{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "import ast\n", "import glob\n", "import boto3\n", "import json\n", "import numpy as np\n", "import pandas as pd\n", "import labelbox as lb\n", "\n", "\n", "from tqdm import tqdm\n", "from langdetect import detect\n", "from bs4 import BeautifulSoup\n", "#from better_profanity import profanity\n", "from suno_utils.utils.s3 import check_s3_file_exists, read_from_s3\n", "from suno_utils.utils.text import read_jsonl\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "227758\n", " index id_x created_at \\\n", "0 1964552 8da17e3f-3b7b-423d-88e6-3eb3b809928a 2024-11-20 18:25:06.433 \n", "1 8795886 be1fc697-e8ca-47b2-94b6-6be632f31edd 2024-11-20 18:25:06.433 \n", "4 45513479 8ab114c9-c229-4451-802a-a47ab9425927 2024-11-28 06:21:06.214 \n", "5 8982344 43b5e2e3-0c32-40eb-894f-6dfbf6d778e3 2024-11-28 06:21:06.214 \n", "6 36763898 0c2988bd-378c-48b1-a573-8aa26d1ef911 2024-11-25 23:04:59.346 \n", "\n", " updated_at time_used \\\n", "0 2024-11-20 18:43:46.423 23.656317 \n", "1 2024-11-23 20:38:56.488 17.028764 \n", "4 2024-11-28 06:33:00.491 20.288259 \n", "5 2024-12-01 03:31:13.357 14.534904 \n", "6 2024-11-25 23:09:20.117 26.165704 \n", "\n", " metadata user_id status \\\n", "0 {'tags': 'Dark industrial rock, cumbia, electr... 16190923 complete \n", "1 {'tags': 'Dark industrial rock, cumbia, electr... 16190923 complete \n", "4 {'tags': 'singer/songwriter fusion blues fushi... 48017012 complete \n", "5 {'tags': 'singer/songwriter fusion blues fushi... 48017012 complete \n", "6 {'tags': ' aggresive, emotional, powerful male... 60688389 complete \n", "\n", " discord_message_id prompt_id ... id_y total_play_count \\\n", "0 None None ... 1.078901e+12 6.0 \n", "1 None None ... 1.079366e+12 3.0 \n", "4 None None ... 1.079016e+12 1.0 \n", "5 None None ... 1.079315e+12 134.0 \n", "6 None None ... 1.079315e+12 1.0 \n", "\n", " auto_play_count total_play_time total_play_count_hour \\\n", "0 0.0 229.982641 0.0 \n", "1 0.0 208.816191 0.0 \n", "4 0.0 200.520000 0.0 \n", "5 88.0 20667.648142 0.0 \n", "6 0.0 225.319979 0.0 \n", "\n", " auto_play_count_hour total_play_time_hour p_date_y p_hour_y \\\n", "0 0.0 0.0 2024-12-01 22 \n", "1 0.0 0.0 2024-12-01 22 \n", "4 0.0 0.0 2024-12-01 22 \n", "5 0.0 0.0 2024-12-01 22 \n", "6 0.0 0.0 2024-12-01 22 \n", "\n", " norm_play_frac \n", "0 1.281955 \n", "1 1.163970 \n", "4 0.999601 \n", "5 103.029153 \n", "6 1.000000 \n", "\n", "[5 rows x 68 columns]\n", "113848\n" ] } ], "source": [ "# load the base metas\n", "base_filepath = \"/home/tony/Data/Preference/up_v1/interesting_clips_up_u_1_20241201_full.pkl\"\n", "base_metas = pd.read_pickle(base_filepath)\n", "print(len(base_metas))\n", "print(base_metas.head())\n", "\n", "\n", "sim_metas_filepath = \"/home/christian/code/christian/metadata/interesting_clips_up_u_1_20241201_full_mfcc_similarity.jsonl\"\n", "sim_metas = read_jsonl(sim_metas_filepath)\n", "print(len(sim_metas))\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# merge the similarity scores with the base metas by request_id\n", "sim_df = pd.DataFrame(sim_metas)\n", " \n", "# Merge with original DataFrame\n", "result_df = base_metas.merge(\n", " sim_df,\n", " on='request_id',\n", " how='left' # Keep all rows from original DataFrame\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 8.244353\n", "1 8.244353\n", "2 5.578719\n", "3 5.578719\n", "4 4.310149\n", "Name: mfcc_similarity, dtype: float64\n" ] } ], "source": [ "print(result_df[\"mfcc_similarity\"].head())" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "base_metas_map = {meta[\"id\"]: meta for meta in base_metas}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "pairs = []\n", "# collect examples \n", "for dir_path in tqdm(dir_paths):\n", " meta = base_metas_map[dir_path.strip(\"/\").split(\"/\")[-1]]\n", "\n", " # load the pair_info.json\n", " pair_info_path = f\"s3://{bucket_name}/{dir_path}pair_info.json\"\n", " pair_info = read_from_s3(pair_info_path)\n", " pair_info = json.loads(pair_info)\n", "\n", " # detect language\n", " lyrics = meta.get(\"text\", None)\n", " if lyrics is not None and lyrics != \"\":\n", " lang = detect(lyrics)\n", " print(f\"language: {lang}\")\n", " if lang != \"en\":\n", " print(f\"skipping {meta['id']} because language is not english\")\n", " continue\n", "\n", " # get mp3 paths of the two examples\n", " a_filename = pair_info[\"a_filename\"]\n", " b_filename = pair_info[\"b_filename\"]\n", "\n", " a_s3_path = f\"s3://{bucket_name}/{dir_path}{a_filename}.mp3\"\n", " b_s3_path = f\"s3://{bucket_name}/{dir_path}{b_filename}.mp3\"\n", "\n", " pairs.append({\"id\": meta[\"id\"], \"a_s3_path\": a_s3_path, \"b_s3_path\": b_s3_path, \"a_filename\": a_filename, \"b_filename\": b_filename, \"a_seed\": pair_info[\"a_seed\"], \"b_seed\": pair_info[\"b_seed\"], \"lyrics\": meta.get(\"text_aligned\", \"\"), \"tags\": meta.get(\"tags\", \"\")})\n", "\n", " if len(pairs) >= 100:\n", " break\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# create a dataset\n", "client = lb.Client(api_key=\"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VySWQiOiJjbHppbmJnY2wwMDYyMDd5bWg2enhiMTd6Iiwib3JnYW5pemF0aW9uSWQiOiJjbHppbmJnY2QwMDYxMDd5bWM4cjM5cHdzIiwiYXBpS2V5SWQiOiJjbHp3cmU3ZnQwYjFzMDd6aWRrNTFnb21mIiwic2VjcmV0IjoiMGU5M2MwNmU4ZWI1Y2Y3NTlmNTk5YTk5MzIwOTU5MzQiLCJpYXQiOjE3MjM4MTU3NTcsImV4cCI6MjM1NDk2Nzc1N30.Z6gZwlzQ85KrqGOydqCdo1RVmUhOEntpev2HhNso4PU\")\n", "dataset = client.create_dataset(name=\"upsample-compare-ab-preference-test\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def get_clip_details(clip_item):\n", " s3_id = clip_item[\"s3_id\"]\n", " string_data = clip_item[\"metadata\"]\n", " dict_data = ast.literal_eval(string_data)\n", " lyrics = dict_data[\"prompt\"]\n", " tags = dict_data[\"tags\"]\n", "\n", " return s3_id, lyrics, tags" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "timestamp = 20241104\n", "# s3://suno-annotation-public/upsample-ab-preference-20241022/\n", "s3_bucket = f\"s3://suno-annotation-public/upsample-compare-ab-preference-{timestamp}/\" # this must have trailing slash\n", "s3_bucket_url = f\"https://suno-annotation-public.s3.amazonaws.com/upsample-compare-ab-preference-{timestamp}\"\n", "output_html_dir = f\"/home/christian/code/christian/outputs/html-upsample-compare-ab-preference-{timestamp}\"\n", "os.makedirs(output_html_dir, exist_ok=True)\n", "\n", "use_prompt = False\n", "if use_prompt:\n", " base_html_filepath = \"/home/christian/code/christian/labelbox/templates/ab_base.html\"\n", "else:\n", " base_html_filepath = \"/home/christian/code/christian/labelbox/templates/ab_base_no_prompt.html\"\n", "\n", "max_num_examples = 100\n", "global_keys = []\n", "\n", "# open the html file\n", "# Read the HTML file\n", "with open(base_html_filepath, \"r\", encoding=\"utf-8\") as file:\n", " soup = BeautifulSoup(file, \"html.parser\")\n", "\n", "metadata = {}\n", "\n", "assets = [] # list of assets to add to the dataset\n", "for pair in tqdm(pairs):\n", " request_id = pair[\"id\"]\n", " lyrics = pair[\"lyrics\"]\n", " tags = pair[\"tags\"]\n", " \n", " clip_a_s3_filepath = f\"{pair['a_s3_path']}\"\n", " clip_b_s3_filepath = f\"{pair['b_s3_path']}\"\n", " clip_a_basename = pair[\"a_filename\"].split(\"/\")[-1]\n", " clip_b_basename = pair[\"b_filename\"].split(\"/\")[-1]\n", "\n", " # copy files to local temporary directory\n", " temp_dir = \"/home/christian/code/christian/notebooks/outputs/temp\"\n", " os.makedirs(temp_dir, exist_ok=True)\n", " os.system(f\"aws s3 cp {clip_a_s3_filepath} {temp_dir}/{clip_a_basename}.mp3\")\n", " os.system(f\"aws s3 cp {clip_b_s3_filepath} {temp_dir}/{clip_b_basename}.mp3\")\n", "\n", " # then loudness normalize to -16db LUFS with ffmpeg force overwrite with 320 kbps bitrate\n", " os.system(f\"ffmpeg -i {temp_dir}/{clip_a_basename}.mp3 -filter:a loudnorm=I=-16:TP=-1.5:LRA=11 -c:a libmp3lame {temp_dir}/{clip_a_basename}_norm.mp3 -y -b:a 320k\")\n", " os.system(f\"ffmpeg -i {temp_dir}/{clip_b_basename}.mp3 -filter:a loudnorm=I=-16:TP=-1.5:LRA=11 -c:a libmp3lame {temp_dir}/{clip_b_basename}_norm.mp3 -y -b:a 320k\")\n", " # copy audio files to s3 bucket\n", " print(f\"copying {temp_dir}/{clip_a_basename}_norm.mp3 to {s3_bucket}{clip_a_basename}_norm.mp3\")\n", " print(f\"copying {temp_dir}/{clip_b_basename}_norm.mp3 to {s3_bucket}{clip_b_basename}_norm.mp3\")\n", " os.system(f\"aws s3 cp {temp_dir}/{clip_a_basename}_norm.mp3 {s3_bucket}{clip_a_basename}_norm.mp3\")\n", " os.system(f\"aws s3 cp {temp_dir}/{clip_b_basename}_norm.mp3 {s3_bucket}{clip_b_basename}_norm.mp3\")\n", "\n", " # edit the html file to add audios and prompt information\n", " soup.find(id=\"audio1\")['src'] = f\"{s3_bucket_url}/{clip_a_basename}_norm.mp3\"\n", " soup.find(id=\"audio2\")['src'] = f\"{s3_bucket_url}/{clip_b_basename}_norm.mp3\"\n", "\n", " # Find the elements by ID\n", " if use_prompt:\n", " lyrics_div = soup.find(id=\"lyrics\")\n", " tags_div = soup.find(id=\"tags\")\n", "\n", " # Clear the existing content\n", " lyrics_div.clear()\n", " tags_div.clear()\n", "\n", " # Insert the content directly\n", " tags_div.string = f\"Tags: {tags}\"\n", " lyrics_div.string = f\"Lyrics: {lyrics}\"\n", "\n", " # Save the modified HTML back to disk\n", " output_filepath = os.path.join(output_html_dir, f\"{request_id}.html\")\n", " with open(output_filepath, \"w\", encoding=\"utf-8\") as fp:\n", " fp.write(str(soup))\n", "\n", " # push html file to s3 public bucket\n", " s3_html_filepath = f\"{s3_bucket}{request_id}.html\"\n", " s3_html_url = f\"{s3_bucket_url}/{request_id}.html\"\n", " print(s3_html_url)\n", " os.system(f\"aws s3 cp {output_filepath} {s3_html_filepath}\")\n", "\n", " # add to data row list\n", " assets.append(\n", " {\n", " \"row_data\": s3_html_url,\n", " \"global_key\": request_id,\n", " }\n", " )\n", "\n", " metadata[request_id] = {\n", " \"clip_a_id\": pair[\"a_seed\"],\n", " \"clip_b_id\": pair[\"b_seed\"],\n", " \"clip_a_url\" : f\"{s3_bucket_url}/{pair['a_filename']}.mp3\",\n", " \"clip_b_url\" : f\"{s3_bucket_url}/{pair['b_filename']}.mp3\",\n", " }\n", "\n", " global_keys.append(request_id)\n", " \n", " if len(assets) >= max_num_examples:\n", " break\n", "\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# save json metadata\n", "metadata_filepath = f\"/home/christian/code/christian/labelbox/metadata/metadata-upsample-compare-ab-preference-{timestamp}.json\"\n", "with open(metadata_filepath, \"w\") as fp:\n", " json.dump(metadata, fp)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Bulk add data rows to the dataset\n", "task = dataset.create_data_rows(assets)\n", "task.wait_till_done()\n", "print(task.errors)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(metadata[\"026eeb93-dbd8-4967-bffe-54547b828bac\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup Ontology and Create Labeling Project" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ontology_builder = lb.OntologyBuilder(\n", " classifications=[ \n", " lb.Classification( \n", " class_type=lb.Classification.Type.RADIO, \n", " name=\"Preference\", \n", " options=[\n", " lb.Option(value=\"A\"),\n", " lb.Option(value=\"B\"),\n", " ]\n", " )\n", " ]\n", ")\n", "\n", "ontology = client.create_ontology(\"Ontology HTML Annotations\", ontology_builder.asdict(), media_type=lb.MediaType.Html)\n", "\n", "project = client.create_project(name=\"music-ab-preference\", \n", " media_type=lb.MediaType.Html)\n", "\n", "# Setup your ontology \n", "project.connect_ontology(ontology)\n", "\n", "# send rows to project\n", "batch = project.create_batch(\n", " \"first-batch-html-demo\", # Each batch in a project must have a unique name\n", " global_keys=global_keys, # Paginated collection of data row objects, list of data row ids or global keys\n", " priority=5 # priority between 1(highest) - 5(lowest)\n", ")\n", "\n", "print(\"Batch: \", batch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Covers" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# find all examples \n", "local_dir = \"/home/christian/code/christian/notebooks/outputs/covers-20240816\"\n", "s3_bucket = \"s3://suno-annotation-public/covers-20240816\"\n", "s3_bucket_url = \"https://suno-annotation-public.s3.amazonaws.com/covers-20240816\"\n", "\n", "# find all json files \n", "metas = glob.glob(os.path.join(local_dir, \"*.json\"))\n", "print(f\"Found {len(metas)} examples...\")\n", "\n", "metas = metas[:10] # just the first 10 for now\n", "\n", "assets = [] # list of assets to add to the dataset\n", "\n", "for meta in metas:\n", " with open(meta, \"r\") as f:\n", " data = json.load(f)\n", "\n", " meta_id = data[\"uid\"]\n", "\n", " # upload the audio files to s3 bucket\n", " #os.system(f\"aws s3 cp {local_dir}/{meta_id}-source.mp3 {s3_bucket}/\")\n", " #os.system(f\"aws s3 cp {local_dir}/{meta_id}-cover-0.mp3 {s3_bucket}/\")\n", " #os.system(f\"aws s3 cp {local_dir}/{meta_id}-cover-1.mp3 {s3_bucket}/\")\n", "\n", " # build paths to audio files with this uid\n", " s3_src_a_path = os.path.join(s3_bucket_url, f\"{meta_id}-source.mp3\")\n", " s3_gen_a_path = os.path.join(s3_bucket_url, f\"{meta_id}-cover-0.mp3\")\n", " s3_gen_b_path = os.path.join(s3_bucket_url, f\"{meta_id}-cover-1.mp3\")\n", "\n", " target_tags = data[\"target_tags\"]\n", " print(\"target tags:\", target_tags)\n", " lyrics = data[\"lyrics\"]\n", "\n", " assets.append(\n", " {\n", " \"global_key\": meta_id,\n", " \"row_data\": s3_html_url,\n", " \n", " }\n", " )\n", " \n", "# Bulk add data rows to the dataset\n", "task = dataset.create_data_rows(assets)\n", "task.wait_till_done()\n", "print(task.errors)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create data payload\n", "# Use global key, a unique ID to identify an asset throughout Labelbox workflow. Learn more: https://docs.labelbox.com/docs/global-keys\n", "# You can add metadata fields to your data rows. Learn more: https://docs.labelbox.com/docs/import-metadata\n" ] } ], "metadata": { "kernelspec": { "display_name": "suno_env", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 2 }