1. 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 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), potentially using SOX. So we chopup the samples 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. This will likely give us far too many samples, so we may want to finally filter them down (to "the best 48 samples"). We could do this by using a library like Librosa to filter somehow (eg based on average amplitude). Perhaps we could run 1-3 analyses of each sample, score them based on results, and then rank them based on the total of these results -- or to keep things simple just rank them based on a single score (average amplitude?) We will likely need to calculate how many samples we are going to get, how/if to rank them, and then generate them acccordingly. I am not sure whether we should filter or just do a bulk import. Finally we will want to save all the files to a server and zip them up.

Sub Agent #1 | Sub Agent #2 | Sub Agent #3

CLAUDE:- final-output-and-component-outputs

Thank you for providing the detailed description of the automated workflow you want to build. Let me address your questions one by one: a) Final output: The final output of this workflow would be a zip file containing a collection of audio samples (.wav files) extracted from a specific YouTube video. The zip file would include a total of 48 samples (or a different number based on your preference) that are filtered and ranked based on certain criteria like average amplitude. b) Components breakdown: To achieve the final output, the following components would be required: 1. YouTube video input: A mechanism to input the desired YouTube video URL or ID. 2. Audio extraction: A component to download the audio from the YouTube video using a library like youtube-dl or pytube. 3. Timestamp input: A way to specify the three specific timestamps (e.g., 5 seconds, 130 seconds, 190 seconds) from which to extract the audio sections. 4. Stem separation: A component to separate the audio into instrumental and vocal stems using a library like Spleeter or Open-Unmix. 5. Audio section extraction: A module to extract 16 beats worth of audio starting from each of the three specified timestamps for both the instrumental and vocal stems. This would result in 6 separate audio sections (3 instrumental and 3 vocal). 6. Beat detection and tempo estimation: A component to detect the beatpoints and estimate the tempo (in BPM) of each audio section using a library like librosa or madmom. 7. Sample chopping: A module to chop the audio sections into samples of varying lengths (1, 2, 4, and 8 beats) based on the detected beatpoints and tempo. This can be done using a library like pydub or sox. 8. Sample analysis and ranking: A component to analyze each sample using librosa features like average amplitude, spectral centroid, or other relevant metrics. The samples can then be scored and ranked based on these analysis results. 9. Sample filtering: A module to filter the samples based on the ranking and select the top 48 samples (or a different desired number). 10. File saving and zipping: A component to save the selected samples as individual .wav files and package them into a zip file. 11. Server upload: A mechanism to upload the generated zip file to a specified server or cloud storage. The core components that would be essential for generating the final output are: - YouTube video URL input - Audio extraction from the video - Timestamp input for audio section extraction - Stem separation (instrumental and vocal) - Sample chopping based on beatpoints and tempo - Sample analysis, ranking, and filtering - Saving samples as .wav files and creating a zip file - Uploading the zip file to a server These components, when combined in a structured workflow, would take the YouTube video input, process it through the various stages, and ultimately produce the desired zip file containing the ranked and filtered audio samples.

GPT4:- final-output-and-component-outputs

a) The final output of this task would be a zip file containing specific audio samples (in .wav format) cut from a specified YouTube video. This would entail extraction of audio at particular timings, separation into component stems, and potentially filtering to retain only a particular number of the "best" samples based on specific criteria like amplitude. b) Here are the core components and perhaps the cyclical process needed to generate the final output: 1. **YouTube Video:** The source of audio samples. The AI will require a method to download or directly interact with the YouTube video. Tools or services like youtube-dl, API keys, or pre-existing services may be leveraged. 2. **Timing Instruction:** An input that precisely specifies at what times the AI should cut the audio from the video. This could be a list/array of time points in a specific format, like seconds. 3. **Audio Extraction and Stem Separation:** The result of processing the YouTube video and timing input. This could involve tools like FFmpeg for extraction and something like Spleeter for stem (vocal and instrumental) separation. The output would be 3 pairs of .wav files (vocals and instruments) from each specified timestamp in the video. 4. **Sampling Instructions:** More inputs that guide the AI on how to slice the audio files into samples. Including beatpoints info, sampling length (1/2/4/8 beats), BPM of the audio, and others for manipulating using tools like Sox. 5. **Audio Samples:** Generated from the separated audio files according to the sampling instructions. These samples would be .wav files corresponding to different segments of the audio at different timestamps. 6. **Filtering Criteria:** Inputs on how the AI should evaluate and filter the samples to reach the optimum number. This could be instructions to use particular metrics (average amplitude, spectral contrast, etc.) from a library like Librosa to assess the samples. 7. **Final Filtered Audio Samples:** The reduced set of samples after applying the filtering criteria. This will be a specific number of .wav files, which were selected based on their suitability according to the metrics used for filtering. 8. **Server and Zip Operations:** The process of uploading the final filtered samples to a server, zipping them, and generating a URL or making them otherwise accessible. This could employ AWS S3 or an FTP server, depending on the server in question, and zip operations. The output of this stage would be a zip file containing the final audio samples. Each of these components will require specific inputs, tools, or services to accomplish, and all of them need to be managed by the AI agent in an orchestrated workflow to successfully complete the task outlined in the command.

claude 3 - extract final output

The final output of this workflow would be a zip file containing a collection of audio samples (.wav files) extracted from a specific YouTube video. The zip file would include a total of 48 samples (or a different number based on your preference) that are filtered and ranked based on certain criteria like average amplitude.

extract component outputs

To achieve the final output, the following components would be required: 1. YouTube video input: A mechanism to input the desired YouTube video URL or ID. 2. Audio extraction: A component to download the audio from the YouTube video using a library like youtube-dl or pytube. 3. Timestamp input: A way to specify the three specific timestamps (e.g., 5 seconds, 130 seconds, 190 seconds) from which to extract the audio sections. 4. Stem separation: A component to separate the audio into instrumental and vocal stems using a library like Spleeter or Open-Unmix. 5. Audio section extraction: A module to extract 16 beats worth of audio starting from each of the three specified timestamps for both the instrumental and vocal stems. This would result in 6 separate audio sections (3 instrumental and 3 vocal). 6. Beat detection and tempo estimation: A component to detect the beatpoints and estimate the tempo (in BPM) of each audio section using a library like librosa or madmom. 7. Sample chopping: A module to chop the audio sections into samples of varying lengths (1, 2, 4, and 8 beats) based on the detected beatpoints and tempo. This can be done using a library like pydub or sox. 8. Sample analysis and ranking: A component to analyze each sample using librosa features like average amplitude, spectral centroid, or other relevant metrics. The samples can then be scored and ranked based on these analysis results. 9. Sample filtering: A module to filter the samples based on the ranking and select the top 48 samples (or a different desired number). 10. File saving and zipping: A component to save the selected samples as individual .wav files and package them into a zip file. 11. Server upload: A mechanism to upload the generated zip file to a specified server or cloud storage. The core components that would be essential for generating the final output are: - YouTube video URL input - Audio extraction from the video - Timestamp input for audio section extraction - Stem separation (instrumental and vocal) - Sample chopping based on beatpoints and tempo - Sample analysis, ranking, and filtering - Saving samples as .wav files and creating a zip file - Uploading the zip file to a server These components, when combined in a structured workflow, would take the YouTube video input, process it through the various stages, and ultimately produce the desired zip file containing the ranked and filtered audio samples.

Claude - research-ideas-and-input-variables

Thank you for the detailed task description. Let me address your questions one by one: a) Clarifying questions for the final output: - How many total samples should be included in the final zip file? - Are there any specific requirements for the sample file format (e.g., bit depth, sample rate)? - Should the samples be labeled or named in a specific way within the zip file? - Is there a preferred method for delivering the final zip file (e.g., via a specific file-sharing service)? b) Inputs required before starting the task: - The YouTube video URL or video ID. - Three specific timestamps within the video to extract samples from. - (Optional) The desired number of samples to include in the final output, if not already specified in the task description. c) Potential task segmentation: I have a few suggestions for segmenting the tasks to ensure a more modular and efficient workflow: 1. YouTube video processing: - Download the audio from the YouTube video. - Extract the specified segments based on the provided timestamps. 2. Stem separation: - Separate the vocals and instrumentals for each extracted segment using a dedicated stem separation tool or library. 3. Sample extraction: - Analyze the tempo and beat positions of each segment. - Extract samples of varying lengths (1, 2, 4, and 8 beats) starting from the beat positions. 4. Sample filtering and selection: - Analyze each sample using Librosa or other audio analysis libraries to compute relevant metrics (e.g., average amplitude). - Rank the samples based on the chosen metric(s). - Select the top N samples (e.g., the best 48) based on the ranking. 5. Output generation: - Save the selected samples to a server or local directory. - Compress the samples into a zip file. - Deliver the zip file through the specified method. By breaking down the task into these specialized areas, the AI Agent can focus on each step independently, making the workflow more modular and easier to develop, test, and maintain.

GPT - research-ideas-and-input-variables

a) Given the task description, some questions that could help specify the final output would include: - What is the desired format for each of the samples? Are .wav files acceptable, or is another file type desired? - Is there a preferred method of slicing? For example: Should the agent start slicing at a beat point and continue for an entire bar, or should it slice for a set length of time? - Is there a preferred BPM or other rhythmic characteristics for the music being sampled? - What are the criteria for determining the "best" samples? The description mentions considering average amplitude - what other factors should be considered if any in ranking and filtering the samples? - What is the exact number of final samples (mentioned as "48")? How many samples per time point and how many samples per each stem (vocal, instrumental)? - Does the user have any particular preference for how the samples are organized within the final zip file? b) The task would need some necessary inputs before launching it: - The YouTube video URL that the user wants to use for the audio sample extraction. - Specific time durations to act as the extraction points (e.g., 5 seconds, 130 seconds, 190 seconds as per the example) - User-defined parameters for chopping up samples using SOX (1/2/4/8 beat lengths). - Criteria needed to determine the "best" samples - Details on server storage where the resultant zip file would be stored.

template-ideas

Thank you for the detailed explanation of your project and the importance of defining the final output. Here are a few suggestions for templates or references that could help refine the definition of a successful output for your music sampling workflow: 1. Sample packs from popular music production websites: - Visit websites like Loopmasters, Splice, or Sample Radar. - Look for sample packs that focus on specific genres or styles relevant to your project. - Review the descriptions and specifications of these packs, such as the number of samples, file format, length of samples, and any additional categorization or tagging. - Analyze customer reviews to understand what makes these packs popular or useful to music producers. 2. GitHub repositories or open-source projects related to music sampling: - Search for repositories or projects on GitHub that involve extracting samples from audio or video files. - Review the project documentation, code structure, and any example outputs provided. - Look for insights on how they handle audio processing, sample extraction, and organization of the resulting samples. 3. YouTube videos or tutorials on creating sample packs: - Search for videos where music producers or content creators demonstrate their process of creating sample packs. - Analyze the steps they follow, the tools they use, and the criteria they consider when selecting and processing samples. - Pay attention to how they organize and present the final sample pack to their audience. 4. Music production forums or communities: - Visit forums or online communities where music producers discuss sampling techniques, workflows, and share their sample packs. - Look for threads or discussions specifically related to extracting samples from YouTube videos or other sources. - Note any best practices, recommended tools, or common pitfalls mentioned by experienced producers. 5. Commercial or free sample packs: - Download and examine a few sample packs (commercial or free) that align with your desired output. - Analyze the structure, naming conventions, and metadata of the samples within these packs. - Consider the overall quality, consistency, and usability of the samples from a music producer's perspective. By collecting and reviewing these templates or references, you can gain valuable insights into what constitutes a successful and useful sample pack. Pay attention to factors such as the number and variety of samples, the quality and consistency of the audio, the organization and labeling of files, and any additional documentation or instructions provided. Use these insights to refine your definition of a successful output and ensure that your automated workflow aligns with industry standards and user expectations. Remember to adapt and customize the templates to suit your specific project requirements and target audience.

claude 3 - searches

Here are 5 specific keyphrases for you to search, to help refine the definition of a successful output for your music sampling project: 1. "royalty free vocal sample pack wav" This search will help find sample packs that specifically contain vocal samples in WAV format, which can serve as a reference for the structure and content of your output. 2. "instrumental samples from youtube videos github" Searching for GitHub repositories related to extracting instrumental samples from YouTube videos will provide insights into existing workflows and code implementations. 3. "creating a sample pack from a song tutorial" This search aims to find video tutorials that demonstrate the process of creating a sample pack from a specific song, which can offer valuable tips and techniques. 4. "reddit mhh discuss youtube sampling workflow" Searching for discussions on the r/makinghiphop subreddit about sampling workflows from YouTube videos can provide insights from the music production community. 5. "free vocal chop sample pack splice" Looking for a free vocal chop sample pack on Splice, a popular platform for music producers, can give you a good example of how samples are typically organized and presented. By searching for these specific keyphrases, you should be able to find relevant templates, examples, and discussions that can help refine your understanding of what constitutes a successful output for your music sampling project. The results will likely include sample packs, code repositories, tutorials, community discussions, and real-world examples that align with your project goals.

subagent-summary

A) FULL TASK SUMMARY: Generate a zip file containing ranked and filtered audio samples extracted from a specific YouTube video. B) FINAL TASK OUTPUT: A zip file containing 48 (or a specified number) of .wav audio samples, each lasting 1, 2, 4, or 8 beats in duration, extracted from instrumental and vocal stems at three specific timestamps of a YouTube video. The samples should be ranked and filtered based on average amplitude or other relevant metrics. C) COMPONENTS OVERVIEW: 1. YouTube video URL input 2. Audio extraction from the video 3. Timestamp input for audio section extraction 4. Stem separation (instrumental and vocal) 5. Beat detection and tempo estimation 6. Sample chopping based on beatpoints and tempo 7. Sample analysis, ranking, and filtering 8. Saving samples as .wav files and creating a zip file 9. Uploading the zip file to a server D) OVERVIEW OF SUBAGENTS: - YouTubeInputAgent: Handles YouTube video URL input and validation. - AudioExtractionAgent: Downloads and extracts audio from the YouTube video. - TimestampInputAgent: Manages timestamp input for audio section extraction. - StemSeparationAgent: Separates the audio into instrumental and vocal stems. - BeatDetectionAgent: Detects beatpoints and estimates tempo for each audio section. - SampleChoppingAgent: Chops audio sections into samples based on beatpoints and tempo. - SampleAnalysisAgent: Analyzes, ranks, and filters samples based on specified metrics. - ZipFileAgent: Saves samples as .wav files and creates a zip file. - ServerUploadAgent: Uploads the zip file to a specified server or cloud storage. E) SUBAGENT 1: YouTubeInputAgent - Name: YouTubeInputAgent - Final Output: Valid YouTube video URL or ID - Accomplishment: This subagent will provide a user interface (e.g., command-line, GUI, or API endpoint) to accept a YouTube video URL or ID as input. It will validate the input to ensure it is a valid YouTube link or ID using regular expressions or by making a request to the YouTube API. If the input is invalid, it will prompt the user to provide a valid URL or ID. - Sequence: Accept input > Validate input > Return valid YouTube URL/ID or prompt for valid input F) SUBAGENT 2: AudioExtractionAgent - Name: AudioExtractionAgent - Final Output: Audio file extracted from the YouTube video - Accomplishment: This subagent will take the valid YouTube URL or ID from the YouTubeInputAgent and use a library like youtube-dl or pytube to download the audio from the video. It will handle any necessary authentication or API calls required to access the video. The extracted audio will be saved as a temporary file for further processing. - Sequence: Receive YouTube URL/ID > Download audio using youtube-dl or pytube > Save audio as a temporary file G) SUBAGENT 3: TimestampInputAgent - Name: TimestampInputAgent - Final Output: Three specific timestamps (in seconds) for audio section extraction - Accomplishment: This subagent will provide a user interface (e.g., command-line, GUI, or API endpoint) to accept three specific timestamps (in seconds) as input. It will validate the input to ensure the timestamps are within the duration of the extracted audio file. If the input is invalid, it will prompt the user to provide valid timestamps. - Sequence: Accept timestamp input > Validate timestamps against audio duration > Return valid timestamps or prompt for valid input H) SUBAGENT 4: StemSeparationAgent - Name: StemSeparationAgent - Final Output: Instrumental and vocal stems for each of the three audio sections - Accomplishment: This subagent will take the extracted audio file and the three timestamps from the TimestampInputAgent. It will use a library like Spleeter or Open-Unmix to separate the audio into instrumental and vocal stems. The stem separation will be performed for each of the three audio sections (e.g., 16 beats worth of audio) starting from the specified timestamps. The resulting stems will be saved as temporary files for further processing. - Sequence: Receive audio file and timestamps > Extract audio sections based on timestamps > Separate each section into instrumental and vocal stems > Save stems as temporary files I) SUBAGENT 5: BeatDetectionAgent - Name: BeatDetectionAgent - Final Output: Detected beatpoints and estimated tempo (in BPM) for each audio section - Accomplishment: This subagent will take the instrumental and vocal stems for each audio section from the StemSeparationAgent. It will use a library like librosa or madmom to detect the beatpoints and estimate the tempo (in BPM) for each section. The detected beatpoints and tempo will be stored for use in the sample chopping process. - Sequence: Receive instrumental and vocal stems for each audio section > Detect beatpoints and estimate tempo using librosa or madmom > Store beatpoints and tempo for each section J) SUBAGENT 6: SampleChoppingAgent - Name: SampleChoppingAgent - Final Output: Chopped samples of varying lengths (1, 2, 4, and 8 beats) for each audio section - Accomplishment: This subagent will take the instrumental and vocal stems, beatpoints, and tempo information from the BeatDetectionAgent. It will use a library like pydub or sox to chop the audio sections into samples of varying lengths (1, 2, 4, and 8 beats) based on the detected beatpoints and tempo. The resulting samples will be saved as temporary files for further analysis and filtering. - Sequence: Receive stems, beatpoints, and tempo for each audio section > Calculate sample lengths based on tempo > Chop audio sections into samples using pydub or sox > Save chopped samples as temporary files K) SUBAGENT 7: SampleAnalysisAgent - Name: SampleAnalysisAgent - Final Output: Ranked and filtered list of top 48 (or a specified number) samples based on average amplitude or other metrics - Accomplishment: This subagent will take the chopped samples from the SampleChoppingAgent. It will use a library like librosa to analyze each sample based on average amplitude or other relevant metrics. The samples will be scored and ranked based on the analysis results. The top 48 samples (or a specified number) will be selected based on the ranking. - Sequence: Receive chopped samples > Analyze samples using librosa features > Score and rank samples based on analysis results > Filter and select top 48 (or specified number) samples L) SUBAGENT 8: ZipFileAgent - Name: ZipFileAgent - Final Output: Zip file containing the selected audio samples as .wav files - Accomplishment: This subagent will take the ranked and filtered list of samples from the SampleAnalysisAgent. It will save each sample as an individual .wav file with a descriptive name (e.g., "section1_instrumental_4beats.wav"). The .wav files will be packaged into a zip file for easy distribution and upload. - Sequence: Receive ranked and filtered list of samples > Save each sample as a .wav file with a descriptive name > Create a zip file containing all the .wav files M) SUBAGENT 9: ServerUploadAgent - Name: ServerUploadAgent - Final Output: Uploaded zip file to a specified server or cloud storage - Accomplishment: This subagent will take the zip file generated by the ZipFileAgent and upload it to a specified server or cloud storage location. It will handle any necessary authentication, file transfer protocols, or API calls required to upload the file. The upload destination can be configured based on user preferences or predefined settings. - Sequence: Receive zip file > Authenticate with the specified server or cloud storage > Upload the zip file using appropriate file transfer protocols or APIs > Confirm successful upload and provide a download link or location These subagents, working together in the described sequence, will generate the desired final output: a zip file containing ranked and filtered audio samples extracted from a specific YouTube video. The modular nature of the subagents allows for flexibility and customization in the workflow, enabling users to adjust parameters such as the number of samples, sample lengths, or analysis metrics based on their specific requirements.

subagent-summary-amended-for-actual-tasks

A) FULL TASK SUMMARY: Generate a zip file containing ranked and filtered audio samples extracted from a specific YouTube video. B) FINAL TASK OUTPUT: A zip file containing 48 (or a specified number) of .wav audio samples, each lasting 1, 2, 4, or 8 beats in duration, extracted from instrumental and vocal stems at three specific timestamps of a YouTube video. The samples should be ranked and filtered based on average amplitude or other relevant metrics. C) COMPONENTS OVERVIEW: 1. Audio extraction from the YouTube video 2. Timestamp input for audio section extraction 3. Stem separation (instrumental and vocal) 4. Beat detection and tempo estimation 5. Sample chopping based on beatpoints and tempo 6. Sample analysis, ranking, and filtering 7. Saving samples as .wav files and creating a zip file 8. Uploading the zip file to a server D) OVERVIEW OF SUBAGENTS: - AudioExtractionAgent: Downloads and extracts audio from the YouTube video. - TimestampInputAgent: Manages timestamp input for audio section extraction. - StemSeparationAgent: Separates the audio into instrumental and vocal stems. - BeatDetectionAgent: Detects beatpoints and estimates tempo for each audio section. - SampleChoppingAgent: Chops audio sections into samples based on beatpoints and tempo. - SampleAnalysisAgent: Analyzes, ranks, and filters samples based on specified metrics. - ZipFileAgent: Saves samples as .wav files and creates a zip file. - ServerUploadAgent: Uploads the zip file to a specified server or cloud storage. E) SUBAGENT 1: AudioExtractionAgent - Name: AudioExtractionAgent - Final Output: Audio file extracted from the YouTube video - Accomplishment: This subagent will use the "Download youtube video to mp4" task to download the video from the provided YouTube URL. It will then use the "Convert mp4 to mp3" task to extract the audio from the downloaded video. The extracted audio will be saved as a temporary file for further processing. - Sequence: Receive YouTube URL > Download video using "Download youtube video to mp4" task > Extract audio using "Convert mp4 to mp3" task > Save audio as a temporary file F) SUBAGENT 2: TimestampInputAgent - Name: TimestampInputAgent - Final Output: Three specific timestamps (in seconds) for audio section extraction - Accomplishment: This subagent will provide a user interface (e.g., command-line, GUI, or API endpoint) to accept three specific timestamps (in seconds) as input. It will validate the input to ensure the timestamps are within the duration of the extracted audio file. If the input is invalid, it will prompt the user to provide valid timestamps. - Sequence: Accept timestamp input > Validate timestamps against audio duration > Return valid timestamps or prompt for valid input G) SUBAGENT 3: StemSeparationAgent - Name: StemSeparationAgent - Final Output: Instrumental and vocal stems for each of the three audio sections - Accomplishment: This subagent will take the extracted audio file and the three timestamps from the TimestampInputAgent. It will use the "Extract instrumental/vocal stems" task to separate the audio into instrumental and vocal stems. The stem separation will be performed for each of the three audio sections (e.g., 16 beats worth of audio) starting from the specified timestamps. The resulting stems will be saved as temporary files for further processing. - Sequence: Receive audio file and timestamps > Extract audio sections based on timestamps using the "Split/extract video by interval" task > Separate each section into instrumental and vocal stems using the "Extract instrumental/vocal stems" task > Save stems as temporary files H) SUBAGENT 4: BeatDetectionAgent - Name: BeatDetectionAgent - Final Output: Detected beatpoints and estimated tempo (in BPM) for each audio section - Accomplishment: This subagent will take the instrumental and vocal stems for each audio section from the StemSeparationAgent. It will use the "Get tempo (BPM) of mp3" task to detect the beatpoints and estimate the tempo (in BPM) for each section. The detected beatpoints and tempo will be stored for use in the sample chopping process. - Sequence: Receive instrumental and vocal stems for each audio section > Detect beatpoints and estimate tempo using the "Get tempo (BPM) of mp3" task > Store beatpoints and tempo for each section I) SUBAGENT 5: SampleChoppingAgent - Name: SampleChoppingAgent - Final Output: Chopped samples of varying lengths (1, 2, 4, and 8 beats) for each audio section - Accomplishment: This subagent will take the instrumental and vocal stems, beatpoints, and tempo information from the BeatDetectionAgent. It will use the "Split/extract video by interval" task to chop the audio sections into samples of varying lengths (1, 2, 4, and 8 beats) based on the detected beatpoints and tempo. The resulting samples will be saved as temporary files for further analysis and filtering. - Sequence: Receive stems, beatpoints, and tempo for each audio section > Calculate sample lengths based on tempo > Chop audio sections into samples using the "Split/extract video by interval" task > Save chopped samples as temporary files J) SUBAGENT 6: SampleAnalysisAgent - Name: SampleAnalysisAgent - Final Output: Ranked and filtered list of top 48 (or a specified number) samples based on average amplitude or other metrics - Accomplishment: This subagent will take the chopped samples from the SampleChoppingAgent. It will use the "GPT4" task to analyze each sample based on average amplitude or other relevant metrics. The samples will be scored and ranked based on the analysis results. The top 48 samples (or a specified number) will be selected based on the ranking. - Sequence: Receive chopped samples > Analyze samples using the "GPT4" task with a prompt to rank samples based on average amplitude or other metrics > Score and rank samples based on analysis results > Filter and select top 48 (or specified number) samples K) SUBAGENT 7: ZipFileAgent - Name: ZipFileAgent - Final Output: Zip file containing the selected audio samples as .wav files - Accomplishment: This subagent will take the ranked and filtered list of samples from the SampleAnalysisAgent. It will save each sample as an individual .wav file with a descriptive name (e.g., "section1_instrumental_4beats.wav") using the "Save content to server" task. The .wav files will be packaged into a zip file using the "Zip and unzip files" task for easy distribution and upload. - Sequence: Receive ranked and filtered list of samples > Save each sample as a .wav file with a descriptive name using the "Save content to server" task > Create a zip file containing all the .wav files using the "Zip and unzip files" task L) SUBAGENT 8: ServerUploadAgent - Name: ServerUploadAgent - Final Output: Uploaded zip file to a specified server or cloud storage - Accomplishment: This subagent will take the zip file generated by the ZipFileAgent and upload it to a specified server or cloud storage location using the "Save external web file/folder to server" task. The upload destination can be configured based on user preferences or predefined settings. - Sequence: Receive zip file > Upload the zip file to the specified server or cloud storage using the "Save external web file/folder to server" task > Confirm successful upload and provide a download link or location These subagents, working together in the described sequence and utilizing the available tasks, will generate the desired final output: a zip file containing ranked and filtered audio samples extracted from a specific YouTube video. The modular nature of the subagents allows for flexibility and customization in the workflow, enabling users to adjust parameters such as the number of samples, sample lengths, or analysis metrics based on their specific requirements.

random-autocomplete

To achieve the end goal of creating a zip file containing a collection of filtered and ranked audio samples extracted from a specific YouTube video, the most relevant tasks might be: 1. Download the audio from the specified YouTube video 2. Extract specific segments of the audio based on the provided timings 3. Separate the extracted audio segments into instrumental and vocal stems 4. Determine the tempo and beatpoints of each audio segment 5. Chop the audio segments into samples of varying lengths (1, 2, 4, and 8 beats) using the tempo and beatpoint information 6. Analyze and rank the samples based on criteria like average amplitude using a library like Librosa 7. Filter the samples to keep only the top 48 (or a different desired number) 8. Save the selected samples as individual .wav files 9. Compress the .wav files into a single zip file 10. Save the zip file to a server or desired location The overall sequence of actions would be: 1. Input YouTube video URL and specific timings 2. Download audio from YouTube video 3. Extract audio segments based on provided timings 4. Separate audio segments into instrumental and vocal stems 5. Determine tempo and beatpoints for each segment 6. Chop audio segments into 1, 2, 4, and 8 beat samples using tempo and beatpoint information 7. Analyze and rank samples based on average amplitude using Librosa 8. Filter samples to keep top 48 (or desired number) 9. Save selected samples as individual .wav files 10. Compress .wav files into a single zip file 11. Save zip file to server or desired location 12. Provide link or access to the final zip file containing the audio samples The Agent could use various APIs and Python libraries to accomplish these tasks, such as: - pytube or youtube-dl for downloading the audio from the YouTube video - pydub or ffmpeg for extracting audio segments and separating stems - librosa for determining tempo, beatpoints, and analyzing audio features - pydub or scipy for chopping the audio segments into samples - python's built-in zipfile library for compressing the .wav files into a zip file - os and shutil for file management and saving the zip file to the desired location Given the Agent's access to a wide range of APIs, Python libraries, and control over a Windows 11 virtual machine, it should be capable of executing this workflow and delivering the desired output of a zip file containing filtered and ranked audio samples extracted from the specified YouTube video.

claude 3 - mermaid-for-all

<!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[Input YouTube URL] --> B{AudioExtractionAgent} B --> |Extracted Audio| C{TimestampInputAgent} C --> |Timestamps| D{StemSeparationAgent} D --> |Instrumental and Vocal Stems| E{BeatDetectionAgent} E --> |Beatpoints and Tempo| F{SampleChoppingAgent} F --> |Chopped Samples| G{SampleAnalysisAgent} G --> |Ranked and Filtered Samples| H{ZipFileAgent} H --> |Zip File| I{ServerUploadAgent} I --> J[Uploaded Zip File] </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>

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SubAgent #1

claude 3 - subagent1

F) SUBAGENT 2: TimestampInputAgent - Name: TimestampInputAgent - Final Output: Three specific timestamps (in seconds) for audio section extraction - Accomplishment: This subagent will provide a user interface (e.g., command-line, GUI, or API endpoint) to accept three specific timestamps (in seconds) as input. It will validate the input to ensure the timestamps are within the duration of the extracted audio file. If the input is invalid, it will prompt the user to provide valid timestamps. - Sequence: Accept timestamp input > Validate timestamps against audio duration > Return valid timestamps or prompt for valid input

subagent1-refined

A) SUBAGENT SUMMARY: The TimestampInputAgent accepts three specific timestamps from the user, validates them against the duration of the extracted audio file, and returns the valid timestamps for audio section extraction. B) FINAL TASK OUTPUT: A JSON string containing three specific timestamps (in seconds) for audio section extraction, e.g., {"timestamps": [5, 130, 190]} C) SUBAGENT INPUT: - Duration of the extracted audio file (in seconds) E) SUBAGENT TASK SUMMARY: 1. Accept user input for three timestamps (in seconds) using the "GPT4" task with a prompt to request the timestamps and provide the audio duration for reference. 2. Validate the user input using the "Code interpreter" task to ensure that the timestamps are within the duration of the extracted audio file and are in the correct format (e.g., positive integers or floats). 3. If the input is invalid, use the "GPT4" task to generate a message prompting the user to provide valid timestamps and repeat steps 1-2. 4. If the input is valid, use the "GPT4" task to format the timestamps as a JSON string, e.g., {"timestamps": [5, 130, 190]}. 5. Save the JSON string containing the valid timestamps to the server using the "Save content to server" task with a file extension of .json. Subagent final input(s) > GPT4 (accept user input) > Code interpreter (validate input) > GPT4 (prompt for valid input, if needed) > GPT4 (format timestamps as JSON) > Save content to server (save JSON string) > Subagent final output(s) F) SILOS: This subagent does not require silos as it performs a single, focused task of accepting and validating user input for three timestamps.

subagent1refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS): 1. Use the "GPT4" task with a prompt to request the first timestamp (in seconds) and provide the audio duration for reference. 2. Use the "Code interpreter" task to validate the first timestamp, ensuring it is within the duration of the extracted audio file and in the correct format (e.g., positive integer or float). 3. If the first timestamp is invalid, use the "GPT4" task to generate a message prompting the user to provide a valid timestamp and repeat steps 1-2 for the first timestamp. 4. Use the "GPT4" task with a prompt to request the second timestamp (in seconds) and provide the audio duration for reference. 5. Use the "Code interpreter" task to validate the second timestamp, ensuring it is within the duration of the extracted audio file and in the correct format (e.g., positive integer or float). 6. If the second timestamp is invalid, use the "GPT4" task to generate a message prompting the user to provide a valid timestamp and repeat steps 4-5 for the second timestamp. 7. Use the "GPT4" task with a prompt to request the third timestamp (in seconds) and provide the audio duration for reference. 8. Use the "Code interpreter" task to validate the third timestamp, ensuring it is within the duration of the extracted audio file and in the correct format (e.g., positive integer or float). 9. If the third timestamp is invalid, use the "GPT4" task to generate a message prompting the user to provide a valid timestamp and repeat steps 7-8 for the third timestamp. 10. If all three timestamps are valid, use the "GPT4" task to format the timestamps as a JSON string, e.g., {"timestamps": [5, 130, 190]}. 11. Save the JSON string containing the valid timestamps to the server using the "Save content to server" task with a file extension of .json.

Subagent1 task-ID-numbers

36,12,36,36,9

subagent1-output-variables

G) FULL TASKS (INCLUDING REPEATING TASKS): 1. Use the "GPT4" task with a prompt to request the first timestamp (in seconds) and provide the audio duration for reference. Output token: [timestamp1-prompt] 2. Use the "Code interpreter" task to validate the first timestamp, ensuring it is within the duration of the extracted audio file and in the correct format (e.g., positive integer or float). Output token: [timestamp1-validated] 3. If the first timestamp is invalid, use the "GPT4" task to generate a message prompting the user to provide a valid timestamp and repeat steps 1-2 for the first timestamp. Output token: [timestamp1-invalid-message] 4. Use the "GPT4" task with a prompt to request the second timestamp (in seconds) and provide the audio duration for reference. Output token: [timestamp2-prompt] 5. Use the "Code interpreter" task to validate the second timestamp, ensuring it is within the duration of the extracted audio file and in the correct format (e.g., positive integer or float). Output token: [timestamp2-validated] 6. If the second timestamp is invalid, use the "GPT4" task to generate a message prompting the user to provide a valid timestamp and repeat steps 4-5 for the second timestamp. Output token: [timestamp2-invalid-message] 7. Use the "GPT4" task with a prompt to request the third timestamp (in seconds) and provide the audio duration for reference. Output token: [timestamp3-prompt] 8. Use the "Code interpreter" task to validate the third timestamp, ensuring it is within the duration of the extracted audio file and in the correct format (e.g., positive integer or float). Output token: [timestamp3-validated] 9. If the third timestamp is invalid, use the "GPT4" task to generate a message prompting the user to provide a valid timestamp and repeat steps 7-8 for the third timestamp. Output token: [timestamp3-invalid-message] 10. If all three timestamps are valid, use the "GPT4" task to format the timestamps as a JSON string, e.g., {"timestamps": [5, 130, 190]}. Output token: [timestamps-json] 11. Save the JSON string containing the valid timestamps to the server using the "Save content to server" task with a file extension of .json. Output token: [timestamps-json-file] H) OUTPUT TOKENS 1. [timestamp1-prompt]: The prompt generated by GPT4 to request the first timestamp from the user. 2. [timestamp1-validated]: The validated first timestamp, ensuring it is within the duration of the extracted audio file and in the correct format. 3. [timestamp1-invalid-message]: The message generated by GPT4 to prompt the user to provide a valid first timestamp if the initial input is invalid. 4. [timestamp2-prompt]: The prompt generated by GPT4 to request the second timestamp from the user. 5. [timestamp2-validated]: The validated second timestamp, ensuring it is within the duration of the extracted audio file and in the correct format. 6. [timestamp2-invalid-message]: The message generated by GPT4 to prompt the user to provide a valid second timestamp if the initial input is invalid. 7. [timestamp3-prompt]: The prompt generated by GPT4 to request the third timestamp from the user. 8. [timestamp3-validated]: The validated third timestamp, ensuring it is within the duration of the extracted audio file and in the correct format. 9. [timestamp3-invalid-message]: The message generated by GPT4 to prompt the user to provide a valid third timestamp if the initial input is invalid. 10. [timestamps-json]: The JSON string containing all three valid timestamps, formatted by GPT4. 11. [timestamps-json-file]: The JSON file containing the valid timestamps, saved to the server using the "Save content to server" task.

subagent1refined-repeating-json-core

A) SUBAGENT SUMMARY: Validate and save user-provided audio timestamps as a JSON file. B) FINAL TASK OUTPUT: A JSON file containing three valid timestamps (in seconds) within the duration of the extracted audio file, saved on the server with a .json extension. C) SUBAGENT INPUT: - Audio duration (in seconds) of the extracted audio file. G) TASKFLOW SUMMARY: 1. Title task: Request first timestamp from user Description of the task: Use GPT4 to generate a prompt asking the user to provide the first timestamp (in seconds) for the audio sample, considering the audio duration. Input(s) involved: [audio_duration] Output token name: [user_input_timestamp1] 2. Title task: Validate first timestamp Description of the task: Use a code interpreter to check if the user-provided first timestamp is within the audio duration and in the correct format (positive integer or float). Input(s) involved: [user_input_timestamp1], [audio_duration] Output token name: [validated_timestamp1] 3. Title task: Request second timestamp from user Description of the task: Use GPT4 to generate a prompt asking the user to provide the second timestamp (in seconds) for the audio sample, considering the audio duration. Input(s) involved: [audio_duration] Output token name: [user_input_timestamp2] 4. Title task: Validate second timestamp Description of the task: Use a code interpreter to check if the user-provided second timestamp is within the audio duration and in the correct format (positive integer or float). Input(s) involved: [user_input_timestamp2], [audio_duration] Output token name: [validated_timestamp2] 5. Title task: Request third timestamp from user Description of the task: Use GPT4 to generate a prompt asking the user to provide the third timestamp (in seconds) for the audio sample, considering the audio duration. Input(s) involved: [audio_duration] Output token name: [user_input_timestamp3] 6. Title task: Validate third timestamp Description of the task: Use a code interpreter to check if the user-provided third timestamp is within the audio duration and in the correct format (positive integer or float). Input(s) involved: [user_input_timestamp3], [audio_duration] Output token name: [validated_timestamp3] 7. Title task: Format timestamps as JSON Description of the task: Use GPT4 to create a JSON string containing the three validated timestamps. Input(s) involved: [validated_timestamp1], [validated_timestamp2], [validated_timestamp3] Output token name: [timestamps_json] 8. Title task: Save JSON file to server Description of the task: Save the JSON string containing the valid timestamps to the server using the "Save content to server" task with a .json file extension. Input(s) involved: [timestamps_json] Output token name: [saved_timestamps_file]

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[Request first timestamp from user] -->|user_input_timestamp1| B[Validate first timestamp] B -->|Invalid| A B -->|validated_timestamp1| C[Request second timestamp from user] C -->|user_input_timestamp2| D[Validate second timestamp] D -->|Invalid| C D -->|validated_timestamp2| E[Request third timestamp from user] E -->|user_input_timestamp3| F[Validate third timestamp] F -->|Invalid| E F -->|validated_timestamp3| G[Format timestamps as JSON] G -->|timestamps_json| H[Save JSON file to server] </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>

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SubAgent #2

claude 3 - subagent2

[subagent2]

subagent2-refined

A) SUBAGENT SUMMARY: The TimestampInputAgent accepts three specific timestamps as input and validates them against the duration of the extracted audio file. B) FINAL TASK OUTPUT: Three specific timestamps (in seconds) for audio section extraction, validated against the duration of the extracted audio file. C) SUBAGENT INPUT: - Extracted audio file from the YouTube video - Three specific timestamps (in seconds) provided by the user E) SUBAGENT TASK SUMMARY: 1. Receive the extracted audio file from the AudioExtractionAgent 2. Prompt the user to input three specific timestamps (in seconds) using the "GPT4" task 3. Validate the user-provided timestamps against the duration of the extracted audio file using the "GPT4" task with a prompt to check if the timestamps are within the audio duration 4. If the timestamps are valid, output the validated timestamps 5. If any of the timestamps are invalid, prompt the user to provide valid timestamps using the "GPT4" task and repeat steps 3-5 until all timestamps are valid Detailed subagent workflow: - Input: Extracted audio file URL from AudioExtractionAgent - Action 1: Use the "GPT4" task to prompt the user to input three specific timestamps (in seconds) for audio section extraction - Action 2: Receive the user-provided timestamps as plain text - Action 3: Use the "GPT4" task to validate the user-provided timestamps against the duration of the extracted audio file - Prompt GPT4 to check if each timestamp is within the audio duration - If all timestamps are valid, proceed to Action 4 - If any timestamp is invalid, use the "GPT4" task to prompt the user to provide valid timestamps and repeat Action 3 - Action 4: Output the validated timestamps as plain text F) SILOS: This subagent does not require silos as it performs a linear sequence of actions without parallel processing.

subagent2refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS): 1. Receive the extracted audio file URL from the AudioExtractionAgent 2. Use the "GPT4" task to prompt the user to input three specific timestamps (in seconds) for audio section extraction 3. Receive the user-provided timestamps as plain text 4. Use the "GPT4" task to validate the first user-provided timestamp against the duration of the extracted audio file - Prompt GPT4 to check if the first timestamp is within the audio duration - If the first timestamp is valid, proceed to task 5 - If the first timestamp is invalid, use the "GPT4" task to prompt the user to provide a valid timestamp for the first timestamp and repeat task 4 5. Use the "GPT4" task to validate the second user-provided timestamp against the duration of the extracted audio file - Prompt GPT4 to check if the second timestamp is within the audio duration - If the second timestamp is valid, proceed to task 6 - If the second timestamp is invalid, use the "GPT4" task to prompt the user to provide a valid timestamp for the second timestamp and repeat task 5 6. Use the "GPT4" task to validate the third user-provided timestamp against the duration of the extracted audio file - Prompt GPT4 to check if the third timestamp is within the audio duration - If the third timestamp is valid, proceed to task 7 - If the third timestamp is invalid, use the "GPT4" task to prompt the user to provide a valid timestamp for the third timestamp and repeat task 6 7. Output the validated timestamps as plain text

Subagent2 task-ID-numbers

4

subagent2-output-variables

G) FULL TASKS (INCLUDING REPEATING TASKS): 1. Receive the extracted audio file URL from the AudioExtractionAgent [extracted-audio-url] 2. Use the "GPT4" task to prompt the user to input three specific timestamps (in seconds) for audio section extraction 3. Receive the user-provided timestamps as plain text [user-timestamps] 4. Use the "GPT4" task to validate the first user-provided timestamp against the duration of the extracted audio file - Prompt GPT4 to check if the first timestamp is within the audio duration - If the first timestamp is valid, proceed to task 5 - If the first timestamp is invalid, use the "GPT4" task to prompt the user to provide a valid timestamp for the first timestamp and repeat task 4 [valid-timestamp1] 5. Use the "GPT4" task to validate the second user-provided timestamp against the duration of the extracted audio file - Prompt GPT4 to check if the second timestamp is within the audio duration - If the second timestamp is valid, proceed to task 6 - If the second timestamp is invalid, use the "GPT4" task to prompt the user to provide a valid timestamp for the second timestamp and repeat task 5 [valid-timestamp2] 6. Use the "GPT4" task to validate the third user-provided timestamp against the duration of the extracted audio file - Prompt GPT4 to check if the third timestamp is within the audio duration - If the third timestamp is valid, proceed to task 7 - If the third timestamp is invalid, use the "GPT4" task to prompt the user to provide a valid timestamp for the third timestamp and repeat task 6 [valid-timestamp3] 7. Output the validated timestamps as plain text [validated-timestamps] H) OUTPUT TOKENS 1. [extracted-audio-url]: The URL of the extracted audio file received from the AudioExtractionAgent 2. [user-timestamps]: The user-provided timestamps as plain text 3. [valid-timestamp1]: The validated first timestamp 4. [valid-timestamp2]: The validated second timestamp 5. [valid-timestamp3]: The validated third timestamp 6. [validated-timestamps]: The final output of the validated timestamps as plain text

subagent2refined-repeating-json-core

A) SUBAGENT SUMMARY: AudioTimestampValidationAgent validates user-provided timestamps against the duration of an extracted audio file. B) FINAL TASK OUTPUT: Plain text containing three validated timestamps (in seconds) for audio section extraction. C) SUBAGENT INPUT: - Extracted audio file URL - User-provided timestamps as plain text E) SUBAGENT TASK SUMMARY: 1. Receive the extracted audio file URL from the AudioExtractionAgent 2. Prompt the user to input three specific timestamps (in seconds) for audio section extraction using GPT4 3. Receive the user-provided timestamps as plain text 4. Validate the first user-provided timestamp against the duration of the extracted audio file using GPT4 - If the first timestamp is valid, proceed to validate the second timestamp - If the first timestamp is invalid, prompt the user to provide a valid timestamp for the first timestamp and repeat validation 5. Validate the second user-provided timestamp against the duration of the extracted audio file using GPT4 - If the second timestamp is valid, proceed to validate the third timestamp - If the second timestamp is invalid, prompt the user to provide a valid timestamp for the second timestamp and repeat validation 6. Validate the third user-provided timestamp against the duration of the extracted audio file using GPT4 - If the third timestamp is valid, output the validated timestamps as plain text - If the third timestamp is invalid, prompt the user to provide a valid timestamp for the third timestamp and repeat validation F) SILOS: None G) TASKFLOW SUMMARY: Task 1: Title task: Receive extracted audio file URL Description of the task: Receive the URL of the extracted audio file from the AudioExtractionAgent to be used for timestamp validation. Input(s) involved: [extracted_audio_file_url] Output token name: [extracted_audio_file_url] Task 2: Title task: Prompt user for timestamps using GPT4 Description of the task: Use GPT4 to prompt the user to input three specific timestamps (in seconds) for audio section extraction. Input(s) involved: None Output token name: [user_timestamps_prompt] Task 3: Title task: Receive user-provided timestamps Description of the task: Receive the user-provided timestamps as plain text for validation against the extracted audio file duration. Input(s) involved: [user_timestamps_prompt] Output token name: [user_provided_timestamps] Task 4: Title task: Validate first timestamp using GPT4 Description of the task: Use GPT4 to validate the first user-provided timestamp against the duration of the extracted audio file. If invalid, prompt the user for a valid timestamp and repeat validation. Input(s) involved: [extracted_audio_file_url], [user_provided_timestamps] Output token name: [validated_first_timestamp] Task 5: Title task: Validate second timestamp using GPT4 Description of the task: Use GPT4 to validate the second user-provided timestamp against the duration of the extracted audio file. If invalid, prompt the user for a valid timestamp and repeat validation. Input(s) involved: [extracted_audio_file_url], [user_provided_timestamps], [validated_first_timestamp] Output token name: [validated_second_timestamp] Task 6: Title task: Validate third timestamp using GPT4 Description of the task: Use GPT4 to validate the third user-provided timestamp against the duration of the extracted audio file. If invalid, prompt the user for a valid timestamp and repeat validation. Input(s) involved: [extracted_audio_file_url], [user_provided_timestamps], [validated_first_timestamp], [validated_second_timestamp] Output token name: [validated_third_timestamp] Task 7: Title task: Output validated timestamps Description of the task: Output the three validated timestamps as plain text for further processing in the audio sampling workflow. Input(s) involved: [validated_first_timestamp], [validated_second_timestamp], [validated_third_timestamp] Output token name: [validated_timestamps]

subagent2mermaid

<!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[Receive extracted audio file URL] --> B[Prompt user for timestamps using GPT4] B --> C[Receive user-provided timestamps] C --> D[Validate first timestamp using GPT4] D --> |Valid| E[Validate second timestamp using GPT4] D --> |Invalid| B E --> |Valid| F[Validate third timestamp using GPT4] E --> |Invalid| B F --> |Valid| G[Output validated timestamps] F --> |Invalid| B </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>

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SubAgent #3

claude 3 - subagent3

G) SUBAGENT 3: StemSeparationAgent - Name: StemSeparationAgent - Final Output: Instrumental and vocal stems for each of the three audio sections - Accomplishment: This subagent will take the extracted audio file and the three timestamps from the TimestampInputAgent. It will use the "Extract instrumental/vocal stems" task to separate the audio into instrumental and vocal stems. The stem separation will be performed for each of the three audio sections (e.g., 16 beats worth of audio) starting from the specified timestamps. The resulting stems will be saved as temporary files for further processing. - Sequence: Receive audio file and timestamps > Extract audio sections based on timestamps using the "Split/extract video by interval" task > Separate each section into instrumental and vocal stems using the "Extract instrumental/vocal stems" task > Save stems as temporary files

subagent1-refined

A) SUBAGENT SUMMARY: The StemSeparationAgent extracts instrumental and vocal stems from three specific audio sections of a YouTube video. B) FINAL TASK OUTPUT: 6 audio files (3 instrumental and 3 vocal stems) in .wav format, each containing 16 beats worth of audio from the specified timestamps. The files will be saved as temporary files for further processing. C) SUBAGENT INPUT: 1. The extracted audio file from the YouTube video (MP3 URL) 2. Three specific timestamps (in seconds) for audio section extraction D) SUBAGENT TASK SUMMARY: 1. Receive audio file (MP3 URL) and timestamps from the TimestampInputAgent 2. For each timestamp: a. Use the "Split/extract video by interval" task to extract 16 beats worth of audio starting from the timestamp b. Save the extracted audio section as a temporary .wav file 3. For each extracted audio section: a. Use the "Extract instrumental/vocal stems" task to separate the audio into instrumental and vocal stems b. Save the resulting stems as temporary .wav files 4. Return the URLs of the 6 temporary .wav files (3 instrumental and 3 vocal stems) E) SILOS: Silo 1: Audio Section Extraction 1. Input: Audio file (MP3 URL) and timestamps 2. Task: "Split/extract video by interval" to extract 16 beats worth of audio for each timestamp 3. Output: 3 temporary .wav files containing the extracted audio sections Silo 2: Stem Separation 1. Input: 3 temporary .wav files containing the extracted audio sections 2. Task: "Extract instrumental/vocal stems" to separate each audio section into instrumental and vocal stems 3. Output: 6 temporary .wav files (3 instrumental and 3 vocal stems) F) DETAILED SUBAGENT WORKFLOW: 1. Receive audio file (MP3 URL) and timestamps from the TimestampInputAgent 2. Silo 1: Audio Section Extraction - For each timestamp: - Use the "Split/extract video by interval" task to extract 16 beats worth of audio starting from the timestamp - Input: Audio file (MP3 URL), start time (timestamp), and duration (16 beats) - Output: Temporary .wav file containing the extracted audio section - Save the extracted audio section as a temporary .wav file 3. Silo 2: Stem Separation - For each extracted audio section: - Use the "Extract instrumental/vocal stems" task to separate the audio into instrumental and vocal stems - Input: Temporary .wav file containing the extracted audio section - Output: 2 temporary .wav files (1 instrumental and 1 vocal stem) - Save the resulting stems as temporary .wav files 4. Return the URLs of the 6 temporary .wav files (3 instrumental and 3 vocal stems) to the next subagent in the workflow (BeatDetectionAgent) This detailed workflow breaks down the StemSeparationAgent into two silos, each focusing on a specific part of the process. The first silo extracts the audio sections based on the provided timestamps, while the second silo separates each audio section into instrumental and vocal stems. By saving the intermediate results as temporary .wav files, the subagent ensures a smooth flow of data between the tasks and silos.

subagent1refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS): 1. Receive audio file (MP3 URL) and timestamps from the TimestampInputAgent 2. Silo 1: Audio Section Extraction - For timestamp 1: - Use the "Split/extract video by interval" task to extract 16 beats worth of audio starting from timestamp 1 - Input: Audio file (MP3 URL), start time (timestamp 1), and duration (16 beats) - Output: Temporary .wav file containing the extracted audio section 1 - Save the extracted audio section 1 as a temporary .wav file - For timestamp 2: - Use the "Split/extract video by interval" task to extract 16 beats worth of audio starting from timestamp 2 - Input: Audio file (MP3 URL), start time (timestamp 2), and duration (16 beats) - Output: Temporary .wav file containing the extracted audio section 2 - Save the extracted audio section 2 as a temporary .wav file - For timestamp 3: - Use the "Split/extract video by interval" task to extract 16 beats worth of audio starting from timestamp 3 - Input: Audio file (MP3 URL), start time (timestamp 3), and duration (16 beats) - Output: Temporary .wav file containing the extracted audio section 3 - Save the extracted audio section 3 as a temporary .wav file 3. Silo 2: Stem Separation - For extracted audio section 1: - Use the "Extract instrumental/vocal stems" task to separate the audio into instrumental and vocal stems - Input: Temporary .wav file containing the extracted audio section 1 - Output: 2 temporary .wav files (1 instrumental and 1 vocal stem for section 1) - Save the resulting stems as temporary .wav files - For extracted audio section 2: - Use the "Extract instrumental/vocal stems" task to separate the audio into instrumental and vocal stems - Input: Temporary .wav file containing the extracted audio section 2 - Output: 2 temporary .wav files (1 instrumental and 1 vocal stem for section 2) - Save the resulting stems as temporary .wav files - For extracted audio section 3: - Use the "Extract instrumental/vocal stems" task to separate the audio into instrumental and vocal stems - Input: Temporary .wav file containing the extracted audio section 3 - Output: 2 temporary .wav files (1 instrumental and 1 vocal stem for section 3) - Save the resulting stems as temporary .wav files 4. Return the URLs of the 6 temporary .wav files (3 instrumental and 3 vocal stems) to the next subagent in the workflow (BeatDetectionAgent) In this full task breakdown, the repetitions for each timestamp and extracted audio section are explicitly listed. This ensures that the subagent will perform the necessary tasks for all three timestamps and their corresponding audio sections, resulting in a total of 6 temporary .wav files (3 instrumental and 3 vocal stems) as the final output.

Subagent1 task-ID-numbers3

34,22

subagent3-output-variables

G) FULL TASKS (INCLUDING REPEATING TASKS): 1. Receive audio file (MP3 URL) and timestamps from the TimestampInputAgent - Input: [audio-file-url], [timestamp1], [timestamp2], [timestamp3] 2. Silo 1: Audio Section Extraction - For timestamp 1: - Use the "Split/extract video by interval" task to extract 16 beats worth of audio starting from timestamp 1 - Input: [audio-file-url], [timestamp1], [duration-16-beats] - Output: [audio-section1-wav] - Save the extracted audio section 1 as a temporary .wav file - For timestamp 2: - Use the "Split/extract video by interval" task to extract 16 beats worth of audio starting from timestamp 2 - Input: [audio-file-url], [timestamp2], [duration-16-beats] - Output: [audio-section2-wav] - Save the extracted audio section 2 as a temporary .wav file - For timestamp 3: - Use the "Split/extract video by interval" task to extract 16 beats worth of audio starting from timestamp 3 - Input: [audio-file-url], [timestamp3], [duration-16-beats] - Output: [audio-section3-wav] - Save the extracted audio section 3 as a temporary .wav file 3. Silo 2: Stem Separation - For extracted audio section 1: - Use the "Extract instrumental/vocal stems" task to separate the audio into instrumental and vocal stems - Input: [audio-section1-wav] - Output: [instrumental-stem1-wav], [vocal-stem1-wav] - Save the resulting stems as temporary .wav files - For extracted audio section 2: - Use the "Extract instrumental/vocal stems" task to separate the audio into instrumental and vocal stems - Input: [audio-section2-wav] - Output: [instrumental-stem2-wav], [vocal-stem2-wav] - Save the resulting stems as temporary .wav files - For extracted audio section 3: - Use the "Extract instrumental/vocal stems" task to separate the audio into instrumental and vocal stems - Input: [audio-section3-wav] - Output: [instrumental-stem3-wav], [vocal-stem3-wav] - Save the resulting stems as temporary .wav files 4. Return the URLs of the 6 temporary .wav files (3 instrumental and 3 vocal stems) to the next subagent in the workflow (BeatDetectionAgent) - Output: [instrumental-stem1-wav-url], [vocal-stem1-wav-url], [instrumental-stem2-wav-url], [vocal-stem2-wav-url], [instrumental-stem3-wav-url], [vocal-stem3-wav-url] H) OUTPUT TOKENS - [audio-file-url]: The URL of the input audio file (MP3) - [timestamp1]: The first timestamp for audio extraction - [timestamp2]: The second timestamp for audio extraction - [timestamp3]: The third timestamp for audio extraction - [duration-16-beats]: The duration of audio to extract (16 beats) - [audio-section1-wav]: Temporary .wav file containing the extracted audio section 1 - [audio-section2-wav]: Temporary .wav file containing the extracted audio section 2 - [audio-section3-wav]: Temporary .wav file containing the extracted audio section 3 - [instrumental-stem1-wav]: Temporary .wav file containing the instrumental stem for section 1 - [vocal-stem1-wav]: Temporary .wav file containing the vocal stem for section 1 - [instrumental-stem2-wav]: Temporary .wav file containing the instrumental stem for section 2 - [vocal-stem2-wav]: Temporary .wav file containing the vocal stem for section 2 - [instrumental-stem3-wav]: Temporary .wav file containing the instrumental stem for section 3 - [vocal-stem3-wav]: Temporary .wav file containing the vocal stem for section 3 - [instrumental-stem1-wav-url]: URL of the temporary .wav file containing the instrumental stem for section 1 - [vocal-stem1-wav-url]: URL of the temporary .wav file containing the vocal stem for section 1 - [instrumental-stem2-wav-url]: URL of the temporary .wav file containing the instrumental stem for section 2 - [vocal-stem2-wav-url]: URL of the temporary .wav file containing the vocal stem for section 2 - [instrumental-stem3-wav-url]: URL of the temporary .wav file containing the instrumental stem for section 3 - [vocal-stem3-wav-url]: URL of the temporary .wav file containing the vocal stem for section 3

subagent3refined-repeating-json-core

G) TASKFLOW SUMMARY 1. Title task: Extract audio sections from timestamps Description of the task: Use the provided timestamps to extract 16 beats worth of audio from each timestamp, saving each extracted section as a temporary .wav file. Input(s) involved: Audio file (MP3 URL), timestamp 1, timestamp 2, timestamp 3 Output token name: Temporary .wav files containing extracted audio sections 1, 2, and 3 2. Title task: Separate instrumental and vocal stems for section 1 Description of the task: Use stem separation techniques to extract instrumental and vocal stems from the extracted audio section 1, saving each stem as a temporary .wav file. Input(s) involved: Temporary .wav file containing extracted audio section 1 Output token name: Temporary .wav files (1 instrumental and 1 vocal stem for section 1) 3. Title task: Separate instrumental and vocal stems for section 2 Description of the task: Use stem separation techniques to extract instrumental and vocal stems from the extracted audio section 2, saving each stem as a temporary .wav file. Input(s) involved: Temporary .wav file containing extracted audio section 2 Output token name: Temporary .wav files (1 instrumental and 1 vocal stem for section 2) 4. Title task: Separate instrumental and vocal stems for section 3 Description of the task: Use stem separation techniques to extract instrumental and vocal stems from the extracted audio section 3, saving each stem as a temporary .wav file. Input(s) involved: Temporary .wav file containing extracted audio section 3 Output token name: Temporary .wav files (1 instrumental and 1 vocal stem for section 3) 5. Title task: Return URLs of temporary .wav files to next subagent Description of the task: Compile the URLs of the 6 temporary .wav files (3 instrumental and 3 vocal stems) and pass them to the next subagent (BeatDetectionAgent) in the workflow. Input(s) involved: Temporary .wav files (3 instrumental and 3 vocal stems) Output token name: URLs of temporary .wav files

subagent3mermaid

<!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"> ```mermaid graph TD A[Receive audio file and timestamps] -->|Audio file, timestamp 1| B(Extract audio section 1) A -->|Audio file, timestamp 2| C(Extract audio section 2) A -->|Audio file, timestamp 3| D(Extract audio section 3) B --> E(Save extracted audio section 1 as temporary .wav file) C --> F(Save extracted audio section 2 as temporary .wav file) D --> G(Save extracted audio section 3 as temporary .wav file) E --> H(Separate instrumental and vocal stems for section 1) F --> I(Separate instrumental and vocal stems for section 2) G --> J(Separate instrumental and vocal stems for section 3) H --> K(Save instrumental stem for section 1 as temporary .wav file) H --> L(Save vocal stem for section 1 as temporary .wav file) I --> M(Save instrumental stem for section 2 as temporary .wav file) I --> N(Save vocal stem for section 2 as temporary .wav file) J --> O(Save instrumental stem for section 3 as temporary .wav file) J --> P(Save vocal stem for section 3 as temporary .wav file) K & L & M & N & O & P --> Q[Return URLs of temporary .wav files to next subagent] ``` </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>

prompts-required

Based on the components listed above, here are the unique prompts that would be required for this agent workflow: Task: Audio Extraction Output of the task: Python code to download the audio from a given YouTube video URL Description: Write a Python script that takes a YouTube video URL as input and downloads the audio from the video using a library like youtube-dl or pytube. The script should save the audio as a separate file. Task: Stem Separation Output of the task: Python code to separate the audio into instrumental and vocal stems Description: Write a Python script that takes an audio file as input and separates it into instrumental and vocal stems using a library like Spleeter or Open-Unmix. The script should save the separated stems as separate audio files. Task: Audio Section Extraction Output of the task: Python code to extract 16 beats worth of audio from specified timestamps Description: Write a Python script that takes an audio file and three timestamps (in seconds) as input. For each timestamp, the script should extract 16 beats worth of audio starting from that timestamp. The script should do this for both the instrumental and vocal stems, resulting in 6 separate audio sections (3 instrumental and 3 vocal). The audio sections should be saved as separate files. Task: Beat Detection and Tempo Estimation Output of the task: Python code to detect beatpoints and estimate tempo of an audio file Description: Write a Python script that takes an audio file as input and detects the beatpoints and estimates the tempo (in BPM) using a library like librosa or madmom. The script should return the detected beatpoints and the estimated tempo. Task: Sample Chopping Output of the task: Python code to chop audio sections into samples of varying lengths Description: Write a Python script that takes an audio file, detected beatpoints, and tempo as input. The script should chop the audio into samples of varying lengths (1, 2, 4, and 8 beats) based on the beatpoints and tempo. The script should use a library like pydub or sox for audio manipulation and save the chopped samples as separate audio files. Task: Sample Analysis and Ranking Output of the task: Python code to analyze and rank audio samples based on features Description: Write a Python script that takes a directory of audio samples as input. The script should analyze each sample using librosa features like average amplitude, spectral centroid, or other relevant metrics. Based on the analysis results, the script should score and rank the samples. The script should return a list of ranked samples. Task: Sample Filtering Output of the task: Python code to filter samples based on ranking and select top samples Description: Write a Python script that takes a list of ranked audio samples and a desired number of samples as input. The script should filter the samples based on the ranking and select the top N samples (where N is the desired number of samples). The script should return the list of selected samples. Task: File Saving and Zipping Output of the task: Python code to save selected samples as .wav files and create a zip file Description: Write a Python script that takes a list of selected audio samples as input. The script should save each sample as an individual .wav file and then package all the .wav files into a single zip file. The script should return the path to the generated zip file. These prompts cover the core components required for the agent workflow. The prompts aim to generate Python code snippets that can be integrated into the overall workflow to achieve the desired functionality.