht zurückgeben und dass das Zeug, das Sie zurückgeben, für die Frage relevant ist. Also zuerst brauchen Sie einen Auswertungs-Datensatz. Viele Leute haben wie menschliche beschriftete Datensätze, wenn Sie im Bau von Sachen in prod sind, Sie könnten wie Benutzer-Feedback auch haben. Wenn nicht, können Sie synthetisch einen Datensatz generieren. Dieser Datensatz ist Eingabe wie Abfrage und Ausgabe der Ideen wie die Rückgabedokumente sind relevant für die Abfrage. So brauchen Sie das irgendwie. Sobald Sie das haben, können Sie Dinge mit Ranging-Metriken messen. Sie können Dinge wie Erfolgsrate, Trefferrate, MR, und DCG der Vielfalt dieser Dinge messen. Und so, wenn Sie in der Lage sind, dies zu bewerten, wie dies wirklich nicht wie ein LLM-Problem. Dies ist wie ein IR-Problem und dies ist rund für mindestens ein Jahrzehnt oder zwei gewesen. Aber eine Menge davon wird immer, wissen Sie, es ist immer noch sehr relevant angesichts der eigentlichen Aufbau dieser alle auf, dass. Das nächste Stück hier ist es ein Retrieve-Teil, richtig, aber dann erzeugen Sie eine Antwort aus. Und dann, wie können Sie tatsächlich bewerten die ganze Sache Ende zu Ende? Also Bewertung der endgültigen Antwort gegeben die Eingabe. Sie wollen immer noch eine Art von Datensatz zu generieren. So können Sie das durch wie menschliche Anmerkungen, Benutzer-Feedback. Sie können wie Grund Wahrheit Referenz Antworten angesichts der Frage, die wirklich zeigt, wie, hey, dies ist die richtige Antwort auf diese Frage. Und Sie können auch wie, wissen Sie, synthetisch erzeugen es mit wie GPD4. Sie führen dies durch die volle Lappen-Pipeline, die Sie gebaut, die Retrieval auf Synthese. Und Sie können wie L auf Basis eVels laufen. Es gibt eine Menge von Projekten in diesen Tagen geht darüber, wie Sie tatsächlich richtig bewerten die Outputs vorhergesagten Outputs eines Sprachmodells. Sobald Sie Ihre eVel Benchmark definiert haben, jetzt wollen Sie darüber nachdenken, wie Sie tatsächlich optimieren Sie Ihre Rag-Systeme. Also habe ich einen Teaser auf der Folie, wie gestern, aber die Art und Weise, wie ich darüber denke, ist, dass, wenn Sie Ihr System tatsächlich verbessern wollen, gibt es wie eine Million Dinge, die Sie tun können, um tatsächlich zu versuchen, Ihr Rag-System zu verbessern. Und wie Sie wahrscheinlich nicht mit dem harten Zeug zuerst beginnen wollen, nur weil, wissen Sie, ein Teil des Wertes von Sprachmodellen ist, wie es Art des demokratisierten Zugriffs zu jedem Entwickler ist. Es ist wirklich einfach gemacht es für die Menschen, aufzustehen und zu laufen. Und so, wenn zum Beispiel Sie in einigen Performance-Probleme mit Lappen laufen, ich wahrscheinlich mit den Grundlagen beginnen. Wie ich es nennen, wie Tischpfähle Rag-Techniken, besser Parsing, so dass Sie nicht nur durch sogar Brocken geteilt, wie die Anpassung Ihrer Brockengrößen, versuchen, Zeug, das bereits in die Vektor-Datenbank integriert ist, wie hybride Suche, sowie wie Metadaten-Filter. Es gibt auch wie erweiterte Retrieval-Methoden, die Sie versuchen könnten. Dies ist wie ein wenig fortgeschrittener. Einige davon zieht aus wie traditionelle IR. Einige von ihm ist eher wie eine Art wirklich wie neu in diesem Zeitalter der wie Online-basierte Apps. Das ist ein traditionelles Konzept. Es gibt auch Konzepte im Lama Index wie rekursives Retrieval, wie den Umgang mit eingebetteten Tabellen, wie kleine bis große Retrieval und viele andere Dinge, die wir haben, die Ihnen helfen können, die Leistung Ihrer Anwendung zu verbessern. Und dann das letzte bisschen wie diese Art von bekommt in ausdrucksvolleren Sachen, die schwieriger zu implementieren sein könnte eine höhere Latenz und Kosten verursachen, aber möglicherweise stärker ist und für das Suchen ist wie Agenten wie, wie Sie Agenten in besser wie Rag Pipelines integrieren, um besser zu beantworten verschiedene Arten von Fragen und synthetisieren Informationen. Und wie finden Sie eigentlich zwei Sachen? Lassen Sie uns ein wenig über die Tischpfähle zuerst sprechen. Tuning Ihre Stückgröße kann übergroße Auswirkungen auf die Leistung haben. Wenn Sie eine Art von wie um die Praxis-Systeme gespielt haben, kann dies für Sie offensichtlich sein oder auch nicht. Was jedoch interessant ist, ist, dass wie mehr Abruf-Token nicht immer mit höherer Leistung gleichzusetzen ist. Und dass, wenn Sie wie Re-Ranking Ihrer Abruf-Token tun, bedeutet es nicht unbedingt, dass Ihre letzte Generation Antwort wird besser sein. Und dies ist wiederum auf Sachen wie Verlust in den mittleren Problemen zurückzuführen, wo Sachen in der Mitte des LLM-Kontextfensters dazu tendieren, verloren zu gehen, wo Sachen am Ende ein wenig mehr gut von der LLM erinnert werden neigt. Und so denke ich, dass wir einen Workshop mit wie entstehen ein paar vor einer Woche, wo im Grunde haben wir Ihnen gezeigt, dass es eine Art von optimaler Brockengröße gegeben Ihre Datensatz und viele Male, wenn Sie Dinge wie Re-Ranking ausprobieren, es tatsächlich erhöht Ihre Fehler Metriken. Metadaten-Filterung. Dies ist eine andere wie sehr Tabelle Stakes Sache, die ich denke, jeder sollte in und ich denke, Vektor-Datenbanken wie Sie wissen, Krama, Planca und Revea, wie diese Vektor-Datenbanken sind alle die Implementierung dieser Fähigkeiten auf Ihrer Haube. Metadaten-Filterung ist im Grunde so wie, wie Sie fügen Sie strukturierten Kontext zu Ihren Brocken wie Ihre Steuer-Blöcke. Und Sie können dies sowohl für die Einbettungen als auch Synthese verwenden, aber es integriert sich auch mit wie die Metadaten-Filter-Funktionen einer Vektor-Datenbank. Metadaten sind also genau wie wieder strukturiertes JSON-Wörterbuch, es könnte wie Seitennummer sein, es könnte der Dokumenttitel sein, es könnte die Zusammenfassung angrenzender Brocken sein, man kann damit auch kreativ werden, man könnte wie Fragen halluzinieren, die der Stamm beantwortet. Und es kann helfen, zu finden, es kann helfen, Ihre Antwortqualität zu erhöhen, es integriert sich auch in die Vektordatenbankfilter. So als Beispiel sagen wir, die Frage ist vorbei wie das SEC wie 10Q-Dokument und können Sie mir sagen, der Risikofaktor ist in 2021. Wenn Sie nur Ross Mantek Suche, in der Regel ist es sehr geringe Präzision, Sie werden eine Reihe von Dingen, die dies kann oder nicht passen zurückgeben, Sie könnten sogar Zeug aus wie andere Jahre zurück, wenn Sie eine Reihe von Dokumenten aus verschiedenen Jahren in der gleichen Vektor-Sammlung. Und so wie Sie sind wie das Rollen der Würfel ein wenig. Aber eine Idee hier ist im Grunde, wenn Sie Zugriff auf die Metadaten der Dokumente haben und Sie eine Frage wie diese stellen, kombinieren Sie im Grunde strukturierte Abfragefunktionen, indem Sie die Metadaten-Filter, wie eine Verschleißklausel in dieser gleichen Aussage, wie ein Jahr gleich 2021 und Sie kombinieren, dass mit semantischer Suche, um die wichtigsten Kandidaten zurückzugeben, gegeben Ihre Anfrage. Und das verbessert die Präzision Ihrer Ergebnisse. Weiter zu Sachen, die vielleicht ein bisschen fortgeschrittener ist, wie Advanced Retrieval ist eine Sache, die wir im Allgemeinen finden, ist diese Idee wie kleine bis große Retrieval. Also, was bedeutet das? Im Grunde jetzt, wenn Sie einen großen Textblock einbetten, Sie synthetisieren auch über diesen Textblock. Und so ist es ein wenig suboptimal, denn was, wenn wie die Einbettungsdarstellungen wie voreingenommen, weil Sie wissen, dass es eine Reihe von flauschigen in diesem Textblock, der eine Reihe von relevanten Informationen enthält, Sie nicht wirklich optimieren Ihre Abrufqualität. So embedding ein großer Textblock fühlt sich manchmal ein wenig suboptimal. Eine Sache, die Sie tun könnten, ist im Grunde Text auf der Satzebene oder auf kleiner Ebene einbetten und dann erweitern Sie dieses Fenster während der Synthesezeit. Und so ist dies in einer Vielzahl von wie Lawman Dex Abstraktionen enthalten. Aber die Idee ist, dass Sie zurückgeben, dass Sie auf mehr granulare Stücke von Informationen abrufen. So kleinere Brocken, dies macht es so, dass diese Brocken sind eher abgerufen werden, wenn Sie tatsächlich eine Abfrage über diese spezifischen Stücke von Kontext fragen. Aber dann wollen Sie sicherstellen, dass das tatsächlich Zugang zu mehr Informationen hat, um tatsächlich ein korrektes Ergebnis synthetisieren. So haben wir dies ausprobiert. Es hilft zu vermeiden, wie ein Verlust in den mittleren Problemen. Statt kleinerer Top-K-Wert wie K gleich zwei, während wie über diesem Datensatz, wenn Sie wie K ist gleich fünf für naives Abrufen über große Textstücke, Sie beginnen im Grunde eine Menge Kontext und diese Art von führt in Fragen, wo Sie vielleicht wissen, der relevante Kontext ist in der Mitte, aber Sie sind nicht in der Lage, oder Sie sind wie die LM ist nicht in der Lage, über diese Informationen synthetisieren. Eine sehr verwandte Idee ist hier wie die Einbettung eines Verweises auf den Stamm der Eltern im Gegensatz zum eigentlichen Stamm der Texte. Wenn Sie also z.B. nicht nur den Stamm des Rohtexts einbetten wollen oder nicht den Stamm des Textes, sondern tatsächlich wie ein kleinerer Block oder eine Zusammenfassung oder eine Frage, die den Stamm beantwortet, haben wir festgestellt, dass das tatsächlich hilft, die Suchleistung bei anständiger Menge zu verbessern. Und es geht wieder so mit dieser Idee einher, wie man oft etwas einbetten möchte, das für die Einbettung eines basierten Retrievals besser geeignet ist. Aber dann möchte man genug Kontext zurückgeben, damit das LLLELL tatsächlich über diese Informationen synthetisieren kann. Das nächste Bit hier ist eigentlich noch fortgeschritteneres Zeug, richtig? Dies geht in Agenten vor, und das geht weiter in die letzte Säule, die ich erwähnt habe, also wie man LLLELLLLMS zur Argumentation verwenden kann, anstatt nur Synthese. Die Intuition hier ist, dass wie für eine Menge von Lappen, wenn Sie nur mit dem LLLLLMS am Ende, Sie sind ein durch die Qualität Ihres Retriever eingeschränkt und Sie sind wirklich nur in der Lage, Dinge wie Fragen beantworten. Und es gibt bestimmte Arten von Fragen und erweiterte Analyse, die Sie vielleicht wollen, dass wie Top-K-Lappen kann nicht wirklich beantworten. Es ist nicht unbedingt nur eine einzige aus Frage zu starten. Sie müssen vielleicht eine ganze Reihe von Argumentationsschritten haben, die tatsächlich ein Stück Information zusammenziehen, oder Sie möchten vielleicht ein Dokument zusammenfassen und mit anderen Dokumenten vergleichen. Eine Art Architektur, die wir im Moment erforschen, ist diese Idee von Multi-Document-Agenten. Was wäre, wenn wir statt wie Lappen ein wenig mehr in Agentengebiet verlegten. Wir haben jedes Dokument nicht nur als eine Sequenz von Textstammen modelliert, sondern als eine Reihe von Werkzeugen, die die Fähigkeit enthalten, sowohl wie Zusammenfassung dieses Dokuments als auch QA über dieses Dokument über bestimmte Fakten zu tun. Und natürlich, wenn Sie skalieren möchten, um Sie wissen, Hunderten oder Tausenden oder Millionen von Dokumenten zu mögen, kann ein Agent in der Regel nur Zugriff auf ein begrenztes Fenster von Werkzeugen haben. Sie möchten also wahrscheinlich eine Art Retrieval auf diesen Werkzeugen durchführen, ähnlich wie Sie Textstamme aus einem Dokument abrufen möchten. Der Hauptunterschied ist, dass Sie, weil diese Werkzeuge sind, tatsächlich darauf reagieren möchten. Sie möchten sie verwenden, anstatt nur den Rohtext zu nehmen und ihn in das Kontextfenster zu stecken. Diese Kombination von Einbettungs-basiertem Retrieval oder jeder Art von Retrieval sowie dem Einsatz von Agentenwerkzeugen zu mischen, ist also ein sehr interessantes Paradigma, das ich für wirklich nur mit diesem Zeitalter der Elemente möglich halte und es vorher nicht wirklich gegeben hat. Eine weitere Art von fortschrittlichem Konzept ist diese Idee der Feinabstimmung. Und so haben auch einige andere Moderatoren darüber gesprochen. Aber die Idee, wie Feinabstimmung in einem Rag-System ist, dass es wirklich optimiert bestimmte Teile dieser Rag-Pipeline für Sie, um irgendwie besser wie die Leistung der Retriever oder Synthese Fähigkeiten zu verbessern. Eine Sache, die Sie tun können, ist Feinabstimmung Ihrer Einbettungen. Ich denke, Anton sprach auch darüber. Wie wenn Sie nur ein vortrainiertes Modell verwenden, werden die Einbettungsdarstellungen nicht über Ihre spezifischen Daten optimiert werden. So manchmal werden Sie nur die falschen Informationen abrufen. Wenn Sie diese Einbettungen irgendwie so einstellen können, dass bei jeder Art von relevanter Frage, die der Benutzer stellen könnte, dass Sie tatsächlich die entsprechende Antwort zurückgeben, dann werden Sie wie bessere Leistung haben. Und die Idee hier ist die Erzeugung synthetische Abfragedaten aus Rohtext-Trunks mit LLuns und verwendet eine feine Melodie und Einbettung Modell. Und Sie können dies tun, wenn wir wirklich schnell zurück gehen. Sie können dies durch im Grunde Art der Feinabstimmung des Basismodells selbst tun. Sie können auch Feintuning ein Adapter auf dem Modell. Und Feintuning ein Adapter auf dem Modell hat ein paar Vorteile, dass Sie nicht benötigen, die Basis-Modell Gewichte, um tatsächlich Feintuning Zeug. Und wenn Sie nur Feintuning die Abfrage, Sie müssen nicht Ihre gesamte Dokument Corpus neu zu indexieren. Es gibt auch Feintuning LLuns, die natürlich wie viele Menschen sind sehr daran interessiert, in diesen Tagen. Und Intuition hier speziell für RAG ist, dass, wenn Sie eine schwächere LLuns wie 3,5 Turbo haben, wie LOMA2, 7B, wie diese schwächeren LLuns sind schlecht, sind nicht schlecht wie, schwächere LLuns sind vielleicht ein wenig schlechter an wie Antwortsynthese, Argumentation, strukturierte Ausgänge, etc. Im Vergleich zu größeren Modellen. Also ist die Lösung hier, was, wenn Sie einen synthetischen Datensatz mit einem größeren Modell wie Ihrem Pd4 generieren können, dies ist etwas, das wir erforschen. So wird es besser in der Kette der Gedanken, längere Antwortqualität, besser strukturierte Ausgänge, und viele andere Möglichkeiten als gut. Also all diese Dinge sind in unseren Docs, es gibt Produktion RAG, es gibt Feinabstimmung, und ich habe noch zwei Sekunden. Also danke Ihnen sehr.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(f\"de_{tv.text_path}\", \"w\") as fp:\n",
        "    fp.write(translated_text)"
      ],
      "metadata": {
        "id": "zI5rV8F9aY7A"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Text to Speech**:"
      ],
      "metadata": {
        "id": "2Ufr_t8nbRyx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gtts"
      ],
      "metadata": {
        "id": "k9n7xE77bg3S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from gtts import gTTS\n",
        "import os"
      ],
      "metadata": {
        "id": "-cdfUaZ_bcIF"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def text_to_speech(text, language='en', output_file='output.mp3'):\n",
        "    \"\"\"\n",
        "    Convert text to speech using gTTS (Google Text-to-Speech).\n",
        "\n",
        "    Parameters:\n",
        "    - text: The input text to be converted.\n",
        "    - language: The language of the text (default is English).\n",
        "    - output_file: The name of the output audio file (default is 'output.mp3').\n",
        "    \"\"\"\n",
        "    tts = gTTS(text=text, lang=language, slow=False)\n",
        "    tts.save(output_file)"
      ],
      "metadata": {
        "id": "CPhWB7lRbWeU"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Text2Speech\n",
        "text_to_speech(translated_text, language='de', output_file='output.mp3')"
      ],
      "metadata": {
        "id": "mMAy73DNbn3u"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "9A9NDxctepcS",
        "outputId": "b566d133-d973-417c-be65-894e7745833f"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.36.1-py3-none-any.whl (8.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.3/8.3 MB\u001b[0m \u001b[31m30.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.4)\n",
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            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.11.17)\n",
            "Installing collected packages: transformers\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.35.2\n",
            "    Uninstalling transformers-4.35.2:\n",
            "      Successfully uninstalled transformers-4.35.2\n",
            "Successfully installed transformers-4.36.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "transformers"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "id": "Y44dKnCufpx4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan\n",
        "from datasets import load_dataset\n",
        "import torch\n",
        "import soundfile as sf"
      ],
      "metadata": {
        "id": "0z_BXR5tceL2"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tts_processor = SpeechT5Processor.from_pretrained(\"microsoft/speecht5_tts\")\n",
        "tts_model = SpeechT5ForTextToSpeech.from_pretrained(\"microsoft/speecht5_tts\")\n",
        "tts_vocoder = SpeechT5HifiGan.from_pretrained(\"microsoft/speecht5_hifigan\")\n",
        "\n",
        "# load xvector containing speaker's voice characteristics from a dataset\n",
        "tts_embeddings_dataset = load_dataset(\"Matthijs/cmu-arctic-xvectors\", split=\"validation\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433,
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        "\n",
        "    Parameters:\n",
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        "    - output_file: The name of the output audio file (default is 'output.wav').\n",
        "    \"\"\"\n",
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