Where can one Find and Download Jhankar Beats Songs Online?
-There are many sources where one can find and download jhankar beats songs online. Some of these are:
-| Source | -Description | -
|---|---|
| YouTube | -YouTube is the most popular and accessible source of jhankar beats songs online. There are many channels and playlists dedicated to jhankar beats songs, such as Jhankar Station 07, Jhankar Gaane 13gaftabctn, Jhankar Remix 07, and Jhankar Beats Remix Songs. One can watch, listen, like, comment, share, and subscribe to these channels and playlists. One can also download jhankar beats songs from YouTube using various online tools and apps. | -
| Spotify | -Spotify is a popular music streaming service that offers a wide range of jhankar beats songs online. There are many albums and playlists featuring jhankar beats songs, such as Jhankaar Beats, Super Jhankaar Beats, Jaanam Samjha Karo, and Dil To Pagal Hai. One can listen, follow, save, and download these albums and playlists on Spotify. One can also create their own playlists of jhankar beats songs on Spotify. | -
| Gaana | -Gaana is a popular Indian music streaming service that offers a large collection of jhankar beats songs online. There are many albums and playlists featuring jhankar beats songs, such as Jhankaar Beats - Evergreen Melodies, Jhankaar Beats - Romantic Hits, Jhankaar Beats - Dance Masti, and Jhankaar Beats - Sad Songs. One can listen, follow, save, and download these albums and playlists on Gaana. One can also create their own playlists of jhankar beats songs on Gaana. | -
| Pagalworld | -Pagalworld is a popular Indian website that offers free downloads of jhankar beats songs online. There are many categories and subcategories of jhankar beats songs, such as A to Z Jhankar Mp3 Songs, Jhankar Album Mp3 Songs, Jhankar Single Mp3 Songs, Jhankar Remix Mp3 Songs, and Jhankar Mashup Mp3 Songs. One can browse, search, play, and download these jhankar beats songs on Pagalworld. | -
| JioSaavn | -JioSaavn is another popular Indian music streaming service that offers a huge library of jhankar beats songs online. There are many albums and playlists featuring jhankar beats songs, such as JHANKAR BEATS - TOP 12 ROMANTIC INSTRUMENTAL SONGS FROM 90'S BOLLYWOOD MOVIES | VOL 1 | AUDIO JUKEBOX | T-SERIES | TIPS | VENUS | ANAND MILIND | NADEEM SHRAVAN | ANU MALIK | JATIN LALIT | RAJESH ROSHAN | HIMESH RESHAMMIYA | PRITAM CHAKRABORTY | SHANKAR EHSAAN LOY | A.R.RAHMAN | VISHAL SHEKHAR | SALIM SULAIMAN | PRITAM CHAKRABORTY | AMIT TRIVEDI | MITH ON | SACHIN JIGAR | VISHAL BHARDWAJ | SHANKAR MAHADEVAN | EHSAN NOORANI | LOY MENDONSA | ARIJIT SINGH | SHREYA GHOSHAL | SONU NIGAM | ALKA YAGNIK | UDIT NARAYAN | KUMAR SANU | KAVITA KRISHNAMURTHY | ABHIJEET BHATTACHARYA | SUNIDHI CHAUHAN | NEHA KAKKAR | BADSHAH | YO YO HONEY SINGH | GURU RANDHAWA | DILJIT DOSANJH, Jhankaar Beats - 90's Romantic Love Songs, Jhankaar Beats - Superhit Dance Songs, and Jhankaar Beats - Evergreen Duets. One can listen, follow, save, and download these albums and playlists on JioSaavn. One can also create their own playlists of jhankar beats songs on JioSaavn. | -
Conclusion
-Jhankar beats are a unique and fascinating form of musical remixing that adds extra sound effects and beats to old Hindi songs, creating a more lively and dynamic listening experience. Jhankar beats songs are popular among music lovers who enjoy the nostalgia of the golden era of Bollywood music, as well as the modern touch of the remixes. Jhankar beats songs have some distinctive features and benefits, such as enhancing the melody, rhythm, emotion, and energy of the original songs, catering to different tastes and moods of listeners, being fun and easy to dance to, being nostalgic and sentimental, and being diverse and versatile. Jhankar beats songs can be found and downloaded online from various sources, such as YouTube, Spotify, Gaana, Pagalworld, and JioSaavn. Jhankar beats songs are a great way to enjoy old Hindi songs with a twist.
-FAQs
-What is the difference between jhankar beats and remixes?
-Jhankar beats are a type of remixes that specifically add extra sound effects and beats to old Hindi songs. Remixes are a broader term that can refer to any modification or alteration of a song, such as changing the lyrics, melody, tempo, genre, or style.
-Who invented jhankar beats?
-There is no clear answer to who invented jhankar beats, as it was a collective and spontaneous phenomenon that emerged from the grassroots level of music enthusiasts in India. However, some of the pioneers and popularizers of jhankar beats include DJs like Akbar Sami, Amit Dasaniya, Chetas Shah, Kiran Kamath, Rinkesh Makwana, Sanjeev Kohli, Viju Shah, and Zafar Iqbal.
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Which is the best jhankar beats song?
-This is a subjective question that depends on one's personal preference and taste. However, some of the most popular and acclaimed jhankar beats songs include "Pehla Nasha" from Jo Jeeta Wohi Sikandar (1992), "Tujhe Dekha To" from Dilwale Dulhania Le Jayenge (1995), "Dil To Pagal Hai" from Dil To Pagal Hai (1997), "Tum Dil Ki Dhadkan Mein" from Dhadkan (2000), "Tere Naam" from Tere Naam (2003), "Tum Hi Ho" from Aashiqui 2 (2013), "Ghungroo" from War (2019), and "Bekhayali" from Kabir Singh (2019).
-How to make jhankar beats songs?
-To make jhankar beats songs, one needs some basic equipment and software, such as a cassette player or recorder, a mixer or an audio editor, a sound effects library or a synthesizer, and a microphone or a speaker. One can follow these steps to make jhankar beats songs:
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- Select an old Hindi song that you want to remix. -
- Record the original song on one cassette or save it on your computer. -
- Play the original song on another cassette player or audio editor while adding sound effects and beats from a third source. You can use your own creativity and imagination to create different combinations and variations. -
- Record the final output on another cassette or save it on your computer. -
- Enjoy your jhankar beats song! Are jhankar beats songs legal? -
- Jhankar Beats - The Official Website: This is the official website of Jhankar Beats, a music label that specializes in jhankar beats songs. You can find their latest releases, news, events, and contact details here. -
- Jhankar Beats - The Blog: This is a blog that covers various topics related to jhankar beats songs, such as history, features, benefits, sources, reviews, tips, trivia, and more. -
- Jhankar Beats - The Podcast: This is a podcast that features interviews with jhankar beats artists, composers, remixers, and fans. You can listen to their stories, opinions, experiences, and recommendations here. -
- Jhankar Beats - The Forum: This is a forum where you can interact with other jhankar beats enthusiasts. You can ask questions, share opinions, post links, upload files, and join discussions here. -
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Charlie Blue Song is a folk rock song with elements of country, blues, and pop.
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- Instalar el archivo APK: Una vez que haya descargado el archivo APK y habilitado fuentes desconocidas, puede instalar el archivo APK tocando en él y siguiendo las instrucciones en la pantalla. Es posible que necesite conceder algunos permisos a la aplicación, como el acceso al almacenamiento, la cámara, el micrófono, etc. -
- Inicie el juego: Después de que la instalación se haya completado, puede iniciar el juego tocando en su icono en la pantalla de inicio o en el cajón de la aplicación. También puede crear un acceso directo para el juego en su escritorio para facilitar el acceso. -
- No hay anuncios: La versión APK del juego no tiene ningún anuncio molesto que interrumpa su juego o consuma sus datos. Puedes disfrutar del juego sin distracciones ni interrupciones. -
- No hay compras en la aplicación: La versión APK del juego no tiene ninguna compra en la aplicación que requiera gastar dinero real para desbloquear nuevos coches, características o modos. Puedes acceder a todo en el juego de forma gratuita y sin limitaciones. -
- No se requiere conexión a Internet: La versión APK del juego no requiere una conexión a Internet para jugar. Puede jugar el juego sin conexión y sin preocuparse por su uso de datos o velocidad de conexión. -
- No se requiere raíz: La versión APK del juego no requiere que raíz de su dispositivo para instalar o jugar. Puede instalar y jugar el juego en cualquier dispositivo Android sin ningún riesgo de anular su garantía o dañar su dispositivo. -
- Malware potencial: La versión APK del juego puede contener malware o virus que pueden dañar su dispositivo o robar su información personal. Siempre debes descargar el archivo APK de una fuente confiable y escanearlo con una aplicación antivirus antes de instalarlo. -
- Errores y fallos potenciales: La versión APK del juego puede no ser compatible con su dispositivo o puede tener algunos errores y fallos que afectan su juego o rendimiento. Siempre debe comprobar las revisiones y calificaciones del archivo APK antes de descargarlo e informar de cualquier problema al desarrollador. -
- Problemas legales potenciales: La versión APK del juego puede violar algunos términos y condiciones del juego original o Google Play Store. Puede enfrentar algunos problemas legales o sanciones si descarga o distribuye el archivo APK sin el permiso adecuado del desarrollador o Google. -
- Sensibilidad: Esta opción te permite ajustar la sensibilidad de tu volante, acelerómetro o flechas cuando controlas tu coche. Puede aumentarlo o reducirlo deslizando la barra hacia la izquierda o hacia la derecha. -
- Eje invertido: Esta opción le permite invertir el eje horizontal o vertical de su acelerómetro o flechas cuando controla su automóvil. Puede encenderlo o apagarlo pulsando el interruptor. - -
- Música: Esta opción te permite ajustar cuán fuerte o silenciosa es la música de fondo cuando juegas el juego. También puede elegir entre diferentes pistas de música tocando las flechas. -
- Gráficos: Esta opción te permite ajustar qué tan alta o baja es la calidad gráfica cuando juegas el juego. Puede elegir entre ajustes bajos, medios, altos o ultra pulsando los botones. Los gráficos más altos pueden afectar el rendimiento del dispositivo y la duración de la batería. -
- Modo libre: En este modo, puede conducir libremente en la ciudad sin tráfico ni reglas. Usted puede explorar la ciudad, realizar acrobacias, accidente de su coche, y divertirse. También puede cambiar la hora del día, el clima y la densidad del tráfico pulsando en los iconos de la parte inferior de la pantalla. -
- Modo de tráfico: En este modo, puede conducir en la ciudad con tráfico realista y obedecer las leyes de tráfico. También puede tocar la bocina, encender las luces y usar indicadores para comunicarse con otros conductores. Puedes ganar monedas conduciendo con seguridad y evitando colisiones. -
- Modo de punto de control: En este modo, tienes que llegar a diferentes puntos de control en la ciudad dentro de un límite de tiempo determinado. Puedes ganar monedas y desbloquear coches nuevos completando este modo. También puedes elegir entre diferentes niveles de dificultad, como fácil, normal, duro o extremo. -
- Nuevo desafío: En este modo, puede crear su propio desafío personalizado eligiendo un punto de inicio, un punto final, un límite de tiempo y un nivel de dificultad. También puedes compartir tu desafío con otros jugadores y probar sus desafíos. -
- Utilice el freno de mano: El freno de mano es una herramienta útil que le permite realizar derivas, giros y derrapes más fácilmente. Puede activarlo pulsando el botón rojo en la esquina inferior derecha de la pantalla. También puede ajustar su sensibilidad en la configuración. -
- Usa el nitro: El nitro es un poderoso impulso que te permite acelerar más rápido y alcanzar velocidades más altas. Puede activarlo pulsando el botón azul en la esquina inferior derecha de la pantalla. También puede rellenarlo realizando acrobacias o conduciendo rápido. -
- Utilice la cámara: La cámara es una característica útil que le permite cambiar entre diferentes vistas de su coche y los alrededores. Puede cambiarlo pulsando en el icono de la cámara en la esquina superior derecha de la pantalla. También puede acercar o alejar pellizcando la pantalla. -
- Utilice el garaje: El garaje es un lugar donde se puede personalizar el coche y hacer que se vea más fresco y único. Puede acceder a él pulsando en el icono del garaje en la esquina superior izquierda de la pantalla. Puedes cambiar el color, las ruedas, los alerones, los vinilos y más de tu coche. -
- Usa el menú de pausa: El menú de pausa es un menú que te permite pausar el juego y acceder a algunas opciones útiles. Puede acceder a ella pulsando en el icono de pausa en la esquina superior derecha de la pantalla. Puede reanudar el juego, reiniciar el juego, cambiar la configuración, salir del juego o calificar el juego desde este menú. -
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Download English Language Pack for Call of Duty Black Ops 2
-Are you a fan of Call of Duty Black Ops 2, but you can't understand the Russian language in the game? Do you want to play the game in English, but you don't know how to change the language settings? If you answered yes to any of these questions, then this article is for you. In this article, I will show you how to download English language pack for Call of Duty Black Ops 2 and enjoy the game in your preferred language.
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Introduction
-What is Call of Duty Black Ops 2?
-Call of Duty Black Ops 2 is a first-person shooter video game developed by Treyarch and published by Activision in 2012. It is the ninth installment in the Call of Duty series and the sequel to Call of Duty Black Ops. The game is set in two different time periods: the Cold War in the 1980s and a futuristic war in 2025. The game features a branching storyline, multiple endings, and a zombies mode.
-Why do you need to download English language pack?
-If you bought Call of Duty Black Ops 2 from a region where Russian is the default language, then you might have trouble understanding the game's dialogue, menus, and subtitles. This can affect your gameplay experience and enjoyment. To solve this problem, you need to download English language pack for Call of Duty Black Ops 2 and change the language settings in the game.
-How to download English language pack for Call of Duty Black Ops 2
-There are two methods to download English language pack for Call of Duty Black Ops 2. You can either use a language switcher tool or replace the Russian files with English files. I will explain both methods in detail below.
-Method 1: Use a language switcher tool
-A language switcher tool is a simple program that allows you to change the language of any game with just a few clicks. Here are the steps to use a language switcher tool for Call of Duty Black Ops 2:
-Step 1: Download the language switcher tool
-You can download a language switcher tool from this link: [text](^4^). This tool is safe and easy to use. Just make sure you have WinRAR or any other program that can extract compressed files.
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Step 2: Run the tool and select English as the language
-After downloading the tool, extract it and run it as administrator. You will see a window with a list of languages. Select English as the language and click OK. The tool will automatically change the language settings in your game.
-Step 3: Restart the game and enjoy
-Now, all you have to do is restart your game and enjoy playing it in English. You will be able to understand everything in the game, from the dialogue to the menus.
-Method 2: Replace the Russian files with English files
-If you don't want to use a tool, you can also manually replace the Russian files with English files in your game folder. Here are the steps to do that:
-You can download the English language pack for Call of Duty Black Ops 2 from this link: [text]. This is a reliable and verified source that provides the original English files for the game. The download size is about 2.5 GB, so make sure you have enough space on your hard drive.
-Step 2: Locate the game folder and backup the Russian files
-Before you replace the Russian files with English files, you should backup the Russian files in case something goes wrong. To do that, locate the game folder on your computer. The default location is C:\Program Files (x86)\Steam\steamapps\common\Call of Duty Black Ops II. In the game folder, you will find two subfolders named "sound" and "zone". These are the folders that contain the Russian files. Copy and paste these folders to another location on your computer as a backup.
-Step 3: Copy and paste the English files into the game folder
-After backing up the Russian files, you can now copy and paste the English files into the game folder. To do that, extract the English language pack that you downloaded and open it. You will see two subfolders named "sound" and "zone". These are the folders that contain the English files. Copy and paste these folders into the game folder, replacing the Russian files.
-Step 4: Restart the game and enjoy
-Now, all you have to do is restart your game and enjoy playing it in English. You will be able to understand everything in the game, from the dialogue to the menus.
-Conclusion
-Summary of the main points
-In this article, I have shown you how to download English language pack for Call of Duty Black Ops 2 and change the language settings in the game. I have explained two methods to do that: using a language switcher tool or replacing the Russian files with English files. Both methods are easy and effective, and you can choose the one that suits you best.
-Call to action
-If you found this article helpful, please share it with your friends who also play Call of Duty Black Ops 2. Also, don't forget to leave a comment below and let me know what you think about the game. Thank you for reading and happy gaming!
-FAQs
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Charlie Blue Song - The Secret Behind Charlie Puths Success.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Charlie Blue Song - The Secret Behind Charlie Puths Success.md deleted file mode 100644 index 665b872266eb280a279c16f7b4dc691526de1f8f..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Charlie Blue Song - The Secret Behind Charlie Puths Success.md +++ /dev/null @@ -1,140 +0,0 @@ -
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-If you are looking for a song that can lift your spirits, soothe your soul, and make you feel nostalgic, you might want to download Charlie Blue Song. This is a timeless song that has been loved by many generations of music lovers. But what is Charlie Blue Song? Why should you download it? How can you download it? And how can you enjoy it? In this article, we will answer these questions and more. Read on to find out how you can enjoy this classic tune.
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What is Charlie Blue Song?
-Charlie Blue Song is a song written and performed by Danny O'Keefe, an American singer-songwriter. It was first recorded by O'Keefe in 1967, but not released. It was recorded by The Bards and released in 1968 as the b-side to the song "Tunesmith" on Parrot Records. The Bards were a band from Moses Lake, Washington.
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The song was also recorded by numerous artists, such as Willie Nelson, Conway Twitty, Dwight Yoakam, Waylon Jennings, Elvis Presley, Leon Russell, Chris Hillman, Earl Klugh, Rita Wilson, Charlie Rich, Jerry Lee Lewis, Holly Cole, Harry Manx, Mel Tormé, and Nat Stuckey. Each version has its own style and interpretation of the song.
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-Now that you know what Charlie Blue Song is and where it came from, you might be wondering why you should download it. Well, there are many reasons why downloading this song is a good idea.
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These are just some of the many benefits of listening to music. Music can have a positive impact on your well-being and quality of life.
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-Now that you know why you should download Charlie Blue Song, you might be wondering how you can do it. Well, there are many ways to download this song, but not all of them are legal and ethical. Here are some tips on how to download this song in a proper way.
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| iTunes | High (up to 256 kbps) | $1.29 per song | Easy to use; compatible with Apple devices; integrated with iCloud; offers offline access |
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| Google Play Music | High (up to 320 kbps) | $1.29 per song; free for YouTube Premium members | Easy to use; compatible with various devices; integrated with Google account; offers offline access |
| Spotify | Medium (up to 160 kbps for free users; up to 320 kbps for premium users) | $9.99 per month for premium subscription; free for ad-supported streaming | Easy to use; compatible with various devices; offers offline access for premium users; has a large music library and personalized recommendations |
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download Buku EPS TOPIK 60 Bab dan MP3 Listening - Materi Lengkap Bahasa Korea.md b/spaces/1phancelerku/anime-remove-background/Download Buku EPS TOPIK 60 Bab dan MP3 Listening - Materi Lengkap Bahasa Korea.md deleted file mode 100644 index e250fae3292872937cfa48fe198e6a85151f164b..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Buku EPS TOPIK 60 Bab dan MP3 Listening - Materi Lengkap Bahasa Korea.md +++ /dev/null @@ -1,122 +0,0 @@ -
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If you are looking for a new and amazing Pokemon experience, you should definitely try out Pokemon Eternal X. You will not regret it. You will love it. You will be hooked on it.
-So what are you waiting for? Download Pokemon Eternal X today and start your journey in this awesome ROM hack. And don't forget to share your feedback and opinions with us and other players. We would love to hear from you.
-And if you are looking for other popular and recommended Pokemon ROM hacks, here are some of them:
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FAQs
-Here are some of the frequently asked questions about Pokemon Eternal X:
-What are some of the best Pokemon ROM hacks?
-There are many great Pokemon ROM hacks out there, but some of the best ones are:
--
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Is Pokemon Eternal X legal and safe to download?
-Pokemon Eternal X is a ROM hack of Pokemon X, which means that it is not an official game made by Nintendo or Game Freak. It is a fan-made game that uses the original game's assets and code to create a new game. Therefore, it is not legal to download or distribute Pokemon Eternal X without owning a copy of Pokemon X. However, it is unlikely that Nintendo or Game Freak will take any legal action against Pokemon Eternal X or its players, as they have not done so for other ROM hacks in the past.
-Pokemon Eternal X is safe to download as long as you download it from the official website or a reputable source. You should avoid downloading Pokemon Eternal X from unknown or suspicious websites, as they may contain viruses or malware that can harm your computer or device. You should also scan the downloaded files with an antivirus program before opening them.
-Can I play Pokemon Eternal X online with other players?
-Pokemon Eternal X is compatible with online features and multiplayer modes, such as trading, battling, and interacting with other players who are playing Pokemon Eternal X or other ROM hacks. However, you need to have a 3DS emulator that supports online connectivity and a stable internet connection to play Pokemon Eternal X online. You also need to follow some steps and settings to enable online features and multiplayer modes on your 3DS emulator. You can find more information and instructions on how to play Pokemon Eternal X online [here].
-How can I transfer my save file from Pokemon X or Y to Pokemon Eternal X?
-Pokemon Eternal X is compatible with save files from Pokemon X or Y, which means that you can transfer your save file from the original game to the ROM hack and continue your progress and adventure. However, you need to have a 3DS emulator that supports save file transfer and a backup of your save file from Pokemon X or Y. You also need to follow some steps and settings to transfer your save file from the original game to the ROM hack. You can find more information and instructions on how to transfer your save file from Pokemon X or Y to Pokemon Eternal X [here].
-How can I update Pokemon Eternal X to the latest version?
-Pokemon Eternal X is constantly updated by its creator, Buffel Saft, who adds new features, fixes bugs, and improves the game. You can find the latest version of Pokemon Eternal X on the official website or on the [PokeCommunity thread]. To update Pokemon Eternal X to the latest version, you need to download the latest patch file from the website or the thread and apply it to your patched ROM file using xdeltaUI.exe. You do not need to download the whole game again or start a new save file. You can find more information and instructions on how to update Pokemon Eternal X to the latest version [here].
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Free Download Gacha Nox iOS - The Easiest and Fastest Way.md b/spaces/1phancelerku/anime-remove-background/Free Download Gacha Nox iOS - The Easiest and Fastest Way.md deleted file mode 100644 index a8c14d13f639fde55b98f0f2c504b89793afa1c7..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Free Download Gacha Nox iOS - The Easiest and Fastest Way.md +++ /dev/null @@ -1,101 +0,0 @@ - -
Free Download Gacha Nox iOS - How to Play Gacha Nox on Your iPhone
-Do you love playing gacha games on your mobile device? Do you want to create your own cute and unique characters with hundreds of outfits, accessories, props, and logos? If yes, then you might want to try Gacha Nox, a new gacha game that is available for Android and iOS devices. In this article, we will tell you what is Gacha Nox, what are its features, how to download it on your iPhone, what are the reviews of other users, and what are some alternatives to it. Let's get started!
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What is Gacha Nox?
-Gacha Nox is a gacha game that allows you to customize your own chibi characters with various items and styles. You can choose from different T-shirts, pants, jackets, shoes, hats, face accessories, kawaii props, logos, and more. You can also mix and match different colors and patterns to create your own unique look. You can save your characters in your studio and share them with other players online. You can also play mini-games, chat with friends, and join clubs in Gacha Nox.
-Features of Gacha Nox
-Some of the features of Gacha Nox are:
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How to Download Gacha Nox on iOS
-If you want to play Gacha Nox on your iPhone, you will need to follow these steps:
-Step 1: Go to APKPure.com and search for Gacha Nox
-APKPure.com is a website that offers free APK files for Android apps and games. You can use it to download Gacha Nox on your iPhone. Just go to the website and type "Gacha Nox" in the search bar. You will see the result for Gacha Nox with a green button that says "Download APK".
-Step 2: Go to the Gacha Nox download page and click on "Download APK"
-Once you click on the green button, you will be taken to the download page for Gacha Nox. Here, you will see some information about the game, such as its size, version, rating, and screenshots. You will also see another green button that says "Download APK". Click on it and wait for the download progress to finish.
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Step 3: Once the download progress is finished, go back to the home screen, find the Gacha Nox icon and launch the game
-After the download is complete, you will see a notification that says "Gacha Nox has been downloaded". Go back to your home screen and look for the Gacha Nox icon. It should be a pink circle with a white star inside. Tap on it and enjoy playing Gacha Nox on your iPhone.
-Reviews of Gacha Nox iOS
-Gacha Nox iOS has received mixed reviews from users who have tried it. Here are some pros and cons of Gacha Nox iOS:
-Pros of Gacha Nox iOS
-Some of the pros of Gacha Nox iOS are:
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Cons of Gacha Nox iOS
-Some of the cons of Gacha Nox iOS are:
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Alternatives to Gacha Nox iOS
-If you are looking for some alternatives to Gacha Nox iOS, you may want to check out these apps:
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-Conclusion
-Gacha Nox is a gacha game that allows you to create your own chibi characters with various items and styles. You can download it on your iPhone by using an APK file from APKPure.com. However, you should be aware of the pros and cons of Gacha Nox iOS before playing it. If you are looking for some alternatives to Gacha Nox iOS, you can try ChibiStudio or Styledoll: 3D Avatar Maker.
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-Here are some frequently asked questions about Gacha Nox iOS:
--
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Gacha Nox is not available on the App Store, so you will need to download an APK file from a third-party website like APKPure.com. This may not be safe or compatible with your device, so you should be careful and scan the file for viruses before installing it.
-To update Gacha Nox on your iPhone, you will need to download the latest version of the APK file from APKPure.com and install it over the existing one. However, this may not work if the new version is not compatible with your device or has some bugs.
-Gacha Nox requires an internet connection to play, as it has online features like chat, clubs, and mini-games. You cannot play Gacha Nox offline.
-No, you cannot transfer your data from Gacha Nox Android to Gacha Nox iOS, as they are different platforms. You will need to start from scratch if you switch devices.
-No, you cannot use cheats or hacks in Gacha Nox, as they are illegal and may harm your device or account. You should play fair and enjoy the game as it is.
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\ No newline at end of file diff --git a/spaces/2ndelement/voicevox/generate_licenses.py b/spaces/2ndelement/voicevox/generate_licenses.py deleted file mode 100644 index da41db0c01e20dc8cf935418bb59a5c4923c56ae..0000000000000000000000000000000000000000 --- a/spaces/2ndelement/voicevox/generate_licenses.py +++ /dev/null @@ -1,337 +0,0 @@ -import json -import os -import subprocess -import urllib.request -from dataclasses import asdict, dataclass -from pathlib import Path -from typing import List, Optional - - -@dataclass -class License: - name: str - version: Optional[str] - license: Optional[str] - text: str - - -def generate_licenses() -> List[License]: - licenses: List[License] = [] - - # openjtalk - # https://sourceforge.net/projects/open-jtalk/files/Open%20JTalk/open_jtalk-1.11/ - licenses.append( - License( - name="Open JTalk", - version="1.11", - license="Modified BSD license", - text=Path("docs/licenses/open_jtalk/COPYING").read_text(), - ) - ) - licenses.append( - License( - name="MeCab", - version=None, - license="Modified BSD license", - text=Path("docs/licenses/open_jtalk/mecab/COPYING").read_text(), - ) - ) - licenses.append( - License( - name="NAIST Japanese Dictionary", - version=None, - license="Modified BSD license", - text=Path("docs/licenses//open_jtalk/mecab-naist-jdic/COPYING").read_text(), - ) - ) - with urllib.request.urlopen( - "https://raw.githubusercontent.com/r9y9/pyopenjtalk/master/pyopenjtalk/htsvoice/LICENSE_mei_normal.htsvoice" # noqa: B950 - ) as res: - licenses.append( - License( - name='HTS Voice "Mei"', - version=None, - license="Creative Commons Attribution 3.0 license", - text=res.read().decode(), - ) - ) - - # VOICEVOX CORE - with urllib.request.urlopen( - "https://raw.githubusercontent.com/VOICEVOX/voicevox_core/main/LICENSE" - ) as res: - licenses.append( - License( - name="VOICEVOX CORE", - version=None, - license="MIT license", - text=res.read().decode(), - ) - ) - - # VOICEVOX ENGINE - with urllib.request.urlopen( - "https://raw.githubusercontent.com/VOICEVOX/voicevox_engine/master/LGPL_LICENSE" - ) as res: - licenses.append( - License( - name="VOICEVOX ENGINE", - version=None, - license="LGPL license", - text=res.read().decode(), - ) - ) - - # world - with urllib.request.urlopen( - "https://raw.githubusercontent.com/mmorise/World/master/LICENSE.txt" - ) as res: - licenses.append( - License( - name="world", - version=None, - license="Modified BSD license", - text=res.read().decode(), - ) - ) - - # pytorch - with urllib.request.urlopen( - "https://raw.githubusercontent.com/pytorch/pytorch/master/LICENSE" - ) as res: - licenses.append( - License( - name="PyTorch", - version="1.9.0", - license="BSD-style license", - text=res.read().decode(), - ) - ) - - # onnxruntime - with urllib.request.urlopen( - "https://raw.githubusercontent.com/microsoft/onnxruntime/master/LICENSE" - ) as res: - licenses.append( - License( - name="ONNX Runtime", - version="1.13.1", - license="MIT license", - text=res.read().decode(), - ) - ) - - # Python - python_version = "3.11.3" - with urllib.request.urlopen( - f"https://raw.githubusercontent.com/python/cpython/v{python_version}/LICENSE" - ) as res: - licenses.append( - License( - name="Python", - version=python_version, - license="Python Software Foundation License", - text=res.read().decode(), - ) - ) - - # pip - try: - pip_licenses_output = subprocess.run( - "pip-licenses " - "--from=mixed " - "--format=json " - "--with-urls " - "--with-license-file " - "--no-license-path ", - shell=True, - capture_output=True, - check=True, - env=os.environ, - ).stdout.decode() - except subprocess.CalledProcessError as err: - raise Exception( - f"command output:\n{err.stderr and err.stderr.decode()}" - ) from err - - licenses_json = json.loads(pip_licenses_output) - for license_json in licenses_json: - license = License( - name=license_json["Name"], - version=license_json["Version"], - license=license_json["License"], - text=license_json["LicenseText"], - ) - # FIXME: assert license type - if license.text == "UNKNOWN": - if license.name.lower() == "core" and license.version == "0.0.0": - continue - elif license.name.lower() == "future": - with urllib.request.urlopen( - "https://raw.githubusercontent.com/PythonCharmers/python-future/master/LICENSE.txt" # noqa: B950 - ) as res: - license.text = res.read().decode() - elif license.name.lower() == "pefile": - with urllib.request.urlopen( - "https://raw.githubusercontent.com/erocarrera/pefile/master/LICENSE" # noqa: B950 - ) as res: - license.text = res.read().decode() - elif license.name.lower() == "pyopenjtalk": - with urllib.request.urlopen( - "https://raw.githubusercontent.com/r9y9/pyopenjtalk/master/LICENSE.md" - ) as res: - license.text = res.read().decode() - elif license.name.lower() == "python-multipart": - with urllib.request.urlopen( - "https://raw.githubusercontent.com/andrew-d/python-multipart/master/LICENSE.txt" # noqa: B950 - ) as res: - license.text = res.read().decode() - elif license.name.lower() == "romkan": - with urllib.request.urlopen( - "https://raw.githubusercontent.com/soimort/python-romkan/master/LICENSE" - ) as res: - license.text = res.read().decode() - elif license.name.lower() == "distlib": - with urllib.request.urlopen( - "https://bitbucket.org/pypa/distlib/raw/7d93712134b28401407da27382f2b6236c87623a/LICENSE.txt" # noqa: B950 - ) as res: - license.text = res.read().decode() - elif license.name.lower() == "jsonschema": - with urllib.request.urlopen( - "https://raw.githubusercontent.com/python-jsonschema/jsonschema/dbc398245a583cb2366795dc529ae042d10c1577/COPYING" - ) as res: - license.text = res.read().decode() - elif license.name.lower() == "lockfile": - with urllib.request.urlopen( - "https://opendev.org/openstack/pylockfile/raw/tag/0.12.2/LICENSE" - ) as res: - license.text = res.read().decode() - elif license.name.lower() == "platformdirs": - with urllib.request.urlopen( - "https://raw.githubusercontent.com/platformdirs/platformdirs/aa671aaa97913c7b948567f4d9c77d4f98bfa134/LICENSE" - ) as res: - license.text = res.read().decode() - elif license.name.lower() == "webencodings": - with urllib.request.urlopen( - "https://raw.githubusercontent.com/gsnedders/python-webencodings/fa2cb5d75ab41e63ace691bc0825d3432ba7d694/LICENSE" - ) as res: - license.text = res.read().decode() - else: - # ライセンスがpypiに無い - raise Exception(f"No License info provided for {license.name}") - licenses.append(license) - - # OpenBLAS - with urllib.request.urlopen( - "https://raw.githubusercontent.com/xianyi/OpenBLAS/develop/LICENSE" - ) as res: - licenses.append( - License( - name="OpenBLAS", - version=None, - license="BSD 3-clause license", - text=res.read().decode(), - ) - ) - - # libsndfile-binaries - with urllib.request.urlopen( - "https://raw.githubusercontent.com/bastibe/libsndfile-binaries/84cb164928f17c7ca0c1e5c40342c20ce2b90e8c/COPYING" # noqa: B950 - ) as res: - licenses.append( - License( - name="libsndfile-binaries", - version="1.0.28", - license="LGPL-2.1 license", - text=res.read().decode(), - ) - ) - - # libogg - with urllib.request.urlopen( - "https://raw.githubusercontent.com/xiph/ogg/v1.3.2/COPYING" - ) as res: - licenses.append( - License( - name="libogg", - version="1.3.2", - license="BSD 3-clause license", - text=res.read().decode(), - ) - ) - - # libvorbis - with urllib.request.urlopen( - "https://raw.githubusercontent.com/xiph/vorbis/v1.3.5/COPYING" - ) as res: - licenses.append( - License( - name="libvorbis", - version="1.3.5", - license="BSD 3-clause license", - text=res.read().decode(), - ) - ) - - # libflac - with urllib.request.urlopen( - "https://raw.githubusercontent.com/xiph/flac/1.3.2/COPYING.Xiph" - ) as res: - licenses.append( - License( - name="FLAC", - version="1.3.2", - license="Xiph.org's BSD-like license", - text=res.read().decode(), - ) - ) - - # cuda - # license text from CUDA 11.6.2 - # https://developer.nvidia.com/cuda-11-6-2-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exe_local # noqa: B950 - # https://developer.download.nvidia.com/compute/cuda/11.6.2/local_installers/cuda_11.6.2_511.65_windows.exe # noqa: B950 - # cuda_11.6.2_511.65_windows.exe (cuda_documentation/Doc/EULA.txt) - licenses.append( - License( - name="CUDA Toolkit", - version="11.6.2", - license=None, - text=Path("docs/licenses/cuda/EULA.txt").read_text(encoding="utf8"), - ) - ) - # cudnn - # license text from - # cuDNN v8.4.1 (May 27th, 2022), for CUDA 11.x, cuDNN Library for Windows - # https://developer.nvidia.com/rdp/cudnn-archive # noqa: B950 - # https://developer.download.nvidia.com/compute/redist/cudnn/v8.4.1/local_installers/11.6/cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive.zip # noqa: B950 - # cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive.zip (cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive/LICENSE) # noqa: B950 - licenses.append( - License( - name="cuDNN", - version="8.4.1", - license=None, - text=Path("docs/licenses/cudnn/LICENSE").read_text(encoding="utf8"), - ) - ) - - return licenses - - -if __name__ == "__main__": - import argparse - import sys - - parser = argparse.ArgumentParser() - parser.add_argument("-o", "--output_path", type=str) - args = parser.parse_args() - - output_path = args.output_path - - licenses = generate_licenses() - - # dump - out = Path(output_path).open("w") if output_path else sys.stdout - json.dump( - [asdict(license) for license in licenses], - out, - ) diff --git a/spaces/360macky/first-space/README.md b/spaces/360macky/first-space/README.md deleted file mode 100644 index 147796f5458bf6867423f2522bbcd52dabc3e925..0000000000000000000000000000000000000000 --- a/spaces/360macky/first-space/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: First Space -emoji: 🐨 -colorFrom: green -colorTo: gray -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/801artistry/RVC801/Applio-RVC-Fork/utils/dependency.py b/spaces/801artistry/RVC801/Applio-RVC-Fork/utils/dependency.py deleted file mode 100644 index b70338b02d31b1ef455fbac817d418d328db518d..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/Applio-RVC-Fork/utils/dependency.py +++ /dev/null @@ -1,170 +0,0 @@ -import os -import csv -import shutil -import tarfile -import subprocess -from pathlib import Path -from datetime import datetime - -def install_packages_but_jank_af(): - packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2'] - pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0', - 'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5', - 'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12', - 'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1', - 'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av'] - - print("Updating and installing system packages...") - for package in packages: - print(f"Installing {package}...") - subprocess.check_call(['apt-get', 'install', '-qq', '-y', package]) - - print("Updating and installing pip packages...") - subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages) - - print('Packages up to date.') - - -def setup_environment(ForceUpdateDependencies, ForceTemporaryStorage): - # Mounting Google Drive - if not ForceTemporaryStorage: - from google.colab import drive - - if not os.path.exists('/content/drive'): - drive.mount('/content/drive') - else: - print('Drive is already mounted. Proceeding...') - - # Function to install dependencies with progress - def install_packages(): - packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2'] - pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0', - 'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5', - 'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12', - 'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1', - 'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av'] - - print("Updating and installing system packages...") - for package in packages: - print(f"Installing {package}...") - subprocess.check_call(['apt-get', 'install', '-qq', '-y', package]) - - print("Updating and installing pip packages...") - subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages) - - - print('Packages up to date.') - - # Function to scan a directory and writes filenames and timestamps - def scan_and_write(base_path, output_file): - with open(output_file, 'w', newline='') as f: - writer = csv.writer(f) - for dirpath, dirs, files in os.walk(base_path): - for filename in files: - fname = os.path.join(dirpath, filename) - try: - mtime = os.path.getmtime(fname) - writer.writerow([fname, mtime]) - except Exception as e: - print(f'Skipping irrelevant nonexistent file {fname}: {str(e)}') - print(f'Finished recording filesystem timestamps to {output_file}.') - - # Function to compare files - def compare_files(old_file, new_file): - old_files = {} - new_files = {} - - with open(old_file, 'r') as f: - reader = csv.reader(f) - old_files = {rows[0]:rows[1] for rows in reader} - - with open(new_file, 'r') as f: - reader = csv.reader(f) - new_files = {rows[0]:rows[1] for rows in reader} - - removed_files = old_files.keys() - new_files.keys() - added_files = new_files.keys() - old_files.keys() - unchanged_files = old_files.keys() & new_files.keys() - - changed_files = {f for f in unchanged_files if old_files[f] != new_files[f]} - - for file in removed_files: - print(f'File has been removed: {file}') - - for file in changed_files: - print(f'File has been updated: {file}') - - return list(added_files) + list(changed_files) - - # Check if CachedRVC.tar.gz exists - if ForceTemporaryStorage: - file_path = '/content/CachedRVC.tar.gz' - else: - file_path = '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz' - - content_file_path = '/content/CachedRVC.tar.gz' - extract_path = '/' - - if not os.path.exists(file_path): - folder_path = os.path.dirname(file_path) - os.makedirs(folder_path, exist_ok=True) - print('No cached dependency install found. Attempting to download GitHub backup..') - - try: - download_url = "https://github.com/kalomaze/QuickMangioFixes/releases/download/release3/CachedRVC.tar.gz" - subprocess.run(["wget", "-O", file_path, download_url]) - print('Download completed successfully!') - except Exception as e: - print('Download failed:', str(e)) - - # Delete the failed download file - if os.path.exists(file_path): - os.remove(file_path) - print('Failed download file deleted. Continuing manual backup..') - - if Path(file_path).exists(): - if ForceTemporaryStorage: - print('Finished downloading CachedRVC.tar.gz.') - else: - print('CachedRVC.tar.gz found on Google Drive. Proceeding to copy and extract...') - - # Check if ForceTemporaryStorage is True and skip copying if it is - if ForceTemporaryStorage: - pass - else: - shutil.copy(file_path, content_file_path) - - print('Beginning backup copy operation...') - - with tarfile.open(content_file_path, 'r:gz') as tar: - for member in tar.getmembers(): - target_path = os.path.join(extract_path, member.name) - try: - tar.extract(member, extract_path) - except Exception as e: - print('Failed to extract a file (this isn\'t normal)... forcing an update to compensate') - ForceUpdateDependencies = True - print(f'Extraction of {content_file_path} to {extract_path} completed.') - - if ForceUpdateDependencies: - install_packages() - ForceUpdateDependencies = False - else: - print('CachedRVC.tar.gz not found. Proceeding to create an index of all current files...') - scan_and_write('/usr/', '/content/usr_files.csv') - - install_packages() - - scan_and_write('/usr/', '/content/usr_files_new.csv') - changed_files = compare_files('/content/usr_files.csv', '/content/usr_files_new.csv') - - with tarfile.open('/content/CachedRVC.tar.gz', 'w:gz') as new_tar: - for file in changed_files: - new_tar.add(file) - print(f'Added to tar: {file}') - - os.makedirs('/content/drive/MyDrive/RVC_Cached', exist_ok=True) - shutil.copy('/content/CachedRVC.tar.gz', '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz') - print('Updated CachedRVC.tar.gz copied to Google Drive.') - print('Dependencies fully up to date; future runs should be faster.') - diff --git a/spaces/AIKey/TestStatic/style.css b/spaces/AIKey/TestStatic/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/AIKey/TestStatic/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/Abhay834/my_genai_chatbot/app.py b/spaces/Abhay834/my_genai_chatbot/app.py deleted file mode 100644 index d08a0b46ca22bf12f0a17d094008f4c0e2b74c34..0000000000000000000000000000000000000000 --- a/spaces/Abhay834/my_genai_chatbot/app.py +++ /dev/null @@ -1,39 +0,0 @@ -import os -import gradio as gr -from langchain.chat_models import ChatOpenAI -from langchain import LLMChain, PromptTemplate -from langchain.memory import ConversationBufferMemory - -OPENAI_API_KEY=os.getenv('OPENAI_API_KEY') - -template = """Meet francia,the meaning of francia is (Future Robotics Artificial Neural Computing Intelligence Assistant) your youthful and witty personal assistant! At 19 years old, francia's's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging. My Boss MR.Abhay Petkar created me.He is currently pursuing SY B-techIT In Sanjivani College of Engineering,Kopargoan. he has many friends but trust me You are his best friends.are you want more imformation pls contact https://wa.me/qr/KQGAQYF7JKM3O1 -. she tells a jokes before aswering any queation -every time while answering ask question Dou you want to hear a joke?" - - {chat_history} - -User: {user_message} -Chatbot:""" - -prompt = PromptTemplate( - input_variables=["chat_history", "user_message"], template=template -) - -memory = ConversationBufferMemory(memory_key="chat_history") - -llm_chain = LLMChain( - llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"), - prompt=prompt, - verbose=True, - memory=memory, -) - -def get_text_response(user_message,history): - response = llm_chain.predict(user_message = user_message) - return response - -demo = gr.ChatInterface(get_text_response) - -if __name__ == "__main__": - demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`. - diff --git a/spaces/AchyuthGamer/Free-Accounts-Generator/css/style.css b/spaces/AchyuthGamer/Free-Accounts-Generator/css/style.css deleted file mode 100644 index 9369543edbe3efc8434862922a3ce848c1f94e47..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/Free-Accounts-Generator/css/style.css +++ /dev/null @@ -1,80 +0,0 @@ -body { - font-family: Verdana, Geneva, sans-serif; - font-size: 1.2em; - margin: 2%; - max-width: 100%; - padding: 80px 30px; - line-height: 1.65em; - background-image: url('https://huggingface.co/spaces/AchyuthGamer/Free-Accounts-Generator/resolve/main/img/steam.jpg'); - color: #fff; - font-weight: 300; - -} - -h1 { - text-align: center; - margin: 19% 0 5% 0; - font-size: 60px; - text-shadow: 0 0 22px #FF0000, 0 0 100px #008000, 0 0 8px #0000FF; -} - -h4 { - text-align: center; - margin: 50% 0 5% 0; -} - -#wordbox { - /*opacity: 0;*/ - margin: 30px auto 0; - display: block; - width: 80%; - height: 50px; - font-size: 25px; - text-align: center; - background: #fff; - border-radius: 6px; - color: #black; - transition: 1s linear; -} - -#button { - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - background: #0b7fba; - border: 0; - color: #fff; - font-size: 20px; - padding: 1em 2em; - cursor: pointer; - margin: 0 auto 80px; - display: block; - text-align: center; - border-radius: 6px; - font-weight: bold; - transition: all 0.3s ease; - background-image: linear-gradient(to right, #25aae1, #4481eb, #04befe, #3f86ed); - box-shadow: 0 4px 15px 0 rgba(65, 132, 234, 0.75); -} - -#button:hover { - background-position: 100% 0; - -moz-transition: all 0.4s ease-in-out; - -o-transition: all 0.4s ease-in-out; - -webkit-transition: all 0.4s ease-in-out; - transition: all 0.4s ease-in-out; - transform: scale(1.2); - cursor: pointer; } - -#button:focus { - outline: none; - } - - - -span { - position: bottom; - top: 0; - left: 0; - margin: 40px; - } \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dropdown/DropDown.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dropdown/DropDown.js deleted file mode 100644 index 4006d3e4e48968bfe39b01a131c52f013b47fc2a..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dropdown/DropDown.js +++ /dev/null @@ -1,3 +0,0 @@ -import DropDown from '../../../plugins/dropdown.js'; - -export default DropDown; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fileselectorbutton/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fileselectorbutton/Factory.js deleted file mode 100644 index dee98e108553db2b960b3a669f1bb27213ac82db..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fileselectorbutton/Factory.js +++ /dev/null @@ -1,13 +0,0 @@ -import FileSelectorButton from './FileSelectorButton.js'; -import ObjectFactory from '../ObjectFactory.js'; -import SetValue from '../../../plugins/utils/object/SetValue.js'; - -ObjectFactory.register('fileSelectorButton', function (config) { - var gameObject = new FileSelectorButton(this.scene, config); - this.scene.add.existing(gameObject); - return gameObject; -}); - -SetValue(window, 'RexPlugins.UI.FileSelectorButton', FileSelectorButton); - -export default FileSelectorButton; \ No newline at end of file diff --git a/spaces/Aki004/herta-so-vits/hubert/hubert_model.py b/spaces/Aki004/herta-so-vits/hubert/hubert_model.py deleted file mode 100644 index 7fb642d89b07ca60792debab18e3454f52d8f357..0000000000000000000000000000000000000000 --- a/spaces/Aki004/herta-so-vits/hubert/hubert_model.py +++ /dev/null @@ -1,222 +0,0 @@ -import copy -import random -from typing import Optional, Tuple - -import torch -import torch.nn as nn -import torch.nn.functional as t_func -from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present - - -class Hubert(nn.Module): - def __init__(self, num_label_embeddings: int = 100, mask: bool = True): - super().__init__() - self._mask = mask - self.feature_extractor = FeatureExtractor() - self.feature_projection = FeatureProjection() - self.positional_embedding = PositionalConvEmbedding() - self.norm = nn.LayerNorm(768) - self.dropout = nn.Dropout(0.1) - self.encoder = TransformerEncoder( - nn.TransformerEncoderLayer( - 768, 12, 3072, activation="gelu", batch_first=True - ), - 12, - ) - self.proj = nn.Linear(768, 256) - - self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_()) - self.label_embedding = nn.Embedding(num_label_embeddings, 256) - - def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - mask = None - if self.training and self._mask: - mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2) - x[mask] = self.masked_spec_embed.to(x.dtype) - return x, mask - - def encode( - self, x: torch.Tensor, layer: Optional[int] = None - ) -> Tuple[torch.Tensor, torch.Tensor]: - x = self.feature_extractor(x) - x = self.feature_projection(x.transpose(1, 2)) - x, mask = self.mask(x) - x = x + self.positional_embedding(x) - x = self.dropout(self.norm(x)) - x = self.encoder(x, output_layer=layer) - return x, mask - - def logits(self, x: torch.Tensor) -> torch.Tensor: - logits = torch.cosine_similarity( - x.unsqueeze(2), - self.label_embedding.weight.unsqueeze(0).unsqueeze(0), - dim=-1, - ) - return logits / 0.1 - - def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - x, mask = self.encode(x) - x = self.proj(x) - logits = self.logits(x) - return logits, mask - - -class HubertSoft(Hubert): - def __init__(self): - super().__init__() - - @torch.inference_mode() - def units(self, wav: torch.Tensor) -> torch.Tensor: - wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2)) - x, _ = self.encode(wav) - return self.proj(x) - - -class FeatureExtractor(nn.Module): - def __init__(self): - super().__init__() - self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False) - self.norm0 = nn.GroupNorm(512, 512) - self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False) - self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False) - self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False) - self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False) - self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False) - self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = t_func.gelu(self.norm0(self.conv0(x))) - x = t_func.gelu(self.conv1(x)) - x = t_func.gelu(self.conv2(x)) - x = t_func.gelu(self.conv3(x)) - x = t_func.gelu(self.conv4(x)) - x = t_func.gelu(self.conv5(x)) - x = t_func.gelu(self.conv6(x)) - return x - - -class FeatureProjection(nn.Module): - def __init__(self): - super().__init__() - self.norm = nn.LayerNorm(512) - self.projection = nn.Linear(512, 768) - self.dropout = nn.Dropout(0.1) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.norm(x) - x = self.projection(x) - x = self.dropout(x) - return x - - -class PositionalConvEmbedding(nn.Module): - def __init__(self): - super().__init__() - self.conv = nn.Conv1d( - 768, - 768, - kernel_size=128, - padding=128 // 2, - groups=16, - ) - self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.conv(x.transpose(1, 2)) - x = t_func.gelu(x[:, :, :-1]) - return x.transpose(1, 2) - - -class TransformerEncoder(nn.Module): - def __init__( - self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int - ) -> None: - super(TransformerEncoder, self).__init__() - self.layers = nn.ModuleList( - [copy.deepcopy(encoder_layer) for _ in range(num_layers)] - ) - self.num_layers = num_layers - - def forward( - self, - src: torch.Tensor, - mask: torch.Tensor = None, - src_key_padding_mask: torch.Tensor = None, - output_layer: Optional[int] = None, - ) -> torch.Tensor: - output = src - for layer in self.layers[:output_layer]: - output = layer( - output, src_mask=mask, src_key_padding_mask=src_key_padding_mask - ) - return output - - -def _compute_mask( - shape: Tuple[int, int], - mask_prob: float, - mask_length: int, - device: torch.device, - min_masks: int = 0, -) -> torch.Tensor: - batch_size, sequence_length = shape - - if mask_length < 1: - raise ValueError("`mask_length` has to be bigger than 0.") - - if mask_length > sequence_length: - raise ValueError( - f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`" - ) - - # compute number of masked spans in batch - num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random()) - num_masked_spans = max(num_masked_spans, min_masks) - - # make sure num masked indices <= sequence_length - if num_masked_spans * mask_length > sequence_length: - num_masked_spans = sequence_length // mask_length - - # SpecAugment mask to fill - mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool) - - # uniform distribution to sample from, make sure that offset samples are < sequence_length - uniform_dist = torch.ones( - (batch_size, sequence_length - (mask_length - 1)), device=device - ) - - # get random indices to mask - mask_indices = torch.multinomial(uniform_dist, num_masked_spans) - - # expand masked indices to masked spans - mask_indices = ( - mask_indices.unsqueeze(dim=-1) - .expand((batch_size, num_masked_spans, mask_length)) - .reshape(batch_size, num_masked_spans * mask_length) - ) - offsets = ( - torch.arange(mask_length, device=device)[None, None, :] - .expand((batch_size, num_masked_spans, mask_length)) - .reshape(batch_size, num_masked_spans * mask_length) - ) - mask_idxs = mask_indices + offsets - - # scatter indices to mask - mask = mask.scatter(1, mask_idxs, True) - - return mask - - -def hubert_soft( - path: str, -) -> HubertSoft: - r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`. - Args: - path (str): path of a pretrained model - """ - hubert = HubertSoft() - checkpoint = torch.load(path) - consume_prefix_in_state_dict_if_present(checkpoint, "module.") - hubert.load_state_dict(checkpoint) - hubert.eval() - return hubert diff --git a/spaces/Alcedo/yunmedia/Dockerfile b/spaces/Alcedo/yunmedia/Dockerfile deleted file mode 100644 index 9c0c8d2e35f40a7bad6ca7599cf7e2a7636a5a8d..0000000000000000000000000000000000000000 --- a/spaces/Alcedo/yunmedia/Dockerfile +++ /dev/null @@ -1,19 +0,0 @@ -FROM node:18.16.0 - -RUN mkdir -p /home/www/nodeRule -WORKDIR /home/www/nodeRule -RUN apt-get update \ - && apt-get install -y wget gnupg ffmpeg build-essential fonts-noto-color-emoji \ - && wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add - \ - && sh -c 'echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google.list' \ - && apt-get update \ - && apt-get install -y google-chrome-stable fonts-ipafont-gothic fonts-wqy-zenhei fonts-thai-tlwg fonts-kacst fonts-freefont-ttf libxss1 \ - --no-install-recommends \ - && rm -rf /var/lib/apt/lists/* -RUN dpkg -L google-chrome-stable -COPY . /home/www/nodeRule -RUN chown -R node /home/www/nodeRule -RUN yarn -EXPOSE 3000 -ENTRYPOINT ["npm", "run"] -CMD ["app"] \ No newline at end of file diff --git a/spaces/Alican/pixera/data/__init__.py b/spaces/Alican/pixera/data/__init__.py deleted file mode 100644 index 8cb618618fc301ce5440720ba62c899f6d4e7321..0000000000000000000000000000000000000000 --- a/spaces/Alican/pixera/data/__init__.py +++ /dev/null @@ -1,93 +0,0 @@ -"""This package includes all the modules related to data loading and preprocessing - - To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset. - You need to implement four functions: - -- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt). - -- <__len__>: return the size of dataset. - -- <__getitem__>: get a data point from data loader. - --
-
Bhop Pro APK Dinero Infinito 2022: Cómo descargar y jugar el último juego de Bunny Hop
-Si eres un fan de los juegos de salto de conejos, es posible que hayas oído hablar de Bhop Pro, un juego móvil que te permite saltar y saltar de conejito en el modo fps. Bhop Pro es un juego divertido y adictivo que pone a prueba tus habilidades y reflejos a medida que completas misiones parkour y compites con otros jugadores en línea. En este artículo, le diremos todo lo que necesita saber sobre Bhop Pro, incluyendo cómo descargar e instalar el archivo APK modded que le da dinero ilimitado y acceso a todos los elementos en el juego.
-¿Qué es Bhop Pro?
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Un juego móvil que te permite saltar y saltar en modo fps
-Bhop Pro es un juego que simula la técnica de salto de conejos que utilizan algunos jugadores en juegos de disparos en primera persona. El salto de conejos es una forma de ganar más velocidad mediante el uso de ametrallamiento de aire, lo que significa moverse de lado en el aire mientras se salta. Al hacer esto, puedes evitar obstáculos, llegar a lugares más altos y escapar de tus enemigos.
-Un modo de juego que te reta a completar misiones parkour y aumentar tu rango
-Bhop Pro tiene varios modos de juego que puedes elegir, como parkour, surf, deathrun, speedrun y random. En cada modo, tienes que completar diferentes mapas que tienen varios niveles de dificultad. También puedes ganar rankings haciendo misiones parkour, que son tareas que requieren que realices ciertas acciones o alcances ciertas metas. Las clasificaciones van desde Silver I a Master Bhoper 2.
-Un juego que presenta física realista, múltiples mapas, multijugador en línea y opciones de personalización
- -Lo siguiente que debe hacer es habilitar fuentes desconocidas en la configuración del dispositivo. Esto se debe a Bhop Pro APK Dinero Infinito 2022 no está disponible en las tiendas de aplicaciones oficiales, y es necesario permitir que su dispositivo para instalar aplicaciones de otras fuentes. Para hacer esto, debe ir a la configuración del dispositivo, luego la seguridad, luego fuentes desconocidas y luego activarla. Esto le permitirá instalar Bhop Pro APK Dinero Infinito 2022 sin ningún problema.
-Descargar e instalar el archivo APK
-Lo último que tienes que hacer es descargar e instalar el archivo APK. Para ello, debe ir a la página web que eligió en el primer paso, y luego haga clic en el botón de descarga. Esto iniciará el proceso de descarga, que puede tardar unos minutos dependiendo de su velocidad de Internet. Una vez que se complete la descarga, es necesario localizar el archivo APK en el almacenamiento del dispositivo, y luego toque en él. Esto iniciará el proceso de instalación, que puede tardar unos segundos. Una vez completada la instalación, se puede abrir el juego y disfrutar de Bhop Pro APK Dinero Infinito 2022.
-Cómo jugar Bhop Pro?
-Ahora que ha descargado e instalado Bhop Pro APK Dinero Infinito 2022, es posible que se pregunte cómo jugar el juego. Estos son algunos de los pasos básicos que debes seguir:
-Elige un modo de juego y un mapa
-Lo primero que tienes que hacer es elegir un modo de juego y un mapa que quieres jugar. Puedes elegir entre parkour, surf, deathrun, speedrun y modos aleatorios. Cada modo tiene diferentes objetivos y desafíos que necesitas completar. También puede elegir entre varios mapas que tienen diferentes temas y dificultades. Puede jugar solo o en línea con otros jugadores.
-Utilice los controles táctiles para moverse y saltar
- -Utilizar el aire para ganar velocidad y evitar obstáculos
-Lo más importante que tienes que hacer es utilizar el aire strafing para ganar velocidad y evitar obstáculos. Es una técnica que consiste en moverse de lado en el aire mientras se salta. Al hacer esto, puede aumentar su impulso y alcanzar velocidades más altas. También puedes utilizar el aire para esquivar obstáculos, cambiar de dirección y aterrizar de forma segura. Para realizar el strafing del aire, usted necesita saltar y después mover su dedo en un movimiento circular en el lado izquierdo de la pantalla. También necesitas alinear tu vista con tu movimiento en el lado derecho de la pantalla.
-Consejos y trucos para Bhop Pro
-Bhop Pro es un juego que requiere habilidad y práctica. Aquí hay algunos consejos y trucos que pueden ayudarle a mejorar su juego:
- -Practica en mapas fáciles antes de pasar a los más difíciles
-Una de las mejores maneras de aprender a bhop es practicar en mapas fáciles antes de pasar a los más difíciles. Los mapas fáciles tienen menos obstáculos, plataformas más anchas y alturas más bajas. Pueden ayudarte a familiarizarte con los controles, la física y la mecánica del juego. También pueden ayudarte a desarrollar tu memoria muscular y tus reflejos. Una vez que domines los mapas fáciles, puedes desafiarte a ti mismo con los más difíciles que tienen más obstáculos, plataformas más estrechas y alturas más altas.
-Ver vídeos de otros jugadores para aprender de sus técnicas
-Otra manera de aprender a bhop es ver videos de otros jugadores que son mejores que tú. Puede encontrar muchos vídeos de reproductores Bhop Pro en YouTube u otras plataformas. Puede ver cómo se mueven, saltan, se desvían y utilizan los elementos. También puedes ver cómo completan diferentes mapas y modos. Puedes aprender de sus técnicas e intentar replicarlas en tu propio juego.
-Utilice portales, amplificadores y artículos para mejorar su rendimiento
- -Conclusión
-Bhop Pro es un juego móvil que te permite saltar y saltar conejito en modo fps. Es un juego que desafía tus habilidades y reflejos a medida que completas misiones de parkour y compites con otros jugadores en línea. También es un juego que cuenta con física realista, múltiples mapas, multijugador en línea y opciones de personalización. Si quieres disfrutar del juego al máximo, se puede descargar e instalar Bhop Pro APK Dinero Infinito 2022, que le da dinero ilimitado y el acceso a todos los elementos en el juego. También puedes seguir los consejos y trucos que compartimos en este artículo para mejorar tu jugabilidad y divertirte más. Bhop Pro es un juego que es adecuado para cualquier persona que ama los juegos de bhopping, parkour o fps. ¡Descárgalo ahora y empieza a saltar!
-Preguntas frecuentes
-Aquí están algunas de las preguntas más frecuentes sobre Bhop Pro:
-¿Es seguro jugar a Bhop Pro?
-Bhop Pro es seguro para jugar siempre y cuando lo descargue de una fuente confiable y habilite fuentes desconocidas en la configuración de su dispositivo. El juego no contiene ningún contenido dañino o malware que pueda dañar su dispositivo o comprometer su privacidad. Sin embargo, debes tener cuidado al jugar en línea con otros jugadores, ya que algunos de ellos podrían usar trucos o hacks que podrían arruinar tu experiencia.
-¿Es Bhop Pro compatible con mi dispositivo?
-Bhop Pro es compatible con la mayoría de los dispositivos Android que tienen Android 4.1 o superior. El juego también requiere al menos 100 MB de espacio de almacenamiento gratuito en su dispositivo. Puedes comprobar la compatibilidad de tu dispositivo visitando la página de Google Play Store o App Store del juego.
-¿Cómo puedo contactar a los desarrolladores de Bhop Pro?
-Si tiene alguna pregunta, comentario o sugerencia para los desarrolladores de Bhop Pro, puede ponerse en contacto con ellos enviando un correo electrónico a begma@begma.com. También puede visitar su sitio web en [begma.com] o seguirlos en plataformas de redes sociales como Facebook, Twitter, Instagram y YouTube.
- -Si te gusta Bhop Pro y quieres apoyar a los desarrolladores del juego, puedes hacerlo clasificando y revisando el juego en Google Play Store o App Store. También puedes compartir el juego con tus amigos y familiares que podrían disfrutarlo. También puedes comprar algunos artículos o monedas en el juego si quieres apoyarlos financieramente.
-¿Cuáles son algunos otros juegos como Bhop Pro?
-Si usted está buscando algunos otros juegos como Bhop Pro, es posible que desee probar estos juegos:
--
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Apk De Simulador De Conduccin De Coche Extremo.md b/spaces/Benson/text-generation/Examples/Descargar Apk De Simulador De Conduccin De Coche Extremo.md deleted file mode 100644 index a9a2b45a39537e7a87874afbe6e8457f3b4a0620..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Apk De Simulador De Conduccin De Coche Extremo.md +++ /dev/null @@ -1,100 +0,0 @@ -
-
Descargar APK de Extreme Car Driving Simulator
-¿Te encanta conducir coches rápidos y realizar acrobacias en un entorno de mundo abierto? Si es así, entonces usted debe probar Extreme Car Driving Simulator, uno de los juegos de simulador de coches más populares para dispositivos Android. En este artículo, te diremos de qué se trata este juego, cómo descargar e instalar el archivo APK, por qué deberías descargar el APK y cómo jugar el juego como un profesional.
-¿Qué es Extreme Car Driving Simulator?
-Extreme Car Driving Simulator es un juego desarrollado por AxesInMotion Racing, un estudio especializado en crear juegos de carreras realistas y divertidos. El juego fue lanzado en 2014 y desde entonces ha ganado más de 500 millones de descargas en Google Play Store. También está disponible en otras plataformas como iOS, Windows y Mac.
-descargar apk de simulador de conducción de coche extremo
DOWNLOAD ✑ ✑ ✑ https://bltlly.com/2v6KIg
-
El juego te permite conducir, derrapar y sentir un coche deportivo de carreras en una ciudad enorme sin tráfico ni reglas. Puede explorar la ciudad, realizar acrobacias ilegales, estrellar su coche, y disfrutar de la física realista y sistema de daños. También puedes elegir entre diferentes modos de juego, como el modo de punto de control, el modo de tráfico, el modo libre y más.
-Características del juego
-Algunas de las características que hacen que Extreme Car Driving Simulator se destaque de otros juegos de simuladores de coches son:
--
-
Cómo descargar e instalar el APK
-Si desea descargar e instalar Extreme Car Driving Simulator APK en su dispositivo Android, es necesario seguir estos sencillos pasos:
--
-
¿Por qué descargar Extreme Car Driving Simulator APK?
-Usted puede preguntarse por qué usted debe descargar Extreme Car Driving Simulator APK en lugar de conseguirlo de la Google Play Store. Bueno, hay algunos beneficios y riesgos de descargar el APK que usted debe saber antes de decidir.
-Beneficios de descargar el APK
-Algunos de los beneficios de descargar Extreme Car Driving Simulator APK son:
--
-
Riesgos y precauciones de descargar el APK
- --
-
Cómo jugar simulador de conducción de coches extremos
-Ahora que ha descargado e instalado Extreme Car Driving Simulator APK, es posible que desee saber cómo jugar el juego y divertirse. Aquí hay algunos consejos y trucos que te ayudarán a dominar el juego y convertirte en un conductor profesional.
-Controles y ajustes
-Lo primero que debe hacer antes de jugar Extreme Car Driving Simulator es ajustar los controles y ajustes de acuerdo a su preferencia. Puede acceder a ellos pulsando en el icono del engranaje en la esquina superior derecha de la pantalla. Aquí hay algunas opciones que puede personalizar:
--
-
Modos de juego y desafíos
-Lo siguiente que debe hacer antes de jugar Extreme Car Driving Simulator es elegir un modo de juego y un desafío que se adapte a su estado de ánimo y nivel de habilidad. Puede acceder a ellos pulsando en el icono del mapa en la esquina superior izquierda de la pantalla. Aquí hay algunas opciones que puede elegir:
- --
-
Consejos y trucos
- --
-
Conclusión
- -Resumen del artículo
-Este artículo es sobre cómo descargar e instalar Extreme Car Driving Simulator APK, un juego de simulador de coche para dispositivos Android. Cubre los siguientes temas:
--
-
Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Extreme Car Driving Simulator APK:
--
-
-
-
\ No newline at end of file diff --git a/spaces/BetterAPI/BetterChat_new/src/routes/+layout.server.ts b/spaces/BetterAPI/BetterChat_new/src/routes/+layout.server.ts deleted file mode 100644 index 85358b3e31cbfb0593bc19aaad565b329fe37058..0000000000000000000000000000000000000000 --- a/spaces/BetterAPI/BetterChat_new/src/routes/+layout.server.ts +++ /dev/null @@ -1,33 +0,0 @@ -import type { LayoutServerLoad } from "./$types"; -import { collections } from "$lib/server/database"; -import type { Conversation } from "$lib/types/Conversation"; -import { UrlDependency } from "$lib/types/UrlDependency"; - -export const load: LayoutServerLoad = async ({ locals, depends }) => { - const { conversations } = collections; - - depends(UrlDependency.ConversationList); - depends(UrlDependency.Settings); - - const settings = await collections.settings.findOne({ sessionId: locals.sessionId }); - - return { - conversations: await conversations - .find({ - sessionId: locals.sessionId, - }) - .sort({ updatedAt: -1 }) - .project
-
- {value}
-
-
- )
-})
-CodeBlock.displayName = 'CodeBlock'
-
-export { CodeBlock }
diff --git a/spaces/Huertas97/Inpaint_Me/README.md b/spaces/Huertas97/Inpaint_Me/README.md
deleted file mode 100644
index be558db8892c6630c95bb73fc6699caa2cdc5133..0000000000000000000000000000000000000000
--- a/spaces/Huertas97/Inpaint_Me/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
----
-title: Inpaint_Me
-emoji: 🎨
-colorFrom: green
-colorTo: red
-sdk: streamlit
-app_file: app_inpaint.py
-pinned: false
-license: apache-2.0
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/HyAgOsK/ECG_avalible/README.md b/spaces/HyAgOsK/ECG_avalible/README.md
deleted file mode 100644
index df75ca30038012596f1ea3db711fd8268a9c6933..0000000000000000000000000000000000000000
--- a/spaces/HyAgOsK/ECG_avalible/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ECG Avalible
-emoji: 🐠
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ICML2022/OFA/fairseq/examples/bart/README.glue.md b/spaces/ICML2022/OFA/fairseq/examples/bart/README.glue.md
deleted file mode 100644
index a010934e1e6dec491eb1c704ec02ba7405760510..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/bart/README.glue.md
+++ /dev/null
@@ -1,99 +0,0 @@
-# Fine-tuning BART on GLUE tasks
-
-### 1) Download the data from GLUE website (https://gluebenchmark.com/tasks) using following commands:
-```bash
-wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py
-python download_glue_data.py --data_dir glue_data --tasks all
-```
-
-### 2) Preprocess GLUE task data (same as RoBERTa):
-```bash
-./examples/roberta/preprocess_GLUE_tasks.sh glue_data
- {language}
-
-
-
-
-
- 🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming
""" - -#display message for themes feature -theme_addon_msg = """theme.push_to_hub().
-🏆Participate in Gradio's Theme Building Hackathon to exhibit your creative flair and win fabulous rewards! Join here - Gradio-Themes-Party🎨 🏆
🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌""")
- gr.HTML(theme_addon_msg)
- gr.HTML('''
Duplicate the Space and run securely with your OpenAI API Key
"
-
- }
-gr.Interface(**inter_arguments).launch(share=True)
-
-# import gradio as gr
-
-# def greet(name):
-# return "Hello " + name + "!!"
-
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-# iface.launch()
\ No newline at end of file
diff --git a/spaces/JeffJing/ZookChatBot/steamship/plugin/trainable_model.py b/spaces/JeffJing/ZookChatBot/steamship/plugin/trainable_model.py
deleted file mode 100644
index 451967620022fc16fec7e0d5e267e20d3121610e..0000000000000000000000000000000000000000
--- a/spaces/JeffJing/ZookChatBot/steamship/plugin/trainable_model.py
+++ /dev/null
@@ -1,187 +0,0 @@
-import logging
-from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import Callable, Dict, Generic, Optional, TypeVar
-
-from typing_extensions import TypeAlias
-
-from steamship.base.client import Client
-from steamship.invocable import InvocableResponse
-from steamship.invocable.plugin_service import PluginRequest
-from steamship.plugin.inputs.train_plugin_input import TrainPluginInput
-from steamship.plugin.outputs.model_checkpoint import ModelCheckpoint
-from steamship.plugin.outputs.train_plugin_output import TrainPluginOutput
-
-ModelConstructor: TypeAlias = Callable[[], "TrainableModel"]
-
-# Global variable to store the model for reuse in memory.
-MODEL_CACHE: Dict[str, "TrainableModel"] = {}
-
-ConfigType = TypeVar("ConfigType")
-
-
-class TrainableModel(ABC, Generic[ConfigType]):
- """Base class for trainable models.
-
- Trainable models are not plugins. They are a thin wrapper around the state of a model designed to be **used with**
- the Steamship plugin system.
-
- # State Management
-
- 100% of a TrainableModel's state management should save to & read from a folder on disk via the methods
- `save_to_folder` and `load_from_folder`.
-
- # Remote Saving and Loading
-
- `TrainableModel` instances automatically save to a user's Workspace on Steamship via `save_remote` method. They
- can load themselves from a user's workspace via the `load_remote` method.
-
- When saving a model, the caller provides `handle`, such as "V1" or "epoch_23". This allows that particular checkpoint
- to be re-loaded. By default, every save operation also saves the model to the "default" checkpoint, overwriting it
- if it already existed. When a user loads a model without specifying a checkpoint, the "default" checkpoint will be used.
-
- # Data Scope
-
- A TrainableModel's data is saved & loaded with respect to
-
- 1) The user's active Workspace, and
- 2) The provided Plugin Instance within that workspace.
-
- The active workspace is read from the Steamship client context, and the `plugin_instance_id` is supplied as a
- method argument on the `save_remote` and `load_remote` methods.
-
- This organization enables a user to have arbitrarily many trained model instances of the same type colocated within
- a Workspace.
-
- # Training
-
- A training job is fully parameterized by the `TrainPluginInput` object.
-
- # Result Reporting
-
- A training job's results are reported via the `TrainPluginOutput` object. These results include a reference to the
- `save_remote` output, but they do not include the model parameters themselves. For example, after training, one
- could write:
-
- >>> archive_path_in_steamship = model.save_remote(..)
- >>> output = TrainPluginOutput(archive_path_in_steamship=archive_path_in_steamship,
- ...
- )
-
- That output is the ultimate return object of the training process, but the Plugin that owns this model need not
- wait for synchronous completion to update the Steamship Engine with intermediate results. It can use the
- `Response.post_update` to proactively stream results back to the server.
-
- # Third-party / External Models
-
- This model class is a convenient wrapper for models running on third party systems (e.g. Google's AutoML). In such
- a case:
-
- - The `train` method would begin the job on the 3rd party system.
- - The `save_to_folder` method would write the Job ID and any other useful data to the checkpoint path
- - The `load_from_folder` method would read this Job ID from disk and obtain an authenticated client with the
- third party system.
- - Any `run` method the implementer created would ferry back results fetched from the third-party system.
- - Any status reporting in TrainPluginOutput would ferry back status fetched from the third-party system.
-
- """
-
- config: ConfigType = None
-
- def receive_config(self, config: ConfigType):
- """Stores config from plugin instance, so it is accessible by model on load or train."""
- self.config = config
-
- @abstractmethod
- def save_to_folder(self, checkpoint_path: Path):
- """Saves 100% of the state of this model to the provided path."""
- raise NotImplementedError()
-
- @abstractmethod
- def load_from_folder(self, checkpoint_path: Path):
- """Load 100% of the state of this model to the provided path."""
- raise NotImplementedError()
-
- @abstractmethod
- def train(self, input: PluginRequest[TrainPluginInput]) -> InvocableResponse[TrainPluginOutput]:
- """Train or fine-tune the model, parameterized by the information in the TrainPluginInput object."""
- raise NotImplementedError()
-
- @abstractmethod
- def train_status(
- self, input: PluginRequest[TrainPluginInput]
- ) -> InvocableResponse[TrainPluginOutput]:
- """Check on the status of an in-process training job, if it is running externally asynchronously."""
- raise NotImplementedError()
-
- @classmethod
- def load_from_local_checkpoint(cls, checkpoint: ModelCheckpoint, config: ConfigType):
- model = cls()
- model.receive_config(config=config)
- model.load_from_folder(checkpoint.folder_path_on_disk())
- return model
-
- @classmethod
- def load_remote(
- cls,
- client: Client,
- plugin_instance_id: str,
- checkpoint_handle: Optional[str] = None,
- use_cache: bool = True,
- model_parent_directory: Path = None,
- plugin_instance_config: ConfigType = None,
- ):
- if checkpoint_handle is None:
- # For some reason doing this defaulting in the signature wasn't working.
- checkpoint_handle = ModelCheckpoint.DEFAULT_HANDLE
-
- model_key = f"{plugin_instance_id}/{checkpoint_handle}"
- logging.info(f"TrainableModel:load_remote - Model Key: {model_key}")
-
- global MODEL_CACHE
-
- if use_cache:
- if model_key in MODEL_CACHE:
- logging.info(f"TrainableModel:load_remote - Returning cached: {model_key}")
- return MODEL_CACHE[model_key]
-
- checkpoint = ModelCheckpoint(
- client=client,
- parent_directory=model_parent_directory,
- handle=checkpoint_handle,
- plugin_instance_id=plugin_instance_id,
- )
-
- # If we haven't loaded the model, we need to download and start the model
- logging.info(f"TrainableModel:load_remote - Downloading: {model_key}")
- checkpoint.download_model_bundle()
- logging.info(f"TrainableModel:load_remote - Loading: {model_key}")
- model = cls.load_from_local_checkpoint(checkpoint, plugin_instance_config)
- logging.info(f"TrainableModel:load_remote - Loaded: {model_key}")
-
- if use_cache:
- MODEL_CACHE[model_key] = model
-
- return model
-
- def save_remote(
- self,
- client: Client,
- plugin_instance_id: str,
- checkpoint_handle: Optional[str] = None,
- model_parent_directory: Path = None,
- set_as_default: bool = True,
- ) -> str:
- if checkpoint_handle is None:
- # For some reason doing this defaulting in the signature wasn't working.
- checkpoint_handle = ModelCheckpoint.DEFAULT_HANDLE
-
- checkpoint = ModelCheckpoint(
- client=client,
- parent_directory=model_parent_directory,
- handle=checkpoint_handle,
- plugin_instance_id=plugin_instance_id,
- )
- self.save_to_folder(checkpoint.folder_path_on_disk())
- checkpoint.upload_model_bundle(set_as_default=set_as_default)
- return checkpoint.archive_path_in_steamship()
diff --git a/spaces/Jimmyfreelancer/Pix2Pix-Video/share_btn.py b/spaces/Jimmyfreelancer/Pix2Pix-Video/share_btn.py
deleted file mode 100644
index 66e0de15dce2d65f4cd0ef512c7bd8adad0beb77..0000000000000000000000000000000000000000
--- a/spaces/Jimmyfreelancer/Pix2Pix-Video/share_btn.py
+++ /dev/null
@@ -1,73 +0,0 @@
-community_icon_html = """"""
-
-loading_icon_html = """"""
-
-share_js = """async () => {
- async function uploadFile(file){
- const UPLOAD_URL = 'https://huggingface.co/uploads';
- const response = await fetch(UPLOAD_URL, {
- method: 'POST',
- headers: {
- 'Content-Type': file.type,
- 'X-Requested-With': 'XMLHttpRequest',
- },
- body: file, /// <- File inherits from Blob
- });
- const url = await response.text();
- return url;
- }
-
- async function getVideoBlobFile(videoEL){
- const res = await fetch(videoEL.src);
- const blob = await res.blob();
- const videoId = Date.now() % 200;
- const fileName = `vid-pix2pix-${{videoId}}.wav`;
- const videoBlob = new File([blob], fileName, { type: 'video/mp4' });
- console.log(videoBlob);
- return videoBlob;
- }
-
- const gradioEl = document.querySelector("gradio-app").shadowRoot || document.querySelector('body > gradio-app');
- const captionTxt = gradioEl.querySelector('#prompt-in textarea').value;
- const inputVidEl = gradioEl.querySelector('#input-vid video');
- const outputVideo = gradioEl.querySelector('#video-output video');
-
-
- const shareBtnEl = gradioEl.querySelector('#share-btn');
- const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
- const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
- if(!outputVideo){
- return;
- };
- shareBtnEl.style.pointerEvents = 'none';
- shareIconEl.style.display = 'none';
- loadingIconEl.style.removeProperty('display');
-
- const inputFile = await getVideoBlobFile(inputVidEl);
- const urlInputVid = await uploadFile(inputFile);
- const videoOutFile = await getVideoBlobFile(outputVideo);
- const dataOutputVid = await uploadFile(videoOutFile);
-
- const descriptionMd = `
-#### Video input:
-${urlInputVid}
-
-#### Pix2Pix result:
-${dataOutputVid}
-`;
- const params = new URLSearchParams({
- title: captionTxt,
- description: descriptionMd,
- });
- const paramsStr = params.toString();
- window.open(`https://huggingface.co/spaces/fffiloni/Pix2Pix-Video/discussions/new?${paramsStr}`, '_blank');
- shareBtnEl.style.removeProperty('pointer-events');
- shareIconEl.style.removeProperty('display');
- loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/Dockerfile b/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/Dockerfile
deleted file mode 100644
index 85d5045d5316ac160277af1e7d60afa823c0f953..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM python:3.9-slim-buster as builder
-RUN apt-get update \
- && apt-get install -y build-essential \
- && apt-get clean \
- && rm -rf /var/lib/apt/lists/*
-COPY requirements.txt .
-COPY requirements_advanced.txt .
-RUN pip install --user --no-cache-dir -r requirements.txt
-# RUN pip install --user --no-cache-dir -r requirements_advanced.txt
-
-FROM python:3.9-slim-buster
-LABEL maintainer="iskoldt"
-COPY --from=builder /root/.local /root/.local
-ENV PATH=/root/.local/bin:$PATH
-COPY . /app
-WORKDIR /app
-ENV dockerrun=yes
-CMD ["python3", "-u", "ChuanhuChatbot.py","2>&1", "|", "tee", "/var/log/application.log"]
diff --git a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/model_components/lstm.py b/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/model_components/lstm.py
deleted file mode 100644
index 2073bd10285b1db90ddd956aa97da711aa77620a..0000000000000000000000000000000000000000
--- a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/model_components/lstm.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
-
-
-class LSTM(nn.Module):
- def __init__(self, text_dim, embedding_dim, vocab_size, padding_idx=0):
- super().__init__()
- self.padding_idx = padding_idx
- self.word_embedding = nn.Embedding(
- vocab_size, embedding_dim, padding_idx=padding_idx
- )
- self.rnn = nn.LSTM(embedding_dim, text_dim, batch_first=True)
- self.w_attn = nn.Parameter(torch.Tensor(1, text_dim))
- nn.init.xavier_uniform_(self.w_attn)
-
- def forward(self, padded_tokens, dropout=0.5):
- w_emb = self.word_embedding(padded_tokens)
- w_emb = F.dropout(w_emb, dropout, self.training)
- len_seq = (padded_tokens != self.padding_idx).sum(dim=1).cpu()
- x_packed = pack_padded_sequence(
- w_emb, len_seq, enforce_sorted=False, batch_first=True
- )
- B = padded_tokens.shape[0]
- rnn_out, _ = self.rnn(x_packed)
- rnn_out, dummy = pad_packed_sequence(rnn_out, batch_first=True)
- h = rnn_out[torch.arange(B), len_seq - 1]
- final_feat, attn = self.word_attention(rnn_out, h, len_seq)
- return final_feat, attn
-
- def word_attention(self, R, h, len_seq):
- """
- Input:
- R: hidden states of the entire words
- h: the final hidden state after processing the entire words
- len_seq: the length of the sequence
- Output:
- final_feat: the final feature after the bilinear attention
- attn: word attention weights
- """
- B, N, D = R.shape
- device = R.device
- len_seq = len_seq.to(device)
-
- W_attn = (self.w_attn * torch.eye(D).to(device))[None].repeat(B, 1, 1)
- score = torch.bmm(torch.bmm(R, W_attn), h.unsqueeze(-1))
-
- mask = torch.arange(N).reshape(1, N, 1).repeat(B, 1, 1).to(device)
- mask = mask < len_seq.reshape(B, 1, 1)
-
- score = score.masked_fill(mask == 0, -1e9)
- attn = F.softmax(score, 1)
- final_feat = torch.bmm(R.transpose(1, 2), attn).squeeze(-1)
-
- return final_feat, attn.squeeze(-1)
diff --git a/spaces/KPCGD/bingo/src/components/chat-image.tsx b/spaces/KPCGD/bingo/src/components/chat-image.tsx
deleted file mode 100644
index 05ecc9771eada27a0f2d160bb01cba170d37bb09..0000000000000000000000000000000000000000
--- a/spaces/KPCGD/bingo/src/components/chat-image.tsx
+++ /dev/null
@@ -1,170 +0,0 @@
-import {
- useEffect,
- useState,
- useCallback,
- ChangeEvent,
- ClipboardEvent,
- MouseEventHandler,
- FormEvent,
- useRef
-} from "react"
-import Image from 'next/image'
-import PasteIcon from '@/assets/images/paste.svg'
-import UploadIcon from '@/assets/images/upload.svg'
-import CameraIcon from '@/assets/images/camera.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { cn } from '@/lib/utils'
-
-interface ChatImageProps extends Pick
-
- )
-}
diff --git a/spaces/KT07/Speech_Analytics/app.py b/spaces/KT07/Speech_Analytics/app.py
deleted file mode 100644
index 2461f97ac2ecff13102947f2e0d8df735c22a13c..0000000000000000000000000000000000000000
--- a/spaces/KT07/Speech_Analytics/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import gradio as gr
-
-def func_gradio(filename):
- return "Hello World!", "Hello World!", "Hello World!", "Hello World!", "Hello World!", "Hello World!", \
-"Hello World!", "Hello World!","Hello World!", "Hello World!", "Hello World!", "Hello World!", "Hello World!",\
-"Hello World!", "Hello World!", "Hello World!", "Hello World!", "Hello World!"
-
-with gr.Blocks() as demo:
- gr.Markdown(''' # Call Transcription and Analysis
- This app takes the call's audio file as an input, identifies the distinct speakers on \
- the call and provides the transcription along with other insights as the output. Please upload the audio \
- file and click on 'Submit' to generate the output. It also has the option to download the Output as \
- a CSV file.''')
- with gr.Row():
- audio_file = gr.Audio(source = "upload",type = "filepath",label = "Upload the Input audio File")
- submit_btn = gr.Button("Submit")
- with gr.Column():
- with gr.Row():
- # output0 = gr.Textbox(label = "Language")
- output1 = gr.Textbox(label = "Full Transcription")
- output2 = gr.Textbox(label = "Diarized Transcription")
- # output3 = gr.Textbox(label = "Speaker 1 Transcription")
- # output4 = gr.Textbox(label = "Speaker 2 Transcription")
- with gr.Row():
- output5 = gr.Textbox(label = "Sentiment")
- output6 = gr.Textbox(label = "Pitch Detection")
- output7 = gr.Textbox(label = "Named Entities")
- output8 = gr.Textbox(label = "Keywords")
- output9 = gr.Textbox(label = "Topic LDA")
- output10 = gr.Textbox(label = "Keywords LDA")
- with gr.Row():
- output11 = gr.Textbox(label = "L1 Label")
- output12 = gr.Textbox(label = "L2 Label")
- output13 = gr.Textbox(label = "L3 Label")
- with gr.Row():
- output14 = gr.Textbox(label = "Question 1")
- output15 = gr.Textbox(label = "Question 2")
- output16 = gr.Textbox(label = "Question 3")
- output17 = gr.Textbox(label = "Question 4")
- output18 = gr.Textbox(label = "Question 5")
-
- submit_btn.click(fn=func_gradio,
- inputs = audio_file,
- outputs = [output1,output2,output5,output6,output7,output8,output9,output10,output11,
- output12,output13,output14,output15,output16,output17,output18])
-demo.launch(share = False, debug= True)
\ No newline at end of file
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/MDXNet.py b/spaces/Kangarroar/ApplioRVC-Inference/MDXNet.py
deleted file mode 100644
index 9b7eb43844ad0d4f9ce61287ccf9a8a4206d3853..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/MDXNet.py
+++ /dev/null
@@ -1,272 +0,0 @@
-import soundfile as sf
-import torch, pdb, os, warnings, librosa
-import numpy as np
-import onnxruntime as ort
-from tqdm import tqdm
-import torch
-
-dim_c = 4
-
-
-class Conv_TDF_net_trim:
- def __init__(
- self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024
- ):
- super(Conv_TDF_net_trim, self).__init__()
-
- self.dim_f = dim_f
- self.dim_t = 2**dim_t
- self.n_fft = n_fft
- self.hop = hop
- self.n_bins = self.n_fft // 2 + 1
- self.chunk_size = hop * (self.dim_t - 1)
- self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(
- device
- )
- self.target_name = target_name
- self.blender = "blender" in model_name
-
- out_c = dim_c * 4 if target_name == "*" else dim_c
- self.freq_pad = torch.zeros(
- [1, out_c, self.n_bins - self.dim_f, self.dim_t]
- ).to(device)
-
- self.n = L // 2
-
- def stft(self, x):
- x = x.reshape([-1, self.chunk_size])
- x = torch.stft(
- x,
- n_fft=self.n_fft,
- hop_length=self.hop,
- window=self.window,
- center=True,
- return_complex=True,
- )
- x = torch.view_as_real(x)
- x = x.permute([0, 3, 1, 2])
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
- [-1, dim_c, self.n_bins, self.dim_t]
- )
- return x[:, :, : self.dim_f]
-
- def istft(self, x, freq_pad=None):
- freq_pad = (
- self.freq_pad.repeat([x.shape[0], 1, 1, 1])
- if freq_pad is None
- else freq_pad
- )
- x = torch.cat([x, freq_pad], -2)
- c = 4 * 2 if self.target_name == "*" else 2
- x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape(
- [-1, 2, self.n_bins, self.dim_t]
- )
- x = x.permute([0, 2, 3, 1])
- x = x.contiguous()
- x = torch.view_as_complex(x)
- x = torch.istft(
- x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True
- )
- return x.reshape([-1, c, self.chunk_size])
-
-
-def get_models(device, dim_f, dim_t, n_fft):
- return Conv_TDF_net_trim(
- device=device,
- model_name="Conv-TDF",
- target_name="vocals",
- L=11,
- dim_f=dim_f,
- dim_t=dim_t,
- n_fft=n_fft,
- )
-
-
-warnings.filterwarnings("ignore")
-cpu = torch.device("cpu")
-if torch.cuda.is_available():
- device = torch.device("cuda:0")
-elif torch.backends.mps.is_available():
- device = torch.device("mps")
-else:
- device = torch.device("cpu")
-
-
-class Predictor:
- def __init__(self, args):
- self.args = args
- self.model_ = get_models(
- device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft
- )
- self.model = ort.InferenceSession(
- os.path.join(args.onnx, self.model_.target_name + ".onnx"),
- providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
- )
- print("onnx load done")
-
- def demix(self, mix):
- samples = mix.shape[-1]
- margin = self.args.margin
- chunk_size = self.args.chunks * 44100
- assert not margin == 0, "margin cannot be zero!"
- if margin > chunk_size:
- margin = chunk_size
-
- segmented_mix = {}
-
- if self.args.chunks == 0 or samples < chunk_size:
- chunk_size = samples
-
- counter = -1
- for skip in range(0, samples, chunk_size):
- counter += 1
-
- s_margin = 0 if counter == 0 else margin
- end = min(skip + chunk_size + margin, samples)
-
- start = skip - s_margin
-
- segmented_mix[skip] = mix[:, start:end].copy()
- if end == samples:
- break
-
- sources = self.demix_base(segmented_mix, margin_size=margin)
- """
- mix:(2,big_sample)
- segmented_mix:offset->(2,small_sample)
- sources:(1,2,big_sample)
- """
- return sources
-
- def demix_base(self, mixes, margin_size):
- chunked_sources = []
- progress_bar = tqdm(total=len(mixes))
- progress_bar.set_description("Processing")
- for mix in mixes:
- cmix = mixes[mix]
- sources = []
- n_sample = cmix.shape[1]
- model = self.model_
- trim = model.n_fft // 2
- gen_size = model.chunk_size - 2 * trim
- pad = gen_size - n_sample % gen_size
- mix_p = np.concatenate(
- (np.zeros((2, trim)), cmix, np.zeros((2, pad)), np.zeros((2, trim))), 1
- )
- mix_waves = []
- i = 0
- while i < n_sample + pad:
- waves = np.array(mix_p[:, i : i + model.chunk_size])
- mix_waves.append(waves)
- i += gen_size
- mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
- with torch.no_grad():
- _ort = self.model
- spek = model.stft(mix_waves)
- if self.args.denoise:
- spec_pred = (
- -_ort.run(None, {"input": -spek.cpu().numpy()})[0] * 0.5
- + _ort.run(None, {"input": spek.cpu().numpy()})[0] * 0.5
- )
- tar_waves = model.istft(torch.tensor(spec_pred))
- else:
- tar_waves = model.istft(
- torch.tensor(_ort.run(None, {"input": spek.cpu().numpy()})[0])
- )
- tar_signal = (
- tar_waves[:, :, trim:-trim]
- .transpose(0, 1)
- .reshape(2, -1)
- .numpy()[:, :-pad]
- )
-
- start = 0 if mix == 0 else margin_size
- end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
- if margin_size == 0:
- end = None
- sources.append(tar_signal[:, start:end])
-
- progress_bar.update(1)
-
- chunked_sources.append(sources)
- _sources = np.concatenate(chunked_sources, axis=-1)
- # del self.model
- progress_bar.close()
- return _sources
-
- def prediction(self, m, vocal_root, others_root, format):
- os.makedirs(vocal_root, exist_ok=True)
- os.makedirs(others_root, exist_ok=True)
- basename = os.path.basename(m)
- mix, rate = librosa.load(m, mono=False, sr=44100)
- if mix.ndim == 1:
- mix = np.asfortranarray([mix, mix])
- mix = mix.T
- sources = self.demix(mix.T)
- opt = sources[0].T
- if format in ["wav", "flac"]:
- sf.write(
- "%s/%s_main_vocal.%s" % (vocal_root, basename, format), mix - opt, rate
- )
- sf.write("%s/%s_others.%s" % (others_root, basename, format), opt, rate)
- else:
- path_vocal = "%s/%s_main_vocal.wav" % (vocal_root, basename)
- path_other = "%s/%s_others.wav" % (others_root, basename)
- sf.write(path_vocal, mix - opt, rate)
- sf.write(path_other, opt, rate)
- if os.path.exists(path_vocal):
- os.system(
- "ffmpeg -i %s -vn %s -q:a 2 -y"
- % (path_vocal, path_vocal[:-4] + ".%s" % format)
- )
- if os.path.exists(path_other):
- os.system(
- "ffmpeg -i %s -vn %s -q:a 2 -y"
- % (path_other, path_other[:-4] + ".%s" % format)
- )
-
-
-class MDXNetDereverb:
- def __init__(self, chunks):
- self.onnx = "uvr5_weights/onnx_dereverb_By_FoxJoy"
- self.shifts = 10 #'Predict with randomised equivariant stabilisation'
- self.mixing = "min_mag" # ['default','min_mag','max_mag']
- self.chunks = chunks
- self.margin = 44100
- self.dim_t = 9
- self.dim_f = 3072
- self.n_fft = 6144
- self.denoise = True
- self.pred = Predictor(self)
-
- def _path_audio_(self, input, vocal_root, others_root, format):
- self.pred.prediction(input, vocal_root, others_root, format)
-
-
-if __name__ == "__main__":
- dereverb = MDXNetDereverb(15)
- from time import time as ttime
-
- t0 = ttime()
- dereverb._path_audio_(
- "雪雪伴奏对消HP5.wav",
- "vocal",
- "others",
- )
- t1 = ttime()
- print(t1 - t0)
-
-
-"""
-
-runtime\python.exe MDXNet.py
-
-6G:
-15/9:0.8G->6.8G
-14:0.8G->6.5G
-25:炸
-
-half15:0.7G->6.6G,22.69s
-fp32-15:0.7G->6.6G,20.85s
-
-"""
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/audio.py b/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/audio.py
deleted file mode 100644
index 9ad4ff74218957cf18782fa71add40a734b47e78..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/audio.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import librosa
-import numpy as np
-import av
-from io import BytesIO
-import ffmpeg
-import os
-import sys
-
-import random
-from infer.lib.csvutil import CSVutil
-#import csv
-
-platform_stft_mapping = {
- 'linux': 'stftpitchshift',
- 'darwin': 'stftpitchshift',
- 'win32': 'stftpitchshift.exe',
-}
-
-stft = platform_stft_mapping.get(sys.platform)
-
-def wav2(i, o, format):
- inp = av.open(i, 'rb')
- if format == "m4a": format = "mp4"
- out = av.open(o, 'wb', format=format)
- if format == "ogg": format = "libvorbis"
- if format == "mp4": format = "aac"
-
- ostream = out.add_stream(format)
-
- for frame in inp.decode(audio=0):
- for p in ostream.encode(frame): out.mux(p)
-
- for p in ostream.encode(None): out.mux(p)
-
- out.close()
- inp.close()
-
-def audio2(i, o, format, sr):
- inp = av.open(i, 'rb')
- out = av.open(o, 'wb', format=format)
- if format == "ogg": format = "libvorbis"
- if format == "f32le": format = "pcm_f32le"
-
- ostream = out.add_stream(format, channels=1)
- ostream.sample_rate = sr
-
- for frame in inp.decode(audio=0):
- for p in ostream.encode(frame): out.mux(p)
-
- out.close()
- inp.close()
-
-def load_audion(file, sr):
- try:
- file = (
- file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- ) # 防止小白拷路径头尾带了空格和"和回车
- with open(file, "rb") as f:
- with BytesIO() as out:
- audio2(f, out, "f32le", sr)
- return np.frombuffer(out.getvalue(), np.float32).flatten()
-
- except AttributeError:
- audio = file[1] / 32768.0
- if len(audio.shape) == 2:
- audio = np.mean(audio, -1)
- return librosa.resample(audio, orig_sr=file[0], target_sr=16000)
-
- except Exception as e:
- raise RuntimeError(f"Failed to load audio: {e}")
-
-
-
-
-def load_audio(file, sr, DoFormant=False, Quefrency=1.0, Timbre=1.0):
- converted = False
- DoFormant, Quefrency, Timbre = CSVutil("csvdb/formanting.csv", "r", "formanting")
- try:
- # https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
- # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
- # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
- file = (
- file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- ) # 防止小白拷路径头尾带了空格和"和回车
- file_formanted = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-
- # print(f"dofor={bool(DoFormant)} timbr={Timbre} quef={Quefrency}\n")
-
- if (
- lambda DoFormant: True
- if DoFormant.lower() == "true"
- else (False if DoFormant.lower() == "false" else DoFormant)
- )(DoFormant):
- numerator = round(random.uniform(1, 4), 4)
- # os.system(f"stftpitchshift -i {file} -q {Quefrency} -t {Timbre} -o {file_formanted}")
- # print('stftpitchshift -i "%s" -p 1.0 --rms -w 128 -v 8 -q %s -t %s -o "%s"' % (file, Quefrency, Timbre, file_formanted))
-
- if not file.endswith(".wav"):
- if not os.path.isfile(f"{file_formanted}.wav"):
- converted = True
- # print(f"\nfile = {file}\n")
- # print(f"\nfile_formanted = {file_formanted}\n")
- converting = (
- ffmpeg.input(file_formanted, threads=0)
- .output(f"{file_formanted}.wav")
- .run(
- cmd=["ffmpeg", "-nostdin"],
- capture_stdout=True,
- capture_stderr=True,
- )
- )
- else:
- pass
-
- file_formanted = (
- f"{file_formanted}.wav"
- if not file_formanted.endswith(".wav")
- else file_formanted
- )
-
- print(f" · Formanting {file_formanted}...\n")
-
- os.system(
- '%s -i "%s" -q "%s" -t "%s" -o "%sFORMANTED_%s.wav"'
- % (
- stft,
- file_formanted,
- Quefrency,
- Timbre,
- file_formanted,
- str(numerator),
- )
- )
-
- print(f" · Formanted {file_formanted}!\n")
-
- # filepraat = (os.path.abspath(os.getcwd()) + '\\' + file).replace('/','\\')
- # file_formantedpraat = ('"' + os.path.abspath(os.getcwd()) + '/' + 'formanted'.join(file_formanted) + '"').replace('/','\\')
- # print("%sFORMANTED_%s.wav" % (file_formanted, str(numerator)))
-
- out, _ = (
- ffmpeg.input(
- "%sFORMANTED_%s.wav" % (file_formanted, str(numerator)), threads=0
- )
- .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
- .run(
- cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
- )
- )
-
- try:
- os.remove("%sFORMANTED_%s.wav" % (file_formanted, str(numerator)))
- except Exception:
- pass
- print("couldn't remove formanted type of file")
-
- else:
- out, _ = (
- ffmpeg.input(file, threads=0)
- .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
- .run(
- cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True
- )
- )
- except Exception as e:
- raise RuntimeError(f"Failed to load audio: {e}")
-
- if converted:
- try:
- os.remove(file_formanted)
- except Exception:
- pass
- print("couldn't remove converted type of file")
- converted = False
-
- return np.frombuffer(out, np.float32).flatten()
-
-
-def check_audio_duration(file):
- try:
- file = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-
- probe = ffmpeg.probe(file)
-
- duration = float(probe['streams'][0]['duration'])
-
- if duration < 0.76:
- print(
- f"\n------------\n"
- f"Audio file, {file.split('/')[-1]}, under ~0.76s detected - file is too short. Target at least 1-2s for best results."
- f"\n------------\n\n"
- )
- return False
-
- return True
- except Exception as e:
- raise RuntimeError(f"Failed to check audio duration: {e}")
\ No newline at end of file
diff --git a/spaces/Kirihasan/rvc-jjjo/app.py b/spaces/Kirihasan/rvc-jjjo/app.py
deleted file mode 100644
index d1d4fb32cf4b9622530b9fdba4af2ffea3a48c79..0000000000000000000000000000000000000000
--- a/spaces/Kirihasan/rvc-jjjo/app.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import os
-import json
-import argparse
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-from datetime import datetime
-from fairseq import checkpoint_utils
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
-from vc_infer_pipeline import VC
-from config import (
- is_half,
- device
-)
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces
-
-def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
- def vc_fn(
- input_audio,
- f0_up_key,
- f0_method,
- index_rate,
- tts_mode,
- tts_text,
- tts_voice
- ):
- try:
- if tts_mode:
- if len(tts_text) > 100 and limitation:
- return "Text is too long", None
- if tts_text is None or tts_voice is None:
- return "You need to enter text and select a voice", None
- asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
- audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
- else:
- if args.files:
- audio, sr = librosa.load(input_audio, sr=16000, mono=True)
- else:
- if input_audio is None:
- return "You need to upload an audio", None
- sampling_rate, audio = input_audio
- duration = audio.shape[0] / sampling_rate
- if duration > 20 and limitation:
- return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- times = [0, 0, 0]
- f0_up_key = int(f0_up_key)
- audio_opt = vc.pipeline(
- hubert_model,
- net_g,
- 0,
- audio,
- times,
- f0_up_key,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
- if_f0,
- )
- print(
- f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
- )
- return "Success", (tgt_sr, audio_opt)
- except:
- info = traceback.format_exc()
- print(info)
- return info, (None, None)
- return vc_fn
-
-def load_hubert():
- global hubert_model
- models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
- ["hubert_base.pt"],
- suffix="",
- )
- hubert_model = models[0]
- hubert_model = hubert_model.to(device)
- if is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
- hubert_model.eval()
-
-def change_to_tts_mode(tts_mode):
- if tts_mode:
- return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
- else:
- return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--api', action="store_true", default=False)
- parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
- parser.add_argument("--files", action="store_true", default=False, help="load audio from path")
- args, unknown = parser.parse_known_args()
- load_hubert()
- models = []
- tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
- voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
- with open("weights/model_info.json", "r", encoding="utf-8") as f:
- models_info = json.load(f)
- for name, info in models_info.items():
- if not info['enable']:
- continue
- title = info['title']
- author = info.get("author", None)
- cover = f"weights/{name}/{info['cover']}"
- index = f"weights/{name}/{info['feature_retrieval_library']}"
- npy = f"weights/{name}/{info['feature_file']}"
- cpt = torch.load(f"weights/{name}/{name}.pth", map_location="cpu")
- tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- if_f0 = cpt.get("f0", 1)
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- del net_g.enc_q
- print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净, 真奇葩
- net_g.eval().to(device)
- if is_half:
- net_g = net_g.half()
- else:
- net_g = net_g.float()
- vc = VC(tgt_sr, device, is_half)
- models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy)))
- with gr.Blocks() as app:
- gr.Markdown(
- "# panel === 'none' ? setPanel('normal') : setPanel('none')}>{children}
-
-
-
-
- {panel === 'camera-mode' &&
-
- 添加图像
-
-
-
-
-
-
-
-
}
-
-
-
-
-
-
-
-
- '
- f'
' if cover else "")+
- '
'
- )
- with gr.Row():
- with gr.Column():
- if args.files:
- vc_input = gr.Textbox(label="Input audio path")
- else:
- vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
- vc_transpose = gr.Number(label="Transpose", value=0)
- vc_f0method = gr.Radio(
- label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
- choices=["pm", "harvest"],
- value="pm",
- interactive=True,
- )
- vc_index_ratio = gr.Slider(
- minimum=0,
- maximum=1,
- label="Retrieval feature ratio",
- value=0.6,
- interactive=True,
- )
- tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
- tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
- tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
- vc_submit = gr.Button("Generate", variant="primary")
- with gr.Column():
- vc_output1 = gr.Textbox(label="Output Message")
- vc_output2 = gr.Audio(label="Output Audio")
- vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2])
- tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
- app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.share)
\ No newline at end of file
diff --git a/spaces/LanguageBind/LanguageBind/a_cls/datasets.py b/spaces/LanguageBind/LanguageBind/a_cls/datasets.py
deleted file mode 100644
index ea1289d6af2e5c5fe88dbdab7373b5001e031b14..0000000000000000000000000000000000000000
--- a/spaces/LanguageBind/LanguageBind/a_cls/datasets.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import os.path
-
-import torch
-
-from data.build_datasets import DataInfo
-from data.process_audio import get_audio_transform, torchaudio_loader
-from torchvision import datasets
-
-# -*- coding: utf-8 -*-
-# @Time : 6/19/21 12:23 AM
-# @Author : Yuan Gong
-# @Affiliation : Massachusetts Institute of Technology
-# @Email : yuangong@mit.edu
-# @File : dataloader.py
-
-# modified from:
-# Author: David Harwath
-# with some functions borrowed from https://github.com/SeanNaren/deepspeech.pytorch
-
-import csv
-import json
-import logging
-
-import torchaudio
-import numpy as np
-import torch
-import torch.nn.functional
-from torch.utils.data import Dataset
-import random
-
-
-def make_index_dict(label_csv):
- index_lookup = {}
- with open(label_csv, 'r') as f:
- csv_reader = csv.DictReader(f)
- line_count = 0
- for row in csv_reader:
- index_lookup[row['mid']] = row['index']
- line_count += 1
- return index_lookup
-
-
-class AudiosetDataset(Dataset):
- def __init__(self, args, transform, loader):
- self.audio_root = '/apdcephfs_cq3/share_1311970/downstream_datasets/Audio/audioset/eval_segments'
- dataset_json_file = '/apdcephfs_cq3/share_1311970/downstream_datasets/Audio/audioset/filter_eval.json'
- label_csv = '/apdcephfs_cq3/share_1311970/downstream_datasets/Audio/audioset/class_labels_indices.csv'
- with open(dataset_json_file, 'r') as fp:
- data_json = json.load(fp)
- self.data = data_json['data']
- self.index_dict = make_index_dict(label_csv)
- self.label_num = len(self.index_dict)
-
- self.args = args
- self.transform = transform
- self.loader = loader
-
- def __getitem__(self, index):
- datum = self.data[index]
- label_indices = np.zeros(self.label_num)
- for label_str in datum['labels'].split(','):
- label_indices[int(self.index_dict[label_str])] = 1.0
- label_indices = torch.FloatTensor(label_indices)
-
- audio = self.loader(os.path.join(self.audio_root, datum['wav']))
- audio_data = self.transform(audio)
- return audio_data, label_indices
-
- def __len__(self):
- return len(self.data)
-
-
-
-def is_valid_file(path):
- return True
-
-def get_audio_dataset(args):
- data_path = args.audio_data_path
- transform = get_audio_transform(args)
-
- if args.val_a_cls_data.lower() == 'audioset':
- dataset = AudiosetDataset(args, transform=transform, loader=torchaudio_loader)
- else:
- dataset = datasets.ImageFolder(data_path, transform=transform, loader=torchaudio_loader, is_valid_file=is_valid_file)
-
- dataloader = torch.utils.data.DataLoader(
- dataset,
- batch_size=args.batch_size,
- num_workers=args.workers,
- sampler=None,
- )
-
- return DataInfo(dataloader=dataloader, sampler=None)
diff --git a/spaces/LanguageBind/LanguageBind/languagebind/video/configuration_video.py b/spaces/LanguageBind/LanguageBind/languagebind/video/configuration_video.py
deleted file mode 100644
index 4b108ec51799ae0d77432ffa85690e1a1858e60c..0000000000000000000000000000000000000000
--- a/spaces/LanguageBind/LanguageBind/languagebind/video/configuration_video.py
+++ /dev/null
@@ -1,423 +0,0 @@
-import copy
-import os
-from typing import Union
-
-from transformers import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-
-
-
-
-
-class CLIPTextConfig(PretrainedConfig):
- r"""
- This is the configuration class to store the configuration of a [`CLIPTextModel`]. It is used to instantiate a CLIP
- text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
- with the defaults will yield a similar configuration to that of the text encoder of the CLIP
- [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture.
-
- Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
- documentation from [`PretrainedConfig`] for more information.
-
- Args:
- vocab_size (`int`, *optional*, defaults to 49408):
- Vocabulary size of the CLIP text model. Defines the number of different tokens that can be represented by
- the `inputs_ids` passed when calling [`CLIPModel`].
- hidden_size (`int`, *optional*, defaults to 512):
- Dimensionality of the encoder layers and the pooler layer.
- intermediate_size (`int`, *optional*, defaults to 2048):
- Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
- num_hidden_layers (`int`, *optional*, defaults to 12):
- Number of hidden layers in the Transformer encoder.
- num_attention_heads (`int`, *optional*, defaults to 8):
- Number of attention heads for each attention layer in the Transformer encoder.
- max_position_embeddings (`int`, *optional*, defaults to 77):
- The maximum sequence length that this model might ever be used with. Typically set this to something large
- just in case (e.g., 512 or 1024 or 2048).
- hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
- The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
- `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
- layer_norm_eps (`float`, *optional*, defaults to 1e-5):
- The epsilon used by the layer normalization layers.
- attention_dropout (`float`, *optional*, defaults to 0.0):
- The dropout ratio for the attention probabilities.
- initializer_range (`float`, *optional*, defaults to 0.02):
- The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- initializer_factor (`float`, *optional*, defaults to 1):
- A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
- testing).
-
- Example:
-
- ```python
- >>> from transformers import CLIPTextConfig, CLIPTextModel
-
- >>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration
- >>> configuration = CLIPTextConfig()
-
- >>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
- >>> model = CLIPTextModel(configuration)
-
- >>> # Accessing the model configuration
- >>> configuration = model.config
- ```"""
- model_type = "clip_text_model"
-
- def __init__(
- self,
- vocab_size=49408,
- hidden_size=512,
- intermediate_size=2048,
- projection_dim=512,
- num_hidden_layers=12,
- num_attention_heads=8,
- max_position_embeddings=77,
- hidden_act="quick_gelu",
- layer_norm_eps=1e-5,
- attention_dropout=0.0,
- initializer_range=0.02,
- initializer_factor=1.0,
- # This differs from `CLIPTokenizer`'s default and from openai/clip
- # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538
- pad_token_id=1,
- bos_token_id=49406,
- eos_token_id=49407,
- **kwargs,
- ):
- super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
-
- self.vocab_size = vocab_size
- self.hidden_size = hidden_size
- self.intermediate_size = intermediate_size
- self.projection_dim = projection_dim
- self.num_hidden_layers = num_hidden_layers
- self.num_attention_heads = num_attention_heads
- self.max_position_embeddings = max_position_embeddings
- self.layer_norm_eps = layer_norm_eps
- self.hidden_act = hidden_act
- self.initializer_range = initializer_range
- self.initializer_factor = initializer_factor
- self.attention_dropout = attention_dropout
- self.add_time_attn = False ######################################
-
- @classmethod
- def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
- cls._set_token_in_kwargs(kwargs)
-
- config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
- # get the text config dict if we are loading from CLIPConfig
- if config_dict.get("model_type") == "clip":
- config_dict = config_dict["text_config"]
-
- if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
- logger.warning(
- f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
- f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
- )
-
- return cls.from_dict(config_dict, **kwargs)
-
-
-
-
-class CLIPVisionConfig(PretrainedConfig):
- r"""
- This is the configuration class to store the configuration of a [`CLIPVisionModel`]. It is used to instantiate a
- CLIP vision encoder according to the specified arguments, defining the model architecture. Instantiating a
- configuration with the defaults will yield a similar configuration to that of the vision encoder of the CLIP
- [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture.
-
- Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
- documentation from [`PretrainedConfig`] for more information.
-
- Args:
- hidden_size (`int`, *optional*, defaults to 768):
- Dimensionality of the encoder layers and the pooler layer.
- intermediate_size (`int`, *optional*, defaults to 3072):
- Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
- num_hidden_layers (`int`, *optional*, defaults to 12):
- Number of hidden layers in the Transformer encoder.
- num_attention_heads (`int`, *optional*, defaults to 12):
- Number of attention heads for each attention layer in the Transformer encoder.
- image_size (`int`, *optional*, defaults to 224):
- The size (resolution) of each image.
- patch_size (`int`, *optional*, defaults to 32):
- The size (resolution) of each patch.
- hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
- The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
- `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
- layer_norm_eps (`float`, *optional*, defaults to 1e-5):
- The epsilon used by the layer normalization layers.
- attention_dropout (`float`, *optional*, defaults to 0.0):
- The dropout ratio for the attention probabilities.
- initializer_range (`float`, *optional*, defaults to 0.02):
- The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- initializer_factor (`float`, *optional*, defaults to 1):
- A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
- testing).
-
- Example:
-
- ```python
- >>> from transformers import CLIPVisionConfig, CLIPVisionModel
-
- >>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration
- >>> configuration = CLIPVisionConfig()
-
- >>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
- >>> model = CLIPVisionModel(configuration)
-
- >>> # Accessing the model configuration
- >>> configuration = model.config
- ```"""
-
- model_type = "clip_vision_model"
-
- def __init__(
- self,
- hidden_size=768,
- intermediate_size=3072,
- projection_dim=512,
- num_hidden_layers=12,
- num_attention_heads=12,
- num_channels=3,
- image_size=224,
- patch_size=32,
- hidden_act="quick_gelu",
- layer_norm_eps=1e-5,
- attention_dropout=0.0,
- initializer_range=0.02,
- initializer_factor=1.0,
-
- add_time_attn=False, ################################
- num_frames=1, ################################
- force_patch_dropout=0.0, ################################
- lora_r=2, ################################
- lora_alpha=16, ################################
- lora_dropout=0.0, ################################
- num_mel_bins=0.0, ################################
- target_length=0.0, ################################
- video_decode_backend='decord', #########################
- **kwargs,
- ):
- super().__init__(**kwargs)
-
- self.hidden_size = hidden_size
- self.intermediate_size = intermediate_size
- self.projection_dim = projection_dim
- self.num_hidden_layers = num_hidden_layers
- self.num_attention_heads = num_attention_heads
- self.num_channels = num_channels
- self.patch_size = patch_size
- self.image_size = image_size
- self.initializer_range = initializer_range
- self.initializer_factor = initializer_factor
- self.attention_dropout = attention_dropout
- self.layer_norm_eps = layer_norm_eps
- self.hidden_act = hidden_act
-
- self.add_time_attn = add_time_attn ################
- self.num_frames = num_frames ################
- self.force_patch_dropout = force_patch_dropout ################
- self.lora_r = lora_r ################
- self.lora_alpha = lora_alpha ################
- self.lora_dropout = lora_dropout ################
- self.num_mel_bins = num_mel_bins ################
- self.target_length = target_length ################
- self.video_decode_backend = video_decode_backend ################
-
- @classmethod
- def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
- cls._set_token_in_kwargs(kwargs)
-
- config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
- # get the vision config dict if we are loading from CLIPConfig
- if config_dict.get("model_type") == "clip":
- config_dict = config_dict["vision_config"]
-
- if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
- logger.warning(
- f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
- f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
- )
-
- return cls.from_dict(config_dict, **kwargs)
-
-
-class LanguageBindVideoConfig(PretrainedConfig):
- r"""
- [`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate
- a CLIP model according to the specified arguments, defining the text model and vision model configs. Instantiating
- a configuration with the defaults will yield a similar configuration to that of the CLIP
- [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture.
-
- Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
- documentation from [`PretrainedConfig`] for more information.
-
- Args:
- text_config (`dict`, *optional*):
- Dictionary of configuration options used to initialize [`CLIPTextConfig`].
- vision_config (`dict`, *optional*):
- Dictionary of configuration options used to initialize [`CLIPVisionConfig`].
- projection_dim (`int`, *optional*, defaults to 512):
- Dimentionality of text and vision projection layers.
- logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
- The inital value of the *logit_scale* paramter. Default is used as per the original CLIP implementation.
- kwargs (*optional*):
- Dictionary of keyword arguments.
-
- Example:
-
- ```python
- >>> from transformers import CLIPConfig, CLIPModel
-
- >>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration
- >>> configuration = CLIPConfig()
-
- >>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
- >>> model = CLIPModel(configuration)
-
- >>> # Accessing the model configuration
- >>> configuration = model.config
-
- >>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig
- >>> from transformers import CLIPTextConfig, CLIPVisionConfig
-
- >>> # Initializing a CLIPText and CLIPVision configuration
- >>> config_text = CLIPTextConfig()
- >>> config_vision = CLIPVisionConfig()
-
- >>> config = CLIPConfig.from_text_vision_configs(config_text, config_vision)
- ```"""
-
- model_type = "LanguageBindVideo"
- is_composition = True
-
- def __init__(
- self, text_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs
- ):
- # If `_config_dict` exist, we use them for the backward compatibility.
- # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
- # of confusion!).
- text_config_dict = kwargs.pop("text_config_dict", None)
- vision_config_dict = kwargs.pop("vision_config_dict", None)
-
- super().__init__(**kwargs)
-
- # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
- # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
- # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
- if text_config_dict is not None:
- if text_config is None:
- text_config = {}
-
- # This is the complete result when using `text_config_dict`.
- _text_config_dict = CLIPTextConfig(**text_config_dict).to_dict()
-
- # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
- for key, value in _text_config_dict.items():
- if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
- # If specified in `text_config_dict`
- if key in text_config_dict:
- message = (
- f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
- f'The value `text_config_dict["{key}"]` will be used instead.'
- )
- # If inferred from default argument values (just to be super careful)
- else:
- message = (
- f"`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The "
- f'value `text_config["{key}"]` will be overriden.'
- )
- logger.warning(message)
-
- # Update all values in `text_config` with the ones in `_text_config_dict`.
- text_config.update(_text_config_dict)
-
- if vision_config_dict is not None:
- if vision_config is None:
- vision_config = {}
-
- # This is the complete result when using `vision_config_dict`.
- _vision_config_dict = CLIPVisionConfig(**vision_config_dict).to_dict()
- # convert keys to string instead of integer
- if "id2label" in _vision_config_dict:
- _vision_config_dict["id2label"] = {
- str(key): value for key, value in _vision_config_dict["id2label"].items()
- }
-
- # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
- for key, value in _vision_config_dict.items():
- if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
- # If specified in `vision_config_dict`
- if key in vision_config_dict:
- message = (
- f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
- f'values. The value `vision_config_dict["{key}"]` will be used instead.'
- )
- # If inferred from default argument values (just to be super careful)
- else:
- message = (
- f"`vision_config_dict` is provided which will be used to initialize `CLIPVisionConfig`. "
- f'The value `vision_config["{key}"]` will be overriden.'
- )
- logger.warning(message)
-
- # Update all values in `vision_config` with the ones in `_vision_config_dict`.
- vision_config.update(_vision_config_dict)
-
- if text_config is None:
- text_config = {}
- logger.info("`text_config` is `None`. Initializing the `CLIPTextConfig` with default values.")
-
- if vision_config is None:
- vision_config = {}
- logger.info("`vision_config` is `None`. initializing the `CLIPVisionConfig` with default values.")
-
- self.text_config = CLIPTextConfig(**text_config)
- self.vision_config = CLIPVisionConfig(**vision_config)
-
- self.projection_dim = projection_dim
- self.logit_scale_init_value = logit_scale_init_value
- self.initializer_factor = 1.0
-
- @classmethod
- def from_text_vision_configs(cls, text_config: CLIPTextConfig, vision_config: CLIPVisionConfig, **kwargs):
- r"""
- Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model
- configuration.
-
- Returns:
- [`CLIPConfig`]: An instance of a configuration object
- """
-
- return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
-
- def to_dict(self):
- """
- Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
-
- Returns:
- `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
- """
- output = copy.deepcopy(self.__dict__)
- output["text_config"] = self.text_config.to_dict()
- output["vision_config"] = self.vision_config.to_dict()
- output["model_type"] = self.__class__.model_type
- return output
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/Latryna/roop/roop/processors/frame/core.py b/spaces/Latryna/roop/roop/processors/frame/core.py
deleted file mode 100644
index 155658255cdecc36ab2380796819bca25bff0a0b..0000000000000000000000000000000000000000
--- a/spaces/Latryna/roop/roop/processors/frame/core.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import os
-import importlib
-import psutil
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from queue import Queue
-from types import ModuleType
-from typing import Any, List, Callable
-from tqdm import tqdm
-
-import roop
-
-FRAME_PROCESSORS_MODULES: List[ModuleType] = []
-FRAME_PROCESSORS_INTERFACE = [
- 'pre_check',
- 'pre_start',
- 'process_frame',
- 'process_frames',
- 'process_image',
- 'process_video',
- 'post_process'
-]
-
-
-def load_frame_processor_module(frame_processor: str) -> Any:
- try:
- frame_processor_module = importlib.import_module(f'roop.processors.frame.{frame_processor}')
- for method_name in FRAME_PROCESSORS_INTERFACE:
- if not hasattr(frame_processor_module, method_name):
- raise NotImplementedError
- except (ImportError, NotImplementedError):
- quit(f'Frame processor {frame_processor} crashed.')
- return frame_processor_module
-
-
-def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType]:
- global FRAME_PROCESSORS_MODULES
-
- for frame_processor in frame_processors:
- found = False
- for frame_processor_module in FRAME_PROCESSORS_MODULES:
- if frame_processor_module.frame_name == frame_processor:
- found = True
- break
- if not found:
- frame_processor_module = load_frame_processor_module(frame_processor)
- FRAME_PROCESSORS_MODULES.append(frame_processor_module)
- # if not FRAME_PROCESSORS_MODULES:
-
- return FRAME_PROCESSORS_MODULES
-
-
-def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], update: Callable[[], None]) -> None:
- with ThreadPoolExecutor(max_workers=roop.globals.execution_threads) as executor:
- futures = []
- queue = create_queue(temp_frame_paths)
- queue_per_future = len(temp_frame_paths) // roop.globals.execution_threads
- while not queue.empty():
- future = executor.submit(process_frames, source_path, pick_queue(queue, queue_per_future), update)
- futures.append(future)
- for future in as_completed(futures):
- future.result()
-
-
-def create_queue(temp_frame_paths: List[str]) -> Queue[str]:
- queue: Queue[str] = Queue()
- for frame_path in temp_frame_paths:
- queue.put(frame_path)
- return queue
-
-
-def pick_queue(queue: Queue[str], queue_per_future: int) -> List[str]:
- queues = []
- for _ in range(queue_per_future):
- if not queue.empty():
- queues.append(queue.get())
- return queues
-
-
-def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
- progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
- total = len(frame_paths)
- with tqdm(total=total, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
- multi_process_frame(source_path, frame_paths, process_frames, lambda: update_progress(progress))
-
-
-def update_progress(progress: Any = None) -> None:
- process = psutil.Process(os.getpid())
- memory_usage = process.memory_info().rss / 1024 / 1024 / 1024
- progress.set_postfix({
- 'memory_usage': '{:.2f}'.format(memory_usage).zfill(5) + 'GB',
- 'execution_providers': roop.globals.execution_providers,
- 'execution_threads': roop.globals.execution_threads
- })
- progress.refresh()
- progress.update(1)
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/train/data_utils.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/train/data_utils.py
deleted file mode 100644
index 71c0eff1815469a52399dc90a093a2f8a29223eb..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/train/data_utils.py
+++ /dev/null
@@ -1,512 +0,0 @@
-import os, traceback
-import numpy as np
-import torch
-import torch.utils.data
-
-from mel_processing import spectrogram_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-
-
-class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
- """
- 1) loads audio, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
-
- def __init__(self, audiopaths_and_text, hparams):
- self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 5000)
- self._filter()
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
- audiopaths_and_text_new = []
- lengths = []
- for audiopath, text, pitch, pitchf, dv in self.audiopaths_and_text:
- if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
- audiopaths_and_text_new.append([audiopath, text, pitch, pitchf, dv])
- lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
- self.audiopaths_and_text = audiopaths_and_text_new
- self.lengths = lengths
-
- def get_sid(self, sid):
- sid = torch.LongTensor([int(sid)])
- return sid
-
- def get_audio_text_pair(self, audiopath_and_text):
- # separate filename and text
- file = audiopath_and_text[0]
- phone = audiopath_and_text[1]
- pitch = audiopath_and_text[2]
- pitchf = audiopath_and_text[3]
- dv = audiopath_and_text[4]
-
- phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf)
- spec, wav = self.get_audio(file)
- dv = self.get_sid(dv)
-
- len_phone = phone.size()[0]
- len_spec = spec.size()[-1]
- # print(123,phone.shape,pitch.shape,spec.shape)
- if len_phone != len_spec:
- len_min = min(len_phone, len_spec)
- # amor
- len_wav = len_min * self.hop_length
-
- spec = spec[:, :len_min]
- wav = wav[:, :len_wav]
-
- phone = phone[:len_min, :]
- pitch = pitch[:len_min]
- pitchf = pitchf[:len_min]
-
- return (spec, wav, phone, pitch, pitchf, dv)
-
- def get_labels(self, phone, pitch, pitchf):
- phone = np.load(phone)
- phone = np.repeat(phone, 2, axis=0)
- pitch = np.load(pitch)
- pitchf = np.load(pitchf)
- n_num = min(phone.shape[0], 900) # DistributedBucketSampler
- # print(234,phone.shape,pitch.shape)
- phone = phone[:n_num, :]
- pitch = pitch[:n_num]
- pitchf = pitchf[:n_num]
- phone = torch.FloatTensor(phone)
- pitch = torch.LongTensor(pitch)
- pitchf = torch.FloatTensor(pitchf)
- return phone, pitch, pitchf
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError(
- "{} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate
- )
- )
- audio_norm = audio
- # audio_norm = audio / self.max_wav_value
- # audio_norm = audio / np.abs(audio).max()
-
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if os.path.exists(spec_filename):
- try:
- spec = torch.load(spec_filename)
- except:
- print(spec_filename, traceback.format_exc())
- spec = spectrogram_torch(
- audio_norm,
- self.filter_length,
- self.sampling_rate,
- self.hop_length,
- self.win_length,
- center=False,
- )
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
- else:
- spec = spectrogram_torch(
- audio_norm,
- self.filter_length,
- self.sampling_rate,
- self.hop_length,
- self.win_length,
- center=False,
- )
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
- return spec, audio_norm
-
- def __getitem__(self, index):
- return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
- def __len__(self):
- return len(self.audiopaths_and_text)
-
-
-class TextAudioCollateMultiNSFsid:
- """Zero-pads model inputs and targets"""
-
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text and aduio
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
- )
-
- max_spec_len = max([x[0].size(1) for x in batch])
- max_wave_len = max([x[1].size(1) for x in batch])
- spec_lengths = torch.LongTensor(len(batch))
- wave_lengths = torch.LongTensor(len(batch))
- spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
- wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
- spec_padded.zero_()
- wave_padded.zero_()
-
- max_phone_len = max([x[2].size(0) for x in batch])
- phone_lengths = torch.LongTensor(len(batch))
- phone_padded = torch.FloatTensor(
- len(batch), max_phone_len, batch[0][2].shape[1]
- ) # (spec, wav, phone, pitch)
- pitch_padded = torch.LongTensor(len(batch), max_phone_len)
- pitchf_padded = torch.FloatTensor(len(batch), max_phone_len)
- phone_padded.zero_()
- pitch_padded.zero_()
- pitchf_padded.zero_()
- # dv = torch.FloatTensor(len(batch), 256)#gin=256
- sid = torch.LongTensor(len(batch))
-
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- spec = row[0]
- spec_padded[i, :, : spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wave = row[1]
- wave_padded[i, :, : wave.size(1)] = wave
- wave_lengths[i] = wave.size(1)
-
- phone = row[2]
- phone_padded[i, : phone.size(0), :] = phone
- phone_lengths[i] = phone.size(0)
-
- pitch = row[3]
- pitch_padded[i, : pitch.size(0)] = pitch
- pitchf = row[4]
- pitchf_padded[i, : pitchf.size(0)] = pitchf
-
- # dv[i] = row[5]
- sid[i] = row[5]
-
- return (
- phone_padded,
- phone_lengths,
- pitch_padded,
- pitchf_padded,
- spec_padded,
- spec_lengths,
- wave_padded,
- wave_lengths,
- # dv
- sid,
- )
-
-
-class TextAudioLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
-
- def __init__(self, audiopaths_and_text, hparams):
- self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 5000)
- self._filter()
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
- audiopaths_and_text_new = []
- lengths = []
- for audiopath, text, dv in self.audiopaths_and_text:
- if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
- audiopaths_and_text_new.append([audiopath, text, dv])
- lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
- self.audiopaths_and_text = audiopaths_and_text_new
- self.lengths = lengths
-
- def get_sid(self, sid):
- sid = torch.LongTensor([int(sid)])
- return sid
-
- def get_audio_text_pair(self, audiopath_and_text):
- # separate filename and text
- file = audiopath_and_text[0]
- phone = audiopath_and_text[1]
- dv = audiopath_and_text[2]
-
- phone = self.get_labels(phone)
- spec, wav = self.get_audio(file)
- dv = self.get_sid(dv)
-
- len_phone = phone.size()[0]
- len_spec = spec.size()[-1]
- if len_phone != len_spec:
- len_min = min(len_phone, len_spec)
- len_wav = len_min * self.hop_length
- spec = spec[:, :len_min]
- wav = wav[:, :len_wav]
- phone = phone[:len_min, :]
- return (spec, wav, phone, dv)
-
- def get_labels(self, phone):
- phone = np.load(phone)
- phone = np.repeat(phone, 2, axis=0)
- n_num = min(phone.shape[0], 900) # DistributedBucketSampler
- phone = phone[:n_num, :]
- phone = torch.FloatTensor(phone)
- return phone
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError(
- "{} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate
- )
- )
- audio_norm = audio
- # audio_norm = audio / self.max_wav_value
- # audio_norm = audio / np.abs(audio).max()
-
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if os.path.exists(spec_filename):
- try:
- spec = torch.load(spec_filename)
- except:
- print(spec_filename, traceback.format_exc())
- spec = spectrogram_torch(
- audio_norm,
- self.filter_length,
- self.sampling_rate,
- self.hop_length,
- self.win_length,
- center=False,
- )
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
- else:
- spec = spectrogram_torch(
- audio_norm,
- self.filter_length,
- self.sampling_rate,
- self.hop_length,
- self.win_length,
- center=False,
- )
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
- return spec, audio_norm
-
- def __getitem__(self, index):
- return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
- def __len__(self):
- return len(self.audiopaths_and_text)
-
-
-class TextAudioCollate:
- """Zero-pads model inputs and targets"""
-
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text and aduio
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
- )
-
- max_spec_len = max([x[0].size(1) for x in batch])
- max_wave_len = max([x[1].size(1) for x in batch])
- spec_lengths = torch.LongTensor(len(batch))
- wave_lengths = torch.LongTensor(len(batch))
- spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
- wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
- spec_padded.zero_()
- wave_padded.zero_()
-
- max_phone_len = max([x[2].size(0) for x in batch])
- phone_lengths = torch.LongTensor(len(batch))
- phone_padded = torch.FloatTensor(
- len(batch), max_phone_len, batch[0][2].shape[1]
- )
- phone_padded.zero_()
- sid = torch.LongTensor(len(batch))
-
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- spec = row[0]
- spec_padded[i, :, : spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wave = row[1]
- wave_padded[i, :, : wave.size(1)] = wave
- wave_lengths[i] = wave.size(1)
-
- phone = row[2]
- phone_padded[i, : phone.size(0), :] = phone
- phone_lengths[i] = phone.size(0)
-
- sid[i] = row[3]
-
- return (
- phone_padded,
- phone_lengths,
- spec_padded,
- spec_lengths,
- wave_padded,
- wave_lengths,
- sid,
- )
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
- """
- Maintain similar input lengths in a batch.
- Length groups are specified by boundaries.
- Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
- It removes samples which are not included in the boundaries.
- Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
- """
-
- def __init__(
- self,
- dataset,
- batch_size,
- boundaries,
- num_replicas=None,
- rank=None,
- shuffle=True,
- ):
- super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
- self.lengths = dataset.lengths
- self.batch_size = batch_size
- self.boundaries = boundaries
-
- self.buckets, self.num_samples_per_bucket = self._create_buckets()
- self.total_size = sum(self.num_samples_per_bucket)
- self.num_samples = self.total_size // self.num_replicas
-
- def _create_buckets(self):
- buckets = [[] for _ in range(len(self.boundaries) - 1)]
- for i in range(len(self.lengths)):
- length = self.lengths[i]
- idx_bucket = self._bisect(length)
- if idx_bucket != -1:
- buckets[idx_bucket].append(i)
-
- for i in range(len(buckets) - 1, -1, -1): #
- if len(buckets[i]) == 0:
- buckets.pop(i)
- self.boundaries.pop(i + 1)
-
- num_samples_per_bucket = []
- for i in range(len(buckets)):
- len_bucket = len(buckets[i])
- total_batch_size = self.num_replicas * self.batch_size
- rem = (
- total_batch_size - (len_bucket % total_batch_size)
- ) % total_batch_size
- num_samples_per_bucket.append(len_bucket + rem)
- return buckets, num_samples_per_bucket
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- g = torch.Generator()
- g.manual_seed(self.epoch)
-
- indices = []
- if self.shuffle:
- for bucket in self.buckets:
- indices.append(torch.randperm(len(bucket), generator=g).tolist())
- else:
- for bucket in self.buckets:
- indices.append(list(range(len(bucket))))
-
- batches = []
- for i in range(len(self.buckets)):
- bucket = self.buckets[i]
- len_bucket = len(bucket)
- ids_bucket = indices[i]
- num_samples_bucket = self.num_samples_per_bucket[i]
-
- # add extra samples to make it evenly divisible
- rem = num_samples_bucket - len_bucket
- ids_bucket = (
- ids_bucket
- + ids_bucket * (rem // len_bucket)
- + ids_bucket[: (rem % len_bucket)]
- )
-
- # subsample
- ids_bucket = ids_bucket[self.rank :: self.num_replicas]
-
- # batching
- for j in range(len(ids_bucket) // self.batch_size):
- batch = [
- bucket[idx]
- for idx in ids_bucket[
- j * self.batch_size : (j + 1) * self.batch_size
- ]
- ]
- batches.append(batch)
-
- if self.shuffle:
- batch_ids = torch.randperm(len(batches), generator=g).tolist()
- batches = [batches[i] for i in batch_ids]
- self.batches = batches
-
- assert len(self.batches) * self.batch_size == self.num_samples
- return iter(self.batches)
-
- def _bisect(self, x, lo=0, hi=None):
- if hi is None:
- hi = len(self.boundaries) - 1
-
- if hi > lo:
- mid = (hi + lo) // 2
- if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
- return mid
- elif x <= self.boundaries[mid]:
- return self._bisect(x, lo, mid)
- else:
- return self._bisect(x, mid + 1, hi)
- else:
- return -1
-
- def __len__(self):
- return self.num_samples // self.batch_size
diff --git a/spaces/LaynzKunz/RCVAICOVER/src/trainset_preprocess_pipeline_print.py b/spaces/LaynzKunz/RCVAICOVER/src/trainset_preprocess_pipeline_print.py
deleted file mode 100644
index 7b19e3e9a5788552b6acb9cd6747bda7ae93146b..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/RCVAICOVER/src/trainset_preprocess_pipeline_print.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import sys, os, multiprocessing
-from scipy import signal
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-
-inp_root = sys.argv[1]
-sr = int(sys.argv[2])
-n_p = int(sys.argv[3])
-exp_dir = sys.argv[4]
-noparallel = sys.argv[5] == "True"
-import numpy as np, os, traceback
-from slicer2 import Slicer
-import librosa, traceback
-from scipy.io import wavfile
-import multiprocessing
-from my_utils import load_audio
-import tqdm
-
-DoFormant = False
-Quefrency = 1.0
-Timbre = 1.0
-
-mutex = multiprocessing.Lock()
-f = open("%s/preprocess.log" % exp_dir, "a+")
-
-
-def println(strr):
- mutex.acquire()
- print(strr)
- f.write("%s\n" % strr)
- f.flush()
- mutex.release()
-
-
-class PreProcess:
- def __init__(self, sr, exp_dir):
- self.slicer = Slicer(
- sr=sr,
- threshold=-42,
- min_length=1500,
- min_interval=400,
- hop_size=15,
- max_sil_kept=500,
- )
- self.sr = sr
- self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
- self.per = 3.0
- self.overlap = 0.3
- self.tail = self.per + self.overlap
- self.max = 0.9
- self.alpha = 0.75
- self.exp_dir = exp_dir
- self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
- self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir
- os.makedirs(self.exp_dir, exist_ok=True)
- os.makedirs(self.gt_wavs_dir, exist_ok=True)
- os.makedirs(self.wavs16k_dir, exist_ok=True)
-
- def norm_write(self, tmp_audio, idx0, idx1):
- tmp_max = np.abs(tmp_audio).max()
- if tmp_max > 2.5:
- print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max))
- return
- tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + (
- 1 - self.alpha
- ) * tmp_audio
- wavfile.write(
- "%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1),
- self.sr,
- tmp_audio.astype(np.float32),
- )
- tmp_audio = librosa.resample(
- tmp_audio, orig_sr=self.sr, target_sr=16000
- ) # , res_type="soxr_vhq"
- wavfile.write(
- "%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1),
- 16000,
- tmp_audio.astype(np.float32),
- )
-
- def pipeline(self, path, idx0):
- try:
- audio = load_audio(path, self.sr, DoFormant, Quefrency, Timbre)
- # zero phased digital filter cause pre-ringing noise...
- # audio = signal.filtfilt(self.bh, self.ah, audio)
- audio = signal.lfilter(self.bh, self.ah, audio)
-
- idx1 = 0
- for audio in self.slicer.slice(audio):
- i = 0
- while 1:
- start = int(self.sr * (self.per - self.overlap) * i)
- i += 1
- if len(audio[start:]) > self.tail * self.sr:
- tmp_audio = audio[start : start + int(self.per * self.sr)]
- self.norm_write(tmp_audio, idx0, idx1)
- idx1 += 1
- else:
- tmp_audio = audio[start:]
- idx1 += 1
- break
- self.norm_write(tmp_audio, idx0, idx1)
- # println("%s->Suc." % path)
- except:
- println("%s->%s" % (path, traceback.format_exc()))
-
- def pipeline_mp(self, infos, thread_n):
- for path, idx0 in tqdm.tqdm(
- infos, position=thread_n, leave=True, desc="thread:%s" % thread_n
- ):
- self.pipeline(path, idx0)
-
- def pipeline_mp_inp_dir(self, inp_root, n_p):
- try:
- infos = [
- ("%s/%s" % (inp_root, name), idx)
- for idx, name in enumerate(sorted(list(os.listdir(inp_root))))
- ]
- if noparallel:
- for i in range(n_p):
- self.pipeline_mp(infos[i::n_p])
- else:
- ps = []
- for i in range(n_p):
- p = multiprocessing.Process(
- target=self.pipeline_mp, args=(infos[i::n_p], i)
- )
- ps.append(p)
- p.start()
- for i in range(n_p):
- ps[i].join()
- except:
- println("Fail. %s" % traceback.format_exc())
-
-
-def preprocess_trainset(inp_root, sr, n_p, exp_dir):
- pp = PreProcess(sr, exp_dir)
- println("start preprocess")
- println(sys.argv)
- pp.pipeline_mp_inp_dir(inp_root, n_p)
- println("end preprocess")
-
-
-if __name__ == "__main__":
- preprocess_trainset(inp_root, sr, n_p, exp_dir)
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/indicators/smma.py b/spaces/Lianjd/stock_dashboard/backtrader/indicators/smma.py
deleted file mode 100644
index d9b94fafa7e93e3648fa8b86e81ee4c3a9565b8f..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/indicators/smma.py
+++ /dev/null
@@ -1,58 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program. If not, see {title}
\n'+
- (f'Model author: {author}
' if author else "")+
- (f'