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Fetch all episode links of Lex Fridman Podcast from https://steno.ai\n", "2. For each episode, transform the transcript in html to json format (Vicuna ShareGPT format)\n", "3. remove the first few sentences from Lex for each episode to remove the introduction and ads. " ], "metadata": { "id": "zRBQFTggAGig" } }, { "cell_type": "markdown", "source": [ "**Problems & Concerns**\n", "1. it's a audio-to-text dataset, it contains inaccurate detections\n", "2. although the speakers are professionals, these are verbal conversations which contain oral languages\n", "3. it may contain ads and personal opinions from Lex Fridman and the speakers\n", "4. ..." ], "metadata": { "id": "lFDpBbo5_CFX" } }, { "cell_type": "markdown", "source": [ "Try for one podcast" ], "metadata": { "id": "ESvQcVl1uGl2" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "b964yXlrNsO8" }, "outputs": [], "source": [ "import requests\n", "from bs4 import BeautifulSoup\n", "import json\n", "\n", "url = 'https://steno.ai/lex-fridman-podcast-10/378-anna-frebel-origin-and-evolution-of-the-universe'\n", "response = requests.get(url)\n", "soup = BeautifulSoup(response.text, 'html.parser')\n", "\n", "transcript_elements = soup.find_all('div', class_=['transcript is-main', 'transcript is-split'])\n", "\n", "conversations = []\n", "current_speaker = None\n", "current_text = ''\n", "starting = True\n", "for element in transcript_elements:\n", " speaker_element = element.find('li', class_='speaker')\n", " text_element = element.find('p', class_='transcript__text tg-paragraph')\n", " if speaker_element and text_element:\n", " speaker = 'lex' if speaker_element.get_text() == 'Speaker 1' else 'guest'\n", " text = text_element.get_text().strip()\n", " if current_speaker is None:\n", " # This is the first speaker\n", " current_speaker = speaker\n", " current_text = text\n", " elif speaker == current_speaker:\n", " # This is the same speaker as the last one, so combine the text\n", " if not starting:\n", " current_text += ' ' + text\n", " else: # for the starting part, use the last paragraph only\n", " current_text = text\n", " else:\n", " # This is a new speaker, so add the previous conversation to the list\n", " starting = False\n", " conversations.append({'from': current_speaker, 'value': current_text})\n", " current_speaker = speaker\n", " current_text = text\n", "\n", "# Add the last conversation\n", "if current_text:\n", " conversations.append({'from': current_speaker, 'value': current_text})\n", "print(len(conversations))\n", "data = {\n", " 'id': '378-anna-frebel',\n", " 'conversations': conversations\n", "}\n", "\n", "# Print JSON to console\n", "print(json.dumps(data, indent=2))\n", "\n", "# Save JSON to file\n", "with open('378-anna-frebel.json', 'w') as f:\n", " json.dump(data, f, indent=2)\n" ] }, { "cell_type": "markdown", "source": [ "Try to get all urls" ], "metadata": { "id": "Js2WIV1r07gE" } }, { "cell_type": "code", "source": [ "import requests, tqdm\n", "\n", "base_url = \"https://steno.ai/lex-fridman-podcast-10/\"\n", "\n", "# This would be a loop over all the pages you are interested in\n", "\n", "episode_urls = []\n", "for i in tqdm.tqdm(range(1, 66)):\n", " response = requests.get(\"https://steno.ai/lex-fridman-podcast-10/fetch-podcast?page={}\".format(i))\n", " data = response.json()\n", "\n", " for episode in data['episodes']['data']:\n", " episode_slug = episode['slug']\n", " episode_url = base_url + episode_slug\n", " episode_urls.append(episode_url)\n", "print(len(episode_urls))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WrJRJlzpuKbL", "outputId": "0fe4daf7-c33b-4a02-9d4b-ae4032cff818" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 65/65 [03:46<00:00, 3.48s/it]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "387\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] } ] }, { "cell_type": "code", "source": [ "episode_urls" ], "metadata": { "id": "D43OXrq-2VK6" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import requests\n", "from bs4 import BeautifulSoup\n", "import json\n", "\n", "def fetch_one_episode(url):\n", " #url = 'https://steno.ai/lex-fridman-podcast-10/378-anna-frebel-origin-and-evolution-of-the-universe'\n", " slug = url.split(\"/\")[-1]\n", " response = requests.get(url)\n", " soup = BeautifulSoup(response.text, 'html.parser')\n", "\n", " transcript_elements = soup.find_all('div', class_=['transcript is-main', 'transcript is-split'])\n", "\n", " conversations = []\n", " current_speaker = None\n", " first_speaker = None\n", " for element in transcript_elements:\n", " speaker_element = element.find('li', class_='speaker')\n", " text_element = element.find('p', class_='transcript__text tg-paragraph')\n", " if speaker_element and text_element:\n", " text = text_element.get_text().strip()\n", " if first_speaker is None:\n", " first_speaker = speaker_element.get_text()\n", " \n", " current_text = ''\n", " starting = True\n", " for element in transcript_elements:\n", " speaker_element = element.find('li', class_='speaker')\n", " text_element = element.find('p', class_='transcript__text tg-paragraph')\n", " if speaker_element and text_element:\n", " speaker = 'lex' if speaker_element.get_text() == first_speaker else 'guest'\n", " text = text_element.get_text().strip()\n", " if current_speaker is None:\n", " # This is the first speaker\n", " current_speaker = speaker\n", " current_text = text\n", " elif speaker == current_speaker:\n", " # This is the same speaker as the last one, so combine the text\n", " if not starting:\n", " current_text += ' ' + text\n", " else: # for the starting part, use the last paragraph only\n", " current_text = text\n", " else:\n", " # This is a new speaker, so add the previous conversation to the list\n", " starting = False\n", " conversations.append({'from': current_speaker, 'value': current_text})\n", " current_speaker = speaker\n", " current_text = text\n", "\n", " # Add the last conversation\n", " if current_text:\n", " conversations.append({'from': current_speaker, 'value': current_text})\n", " #print(len(conversations))\n", " data = {\n", " 'id': slug,\n", " 'conversations': conversations\n", " }\n", " return data\n" ], "metadata": { "id": "jCSL7iH409DK" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "all_data = []\n", "for episode_url in tqdm.tqdm(episode_urls):\n", " episode_data = fetch_one_episode(episode_url)\n", " all_data.append(episode_data)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_9db5SSw15xV", "outputId": "3ee3b805-b28b-4ca9-b76f-e7f9a8498633" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 387/387 [25:06<00:00, 3.89s/it]\n" ] } ] }, { "cell_type": "code", "source": [ "with open('lex_fridman_podcast_for_llm_vicuna_original.json', 'w') as f:\n", " json.dump(all_data, f, indent=2)" ], "metadata": { "id": "DLUyJX7v3GgP" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "all_data = json.loads(open(\"lex_fridman_podcast_for_llm_vicuna_original.json\").read())" ], "metadata": { "id": "n5cG6tSsQA7k" }, "execution_count": 98, "outputs": [] }, { "cell_type": "code", "source": [ "def chunk_conversation(conversation, limit=1200):\n", " chunked_conversations = []\n", " chunk = []\n", " word_count = 0\n", " chunk_id = 0\n", " count = 0\n", "\n", " for entry in conversation[\"conversations\"]:\n", " entry_word_count = len(entry['value'].split())\n", " if word_count + entry_word_count > limit and count % 2 == 0: # count % 2 == 0: making sure each conversation starts with lex\n", " chunked_conversations.append({\n", " \"id\": f\"{conversation['id']}-{chunk_id}\",\n", " \"conversations\": chunk\n", " })\n", " chunk = []\n", " word_count = 0\n", " chunk_id += 1\n", " chunk.append(entry)\n", " word_count += entry_word_count\n", " count += 1\n", "\n", " if chunk: # add the last chunk if any left\n", " chunked_conversations.append({\n", " \"id\": f\"{conversation['id']}-{chunk_id}\",\n", " \"conversations\": chunk\n", " })\n", "\n", " return chunked_conversations\n" ], "metadata": { "id": "3yiwT2Oy-RUg" }, "execution_count": 99, "outputs": [] }, { "cell_type": "code", "source": [ "all_data_chunked = []\n", "for each in all_data:\n", " each_data_chunked = chunk_conversation(each)\n", " all_data_chunked += each_data_chunked" ], "metadata": { "id": "pLTT5vMEArlx" }, "execution_count": 100, "outputs": [] }, { "cell_type": "code", "source": [ "with open('lex_fridman_podcast_for_llm_vicuna_chunked.json', 'w') as f:\n", " json.dump(all_data_chunked, f, indent=2)" ], "metadata": { "id": "HzARTP3TDIQC" }, "execution_count": 101, "outputs": [] }, { "cell_type": "markdown", "source": [ "change \"lex\" & \"guest\" to \"human\" & \"gpt\" for vicuna training" ], "metadata": { "id": "BpDq8kIbDVpN" } }, { "cell_type": "code", "source": [ "for conv in all_data_chunked:\n", " for each in conv[\"conversations\"]:\n", " each[\"from\"] = \"human\" if each[\"from\"] == \"lex\" else \"gpt\"" ], "metadata": { "id": "60lGbAppDSOC" }, "execution_count": 102, "outputs": [] }, { "cell_type": "code", "source": [ "with open('lex_fridman_podcast_for_llm_vicuna_chunked_gpt.json', 'w') as f:\n", " json.dump(all_data_chunked, f, indent=2)" ], "metadata": { "id": "zb4UT0MGDhPo" }, "execution_count": 103, "outputs": [] }, { "cell_type": "markdown", "source": [ "Save to huggingface" ], "metadata": { "id": "8l9K5EywSWci" } }, { "cell_type": "code", "source": [ "!pip install huggingface_hub" ], "metadata": { "id": "TIIYwRvYLi0p" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login, HfApi\n", "notebook_login()" ], "metadata": { "id": "tIrOADX8LYCJ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "api = HfApi()\n", "api.upload_file(\n", " path_or_fileobj=\"/content/lex_fridman_podcast_for_llm_vicuna_chunked.json\",\n", " path_in_repo=\"/other_versions/lex_fridman_podcast_for_llm_vicuna_chunked.json\",\n", " repo_id=\"64bits/lex_fridman_podcast_for_llm_vicuna\",\n", " repo_type=\"dataset\",\n", ")" ], "metadata": { "id": "6QxFaTysLdg8" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Use Dataset" ], "metadata": { "id": "BarPnjBsRfgp" } }, { "cell_type": "code", "source": [ "!pip install -q datasets" ], "metadata": { "id": "vYN92u8wOUoe" }, "execution_count": 68, "outputs": [] }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"64bits/lex_fridman_podcast_for_llm_vicuna\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241, "referenced_widgets": [ "a928d1a24153425b917306e909f53864", "a6eebe88ec9c45a595ae6c357de33282", "ad3d32220c7b486a8f79119635224a2b", "234265f053d54adc903afc70a73309c3", "eb7a3f13915549179b6417d4a69b75b1", "b4d5724b989c470faf75e690a7276412", 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"5367328e186744b89b0ee21a301b8dab" ] }, "id": "PzRHJ8S3N-I_", "outputId": "5797e0a5-b716-4bd1-8bf5-b1da786288fb" }, "execution_count": 111, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/2.01k [00:00