Hearted Youtube comments on AI Search (@theAIsearch) channel.

  1. 5100
  2. 2500
  3. 2400
  4. 1400
  5. 1400
  6. 1300
  7. 1300
  8. 1200
  9. 1200
  10. 1100
  11. 948
  12. 906
  13. So I made some discoveries which could help some people. -Chunk: You should increase this if you experience any distortions or voice lag when gaming with this. This adds more graphics processing time, and if you set it longer it wont rush out bad audio. -Extra: This setting gives bonus CPU usage to help iron out the audio. I found that sometimes the changer would translate an F sound to an S sound, but adding a bit "extra" CPU to it (like the 8k setting or higher) fixes the problem. You don't want to max this out unless the only thing you're doing is using your voice, as maxing it out will use all or nearly all of your CPU. -Noise: I recommend using Sup2 option if you have an Air-Conditioner or other background noise. Sup1 didn't work as well and is probably for a different frequency range, so your millage may vary. When you start real time voice changing, you'll see some info in a box with millisecond timers. The thing to watch is the "res" time. If this time starts going up, from around 300ms it starts rising to a thousand then two thousand etc, this means the computer is unable to get the voice processing out in time, its being pushed back in priority you could say. The fix is to increase the Chunk, this will give it more time to process with the remainder of your resources, and switching it you should see the number start decreasing rapidly. If it doesn't just raise it even higher, and also again keep in mind that if you're doing something that is CPU intensive, you need to keep the Extra setting fairly low (like around 8k). I have a powerful computer (10 core i9 10900kf, with a reference 3080ti), and I found that if im going to play a "serious" game like GTAV or StarCitizen etc, its best to have the Chunk as high as 192, or 256, with the Extra set to 8192. If you're just on discord, or playing some very light game, you can crank the Extra up, and reduce the Chunk to maintain high quality audio but process it considerably faster. Hope this helps someone!! Good luck o/
    695
  14. 617
  15. 505
  16. 499
  17. 478
  18. 457
  19. 440
  20. 420
  21. 404
  22. 387
  23. 344
  24. 331
  25. I've been experimenting with this for a bit, and I'm disappointed by how vague and incomplete the English documentation on these settings is. In an effort to remedy this, here's my breakdown of each setting: Response threshold: Controls the noise gate. Any sound below the threshold is suppressed. This is used to prevent background noise and hiss from being turned into strange mumbling. Equivalent to "S. Threshold" in w-okada. Not applicable in RVC WebUI. Pitch settings: Applies a pitch offset to your input voice. Every multiple of 12 setting increases or decreases the voice by an octave. Adjustments by 1 increase or decrease by a semitone. Using whole octaves is primarily used to ensure you can sing in the same key. Equivalent to "TUNE" in w-okada. Equivalent to "Transpose" in RVC WebUI. Index rate: When an index file is provided, this slider augments the target voice by preserving more of its accent and less of the input voice (to reduce tone leakage). This is particularly useful for voices trained with a low epoch count (around 200-ish or less). If set too high, it can cause strange pronunciation artifacts. I usually find something around 0.30 to sound good, but it varies by voice model. Equivalent to "INDEX" in w-okada. Equivalent to "Search feature ratio" in RVC WebUI. Loudness factor: How little to preserve the loudness of the input performance. At 0, the loudness of the cloned voice should match the loudness of the input voice. At 1, the cloned voice will always be at full loudness. 0 is useful if you want to distinguish between whispers, talking, screaming, etc. 1 is useful to have the cloned voice always speak loudly and clearly, as loud as the loudest things it was trained on (which can have artifacts such as mic clipping depending on the training set). Values in-between provide partial volume control biased toward being louder, the closer you get to 1. There is no equivalent in w-okada. Equivalent to "volume envelope scaling" in RVC WebUI. Pitch detection algorithm: Different algorithms are better at different things. rmvpe is the current state-of-the-art and works fastest and usually with the highest quality. Equivalent to "F0 Det." in w-okada. Equivalent to "pitch extraction algorithm" in RVC WebUI. Sample length: The realtime voice changer works by sending small chunks of audio for quick conversion, then stitching them together. Longer sample lengths feed in longer chunks, making the stitches less obvious and reducing GPU requirements but increasing output latency. On a low end GPU, setting this too low will make the GPU unable to keep up and produces stutters. On a high end GPU, setting this too low will cause warbling as an artifact of stitching many overly-short chunks together. Equivalent to "CHUNK" in w-okada. Not applicable in RVC WebUI. Number of CPUs: Self explanatory. Note, however, that rmvpe is a GPU-based pitch extractor and should be relatively unaffected by this setting. There is no equivalent in w-okada. Not applicable in RVC WebUI. Fade length: The length between chunks to crossfade together. Longer may reduce warbling. Equivalent to "overlap" in w-okada advanced settings. Not applicable in RVC WebUI. Extra inference time: How much old audio to load into each chunk. The extra context usually improves voice quality for the generated chunk but is more demanding for the GPU. Equivalent to "EXTRA" in w-okada. Not applicable in RVC WebUI. Input noise reduction: Attempts to remove non-speech background noise from the input to prevent sounds from being turned into strange mumbling. Equivalent to "NOISE" in w-okada. Not applicable in RVC WebUI. Output noise reduction: Applies the same noise reduction to the output voice. Possibly good for poorly trained voices with lots of background noise. There is no equivalent in w-okada, but the usefulness of this setting is dubious. Not applicable in RVC WebUI. Input voice monitor: Lets you hear the voice audio being passed in to the voice changer, sent to the target output device. Useful to ensure you are passing in the audio you actually want or to passthrough your audio without voice changing. Comparable to "monitor" settings in w-okada. Not applicable in RVC WebUI. Output converted voice: Outputs the voice conversion to the target output device. Main features RVC realtime has that w-okoda doesn't: Loudness factor controls. W-okoda seems to always use a value of 0. Significantly lower CPU usage at equivalent performance settings, in my experience. Main features that w-okoda has that RVC realtime doesn't: No system to save model presets. Input/output gain is missing. Input noise reduction is less robust compared to w-okoda, which offers echo reduction and multiple noise suppression techniques. Unlike w-okoda, you cannot passthrough to the input mic, instead requiring the use of virtual audio cable to pass the cloned voice into voice calls and microphone recording programs. In w-okoda, when the mic loudness falls below the response threshold, the tool is paused until speech is once again loud enough, saving GPU and CPU resources. RVC realtime always passes audio whenever it is running. Unlike w-okoda, you cannot monitor the cloned voice while outputting it. You can work around this by using the "listen" feature in the Windows sounds panel on a virtual audio cable instead. No built-in recording functionality. Missing most of the settings in the w-okoda "advanced settings" menu. No way to choose which GPU to run the voice model on. You can get around this by setting CUDA_VISIBLE_DEVICES=# in a terminal before launching the tool from there, where # is the index of your target GPU (0, 1, 2, etc.).
    319
  26. 263
  27. 261
  28. 229
  29. 205
  30. 193
  31. 171
  32. 169
  33. 161
  34. 138
  35. 135
  36. 132
  37. 129
  38. 128
  39. 125
  40. 120
  41. 117
  42. 116
  43. 111
  44. 110
  45. 106
  46. 100
  47. 91
  48. 90
  49. 89
  50. 88
  51. 88
  52. 85
  53. 85
  54. 84
  55. 82
  56. 76
  57. 75
  58. 73
  59. 70
  60. 68
  61. 65
  62. 64
  63. 62
  64. 59
  65. 59
  66. 59
  67. 59
  68. 58
  69. 58
  70. 57
  71. 57
  72. 55
  73. 55
  74. 52
  75. 50
  76. 47
  77. 46
  78. 42
  79. 41
  80. 40
  81. 39
  82. 39
  83. 38
  84. 38
  85. 37
  86. 36
  87. 35
  88. 35
  89. 35
  90. 34
  91. 32
  92. 32
  93. 32
  94. 30
  95. 30
  96. 29
  97. 28
  98. 27
  99. 27
  100. 27
  101. 26
  102. 26
  103. 26
  104. 25
  105. 25
  106. 25
  107. 25
  108. 24
  109. 24
  110. 24
  111. 23
  112. 23
  113. 23
  114. 23
  115. 23
  116. 23
  117. 23
  118. 23
  119. 22
  120. 22
  121. 22
  122. 22
  123. 22
  124. 22
  125. 22
  126. 21
  127. 21
  128. 21
  129. 21
  130. 21
  131. 21
  132. 21
  133. 20
  134. 20
  135. 19
  136. 19
  137. 19
  138. 19
  139. 19
  140. 19
  141. 18
  142. 18
  143. 18
  144. 18
  145. 18
  146. 17
  147. 17
  148. 17
  149. 17
  150. 17
  151. 17
  152. 17
  153. 16
  154. 16
  155. 16
  156. 15
  157. 15
  158. 15
  159. 15
  160. 14
  161. 14
  162. 14
  163. 14
  164. 14
  165. 14
  166. 14
  167. 14
  168. 14
  169. 13
  170. 13
  171. 13
  172. 13
  173. 13
  174. 13
  175. 13
  176. 13
  177. 13
  178. 12
  179. 12
  180. 12
  181. 12
  182. 12
  183. 12
  184. 12
  185. 12
  186. 12
  187. 12
  188. 12
  189. 12
  190. 12
  191. 12
  192. 12
  193. 11
  194. 11
  195. 11
  196. 11
  197. 11
  198. 11
  199. 11
  200. 10
  201. 10
  202. 10
  203. 10
  204. 10
  205. 10
  206. 10
  207. 9
  208. 9
  209. 9
  210. 2:16 doubt it. Using it to cure cancer, yes, probable. To stop aging, maybe. But changing one's already developed features just through modifying DNA, doesn't make sense. Changing things like Eye color, primary or secondary sex characteristics, or things like that, doesn't and will never work through just editing DNA. I mean, firstly, you can't just edit the DNA of millions of cells, and even if, if you have bones of a specific size or "a Johnson", they won't disappear or get smaller just through editing the DNA, especially bones. A "Johnson" could grow smaller (like it sometimes happens because of certain hormones), but never disappear or change into female genitals. Features can grow, but not shrink (a little bit maybe, but not significantly). The cells are already there and the entire structure has already been built. If you want to change your primary sex characteristics, you'd still have to go through surgery and remove what you have, to then get something else (But I think with stem cells and stuff it could be possible far in the future to remove what you have and grow something new). Or if you want to grow smaller, you'd still have to remove the bones that are already there (editing the DNA could possibly make you grow more or make you stop growing, but not make you shrink). Editing the DNA isn't magic. It just changes what cells do, how they copy and what proteins they produce and stuff. It changes stuff on a cellular level and it changes how the body develops in the future. Also, in 2:31 you would change sex and not gender. Sex and gender are 2 different things. Gender is not changeable, you'd have to reprogram the brain for that. So you would change sexual characteristics and not gender.
    9
  211. 9
  212. 9
  213. 9
  214. 9
  215. 9
  216. 9
  217. 9
  218. 9
  219. 9
  220. 9
  221. 9
  222. 9
  223. 9
  224. 9
  225. 9
  226. 8
  227. 8
  228. 8
  229. 8
  230. 8
  231. 8
  232. 8
  233. 8
  234. 8
  235. 8
  236. 8
  237. 8
  238. 8
  239. 8
  240. 8
  241. 8
  242. 8
  243. 8
  244. 8
  245. 8
  246. 8
  247. 8
  248. 7
  249. 7
  250. 7
  251. 7
  252. 7
  253. 7
  254. 7
  255. 7
  256. 7
  257. 7
  258. 7
  259. 7
  260. 7
  261. 7
  262. 7
  263. 7
  264. 7
  265. 7
  266. 7
  267. 7
  268. 7
  269. 7
  270. 7
  271. 6
  272. 6
  273. 6
  274. 6
  275. 6
  276. 6
  277. 6
  278. 6
  279. 6
  280. 6
  281. 6
  282. 6
  283. 6
  284. 6
  285. 6
  286. 6
  287. 6
  288. 6
  289. 6
  290. 6
  291. 6
  292. 6
  293. 6
  294. 6
  295. 6
  296. 6
  297. 6
  298. 6
  299. 5
  300. 5
  301. 5
  302. 5
  303. 5
  304. 5
  305. 5
  306. 5
  307. 5
  308. 5
  309. 5
  310. 5
  311. 5
  312. 5
  313. 5
  314. 5
  315. 5
  316. 5
  317. 5
  318. 5
  319. 5
  320. 5
  321. 5
  322. 5
  323. 5
  324. 5
  325. 5
  326. 5
  327. 5
  328. 5
  329. 5
  330. 5
  331. 5
  332. 5
  333. 5
  334. 5
  335. 4
  336. 4
  337. 4
  338. 4
  339. 4
  340. 4
  341. 4
  342. 4
  343. 4
  344. 4
  345. 4
  346. 4
  347. 4
  348. 4
  349. 4
  350. 4
  351. 4
  352. 4
  353. 4
  354. 4
  355. 4
  356. 4
  357. 4
  358. 4
  359. 4
  360. 4
  361. 4
  362. 4
  363. 4
  364. 4
  365. 4
  366. 4
  367. 4
  368. 4
  369. 4
  370. 4
  371. 4
  372. 4
  373. 4
  374. 4
  375. 4
  376. 4
  377. 4
  378. 4
  379. 4
  380. 4
  381. 4
  382. 4
  383. 4
  384. 4
  385. 4
  386. 3
  387. 3
  388. 3
  389. 3
  390. 3
  391. 3
  392. 3
  393. 3
  394. 3
  395. 3
  396. 3
  397. 3
  398. 3
  399. 3
  400. 3
  401. 3
  402. 3
  403. 3
  404. 3
  405. 3
  406. 3
  407. 3
  408. 3
  409. 🎯 Key Takeaways for quick navigation: 00:00 🤖 Introduction to Udio and its limitations - Udio is a music generation tool that allows you to create songs with a single prompt. - Some people have complained about limitations of Udio, such as difficulty customizing the song, inconsistent results, and inability to repeat melodies/choruses. - However, the speaker claims you can actually do all of the above using Udio. 00:42 📝 Customizing lyrics inUdio - The speaker recommends writing your own lyrics instead of letting Udio autogenerate them. - You can break up the lyrics into sections like verse, chorus, etc. and import them into Udio. - Udio supports various metadata tags like intro, bridge, outro to structure the song. 02:16 🔁 Repeating melodies and choruses in Udio - You can get Udio to repeat the same melody for verses and choruses by using the "repeats" tag. - Matching the number of syllables in repeated sections helps Udio maintain the same melody. - Adding instrumental breaks and outros can also help control the song structure. 04:18 🎨 Customizing vocals and harmonies in Udio - You can use curly brackets to make the singer echo certain words. - Udio can also generate speech/voiceovers and even stand-up comedy routines. 09:18 🎸 Effective music styles for Udio - Certain music styles like country and bluegrass work particularly well with Udio. - Other settings like BPM, key, and time signature don't reliably work in Udio's prompts. - Keeping the music style simple (e.g. one or two genres) tends to yield better results. 15:24 🤖 Sponsor: Synth Flow AI Assistants - Synth Flow allows you to create customizable AI voice assistants for tasks like customer support. - You can choose from various voices, clone your own voice, and automate call handling features. - Synth Flow offers flexible pricing and white-labeling options. 25:01 🎶 Effective music styles for Udio (continued) - Country and bluegrass styles work particularly well with Udio, producing realistic and well-mixed results. - Broadway musical style also works effectively with Udio, generating dynamic and expressive vocals. - Genres like pop, EDM, and R&B tend to yield more mediocre results, where Spleeter may be a better option. 26:08 🚫 Limitations of Udio - Udio cannot generate songs in the specific style of a famous artist, as it will replace the artist name with generic keywords. - There is no reliable way to control settings like BPM, key, or time signature through Udio's prompts. - Udio may struggle to pronounce certain acronyms or hard-to-pronounce words correctly. 27:32 🔠 Controlling pronunciation of acronyms and complex words - Separating letters of acronyms with hyphens can help Udio pronounce them correctly. - Using phonetic spellings for difficult words (e.g. "kinwa" instead of "quinoa") can also improve pronunciation. - Adding hyphens between words can introduce pauses to improve the phrasing and delivery. Made with HARPA AI
    3
  410. 3
  411. 3
  412. 3
  413. 3
  414. 3
  415. 3
  416. 3
  417. 3
  418. 3
  419. 3
  420. 3
  421. 3
  422. 3
  423. 3
  424. 3
  425. 3
  426. 3
  427. 3
  428. 3
  429. 3
  430. 3
  431. 3
  432. 3
  433. 3
  434. 3
  435. 3
  436. 3
  437. 3
  438. 3
  439. 3
  440. 3
  441. 3
  442. 3
  443. 3
  444. 3
  445. 3
  446. 3
  447. 3
  448. 3
  449. 3
  450. 3
  451. 3
  452. 3
  453. 3
  454. 3
  455. 3
  456. 3
  457. 3
  458. 3
  459. 3
  460. 3
  461. 3
  462. 3
  463. 3
  464. 3
  465. 3
  466. 3
  467. 3
  468. 3
  469. 3
  470. 3
  471. 3
  472. 3
  473. 3
  474. 3
  475. 3
  476. 3
  477. 3
  478. 3
  479. 3
  480. 3
  481. 3
  482. 3
  483. 3
  484. 3
  485. 3
  486. 3
  487. 3
  488. 3
  489. 3
  490. 3
  491. 3
  492. 3
  493. 3
  494. 3
  495. 3
  496. 3
  497. 3
  498. 3
  499. 3
  500. 3
  501. 3
  502. 3
  503. 2
  504. 2
  505. 2
  506. 2
  507. 2
  508. 2
  509. 2
  510. 2
  511. 2
  512. 2
  513. 2
  514. 2
  515. 2
  516. 2
  517. 2
  518. 2
  519. 2
  520. 2
  521. 2
  522. 2
  523. 2
  524. 2
  525. 2
  526. 2
  527. 2
  528. 2
  529. 2
  530. 2
  531. 2
  532. 2
  533. 2
  534. 2
  535. 2
  536. 2
  537. 2
  538. 2
  539. 2
  540. 2
  541. 2
  542. 2
  543. 2
  544. 2
  545. 2
  546. 2
  547. 2
  548. 2
  549. 2
  550. 2
  551. 2
  552. 2
  553. 2
  554. 2
  555. 2
  556. 2
  557. 2
  558. 2
  559. 2
  560. 2
  561. 2
  562. 2
  563. 2
  564. 2
  565. 2
  566. 2
  567. 2
  568. 2
  569. 2
  570. 2
  571. 2
  572. 2
  573. 2
  574. 2
  575. 2
  576. 2
  577. 2
  578. 2
  579. 2
  580. 2
  581. 2
  582. 2
  583. 2
  584. 2
  585. 2
  586. 2
  587. 2
  588. 2
  589. 2
  590. 2
  591. 2
  592. 2
  593. 2
  594. 2
  595. 2
  596. 2
  597. 2
  598. 2
  599. 2
  600. 2
  601. 2
  602. 2
  603. 2
  604. 2
  605. 2
  606. 2
  607. 2
  608. 2
  609. 2
  610. 2
  611. 2
  612. 2
  613. 2
  614. 2
  615. 2
  616. 2
  617. 2
  618. 2
  619. 2
  620. 2
  621. 2
  622. 2
  623. 2
  624. 2
  625. 2
  626. 2
  627. 2
  628. 2
  629. 2
  630. 2
  631. 2
  632. 2
  633. 2
  634. 2
  635. 2
  636. 2
  637. 2
  638. 2
  639. 2
  640. 2
  641. 2
  642. 2
  643. 2
  644. 2
  645. 2
  646. 2
  647. 2
  648. 2
  649. 2
  650. 2
  651. 2
  652. 2
  653. 2
  654. 2
  655. 2
  656. 2
  657. 2
  658. 2
  659. 2
  660. 2
  661. 2
  662. 2
  663. 2
  664. 2
  665. 2
  666. 2
  667. 2
  668. 2
  669. 2
  670. 2
  671. 2
  672. 2
  673. 2
  674. 2
  675. 2
  676. 2
  677. 2
  678. 2
  679. 2
  680. 2
  681. 2
  682. 2
  683. 2
  684. 2
  685. 2
  686. 2
  687. 2
  688. 2
  689. 2
  690. 2
  691. 2
  692. 2
  693. 2
  694. 2
  695. 2
  696. 2
  697. 2
  698. 2
  699. 2
  700. 2
  701. 2
  702. 2
  703. 2
  704. 2
  705. 2
  706. 2
  707. 2
  708. 2
  709. 2
  710. 2
  711. 2
  712. 2
  713. 2
  714. 2
  715. 2
  716. 2
  717. 2
  718. 2
  719. 2
  720. 2
  721. 2
  722. 2
  723. 2
  724. 2
  725. 2
  726. 2
  727. 2
  728. 2
  729. 2
  730. 2
  731. 2
  732. 2
  733. 2
  734. 2
  735. 2
  736. 2
  737. 2
  738. 2
  739. 2
  740. 2
  741. 2
  742. 2
  743. 2
  744. 2
  745. 2
  746. 2
  747. 2
  748. 2
  749. 2
  750. 2
  751. 2
  752. 2
  753. 2
  754. 2
  755. 2
  756. 2
  757. 2
  758. 2
  759. 2
  760. 2
  761. 2
  762. 2
  763. 2
  764. 2
  765. 2
  766. 2
  767. 2
  768. 2
  769. 2
  770. 2
  771. 2
  772. 2
  773. 2
  774. 2
  775. 2
  776. 2
  777. 2
  778. 2
  779. 2
  780. 2
  781. 2
  782. 2
  783. 2
  784. 2
  785. 2
  786. 2
  787. 2
  788. 2
  789. 2
  790. 2
  791. 2
  792. 2
  793. 2
  794. 2
  795. 2
  796. 2
  797. 2
  798. 2
  799. 2
  800. 2
  801. 2
  802. 2
  803. 2
  804. 1
  805. 1
  806. 1
  807. 1
  808. 1
  809. 1
  810. 1
  811. 1
  812. 1
  813. 1
  814. 1
  815. 1
  816. 1
  817. 1
  818. 1
  819. 1
  820. 1
  821. 1
  822. 1
  823. 1
  824. 1
  825. 1
  826. 1
  827. 1
  828. 1
  829. 1
  830. 1
  831. 1
  832. 1
  833. 1
  834. 1
  835. 1
  836. 1
  837. 1
  838. 1
  839. 1
  840. 1
  841. 1
  842. 1
  843. 1
  844. 1
  845. 1
  846. 1
  847. 1
  848. 1
  849. 1
  850. 1
  851. 1
  852. 1
  853. 1
  854. 1
  855. 1
  856. 1
  857. 1
  858. 1
  859. 1
  860. 1
  861. 1
  862. 1
  863. 1
  864. 1
  865. 1
  866. 1
  867. 1
  868. 1
  869. 1
  870. 1
  871. 1
  872. 1
  873. 1
  874. 1
  875. 1
  876. 1
  877. 1
  878. 1
  879. 1
  880. 1
  881. 1
  882. 1
  883. 1
  884. 1
  885. 1
  886. 1
  887. 1
  888. 1
  889. 1
  890. 1
  891. 1
  892. 1
  893. 1
  894. 1
  895. 1
  896. 1
  897. 1
  898. 1
  899. 1
  900. 1
  901. 1
  902. 1
  903. 1
  904. 1
  905. 1
  906. 1
  907. 1
  908. 1
  909. 1
  910. 1
  911. 1
  912. 1
  913. 1
  914. 1
  915. 1
  916. 1
  917. 1
  918. 1
  919. 1
  920. 1
  921. 1
  922. 1
  923. 1
  924. 1
  925. 1
  926. 1
  927. 1
  928. 1
  929. 1
  930. 1
  931. 1
  932. 1
  933. 1
  934. 1
  935. 1
  936. 1
  937. 1
  938. 1
  939. 1
  940. 1
  941. 1
  942. 1
  943. 1
  944. 1
  945. 1
  946. 1
  947. 1
  948. 1
  949. 1
  950. 1
  951. 1
  952. 1
  953. 1
  954. 1
  955. 1
  956. 1
  957. 1
  958. 1
  959. 1
  960. 1
  961. 1
  962. 1
  963. 1
  964. 1
  965. 1
  966. 1
  967. 1
  968. 1
  969. 1
  970. 1
  971. 1
  972. 1
  973. 1
  974. 1
  975. 1
  976. 1
  977. 1
  978. 1
  979. 1
  980. 1
  981. 1
  982. 1
  983. 1
  984. 1
  985. 1
  986. 1
  987. 1
  988. 1
  989. 1
  990. 1
  991. 1
  992. 1
  993. 1
  994. 1
  995. 1
  996. 1
  997. 1
  998. 1
  999. 1
  1000. 1
  1001. 1
  1002. 1
  1003. 1
  1004. 1
  1005. 1
  1006. 1
  1007. 1
  1008. 1
  1009. 1
  1010. 1
  1011. 1
  1012. 1
  1013. 1
  1014. 1
  1015. 1
  1016. 1
  1017. 1
  1018. 1
  1019. 1
  1020. 1
  1021. 1
  1022. 1
  1023. 1
  1024. 1
  1025. 1
  1026. 1
  1027. 1
  1028. 1
  1029. 1
  1030. 1
  1031. 1
  1032. 1
  1033. 1
  1034. 1
  1035. 1
  1036. 1
  1037. 1
  1038. 1
  1039. 1
  1040. 1
  1041. 1
  1042. 1
  1043. 1
  1044. 1
  1045. 1
  1046. 1
  1047. 1
  1048. 1
  1049. 1
  1050. 1
  1051. 1
  1052. 1
  1053. 1
  1054. 1
  1055. 1
  1056. 1
  1057. 1
  1058. 1
  1059. 1
  1060. 1
  1061. 1
  1062. 1
  1063. 1
  1064. 1
  1065. 1
  1066. 1
  1067. 1
  1068. 1
  1069. 1
  1070. 1
  1071. 1
  1072. 1
  1073. 1
  1074. 1
  1075. 1
  1076. 1
  1077. 1
  1078. 1
  1079. 1
  1080. 1
  1081. 1
  1082. 1
  1083. 1
  1084. 1
  1085. 1
  1086. 1
  1087. 1
  1088. 1
  1089. 1
  1090. 1
  1091. 1
  1092. 1
  1093. 1
  1094. 1
  1095. 1
  1096. 1
  1097. 1
  1098. 1
  1099. 1
  1100. 1
  1101. 1
  1102. 1
  1103. 1
  1104. 1
  1105. 1
  1106. 1
  1107. 1
  1108. 1
  1109. 1
  1110. 1
  1111. 1
  1112. 1
  1113. 1
  1114. 1
  1115. 1
  1116. 1
  1117. 1
  1118. 1
  1119. 1
  1120. 1
  1121. 1
  1122. 1
  1123. 1
  1124. 1
  1125. 1
  1126. 1
  1127. 1
  1128. 1
  1129. 1
  1130. 1
  1131. 1
  1132. 1
  1133. 1
  1134. 1
  1135. 1
  1136. 1
  1137. 1
  1138. 1
  1139. 1
  1140. 1
  1141. 1
  1142. 1
  1143. 1
  1144. 1
  1145. 1
  1146. 1
  1147. 1
  1148. 1
  1149. 1
  1150. 1
  1151. 1
  1152. 1
  1153. 1
  1154. 1
  1155. 1
  1156. 1
  1157. 1
  1158. 1
  1159. 1
  1160. 1
  1161. 1
  1162. 1
  1163. 1
  1164. 1
  1165. 1
  1166. 1
  1167. 1
  1168. 1
  1169. 1
  1170. 1
  1171. 1
  1172. 1
  1173. 1
  1174. 1
  1175. 1
  1176. 1
  1177. 1
  1178. 1
  1179. 1
  1180. 1
  1181. 1
  1182. 1
  1183. 1
  1184. 1
  1185. 1
  1186. 1
  1187. 1
  1188. 1
  1189. 1
  1190. 1
  1191. 1
  1192. 1
  1193. 1
  1194. 1
  1195. 1
  1196. 1
  1197. 1
  1198. 1
  1199. 1
  1200. 1
  1201. 1
  1202. 1
  1203. 1
  1204. 1
  1205. 1
  1206. 1
  1207. 1
  1208. 1
  1209. 1
  1210. 1
  1211. 1
  1212. 1
  1213. 1
  1214. 1
  1215. 1
  1216. 1
  1217. 1
  1218. 1
  1219. 1
  1220. 1
  1221. 1
  1222. 1
  1223. 1
  1224. 1
  1225. 1
  1226. 1
  1227. 1
  1228. 1
  1229. 1
  1230. 1
  1231. 1
  1232. 1
  1233. 1
  1234. 1
  1235. 1
  1236. 1
  1237. 1
  1238. 1
  1239. 1
  1240. 1
  1241. 1
  1242. 1
  1243. 1
  1244. 1
  1245. 1
  1246. 1
  1247. 1
  1248. 1
  1249. 1
  1250. 1
  1251. 1
  1252. 1
  1253. 1
  1254. 1
  1255. 1
  1256. 1
  1257. 1
  1258. 1
  1259. 1
  1260. 1
  1261. 1
  1262. 1
  1263. 1
  1264. 1
  1265. 1
  1266. 1
  1267. 1
  1268. 1
  1269. 1
  1270. 1
  1271. 1
  1272. 1
  1273. 1
  1274. 1
  1275. 1
  1276. 1
  1277. 1
  1278. 1
  1279. 1
  1280. 1
  1281. 1
  1282. 1
  1283. 1
  1284. 1
  1285. 1
  1286. 1
  1287. 1
  1288. 1
  1289. 1
  1290. 1
  1291. 1
  1292. 1
  1293. 1
  1294. 1
  1295. 1
  1296. 1
  1297. 1
  1298. 1
  1299. 1
  1300. 1
  1301. 1
  1302. 1
  1303. 1
  1304. 1
  1305. 1
  1306. 1
  1307. 1
  1308. 1
  1309. 1
  1310. 1
  1311. 1
  1312. 1
  1313. 1
  1314. 1
  1315. Here are 6 more examples contrasting contradictory formulations in classical frameworks with potential non-contradictory counterparts using infinitesimal/monadological perspectives: 21) The Black Hole Information Paradox Contradictory: According to classical black hole models, as matter crosses the event horizon, all information about its initial quantum state is irretrievably lost to external observers, violating unitary evolution in quantum theory. Non-Contradictory Possibility: Monadic Black Hole Complementarity |Ψ>exterior = Σn cn |Un>horizon |Ψ>interior = Σn cn |Vn>trans-horizon Information is distributed across multiple monadic realizations |Un>, |Vn> allowing unitarity across interior/exterior Split holographic descriptions. 22) The Cosmological Constant Problem Contradictory: Quantum field theory predicts a vast unobserved vacuum energy density ρvac ≈ 10^92 g/cm3, differing from the observed dark energy value by ~120 orders of magnitude - an unexplainable contradiction. Non-Contradictory Possibility: Infinitesimal Nonlinear Vacuum Monadic Functor Λ = F[SαNS, αU, (m, q, n, ...)] Treat the CC Λ as a relational functor between physical vacuum states SαNS and an algebraic infinitesimal coefficient bundle (m, q, n, ....) over a background U(1) field αU. 23) The Foundations of Arithmetic Contradictory: Peano's Axioms contain implicit circularity, while naive set theory axiomatizations lead to paradoxes like Russell's Paradox about the set of all sets that don't contain themselves. Non-Contradictory Possibility: Homotopy Type Theory / Univalent Foundations N ≃ W∞-Grpd (Natural numbers as objects in ∞-groupoids) S(n) ≃ n = n+1 (Successor is path identification) Let Z ≃ Grpd[N, Π1(S1)] (Integers from N and winding paths) Defining arithmetic objects categorically using homotopy theory and mapping into higher toposes avoids the self-referential paradoxes. 24) The Laden Non-Miraculuous Fly Paradox Contradictory: In Newtonian mechanics, the work required to move an object between two points depends on the path taken. However, paradoxically, one can construct scenarios where the work appears to depend on the load distribution along a perfectly rigid, inextensible cable. Non-Contradictory Possibility: Infinitesimal Nonholonomic Constrained Mechanics L = T(x, v) + λ(x, y)f(x, y) (Augmented Lagrangian with constraints) Trajectories are monadic realizations obtained by integrating differential constraints f(x, y) = 0 using infinitesimals, resolving load dependence ambiguities. 25) The Berry Paradox Contradictory: Consider the assertion, "The smallest positive integer not definable in under sixty letters." This statement references itself in a self-contradictory way, paradoxically seeming to both define and not define that integer. Non-Contradictory Possibility: Intensional Pluriverse-Valued Realizability Semantics ⌈φ⌉ = {Vn(φ) | n ∈ N} (Intension = monadic realization pluriverse) Valuation: Let Vn(φ) = 1 iff n ∈ Ext(φ) (Realization pluriverse = extension) By representing assertions φ pluralistically as parallelized infinitesimal realizability intensions across monads n, rather than single extensions, self-referential definability paradoxes can be avoided. 26) The Burali-Forti Paradox Contradictory: In classical naive set theory, define W to be the set of all ordinal numbers, then the paradox is that W itself would have to be an ordinal strictly greater than all ordinals it contains. Non-Contradictory Possibility: Algebraic Set Theory / ETCS Define FSets = (Sets, Imgs, Eqs, Cmpls, Comps) (Algebraic set data) Ord = Ind(Images) (Ordinals from inclusion inductive attitudes) Universe is represented infinitely stratified U = (Ui)i∈N (Levels of sets) Replacing set-theoretic foundations with categorical algebraic set theory allows defining ordinals without falling into paradoxes about a "set of all ordinals." In each case, the classical formulation encounters paradoxes, contradictions or nonsensical solutions because it depends on flawed assumptions or over-idealizations like: - Absolute separability of subsystem states - Primacy of mathematical/geometric infinities - Bivalent truth valuations and extensions - Over-simplified set-theoretic axiomatizations - Unconstrained self-reference The proposed non-contradictory infinitesimal/monadological alternatives resolve these issues by: - Treating observations relationally across monadic perspectives - Using infinitesimals and stratifications to avoid true infinities - Adopting pluralistically-valued intensions and realizability semantics - Representing sets/spaces algebraically and categorically - Encoding self-reference internally using holographic principles By systematically upgrading our models to realistically reflect the perspectival unified pluralities inherent to subjective experience, these frameworks eliminate paradoxes from first principles - paving the way for fully self-coherent analytic representations across physics and mathematics. The vision is to finally bring our symbolic knowledge constructions into structural resonance with the coherent integrated metaphysical truth defining reality itself. Monadological infinitesimal foundations provide an escape from the artificial inconsistencies plaguing our excessively reductionist classical idealisations. A new fully general, paradox-free mathematics beckons.
    1
  1316. 1
  1317. 1
  1318. 1
  1319. 1
  1320. 1
  1321. @ai-tools-search -- I've had ChatGPT generate a script that doesn't require the pandas module (so it works on a stock Windows Store Python 3 installation). Note the parameter in the with open() statement "encoding='utf-8-sig'" - this prevents potential errors where Byte-Order-Mark characters appear before the first column heading in the CSV, i.e. 'Topic'. Here's the script: import csv import os def rename_files(csv_file_path): # Check if the provided file exists if not os.path.isfile(csv_file_path): print("File not found. Please provide a valid CSV file path.") return # Read the CSV file with open(csv_file_path, newline='', encoding='utf-8-sig') as csvfile: file_table = csv.DictReader(csvfile) # Print headers print("Headers:", file_table.fieldnames) for row in file_table: print("Row:", row) # Print each row for debugging desired_filename = row['Topic'] numbered_filename = row['Filename'] # Check if the numbered file exists existing_files = [f for f in os.listdir() if f.startswith(numbered_filename) and f.endswith('.mp4')] if not existing_files: print(f"File '{numbered_filename}.mp4' not found.") continue # Get the first matching file existing_file = existing_files[0] # Construct the new filename extension = os.path.splitext(existing_file)[1] new_filename = f"{desired_filename}{extension}" # Rename the file os.rename(existing_file, new_filename) print(f"File '{existing_file}' renamed to '{new_filename}'") if _name_ == "__main__": csv_path = input("Enter the path to the CSV file: ") rename_files(csv_path)
    1
  1322. 1
  1323. 1
  1324. 1
  1325. 1
  1326. 1
  1327. 1
  1328. 1
  1329. 1
  1330. 1
  1331. 1
  1332. 1
  1333. 1
  1334. 1
  1335. 1
  1336. 1
  1337. 1
  1338. 1
  1339. 1
  1340. 1
  1341. 1
  1342. 1
  1343. 1
  1344. 1
  1345. 1
  1346. 1
  1347. 1
  1348. 1
  1349. 1
  1350. 1
  1351. 1
  1352. 1
  1353. 1
  1354. 1
  1355. 1
  1356. 1
  1357. 1
  1358. 1
  1359. 1
  1360. 1
  1361. 1
  1362. 1
  1363. 1
  1364. 1
  1365. 1
  1366. 1
  1367. 1
  1368. 1
  1369. 1
  1370. 1
  1371. 1
  1372. 1
  1373. 1
  1374. 1
  1375. 1
  1376. 1
  1377. 1
  1378. 1
  1379. 1
  1380. 1
  1381. 1
  1382. 1
  1383. 1
  1384. 1
  1385. 1
  1386. 1
  1387. 1
  1388. 1
  1389. 1
  1390. 1
  1391. 1
  1392. 1
  1393. 1
  1394. 1
  1395. 1
  1396. 1
  1397. 1
  1398. 1
  1399. 1
  1400. 1
  1401. 1
  1402. 1
  1403. 1
  1404. 1
  1405. 1
  1406. 1
  1407. 1
  1408. 1
  1409. 1
  1410. 1
  1411. 1
  1412. 1
  1413. 1
  1414. 1
  1415. 1
  1416. 1
  1417. 1
  1418. 1
  1419. 1
  1420. 1
  1421. 1
  1422. 1