Sample pack from YT video

I want to input a youtube video, then get a collection of music samples returned to me (.wav files in a zip). I think I should specify 3 specific times (eg 5 seconds, 130 seconds, 190 seconds, and then the agent rip the audio file from YouTube, then extract stems (instrumentals, vocals), and then extract (say) 16 beats worth of audio - beginning from the 3 timings I've entered. This gives us 3x separate audio sections to sample, and if we then separate them into vocals and instrumentals that then gives us 6x separate audio files to sample from (although we will use the initial tempo/beatpoint for all 6). Next we will want to use the beatpoint and tempo to extract samples (eg 1/2/4/8 beat in lengths, beginning from the beatpoints). So we chopup the samples eg using SOX, perhaps chopping based on beat length (if BPM = 90bpm, then 1 beat is 0.66secs) and beat position, and chopping maybe a mix of 1/2/4/8 beat samples. Finally we will want to save all the files to a server and zip them up.

First, pre-entered info...

final-output2

The final output of this process would be a zip file containing a curated selection of audio samples in .wav format. These samples would be extracted from a YouTube video at specified timestamps, separated into instrumental and vocal stems, and then chopped into various lengths (1, 2, 4, and 8 beats) based on the tempo and beat positions of the original audio. The zip file would contain the best 48 samples, filtered and ranked based on criteria such as average amplitude or other audio features.

subagent-summary-amended-for-actual-tasks

A) SUBAGENT SUMMARY: TempoBeatsAnalyzerAgent extracts tempo and beat position information from instrumental MP3 files (user wants to discard vocals, upon reflection, to simplify the subagent). B) FINAL TASK OUTPUT: JSON file containing the tempo (in BPM) and beat positions (in seconds) for each of the three instrumental MP3 files. C) SUBAGENT INPUT: Three MP3 URLs of the instrumental stems from StemSeparatorAgent (vocals can be discarded). E) SUBAGENT TASK SUMMARY: The TempoBeatsAnalyzerAgent will take the three instrumental MP3 files and perform the following steps for each file: subagent final input (instrumental MP3 URL) > #195 - Extract Beatpoints & Tempo of MP3 > subagent final output (tempo and beat position information) This process will be repeated for each of the three instrumental MP3 files, resulting in three separate outputs containing the tempo and beat position information for each file. F) SILOS: Silo 1: - Input: Instrumental MP3 URL 1 - #195 - Extract Beatpoints & Tempo of MP3 - Output: Tempo and beat position information for MP3 1 Silo 2: - Input: Instrumental MP3 URL 2 - #195 - Extract Beatpoints & Tempo of MP3 - Output: Tempo and beat position information for MP3 2 Silo 3: - Input: Instrumental MP3 URL 3 - #195 - Extract Beatpoints & Tempo of MP3 - Output: Tempo and beat position information for MP3 3 After completing the three silos, the subagent will return the tempo and beat position information for all three instrumental MP3 files. This information will be the final output of the TempoBeatsAnalyzerAgent and will be passed to the next subagent in the workflow.

subagentnumber

OVERVIEW OF SUBAGENTS: SUBAGENT 1: YouTube Video Processor - SKILL: #224 - Extract & Download Section Of YouTube Video URL - DESCRIPTION: This subagent will use the provided YouTube video URL and extract the audio from the three specified timestamp sections as MP4 files. - INPUT NEEDED: YouTube URL, start time (secs), and duration (secs) for each of the three sections - OUTPUT: Three MP4 URLs corresponding to the specified timestamp sections - SUBAGENT FINAL OUTPUT: [youtube-timestamp-mp4] SUBAGENT 2: Audio Converter - SKILL: #241 - Convert mp4 to mp3 - DESCRIPTION: This subagent will convert the extracted MP4 files to MP3 format for further processing. - INPUT NEEDED: MP4 URLs of the three timestamp sections - OUTPUT: Three MP3 URLs corresponding to the specified timestamp sections - SUBAGENT FINAL OUTPUT: [timestamp-mp3-audio] SUBAGENT 3: Stem Separator - SKILL: #204 - Extract Instrumental & Vocal From MP3 - DESCRIPTION: This subagent will separate the instrumental and vocal stems from the MP3 files obtained from the Audio Converter. - INPUT NEEDED: MP3 URLs of the three timestamp sections - OUTPUT: Six MP3 URLs (instrumental and vocal for each timestamp section) - SUBAGENT FINAL OUTPUT: [instrumental-vocal-stems] SUBAGENT 4: Beat Detector - SKILL: #195 - Extract Beatpoints & Tempo of MP3 - DESCRIPTION: This subagent will analyze the MP3 files to detect the tempo (BPM) and beat positions for each audio stem. - INPUT NEEDED: MP3 URLs of the instrumental and vocal stems for each timestamp section - OUTPUT: Tempo (BPM) and beat positions for each audio stem - SUBAGENT FINAL OUTPUT: [stem-beat-data] SUBAGENT 5: Audio Chopper - SKILL: #197 - Cut Wav/mp3 Audio into Multiple Pieces/Samples - DESCRIPTION: This subagent will chop the audio stems into 1, 2, 4, and 8 beat lengths based on the detected tempo and beat positions. - INPUT NEEDED: MP3 URLs of the instrumental and vocal stems, tempo (BPM), and beat positions for each timestamp section - OUTPUT: Multiple WAV files for each stem and timestamp section, representing the chopped samples of 1, 2, 4, and 8 beat lengths - SUBAGENT FINAL OUTPUT: [chopped-audio-samples] SUBAGENT 6: Sample Curator - SKILL: #196 - Create Visual Waveform Of Wav/mp3 File - SKILL: #198 - Analyze An Image With GPT Vision & Return Text - DESCRIPTION: This subagent will create visual waveforms of the chopped samples, analyze them using GPT Vision, and rank the samples based on criteria such as average amplitude. It will then select the top 48 samples across all stems and timestamp sections. - INPUT NEEDED: WAV files of the chopped samples for each stem and timestamp section - OUTPUT: 48 selected WAV files - SUBAGENT FINAL OUTPUT: [curated-48-samples] SUBAGENT 7: File Organizer - SKILL: #168 - Save text/html to server - DESCRIPTION: This subagent will apply a consistent naming convention and folder structure to the selected sample files. - INPUT NEEDED: 48 selected WAV files - OUTPUT: Consistently named and organized WAV files, ready for compression - SUBAGENT FINAL OUTPUT: [organized-sample-files] SUBAGENT 8: Zip Creator and Uploader - SKILL: #175 - Zip Internal URLs - DESCRIPTION: This subagent will compress the organized sample files into a zip file and upload it to a server for easy access and distribution. - INPUT NEEDED: Organized WAV files - OUTPUT: URL of the uploaded zip file containing the audio samples - SUBAGENT FINAL OUTPUT: [uploaded-sample-zip] UPDATED WORKFLOW: 1. User inputs YouTube video URL and three specific timestamps 2. YouTube Video Processor (#224) extracts audio from the specified timestamp sections as MP4 files 3. Audio Converter (#241) converts the extracted MP4 files to MP3 format 4. Stem Separator (#204) separates the instrumental and vocal stems from the MP3 files 5. Beat Detector (#195) analyzes the MP3 stems to detect tempo and beat positions 6. Audio Chopper (#197) chops the audio stems into 1, 2, 4, and 8 beat lengths based on the detected tempo and beat positions 7. Sample Curator (#196, #198) creates visual waveforms of the chopped samples, analyzes them using GPT Vision, ranks the samples, and selects the top 48 8. File Organizer (#168) applies a consistent naming convention and folder structure to the selected sample files 9. Zip Creator and Uploader (#175) compresses the organized sample files into a zip file and uploads it to a server for easy access and distribution ADDITIONAL NOTES: - The user interface for inputting the YouTube video URL and timestamps will be provided by the main agent. - The combination of the provided skills covers all the necessary functionality to complete the task, and no additional APIs or Python libraries are required.

Then, agent response...

subagent1refined-new-json-core

G) TASKFLOW SUMMARY - ID: #195 - Title task: Extract Beatpoints & Tempo of MP3 - Description of the task: This task takes an instrumental MP3 file as input, analyzes its audio content to determine the tempo (in BPM) and the positions of the beats (in seconds), and outputs this information in a structured format. - Input(s) involved: [instrumental MP3 URL] - Output token name: [tempo and beat position information]

subagent1mermaid

<!DOCTYPE html> <html> <head> <title>Mermaid Diagram Example</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script> </head> <body> <div class="mermaid"> graph TD A[Instrumental MP3 URL 1] --> B{#195 - Extract Beatpoints & Tempo of MP3} B --> C[Tempo and beat position information for MP3 1] D[Instrumental MP3 URL 2] --> E{#195 - Extract Beatpoints & Tempo of MP3} E --> F[Tempo and beat position information for MP3 2] G[Instrumental MP3 URL 3] --> H{#195 - Extract Beatpoints & Tempo of MP3} H --> I[Tempo and beat position information for MP3 3] C --> J[TempoBeatsAnalyzerAgent Final Output] F --> J I --> J </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>

Mermaid Image - https://static.aiz.ac/1720012288-mermaid/mermaid1.png

prompts-required

ID #: 195 Task: Extract Beatpoints & Tempo of MP3 Output of the task/skill: JSON file containing the tempo (in BPM) and beat positions (in seconds) for the input instrumental MP3 file. Description: Here is a Python script that takes an instrumental MP3 file as input, analyzes its audio content using the librosa library to determine the tempo (in BPM) and the positions of the beats (in seconds), and outputs this information as a JSON file: ```python import librosa import json # Load the instrumental MP3 file y, sr = librosa.load('instrumental.mp3') # Estimate the tempo tempo, _ = librosa.beat.beat_track(y=y, sr=sr) # Detect the beat positions beat_frames = librosa.beat.beat_track(y=y, sr=sr)[1] beat_times = librosa.frames_to_time(beat_frames, sr=sr) # Create a dictionary with the tempo and beat positions data = { 'tempo': tempo, 'beat_positions': beat_times.tolist() } # Save the data as a JSON file with open('tempo_beats.json', 'w') as f: json.dump(data, f) ``` This script does the following: 1. It loads the instrumental MP3 file using `librosa.load()`, which returns the audio time series `y` and the sampling rate `sr`. 2. It estimates the tempo of the audio using `librosa.beat.beat_track()`, which returns the tempo in BPM. 3. It detects the positions of the beats in the audio using `librosa.beat.beat_track()`, which returns the frame indices of the beats. These frame indices are then converted to timestamps in seconds using `librosa.frames_to_time()`. 4. It creates a dictionary `data` containing the tempo and the list of beat positions. 5. Finally, it saves the `data` dictionary as a JSON file named `tempo_beats.json`. The output JSON file will have the following structure: ```json { "tempo": 120.0, "beat_positions": [0.23, 0.69, 1.16, 1.62, ...] } ``` This JSON file can then be passed to the next step in the workflow for further processing. Note: Make sure to replace `'instrumental.mp3'` with the actual filename or URL of the instrumental MP3 file you want to analyze.

subagent1-json-array

[ { "taskID": 195, "semanticTitleOfTask": "Extract Tempo and Beat Positions from Instrumental MP3", "taskDescription": "This task takes an instrumental MP3 file as input, analyzes its audio content to determine the tempo (in BPM) and the positions of the beats (in seconds), and outputs this information in a structured format.", "inputDescription": "The input is an instrumental MP3 URL, which is the URL of the MP3 file containing the instrumental audio.", "inputRequired": ["instrumental MP3 URL"], "outputDescription": "The output is a JSON file containing the tempo (in BPM) and beat positions (in seconds) for the input instrumental MP3 file.", "outputName": "tempo and beat position information", "promptInstruction": "```python\nimport librosa\nimport json\n\n# Load the instrumental MP3 file\ny, sr = librosa.load('instrumental.mp3')\n\n# Estimate the tempo\ntempo, _ = librosa.beat.beat_track(y=y, sr=sr)\n\n# Detect the beat positions\nbeat_frames = librosa.beat.beat_track(y=y, sr=sr)[1]\nbeat_times = librosa.frames_to_time(beat_frames, sr=sr)\n\n# Create a dictionary with the tempo and beat positions\ndata = {\n 'tempo': tempo,\n 'beat_positions': beat_times.tolist()\n}\n\n# Save the data as a JSON file\nwith open('tempo_beats.json', 'w') as f:\n json.dump(data, f)\n```" } ]

subagent1-json-array-mermaid

<!DOCTYPE html> <html> <head> <title>Mermaid Diagram Example</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script> </head> <body> <div class="mermaid"> graph TD A[Instrumental MP3 URL] --> B{Extract Tempo and Beat Positions from Instrumental MP3} B --> C[Tempo and beat position information in JSON format] </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>