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d5da3c52bfDurood E Tanjeena is a powerful prayer that is often recited by Muslims in times of difficulty. It is said to be very beneficial and is believed to bring blessings and protection from Allah. In this article, we will show you how to download and recite Durood E Tanjeena PDF for free.
-Durood E Tanjeena is a supplication that can be translated to "Praise be to Allah, the Highest." It is a short prayer that consists of 11 words in Arabic. The prayer is as follows:
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--اÙÙÙ٠ص٠عÙÙ Ù Ø٠د ÙعÙ٠آ٠٠Ø٠د Ù٠ا صÙÙت عÙ٠إبراÙÙÙ ÙعÙ٠آ٠إبراÙÙ٠إÙÙ ØÙ Ùد ٠جÙد
-Allahumma salli ala Muhammad wa ala ali Muhammad kama sallayta ala Ibrahim wa ala ali Ibrahim innaka Hamidun Majid
-
The meaning of Durood E Tanjeena can be translated to "O Allah, send blessings upon Muhammad and upon the family of Muhammad, as You sent blessings upon Ibrahim and upon the family of Ibrahim. Verily, You are Praiseworthy and Glorious."
-If you want to download Durood E Tanjeena PDF for free, you can use one of the following links:
-These links will allow you to download Durood E Tanjeena PDF with clear Arabic text and Urdu translation. You can also read online or print the PDF file for your convenience.
-There are different ways to recite Durood E Tanjeena, depending on your intention and situation. Here are some common methods:
-When reciting Durood E Tanjeena, you should have faith and sincerity in your heart. You should also send salutations upon the Prophet Muhammad (peace be upon him) before and after reciting it.
-Durood E Tanjeena has many benefits for those who recite it regularly. Some of these benefits include:
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Archer Attack 3D: Shooter War is an action game developed by Matchingham Games that will give you an exciting archer experience with its unique graphics and designs. In this game, you will be the hero and you will be able to direct the bowman the way you want. The decisions will be entirely yours, you will destroy the targets step by step and achieve victory.
-The game has a simple but engaging story that will take you to various conflict areas and scenarios, such as the capture of a military base by militants. You will have to use your bow and arrow to eliminate the enemies and complete the missions. You will also have to face different challenges and obstacles along the way, such as moving targets, traps, bombs, helicopters, tanks, and more.
-The gameplay of Archer Attack 3D: Shooter War is easy to learn but hard to master. You will have to swipe on the screen to aim and release to shoot your arrow. You will also have to adjust your angle and power according to the distance and wind. You will have to be careful not to miss or hit the wrong target, as that will cost you time and health. You will also have to be quick and accurate, as the enemies will shoot back at you or try to escape.
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Archer Attack 3D: Shooter War is a game that will impress you with its amazing graphics and features. You will not get bored with this game, as it offers a lot of variety and challenge. You will also enjoy the thrill of shooting arrows and hitting your targets with precision and skill.
-If you are interested in playing Archer Attack 3D: Shooter War, you will be glad to know that it is free to download and play on your Android device. There are different ways to download this game, depending on your preference and convenience. Here are some of the methods you can use:
-This is the easiest and most recommended way to download Archer Attack 3D: Shooter War on your device. All you have to do is follow these simple steps:
-This method ensures that you get the latest version of the game and that it is compatible with your device. You will also be able to update the game automatically whenever there is a new version available.
-If you are unable to access the Google Play Store or if you want to download an older version of the game, you can use APKCombo, a website that provides APK files for various Android apps and games. To download Archer Attack 3D: Shooter War from APKCombo, follow these steps:
-This method allows you to download any version of the game you want, even if it is not available on the Google Play Store. However, you have to be careful about the source of the APK file, as some websites may contain malware or viruses. You also have to update the game manually whenever there is a new version available.
-If you want to play Archer Attack 3D: Shooter War on your PC, you can use BlueStacks, an emulator that allows you to run Android apps and games on your computer. To download Archer Attack 3D: Shooter War on PC using BlueStacks, follow these steps:
-This method allows you to play Archer Attack 3D: Shooter War on a bigger screen and with better controls. You can also use your keyboard and mouse to aim and shoot your arrows. However, you have to make sure that your PC meets the minimum requirements for running BlueStacks smoothly.
-Now that you have downloaded Archer Attack 3D: Shooter War on your device, you may be wondering how to play it like a pro. Well, don't worry, we have some tips and tricks for you that will help you improve your skills and performance in this game. Here are some of them:
-Archer Attack 3D: Shooter War is a game that will appeal to anyone who loves archery games or shooting games. It is a game that will provide you with hours of fun and entertainment with its addictive gameplay and stunning graphics. Here are some of the reasons why you should play Archer Attack 3D: Shooter War:
-Playing Archer Attack 3D: Shooter War can have many benefits for you, such as:
-Archer Attack 3D: Shooter War is a game that has received positive reviews and ratings from many players and critics. The game has a rating of 4.2 out of 5 stars on the Google Play Store, based on over 10,000 reviews. Some of the comments from the players are:
---"This is one of the best archer games I have ever played. The graphics are amazing and the gameplay is addictive. I love the different modes and levels, they are challenging and fun. I also like the different arrows and gear, they are cool and useful. I highly recommend this game to anyone who likes archery games or shooting games."
-
--"This game is awesome! It is very realistic and exciting. The physics and the effects are very good. The controls are smooth and easy to use. The missions and the scenarios are very interesting and varied. The sound and the music are also very good. This game is a must-have for archer fans."
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--"This game is very entertaining and enjoyable. It is a great way to pass time and have fun. The graphics are beautiful and the design is unique. The gameplay is simple but challenging. The arrows and the gear are awesome and customizable. The modes and the levels are diverse and rewarding. This game is a great archer game."
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Archer Attack 3D: Shooter War is a game that will give you an amazing archer experience with its stunning graphics and features. You will be able to download it for free on your Android device or your PC using different methods. You will also be able to play it like a pro using some tips and tricks that we have shared with you. You will also be able to enjoy the benefits of playing this game, such as improving your concentration, coordination, creativity, problem-solving skills, stress relief, and boredom relief. You will also be able to see the positive reviews and ratings of this game from other players and critics.
-If you are looking for a fun and exciting archer game that will challenge your skills and immerse you in an action-packed adventure, you should definitely try Archer Attack 3D: Shooter War. It is one of the best bowman games and shooting games available on Android devices. Download it now and enjoy!
-Here are some of the frequently asked questions about Archer Attack 3D: Shooter War:
-A: Yes, Archer Attack 3D: Shooter War is free to download and play on your Android device or your PC using BlueStacks. However, the game may contain some in-app purchases that can enhance your gameplay or remove ads.
-A: You can contact the developer of Archer Attack 3D: Shooter War by sending an email to matchinghamgames@gmail.com. You can also follow them on Facebook at https://www.facebook.com/matchinghamgames/.
-A: You can report a bug or a problem in Archer Attack 3D: Shooter War by sending an email to matchinghamgames@gmail.com. You can also leave a comment on the Google Play Store page of the game or on their Facebook page.
-A: You can share your feedback or suggestions for Archer Attack 3D: Shooter War by sending an email to matchinghamgames@gmail.com. You can also leave a review on the Google Play Store page of the game or on their Facebook page.
-A: You can support Archer Attack 3D: Shooter War by doing any of the following things:
-These actions will help the developer to improve the game and to create more games like this in the future.
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¿Te gustan los juegos de simulación que te permiten crear tu propia familia virtual y vivir una vida feliz? Si es así, entonces deberías probar Virtual Families 3, la última entrega de la popular serie de Last Day of Work. En este juego, puede adoptar a una persona pequeña de miles de opciones, construir un hogar para ellos, y ayudarles a lograr sus sueños. También puedes interactuar con otros jugadores online y visitar sus casas. ¿Pero qué pasa si quieres disfrutar del juego sin limitaciones o restricciones? Bueno, usted puede hacer eso mediante la descarga de familias virtuales 3 mod apk dinero ilimitado. En este artículo, le diremos qué es Virtual Families 3, por qué debe descargar la versión apk mod, y cómo instalarlo en su dispositivo. Así que, vamos a empezar!
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Virtual Families 3 es un juego que ofrece muchas características para hacerlo agradable y realista. Estas son algunas de las características principales del juego:
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" - -examples=[['source.wav','ref.wav']] - -gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, enable_queue=True).launch() \ No newline at end of file diff --git a/spaces/akhaliq/t5-base-lm-adapt/app.py b/spaces/akhaliq/t5-base-lm-adapt/app.py deleted file mode 100644 index 660be12b987822e73cd779c160213007c40373f0..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/t5-base-lm-adapt/app.py +++ /dev/null @@ -1,8 +0,0 @@ -import gradio as gr -title = "T5" -description = "Gradio Demo for T5, Read more at the links below." -article = "" -examples = [ - ['Sevilla is a city in Spain'] -] -gr.Interface.load("huggingface/google/t5-base-lm-adapt", inputs=gr.inputs.Textbox(lines=5, label="Input Text"),title=title,description=description,article=article, examples=examples,enable_queue=True).launch() \ No newline at end of file diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/compatibility_tags.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/compatibility_tags.py deleted file mode 100644 index b6ed9a78e552806cb23d8ac48ada6d41db5b4de5..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/compatibility_tags.py +++ /dev/null @@ -1,165 +0,0 @@ -"""Generate and work with PEP 425 Compatibility Tags. -""" - -import re -from typing import List, Optional, Tuple - -from pip._vendor.packaging.tags import ( - PythonVersion, - Tag, - compatible_tags, - cpython_tags, - generic_tags, - interpreter_name, - interpreter_version, - mac_platforms, -) - -_osx_arch_pat = re.compile(r"(.+)_(\d+)_(\d+)_(.+)") - - -def version_info_to_nodot(version_info: Tuple[int, ...]) -> str: - # Only use up to the first two numbers. - return "".join(map(str, version_info[:2])) - - -def _mac_platforms(arch: str) -> List[str]: - match = _osx_arch_pat.match(arch) - if match: - name, major, minor, actual_arch = match.groups() - mac_version = (int(major), int(minor)) - arches = [ - # Since we have always only checked that the platform starts - # with "macosx", for backwards-compatibility we extract the - # actual prefix provided by the user in case they provided - # something like "macosxcustom_". It may be good to remove - # this as undocumented or deprecate it in the future. - "{}_{}".format(name, arch[len("macosx_") :]) - for arch in mac_platforms(mac_version, actual_arch) - ] - else: - # arch pattern didn't match (?!) - arches = [arch] - return arches - - -def _custom_manylinux_platforms(arch: str) -> List[str]: - arches = [arch] - arch_prefix, arch_sep, arch_suffix = arch.partition("_") - if arch_prefix == "manylinux2014": - # manylinux1/manylinux2010 wheels run on most manylinux2014 systems - # with the exception of wheels depending on ncurses. PEP 599 states - # manylinux1/manylinux2010 wheels should be considered - # manylinux2014 wheels: - # https://www.python.org/dev/peps/pep-0599/#backwards-compatibility-with-manylinux2010-wheels - if arch_suffix in {"i686", "x86_64"}: - arches.append("manylinux2010" + arch_sep + arch_suffix) - arches.append("manylinux1" + arch_sep + arch_suffix) - elif arch_prefix == "manylinux2010": - # manylinux1 wheels run on most manylinux2010 systems with the - # exception of wheels depending on ncurses. PEP 571 states - # manylinux1 wheels should be considered manylinux2010 wheels: - # https://www.python.org/dev/peps/pep-0571/#backwards-compatibility-with-manylinux1-wheels - arches.append("manylinux1" + arch_sep + arch_suffix) - return arches - - -def _get_custom_platforms(arch: str) -> List[str]: - arch_prefix, arch_sep, arch_suffix = arch.partition("_") - if arch.startswith("macosx"): - arches = _mac_platforms(arch) - elif arch_prefix in ["manylinux2014", "manylinux2010"]: - arches = _custom_manylinux_platforms(arch) - else: - arches = [arch] - return arches - - -def _expand_allowed_platforms(platforms: Optional[List[str]]) -> Optional[List[str]]: - if not platforms: - return None - - seen = set() - result = [] - - for p in platforms: - if p in seen: - continue - additions = [c for c in _get_custom_platforms(p) if c not in seen] - seen.update(additions) - result.extend(additions) - - return result - - -def _get_python_version(version: str) -> PythonVersion: - if len(version) > 1: - return int(version[0]), int(version[1:]) - else: - return (int(version[0]),) - - -def _get_custom_interpreter( - implementation: Optional[str] = None, version: Optional[str] = None -) -> str: - if implementation is None: - implementation = interpreter_name() - if version is None: - version = interpreter_version() - return f"{implementation}{version}" - - -def get_supported( - version: Optional[str] = None, - platforms: Optional[List[str]] = None, - impl: Optional[str] = None, - abis: Optional[List[str]] = None, -) -> List[Tag]: - """Return a list of supported tags for each version specified in - `versions`. - - :param version: a string version, of the form "33" or "32", - or None. The version will be assumed to support our ABI. - :param platform: specify a list of platforms you want valid - tags for, or None. If None, use the local system platform. - :param impl: specify the exact implementation you want valid - tags for, or None. If None, use the local interpreter impl. - :param abis: specify a list of abis you want valid - tags for, or None. If None, use the local interpreter abi. - """ - supported: List[Tag] = [] - - python_version: Optional[PythonVersion] = None - if version is not None: - python_version = _get_python_version(version) - - interpreter = _get_custom_interpreter(impl, version) - - platforms = _expand_allowed_platforms(platforms) - - is_cpython = (impl or interpreter_name()) == "cp" - if is_cpython: - supported.extend( - cpython_tags( - python_version=python_version, - abis=abis, - platforms=platforms, - ) - ) - else: - supported.extend( - generic_tags( - interpreter=interpreter, - abis=abis, - platforms=platforms, - ) - ) - supported.extend( - compatible_tags( - python_version=python_version, - interpreter=interpreter, - platforms=platforms, - ) - ) - - return supported diff --git a/spaces/alibidaran/General_image_captioning/README.md b/spaces/alibidaran/General_image_captioning/README.md deleted file mode 100644 index c199b979e98b3d1867a0dcb643b5f4bcde38ecaf..0000000000000000000000000000000000000000 --- a/spaces/alibidaran/General_image_captioning/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: General Image Captioning -emoji: 🌍 -colorFrom: indigo -colorTo: pink -sdk: gradio -sdk_version: 3.32.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/allinaigc/internet_GPT_venice/README.md b/spaces/allinaigc/internet_GPT_venice/README.md deleted file mode 100644 index d54a682dbfd8b04a4cb6cc97326d3d670eaeec32..0000000000000000000000000000000000000000 --- a/spaces/allinaigc/internet_GPT_venice/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: GPT Internet Temp -emoji: 😻 -colorFrom: green -colorTo: yellow -sdk: gradio -sdk_version: 3.28.3 -app_file: app.py -pinned: false -duplicated_from: allinaigc/internet_GPT_1105 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/allknowingroger/Image-Models-Test171/app.py b/spaces/allknowingroger/Image-Models-Test171/app.py deleted file mode 100644 index 69f744681dfcf035354a808fa14d91966149351c..0000000000000000000000000000000000000000 --- a/spaces/allknowingroger/Image-Models-Test171/app.py +++ /dev/null @@ -1,144 +0,0 @@ -import gradio as gr -# import os -# import sys -# from pathlib import Path -import time - -models =[ - "gowri-39/nature-gsh", - "Priyakatta02/peacock", - "4safetest/nvhai", - "Yntec/DucHaitenNiji", - "Deepika1234/my-pet-dog", - "12345678M/my-pet-dog", - "rohitasnair/Test", - "DmatryMakeev/anna-asti-2", - "livingbox/model-test-sept-v2-768", -] - - -model_functions = {} -model_idx = 1 -for model_path in models: - try: - model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False) - except Exception as error: - def the_fn(txt): - return None - model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"]) - model_idx+=1 - - -def send_it_idx(idx): - def send_it_fn(prompt): - output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt) - return output - return send_it_fn - -def get_prompts(prompt_text): - return prompt_text - -def clear_it(val): - if int(val) != 0: - val = 0 - else: - val = 0 - pass - return val - -def all_task_end(cnt,t_stamp): - to = t_stamp + 60 - et = time.time() - if et > to and t_stamp != 0: - d = gr.update(value=0) - tog = gr.update(value=1) - #print(f'to: {to} et: {et}') - else: - if cnt != 0: - d = gr.update(value=et) - else: - d = gr.update(value=0) - tog = gr.update(value=0) - #print (f'passing: to: {to} et: {et}') - pass - return d, tog - -def all_task_start(): - print("\n\n\n\n\n\n\n") - t = time.gmtime() - t_stamp = time.time() - current_time = time.strftime("%H:%M:%S", t) - return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0) - -def clear_fn(): - nn = len(models) - return tuple([None, *[None for _ in range(nn)]]) - - - -with gr.Blocks(title="SD Models") as my_interface: - with gr.Column(scale=12): - # with gr.Row(): - # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""") - with gr.Row(): - with gr.Row(scale=6): - primary_prompt=gr.Textbox(label="Prompt", value="") - # real_prompt=gr.Textbox(label="Real prompt") - with gr.Row(scale=6): - # improve_prompts_btn=gr.Button("Improve") - with gr.Row(): - run=gr.Button("Run",variant="primary") - clear_btn=gr.Button("Clear") - with gr.Row(): - sd_outputs = {} - model_idx = 1 - for model_path in models: - with gr.Column(scale=3, min_width=320): - with gr.Box(): - sd_outputs[model_idx] = gr.Image(label=model_path) - pass - model_idx += 1 - pass - pass - - with gr.Row(visible=False): - start_box=gr.Number(interactive=False) - end_box=gr.Number(interactive=False) - tog_box=gr.Textbox(value=0,interactive=False) - - start_box.change( - all_task_end, - [start_box, end_box], - [start_box, tog_box], - every=1, - show_progress=False) - - primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box]) - run.click(all_task_start, None, [start_box, end_box, tog_box]) - runs_dict = {} - model_idx = 1 - for model_path in models: - runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]]) - model_idx += 1 - pass - pass - - # improve_prompts_btn_clicked=improve_prompts_btn.click( - # get_prompts, - # inputs=[primary_prompt], - # outputs=[primary_prompt], - # cancels=list(runs_dict.values())) - clear_btn.click( - clear_fn, - None, - [primary_prompt, *list(sd_outputs.values())], - cancels=[*list(runs_dict.values())]) - tog_box.change( - clear_it, - tog_box, - tog_box, - cancels=[*list(runs_dict.values())]) - -my_interface.queue(concurrency_count=600, status_update_rate=1) -my_interface.launch(inline=True, show_api=False) - \ No newline at end of file diff --git a/spaces/amirDev/crowd-counting-p2p/util/misc.py b/spaces/amirDev/crowd-counting-p2p/util/misc.py deleted file mode 100644 index 7cfe7d73bbf4b51a1a30c2c9e88f423409e84095..0000000000000000000000000000000000000000 --- a/spaces/amirDev/crowd-counting-p2p/util/misc.py +++ /dev/null @@ -1,518 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Misc functions, including distributed helpers. - -Mostly copy-paste from torchvision references. -""" -import os -import subprocess -import time -from collections import defaultdict, deque -import datetime -import pickle -from typing import Optional, List - -import torch -import torch.distributed as dist -from torch import Tensor - -import torch.nn as nn -import torch.nn.functional as F -from torch.autograd import Variable - -# needed due to empty tensor bug in pytorch and torchvision 0.5 -import torchvision -# if float(torchvision.__version__[:3]) < 0.7: -# from torchvision.ops import _new_empty_tensor -# from torchvision.ops.misc import _output_size - - -class SmoothedValue(object): - """Track a series of values and provide access to smoothed values over a - window or the global series average. - """ - - def __init__(self, window_size=20, fmt=None): - if fmt is None: - fmt = "{median:.4f} ({global_avg:.4f})" - self.deque = deque(maxlen=window_size) - self.total = 0.0 - self.count = 0 - self.fmt = fmt - - def update(self, value, n=1): - self.deque.append(value) - self.count += n - self.total += value * n - - def synchronize_between_processes(self): - """ - Warning: does not synchronize the deque! - """ - if not is_dist_avail_and_initialized(): - return - t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') - dist.barrier() - dist.all_reduce(t) - t = t.tolist() - self.count = int(t[0]) - self.total = t[1] - - @property - def median(self): - d = torch.tensor(list(self.deque)) - return d.median().item() - - @property - def avg(self): - d = torch.tensor(list(self.deque), dtype=torch.float32) - return d.mean().item() - - @property - def global_avg(self): - return self.total / self.count - - @property - def max(self): - return max(self.deque) - - @property - def value(self): - return self.deque[-1] - - def __str__(self): - return self.fmt.format( - median=self.median, - avg=self.avg, - global_avg=self.global_avg, - max=self.max, - value=self.value) - - -def all_gather(data): - """ - Run all_gather on arbitrary picklable data (not necessarily tensors) - Args: - data: any picklable object - Returns: - list[data]: list of data gathered from each rank - """ - world_size = get_world_size() - if world_size == 1: - return [data] - - # serialized to a Tensor - buffer = pickle.dumps(data) - storage = torch.ByteStorage.from_buffer(buffer) - tensor = torch.ByteTensor(storage).to("cuda") - - # obtain Tensor size of each rank - local_size = torch.tensor([tensor.numel()], device="cuda") - size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] - dist.all_gather(size_list, local_size) - size_list = [int(size.item()) for size in size_list] - max_size = max(size_list) - - # receiving Tensor from all ranks - # we pad the tensor because torch all_gather does not support - # gathering tensors of different shapes - tensor_list = [] - for _ in size_list: - tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) - if local_size != max_size: - padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") - tensor = torch.cat((tensor, padding), dim=0) - dist.all_gather(tensor_list, tensor) - - data_list = [] - for size, tensor in zip(size_list, tensor_list): - buffer = tensor.cpu().numpy().tobytes()[:size] - data_list.append(pickle.loads(buffer)) - - return data_list - - -def reduce_dict(input_dict, average=True): - """ - Args: - input_dict (dict): all the values will be reduced - average (bool): whether to do average or sum - Reduce the values in the dictionary from all processes so that all processes - have the averaged results. Returns a dict with the same fields as - input_dict, after reduction. - """ - world_size = get_world_size() - if world_size < 2: - return input_dict - with torch.no_grad(): - names = [] - values = [] - # sort the keys so that they are consistent across processes - for k in sorted(input_dict.keys()): - names.append(k) - values.append(input_dict[k]) - values = torch.stack(values, dim=0) - dist.all_reduce(values) - if average: - values /= world_size - reduced_dict = {k: v for k, v in zip(names, values)} - return reduced_dict - - -class MetricLogger(object): - def __init__(self, delimiter="\t"): - self.meters = defaultdict(SmoothedValue) - self.delimiter = delimiter - - def update(self, **kwargs): - for k, v in kwargs.items(): - if isinstance(v, torch.Tensor): - v = v.item() - assert isinstance(v, (float, int)) - self.meters[k].update(v) - - def __getattr__(self, attr): - if attr in self.meters: - return self.meters[attr] - if attr in self.__dict__: - return self.__dict__[attr] - raise AttributeError("'{}' object has no attribute '{}'".format( - type(self).__name__, attr)) - - def __str__(self): - loss_str = [] - for name, meter in self.meters.items(): - loss_str.append( - "{}: {}".format(name, str(meter)) - ) - return self.delimiter.join(loss_str) - - def synchronize_between_processes(self): - for meter in self.meters.values(): - meter.synchronize_between_processes() - - def add_meter(self, name, meter): - self.meters[name] = meter - - def log_every(self, iterable, print_freq, header=None): - i = 0 - if not header: - header = '' - start_time = time.time() - end = time.time() - iter_time = SmoothedValue(fmt='{avg:.4f}') - data_time = SmoothedValue(fmt='{avg:.4f}') - space_fmt = ':' + str(len(str(len(iterable)))) + 'd' - if torch.cuda.is_available(): - log_msg = self.delimiter.join([ - header, - '[{0' + space_fmt + '}/{1}]', - 'eta: {eta}', - '{meters}', - 'time: {time}', - 'data: {data}', - 'max mem: {memory:.0f}' - ]) - else: - log_msg = self.delimiter.join([ - header, - '[{0' + space_fmt + '}/{1}]', - 'eta: {eta}', - '{meters}', - 'time: {time}', - 'data: {data}' - ]) - MB = 1024.0 * 1024.0 - for obj in iterable: - data_time.update(time.time() - end) - yield obj - iter_time.update(time.time() - end) - if i % print_freq == 0 or i == len(iterable) - 1: - eta_seconds = iter_time.global_avg * (len(iterable) - i) - eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) - if torch.cuda.is_available(): - print(log_msg.format( - i, len(iterable), eta=eta_string, - meters=str(self), - time=str(iter_time), data=str(data_time), - memory=torch.cuda.max_memory_allocated() / MB)) - else: - print(log_msg.format( - i, len(iterable), eta=eta_string, - meters=str(self), - time=str(iter_time), data=str(data_time))) - i += 1 - end = time.time() - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - print('{} Total time: {} ({:.4f} s / it)'.format( - header, total_time_str, total_time / len(iterable))) - - -def get_sha(): - cwd = os.path.dirname(os.path.abspath(__file__)) - - def _run(command): - return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() - sha = 'N/A' - diff = "clean" - branch = 'N/A' - try: - sha = _run(['git', 'rev-parse', 'HEAD']) - subprocess.check_output(['git', 'diff'], cwd=cwd) - diff = _run(['git', 'diff-index', 'HEAD']) - diff = "has uncommited changes" if diff else "clean" - branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) - except Exception: - pass - message = f"sha: {sha}, status: {diff}, branch: {branch}" - return message - - -def collate_fn(batch): - batch = list(zip(*batch)) - batch[0] = nested_tensor_from_tensor_list(batch[0]) - return tuple(batch) - -def collate_fn_crowd(batch): - # re-organize the batch - batch_new = [] - for b in batch: - imgs, points = b - if imgs.ndim == 3: - imgs = imgs.unsqueeze(0) - for i in range(len(imgs)): - batch_new.append((imgs[i, :, :, :], points[i])) - batch = batch_new - batch = list(zip(*batch)) - batch[0] = nested_tensor_from_tensor_list(batch[0]) - return tuple(batch) - - -def _max_by_axis(the_list): - # type: (List[List[int]]) -> List[int] - maxes = the_list[0] - for sublist in the_list[1:]: - for index, item in enumerate(sublist): - maxes[index] = max(maxes[index], item) - return maxes - -def _max_by_axis_pad(the_list): - # type: (List[List[int]]) -> List[int] - maxes = the_list[0] - for sublist in the_list[1:]: - for index, item in enumerate(sublist): - maxes[index] = max(maxes[index], item) - - block = 128 - - for i in range(2): - maxes[i+1] = ((maxes[i+1] - 1) // block + 1) * block - return maxes - - -def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): - # TODO make this more general - if tensor_list[0].ndim == 3: - - # TODO make it support different-sized images - max_size = _max_by_axis_pad([list(img.shape) for img in tensor_list]) - # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) - batch_shape = [len(tensor_list)] + max_size - b, c, h, w = batch_shape - dtype = tensor_list[0].dtype - device = tensor_list[0].device - tensor = torch.zeros(batch_shape, dtype=dtype, device=device) - for img, pad_img in zip(tensor_list, tensor): - pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) - else: - raise ValueError('not supported') - return tensor - -class NestedTensor(object): - def __init__(self, tensors, mask: Optional[Tensor]): - self.tensors = tensors - self.mask = mask - - def to(self, device): - # type: (Device) -> NestedTensor # noqa - cast_tensor = self.tensors.to(device) - mask = self.mask - if mask is not None: - assert mask is not None - cast_mask = mask.to(device) - else: - cast_mask = None - return NestedTensor(cast_tensor, cast_mask) - - def decompose(self): - return self.tensors, self.mask - - def __repr__(self): - return str(self.tensors) - - -def setup_for_distributed(is_master): - """ - This function disables printing when not in master process - """ - import builtins as __builtin__ - builtin_print = __builtin__.print - - def print(*args, **kwargs): - force = kwargs.pop('force', False) - if is_master or force: - builtin_print(*args, **kwargs) - - __builtin__.print = print - - -def is_dist_avail_and_initialized(): - if not dist.is_available(): - return False - if not dist.is_initialized(): - return False - return True - - -def get_world_size(): - if not is_dist_avail_and_initialized(): - return 1 - return dist.get_world_size() - - -def get_rank(): - if not is_dist_avail_and_initialized(): - return 0 - return dist.get_rank() - - -def is_main_process(): - return get_rank() == 0 - - -def save_on_master(*args, **kwargs): - if is_main_process(): - torch.save(*args, **kwargs) - - -def init_distributed_mode(args): - if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: - args.rank = int(os.environ["RANK"]) - args.world_size = int(os.environ['WORLD_SIZE']) - args.gpu = int(os.environ['LOCAL_RANK']) - elif 'SLURM_PROCID' in os.environ: - args.rank = int(os.environ['SLURM_PROCID']) - args.gpu = args.rank % torch.cuda.device_count() - else: - print('Not using distributed mode') - args.distributed = False - return - - args.distributed = True - - torch.cuda.set_device(args.gpu) - args.dist_backend = 'nccl' - print('| distributed init (rank {}): {}'.format( - args.rank, args.dist_url), flush=True) - torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) - torch.distributed.barrier() - setup_for_distributed(args.rank == 0) - - -@torch.no_grad() -def accuracy(output, target, topk=(1,)): - """Computes the precision@k for the specified values of k""" - if target.numel() == 0: - return [torch.zeros([], device=output.device)] - maxk = max(topk) - batch_size = target.size(0) - - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target.view(1, -1).expand_as(pred)) - - res = [] - for k in topk: - correct_k = correct[:k].view(-1).float().sum(0) - res.append(correct_k.mul_(100.0 / batch_size)) - return res - - -def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): - # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor - """ - Equivalent to nn.functional.interpolate, but with support for empty batch sizes. - This will eventually be supported natively by PyTorch, and this - class can go away. - """ - if float(torchvision.__version__[:3]) < 0.7: - if input.numel() > 0: - return torch.nn.functional.interpolate( - input, size, scale_factor, mode, align_corners - ) - - output_shape = _output_size(2, input, size, scale_factor) - output_shape = list(input.shape[:-2]) + list(output_shape) - return _new_empty_tensor(input, output_shape) - else: - return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) - - -class FocalLoss(nn.Module): - r""" - This criterion is a implemenation of Focal Loss, which is proposed in - Focal Loss for Dense Object Detection. - - Loss(x, class) = - \alpha (1-softmax(x)[class])^gamma \log(softmax(x)[class]) - - The losses are averaged across observations for each minibatch. - - Args: - alpha(1D Tensor, Variable) : the scalar factor for this criterion - gamma(float, double) : gamma > 0; reduces the relative loss for well-classified examples (p > .5), - putting more focus on hard, misclassified examples - size_average(bool): By default, the losses are averaged over observations for each minibatch. - However, if the field size_average is set to False, the losses are - instead summed for each minibatch. - - - """ - def __init__(self, class_num, alpha=None, gamma=2, size_average=True): - super(FocalLoss, self).__init__() - if alpha is None: - self.alpha = Variable(torch.ones(class_num, 1)) - else: - if isinstance(alpha, Variable): - self.alpha = alpha - else: - self.alpha = Variable(alpha) - self.gamma = gamma - self.class_num = class_num - self.size_average = size_average - - def forward(self, inputs, targets): - N = inputs.size(0) - C = inputs.size(1) - P = F.softmax(inputs) - - class_mask = inputs.data.new(N, C).fill_(0) - class_mask = Variable(class_mask) - ids = targets.view(-1, 1) - class_mask.scatter_(1, ids.data, 1.) - - if inputs.is_cuda and not self.alpha.is_cuda: - self.alpha = self.alpha.cuda() - alpha = self.alpha[ids.data.view(-1)] - - probs = (P*class_mask).sum(1).view(-1,1) - - log_p = probs.log() - batch_loss = -alpha*(torch.pow((1-probs), self.gamma))*log_p - - if self.size_average: - loss = batch_loss.mean() - else: - loss = batch_loss.sum() - return loss \ No newline at end of file diff --git a/spaces/anaclaudia13ct/insect_detection/utils/aws/userdata.sh b/spaces/anaclaudia13ct/insect_detection/utils/aws/userdata.sh deleted file mode 100644 index 5fc1332ac1b0d1794cf8f8c5f6918059ae5dc381..0000000000000000000000000000000000000000 --- a/spaces/anaclaudia13ct/insect_detection/utils/aws/userdata.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html -# This script will run only once on first instance start (for a re-start script see mime.sh) -# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir -# Use >300 GB SSD - -cd home/ubuntu -if [ ! -d yolov5 ]; then - echo "Running first-time script." # install dependencies, download COCO, pull Docker - git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 - cd yolov5 - bash data/scripts/get_coco.sh && echo "COCO done." & - sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & - python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & - wait && echo "All tasks done." # finish background tasks -else - echo "Running re-start script." # resume interrupted runs - i=0 - list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' - while IFS= read -r id; do - ((i++)) - echo "restarting container $i: $id" - sudo docker start $id - # sudo docker exec -it $id python train.py --resume # single-GPU - sudo docker exec -d $id python utils/aws/resume.py # multi-scenario - done <<<"$list" -fi diff --git a/spaces/aodianyun/stable-diffusion-webui/html/extra-networks-card.html b/spaces/aodianyun/stable-diffusion-webui/html/extra-networks-card.html deleted file mode 100644 index 8a5e2fbd223e71abacca9a602bd1be154f5fb520..0000000000000000000000000000000000000000 --- a/spaces/aodianyun/stable-diffusion-webui/html/extra-networks-card.html +++ /dev/null @@ -1,12 +0,0 @@ -While the increase of the Ni ratio in NCM contributes to an enhanced specific discharge capacity, it also results in severe capacity degradation caused by cation mixing, surface side reactions, and crack propagation with structural instability1. To better understand these challenges, Jung et al. investigated the degradation mechanism of the phase transformation induced by cation mixing from the surface to bulk using ex situ structural analysis7. Similarly, Lin et al. described the surface reconstruction and chemical evolution of the rhombohedral layered structure to a cubic spinel structure using high-throughput X-ray absorption spectroscopy8. As theoretical approaches, electronic correlations for the redox reactions between the multivalent transition metals in the Ni-rich NCM9 and stability analysis with respect to the various ratios of the Ni, Co, and Mn components in the NCM10 have been performed through first-principles calculations. On the bases of these fundamental data, many researchers have suggested solutions to resolve the cyclic degradation problem. Along with diverse approaches such as morphology control11, elemental doping12,13,14,15 and surface coating16,17,18,19,20, Sun et al. have suggested various effective ways to reduce cyclic degradation and improve electrochemical performance through the design of core-shell21, gradient core-shell3,22, and full concentration gradient structures2,23,24 for Ni-rich NCM cathodes.
-Recently, Meng et al. reported that the severe crack generation in NCM811 particles induces a significant performance degradation29. According to the scanning electron microscopy (SEM) observation, particle fractures and fragmentation of NCM811 particles were evident after cycles. Due to this crack generation, the discharge capacity of NCM811 was remarkably decreased with increasing overpotentials during cycles. From our fundamental understanding, we suggest that the origin of crack generation is the contraction of primary particles with a mechanical instability caused by heterogeneous phase transformation and anisotropic strain changes. In addition, the lower Gc at delithiated states contributes to a severe crack propagation. Finally, it is expected that these could be resolved by reducing the inhomogeneity and anisotropy of structural changes and increasing Gc.
-Download ===== https://urloso.com/2uyPnt
Flexible electrodes and strain sensors have seen tremendous growth in their demand due to their ease of integration with non-conventional interfaces. In this regard, mainly two strategies have been adopted. One strategy relies on layered thin films cast on a flexible substrate, while an alternate strategy utilizes flexible composite films. While both have been studied extensively, each has its niche area of applications. For optoelectronic and device applications like solar cells1, transistors2, organic LEDs3, photodetector4, that necessitate energy band alignment, layered films have found favor due to the ease of interfacing. For applications in strain sensors, composites have shown more robust performance under mechanical deformation5,6 while layered films typically have relatively higher gauge factor. However, the success of these strategies highly depends upon their ability to withstand various forms of mechanically induced deformations like cracks and wrinkles. These deformations in layered morphology constitute as important attributes in flexible substrates and devices, and their effects have been studied extensively. Bowden et al.7 and others suggest methods to control the wrinkling/buckling of PDMS under oxygen plasma treatment, and thermal cycling on metal deposited substrates8,9. Conventional inorganic materials that have been employed as an electrode in such devices, have not been able to withstand strain more than 1.75%10,11, wherein the crack formation led to material failure, thus degrading the performance of the devices12,13. However, to develop commercially viable flexible devices, effect of cracks on electrode performance in such devices needs to be studied.
-Graphene, an atomically thin 2-D membrane, due to its robust mechanical properties14 has been successfully shown to replace the existing conventional electrode materials in flexible devices15,16,17. Interestingly, when compared to pristine graphene, defected graphene was found to be more resistant to crack propagation, since the defects make the cracking process energetically less favorable18. Graphene oxide (GO) a chemically modified form of graphene, contains structural defects in the form of oxygen functional groups especially hydroxyls and epoxides which determine the crack formation and propagation19 and also the relative concentration of these groups controls mechanical failure of GO20. Thus one can tune the cracking of GO films by modifying the chemical composition which can be also achieved by reduction of GO to reduced graphene oxide (rGO), by chemical21 or thermal methods22. For commercial viability of rGO as an electrode material in flexible devices3,23,24 it is important to have an understanding of crack formation under strain and the limitations it imposes on electrical properties. Thomas et al. illustrated that buckling and cracking of GO films can be controlled by pre-straining the substrate25. Graphene layers have recently been used as a meta-interface to limit the cracking of Indium Tin Oxide (ITO) on polyethylene terephthalate (PET) substrates26. However, the interplay of strain-induced cracks, film thickness and the resultant strain-dependent electrical response of rGO on flexible substrates needs detailed investigation.
-Here we report crack propagation and tunability in electrical response of conducting rGO films coated on PDMS under uniaxial strain. We show that by optimizing the thickness, rGO films can largely be made to retain their conductivity under strain up to 5%, a value which is well above the limit for conventional flexible electrode materials. Our study also reveals a facile way to achieve periodic cracking and control the crack density and crack width by varying the thickness. This is then utilized for strain sensing and also for strain resistant flexible photoconductor applications.
-SEM images for (a) 1-coat (arrows indicate the position of cracks) (b) 3-coat (c) 6-coat rGO films on PDMS substrate at 5% applied strain. (d) Schematic diagram to illustrate the thickness dependent crack formation in rGO films under uniaxial strain.
-To understand the cracking process in detail, we studied the progressive formation of cracks as a function of applied strain for different thickness. Figure S3 show the optical images of a representative 6-coat sample under the application of strain from 0 to 5%. The first notable feature about cracking is that there is a critical strain value (~3.6%), after which the cracks begin to appear. Two modes of cracking have been described in the literature: (i) sequential cracking and (ii) simultaneous cracking39. The former is observed in our samples, since the crack density monotonically increasing with strain beyond the critical strain value. The optical images also reveal new cracks forming between existing cracks, confirming the sequential cracking.
-So far we have discussed the morphology of crack formation at different values of uniaxial strain, and also the dependence of this phenomenon on film thickness. These morphological changes can also be expected to strongly influence the electrical response under strain. With this motivation, we prepared six kinds of samples ranging from 1-coat to 6-coat to evaluate the strain response as a function of thickness of rGO on PDMS substrate. The uniaxial strain was applied using a linear micro-manipulator stage. Figure 4a shows the variation of normalized resistance for 1-coat, 3-coat and 6-coat samples as a function of strain. For 1-coat sample, the resistance takes-off at an applied strain value of 3.5% and shows an eight fold increase in resistance for maximum strain value of 5%.
-To get more insight into this change in resistance upon straining, a detailed study was conducted which is discussed as follows. Figure 5a shows the cross-section SEM of 3-coat sample under 5% strain. Arrows point towards the crack formation in both buckled and planar region of the sample. To further investigate the effect of cracks on fractional resistance change as the strain is varied for 3-coat sample, variation in crack density (n) was studied as a function of applied strain shown in Fig. 5b. Both the fractional resistance and crack density vary exponentially after a critical strain value (~3.6%) is breached. The similar response of both parameters arises since crack density determines the fractional resistance change. The variation in crack density as a function of strain is shown in the representative optical images in Fig. 5c,d and e at 0, 3.6 and 5% strain, respectively.
-So far we have demonstrated that very thin (1-coat) or thick (6-coat) films can be adapted for strain sensing. The intermediate thickness (3-coat) shows the least response to applied strain and is therefore suited in applications like flexible optoelectronics, where strain-related effects are not a desirable feature. To discuss the role of thickness-dependent crack propagation in a strained optoelectronic device, we prepared a hybrid of a standard UV active material - TiO2 nanoparticles with rGO on flexible PDMS substrate.
- -Figure 7a illustrates the UV response of TiO2-rGO system with 1-coat rGO as electrode. As can be seen from figure, the resistance under dark and light conditions continues to increases as a function of applied strain and does not show a stable response. For example, the strain dependence of dark current implies that the change in resistance of a flexible photodetector can be attributed either to the strain and its associated crack propagation or to the generation of photocarriers. The device, when used in the flexible configuration, is not capable of distinguishing these two factors since both influence the resistance. Figure 7b shows the response of TiO2-rGO system with 3-coat rGO as electrode. Both the dark resistance and resistance under illumination have a much weaker dependence on strain. For this case, a change of resistance of the device can directly be attributed to photodetection. Thus optimization of rGO thickness is crucial for applications which require resistance to strain.
aaccfb2cb3Download File https://urloso.com/2uyQd0
''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
- elif(option == "person"):
- instance_prompt_example = "julcto"
- freeze_for = 100
- return [f"You are going to train a `person`(s), upload 10-20 images of each person you are planning on training on from different angles/perspectives. {mandatory_liability}:", '''
''', f"You should name the files with a unique word that represent your concept (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
- elif(option == "style"):
- instance_prompt_example = "trsldamrl"
- freeze_for = 10
- return [f"You are going to train a `style`, upload 10-20 images of the style you are planning on training on. Name the files with the words you would like {mandatory_liability}:", '''
''', f"You should name your files with a unique word that represent your concept (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
-
-def count_files(*inputs):
- file_counter = 0
- concept_counter = 0
- for i, input in enumerate(inputs):
- if(i < maximum_concepts-1):
- files = inputs[i]
- if(files):
- concept_counter+=1
- file_counter+=len(files)
- uses_custom = inputs[-1]
- type_of_thing = inputs[-4]
- if(uses_custom):
- Training_Steps = int(inputs[-3])
- else:
- if(type_of_thing == "person"):
- Training_Steps = file_counter*200*2
- else:
- Training_Steps = file_counter*200
- return(gr.update(visible=True, value=f"You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps. This should take around {round(Training_Steps/1.5, 2)} seconds, or {round((Training_Steps/1.5)/3600, 2)} hours. As a reminder, the T4 GPU costs US$0.60 for 1h. Once training is over, don't forget to swap the hardware back to CPU."))
-
-def train(*inputs):
- if "IS_SHARED_UI" in os.environ:
- raise gr.Error("This Space only works in duplicated instances")
- if os.path.exists("output_model"): shutil.rmtree('output_model')
- if os.path.exists("instance_images"): shutil.rmtree('instance_images')
- if os.path.exists("diffusers_model.zip"): os.remove("diffusers_model.zip")
- if os.path.exists("model.ckpt"): os.remove("model.ckpt")
- file_counter = 0
- for i, input in enumerate(inputs):
- if(i < maximum_concepts-1):
- if(input):
- os.makedirs('instance_images',exist_ok=True)
- files = inputs[i+(maximum_concepts*2)]
- prompt = inputs[i+maximum_concepts]
- if(prompt == "" or prompt == None):
- raise gr.Error("You forgot to define your concept prompt")
- for j, file_temp in enumerate(files):
- file = Image.open(file_temp.name)
- width, height = file.size
- side_length = min(width, height)
- left = (width - side_length)/2
- top = (height - side_length)/2
- right = (width + side_length)/2
- bottom = (height + side_length)/2
- image = file.crop((left, top, right, bottom))
- image = image.resize((512, 512))
- extension = file_temp.name.split(".")[1]
- image = image.convert('RGB')
- image.save(f'instance_images/{prompt}_({j+1}).jpg', format="JPEG", quality = 100)
- file_counter += 1
-
- os.makedirs('output_model',exist_ok=True)
- uses_custom = inputs[-1]
- type_of_thing = inputs[-4]
- if(uses_custom):
- Training_Steps = int(inputs[-3])
- Train_text_encoder_for = int(inputs[-2])
- else:
- Training_Steps = file_counter*200
- if(type_of_thing == "object"):
- Train_text_encoder_for=30
- elif(type_of_thing == "person"):
- Train_text_encoder_for=60
- elif(type_of_thing == "style"):
- Train_text_encoder_for=15
-
- class_data_dir = None
- stptxt = int((Training_Steps*Train_text_encoder_for)/100)
- args_general = argparse.Namespace(
- image_captions_filename = True,
- train_text_encoder = True,
- stop_text_encoder_training = stptxt,
- save_n_steps = 0,
- pretrained_model_name_or_path = model_to_load,
- instance_data_dir="instance_images",
- class_data_dir=class_data_dir,
- output_dir="output_model",
- instance_prompt="",
- seed=42,
- resolution=512,
- mixed_precision="fp16",
- train_batch_size=1,
- gradient_accumulation_steps=1,
- use_8bit_adam=True,
- learning_rate=2e-6,
- lr_scheduler="polynomial",
- lr_warmup_steps = 0,
- max_train_steps=Training_Steps,
- )
- run_training(args_general)
- torch.cuda.empty_cache()
- #convert("output_model", "model.ckpt")
- #shutil.rmtree('instance_images')
- #shutil.make_archive("diffusers_model", 'zip', "output_model")
- with zipfile.ZipFile('diffusers_model.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:
- zipdir('output_model/', zipf)
- torch.cuda.empty_cache()
- return [gr.update(visible=True, value=["diffusers_model.zip"]), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)]
-
-def generate(prompt):
- from diffusers import StableDiffusionPipeline
-
- pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16)
- pipe = pipe.to("cuda")
- image = pipe(prompt).images[0]
- return(image)
-
-def push(model_name, where_to_upload, hf_token):
- if(not os.path.exists("model.ckpt")):
- convert("output_model", "model.ckpt")
- from huggingface_hub import HfApi, HfFolder, CommitOperationAdd
- from huggingface_hub import create_repo
- model_name_slug = slugify(model_name)
- api = HfApi()
- your_username = api.whoami(token=hf_token)["name"]
- if(where_to_upload == "My personal profile"):
- model_id = f"{your_username}/{model_name_slug}"
- else:
- model_id = f"sd-dreambooth-library/{model_name_slug}"
- headers = {"Authorization" : f"Bearer: {hf_token}", "Content-Type": "application/json"}
- response = requests.post("https://huggingface.co/organizations/sd-dreambooth-library/share/SSeOwppVCscfTEzFGQaqpfcjukVeNrKNHX", headers=headers)
-
- images_upload = os.listdir("instance_images")
- image_string = ""
- instance_prompt_list = []
- previous_instance_prompt = ''
- for i, image in enumerate(images_upload):
- instance_prompt = image.split("_")[0]
- if(instance_prompt != previous_instance_prompt):
- title_instance_prompt_string = instance_prompt
- instance_prompt_list.append(instance_prompt)
- else:
- title_instance_prompt_string = ''
- previous_instance_prompt = instance_prompt
- image_string = f'''{title_instance_prompt_string}
-{image_string}})'''
- readme_text = f'''---
-license: creativeml-openrail-m
-tags:
-- text-to-image
----
-### {model_name} Dreambooth model trained by {api.whoami(token=hf_token)["name"]} with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training)
-
-You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
-
-Sample pictures of this concept:
-{image_string}
-'''
- #Save the readme to a file
- readme_file = open("README.md", "w")
- readme_file.write(readme_text)
- readme_file.close()
- #Save the token identifier to a file
- text_file = open("token_identifier.txt", "w")
- text_file.write(', '.join(instance_prompt_list))
- text_file.close()
- create_repo(model_id,private=True, token=hf_token)
- operations = [
- CommitOperationAdd(path_in_repo="token_identifier.txt", path_or_fileobj="token_identifier.txt"),
- CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="README.md"),
- CommitOperationAdd(path_in_repo=f"model.ckpt",path_or_fileobj="model.ckpt")
- ]
- api.create_commit(
- repo_id=model_id,
- operations=operations,
- commit_message=f"Upload the model {model_name}",
- token=hf_token
- )
- api.upload_folder(
- folder_path="output_model",
- repo_id=model_id,
- token=hf_token
- )
- api.upload_folder(
- folder_path="instance_images",
- path_in_repo="concept_images",
- repo_id=model_id,
- token=hf_token
- )
- return [gr.update(visible=True, value=f"Successfully uploaded your model. Access it [here](https://huggingface.co/{model_id})"), gr.update(visible=True, value=["diffusers_model.zip", "model.ckpt"])]
-
-def convert_to_ckpt():
- convert("output_model", "model.ckpt")
- return gr.update(visible=True, value=["diffusers_model.zip", "model.ckpt"])
-
-with gr.Blocks(css=css) as demo:
- with gr.Box():
- if "IS_SHARED_UI" in os.environ:
- gr.HTML('''
- For it to work, you have to duplicate the Space and run it on your own profile where a (paid) private GPU will be attributed to it during runtime. As each T4 costs US$0,60/h, it should cost < US$1 to train a model with less than 100 images on default settings!
-
- 
- If you haven't already, attribute a T4 GPU to it (via the Settings tab) and run the training below. You will be billed by the minute from when you activate the GPU until when you turn it off.
-''')
- things_naming = gr.Markdown("You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `cttoy` here). Images will be automatically cropped to 512x512.")
- with gr.Column():
- file_collection = []
- concept_collection = []
- buttons_collection = []
- delete_collection = []
- is_visible = []
-
- row = [None] * maximum_concepts
- for x in range(maximum_concepts):
- ordinal = lambda n: "%d%s" % (n, "tsnrhtdd"[(n // 10 % 10 != 1) * (n % 10 < 4) * n % 10::4])
- if(x == 0):
- visible = True
- is_visible.append(gr.State(value=True))
- else:
- visible = False
- is_visible.append(gr.State(value=False))
-
- file_collection.append(gr.File(label=f"Upload the images for your {ordinal(x+1)} concept", file_count="multiple", interactive=True, visible=visible))
- with gr.Column(visible=visible) as row[x]:
- concept_collection.append(gr.Textbox(label=f"{ordinal(x+1)} concept prompt - use a unique, made up word to avoid collisions"))
- with gr.Row():
- if(x < maximum_concepts-1):
- buttons_collection.append(gr.Button(value="Add +1 concept", visible=visible))
- if(x > 0):
- delete_collection.append(gr.Button(value=f"Delete {ordinal(x+1)} concept"))
-
- counter_add = 1
- for button in buttons_collection:
- if(counter_add < len(buttons_collection)):
- button.click(lambda:
- [gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), True, None],
- None,
- [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], buttons_collection[counter_add], is_visible[counter_add], file_collection[counter_add]], queue=False)
- else:
- button.click(lambda:[gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), True], None, [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], is_visible[counter_add]], queue=False)
- counter_add += 1
-
- counter_delete = 1
- for delete_button in delete_collection:
- if(counter_delete < len(delete_collection)+1):
- delete_button.click(lambda:[gr.update(visible=False),gr.update(visible=False), gr.update(visible=True), False], None, [file_collection[counter_delete], row[counter_delete], buttons_collection[counter_delete-1], is_visible[counter_delete]], queue=False)
- counter_delete += 1
-
-
-
- with gr.Accordion("Custom Settings", open=False):
- swap_auto_calculated = gr.Checkbox(label="Use custom settings")
- gr.Markdown("If not checked, the number of steps and % of frozen encoder will be tuned automatically according to the amount of images you upload and whether you are training an `object`, `person` or `style` as follows: The number of steps is calculated by number of images uploaded multiplied by 20. The text-encoder is frozen after 10% of the steps for a style, 30% of the steps for an object and is fully trained for persons.")
- steps = gr.Number(label="How many steps", value=800)
- perc_txt_encoder = gr.Number(label="Percentage of the training steps the text-encoder should be trained as well", value=30)
-
- type_of_thing.change(fn=swap_text, inputs=[type_of_thing], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder], queue=False)
- training_summary = gr.Textbox("", visible=False, label="Training Summary")
- steps.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
- perc_txt_encoder.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
- for file in file_collection:
- file.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
- train_btn = gr.Button("Start Training")
- with gr.Box(visible=False) as try_your_model:
- gr.Markdown("## Try your model")
- with gr.Row():
- prompt = gr.Textbox(label="Type your prompt")
- result_image = gr.Image()
- generate_button = gr.Button("Generate Image")
- with gr.Box(visible=False) as push_to_hub:
- gr.Markdown("## Push to Hugging Face Hub")
- model_name = gr.Textbox(label="Name of your model", placeholder="Tarsila do Amaral Style")
- where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], label="Upload to")
- gr.Markdown("[A Hugging Face write access token](https://huggingface.co/settings/tokens), go to \"New token\" -> Role : Write. A regular read token won't work here.")
- hf_token = gr.Textbox(label="Hugging Face Write Token")
- push_button = gr.Button("Push to the Hub")
- result = gr.File(label="Download the uploaded models in the diffusers format", visible=True)
- success_message_upload = gr.Markdown(visible=False)
- convert_button = gr.Button("Convert to CKPT", visible=False)
-
- train_btn.click(fn=train, inputs=is_visible+concept_collection+file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[result, try_your_model, push_to_hub, convert_button])
- generate_button.click(fn=generate, inputs=prompt, outputs=result_image)
- push_button.click(fn=push, inputs=[model_name, where_to_upload, hf_token], outputs=[success_message_upload, result])
- convert_button.click(fn=convert_to_ckpt, inputs=[], outputs=result)
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/clip-italian/clip-italian-demo/utils.py b/spaces/clip-italian/clip-italian-demo/utils.py
deleted file mode 100644
index aafbdb79bea01585823f4cd9a3e44c92df01fa20..0000000000000000000000000000000000000000
--- a/spaces/clip-italian/clip-italian-demo/utils.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import os
-import natsort
-from tqdm import tqdm
-import torch
-from jax import numpy as jnp
-from PIL import Image as PilImage
-
-
-class CustomDataSet(torch.utils.data.Dataset):
- def __init__(self, main_dir, transform):
- self.main_dir = main_dir
- self.transform = transform
- all_imgs = os.listdir(main_dir)
- self.total_imgs = natsort.natsorted(all_imgs)
-
- def __len__(self):
- return len(self.total_imgs)
-
- def get_image_name(self, idx):
- return self.total_imgs[idx]
-
- def __getitem__(self, idx):
- img_loc = os.path.join(self.main_dir, self.total_imgs[idx])
- image = PilImage.open(img_loc).convert("RGB")
- tensor_image = self.transform(image)
- return tensor_image
-
-
-def text_encoder(text, model, tokenizer):
- inputs = tokenizer(
- [text],
- max_length=96,
- truncation=True,
- padding="max_length",
- return_tensors="np",
- )
- embedding = model.get_text_features(
- inputs["input_ids"],
- inputs["attention_mask"])[0]
- norms = jnp.linalg.norm(embedding, axis=-1, keepdims=True)
- embedding = embedding / norms
- return jnp.expand_dims(embedding, axis=0), norms
-
-
-def image_encoder(image, model):
- image = image.permute(1, 2, 0).numpy()
- image = jnp.expand_dims(image, axis=0) # add batch size
- features = model.get_image_features(image,)
- norms = jnp.linalg.norm(features, axis=-1, keepdims=True)
- features = features / norms
- return features, norms
-
-
-def precompute_image_features(model, loader):
- image_features = []
- for i, (images) in enumerate(tqdm(loader)):
- images = images.permute(0, 2, 3, 1).numpy()
- features = model.get_image_features(images,)
- features /= jnp.linalg.norm(features, axis=-1, keepdims=True)
- image_features.extend(features)
- return jnp.array(image_features)
-
-
-def find_image(text_query, model, dataset, tokenizer, image_features, n, dataset_name):
- zeroshot_weights, _ = text_encoder(text_query, model, tokenizer)
- distances = jnp.dot(image_features, zeroshot_weights.reshape(-1, 1))
- file_paths = []
- for i in range(1, n + 1):
- idx = jnp.argsort(distances, axis=0)[-i, 0]
-
- if dataset_name == "Unsplash":
- file_paths.append("photos/" + dataset.get_image_name(idx))
- elif dataset_name == "CC":
- file_paths.append(dataset[idx])
- else:
- raise ValueError(f"{dataset_name} not supported here")
- return file_paths
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/_m_o_r_x.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/_m_o_r_x.py
deleted file mode 100644
index da299c6d85893e4113c459d503d77c6a120128ae..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/_m_o_r_x.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6morx.html
-class table__m_o_r_x(BaseTTXConverter):
- pass
diff --git a/spaces/cncanon/gpt4/README.md b/spaces/cncanon/gpt4/README.md
deleted file mode 100644
index 692f4d5d704736314c47a0cd3d7d78ec1cf43af9..0000000000000000000000000000000000000000
--- a/spaces/cncanon/gpt4/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: GPT-4
-emoji: ⚡
-colorFrom: purple
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/codedog-ai/edu-assistant/edu_assistant/version.py b/spaces/codedog-ai/edu-assistant/edu_assistant/version.py
deleted file mode 100644
index 7874da15e5c431dc940f8ece5af8aee2dfe05061..0000000000000000000000000000000000000000
--- a/spaces/codedog-ai/edu-assistant/edu_assistant/version.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# -- Project information -----------------------------------------------------
-
-PROJECT = "edu-assistant"
-VERSION = "0.3.0"
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_mvpred.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_mvpred.h
deleted file mode 100644
index 46ae2738f921468360bd3992bf58b220432f1f8a..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_mvpred.h
+++ /dev/null
@@ -1,835 +0,0 @@
-/*
- * H.26L/H.264/AVC/JVT/14496-10/... motion vector prediction
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| Challenging and fun gameplay | -Frequent updates and in-app purchases | -
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| Addictive and fun gameplay | Sometimes crashes or freezes on some devices | -
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Euro Truck Simulator 2 is a simulation game developed by SCS Software and released in 2012. It is the sequel to the popular Euro Truck Simulator and features more realistic graphics, physics, and gameplay. In Euro Truck Simulator 2, you can drive various licensed trucks from famous brands such as Volvo, Scania, MAN, DAF, Renault, and more. You can also customize your trucks with different parts, paint jobs, and accessories. You can travel across more than 60 European cities and countries, including the UK, Germany, France, Italy, Spain, Poland, and more. You can also run your own trucking business by hiring drivers, buying garages, and managing your finances.
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A reliable source is one that provides you with a safe and secure download link for Euro Truck Simulator 2 free full version no password. A reliable source should also offer you a fast and easy download process, without any surveys, pop-ups, or malware. A reliable source should also have positive feedback from other users who have downloaded the game successfully.
-There are three main places where you can find a source for downloading Euro Truck Simulator 2 free full version no password: the official website, Steam, and third-party websites. Let's look at each one in detail.
-The official website of Euro Truck Simulator 2 is https://eurotrucksimulator2.com/. Here you can find all the information about the game, such as the features, screenshots, videos, news, and updates. You can also buy the game and the DLCs from the official website. However, if you want to download the game for free, you can also find a demo version of the game on the official website. The demo version allows you to play the game for one hour and explore some of the maps and trucks. To download the demo version, you need to click on the "Download Demo" button on the homepage and follow the instructions. The demo version is compatible with Windows 7/8/10 and requires 4 GB of RAM and 3 GB of disk space.
-Steam is a digital distribution platform that offers thousands of games for PC, Mac, and Linux. You can find Euro Truck Simulator 2 on Steam at https://store.steampowered.com/app/227300/Euro_Truck_Simulator_2/. Here you can buy the game and the DLCs, as well as read reviews, join discussions, and watch live streams. You can also download the game for free from Steam if you have an account and a Steam client installed on your computer. To download the game for free from Steam, you need to follow these steps:
-This code is a promotional code that was given away by SCS Software in 2020 to celebrate their 8th anniversary. It allows you to download Euro Truck Simulator 2 free full version no password. However, this code might not work forever, so hurry up and use it before it expires.
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-If you are a fan of truck driving simulator games, you should definitely try Euro Truck Simulator 2. It is one of the best games in its genre and it will keep you entertained for hours. You can download Euro Truck Simulator 2 free full version no password from any of the sources that we have mentioned in this article. However, if you want to support the developers and get access to more content and features, you can also buy the game and the DLCs from the official website or Steam. Either way, you will not regret playing Euro Truck Simulator 2.
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-You can update Euro Truck Simulator 2 automatically if you have downloaded it from Steam or manually if you have downloaded it from other sources. To update it automatically, you need to open Steam and go to the Library tab. Find Euro Truck Simulator 2 in your list of games and right-click on it. Select "Properties" and go to the "Updates" tab. Make sure that "Always keep this game up to date" is selected. Steam will then download and install the latest version of the game for you. To update it manually, you need to go to the official website of Euro Truck Simulator 2 and find the latest patch for the game. You can find the patches at https://eurotrucksimulator2.com/update.php. Download the patch that matches your game version and save it on your computer. Then, run the patch file and follow the instructions on the screen. The patch will update your game to the latest version.
-You can play Euro Truck Simulator 2 online with other players by using a mod called TruckersMP. TruckersMP is a multiplayer mod that allows you to join servers and drive with or against other players in real time. You can also chat, voice, and text with other players, join or create companies, and participate in events and convoys. To play Euro Truck Simulator 2 online with TruckersMP, you need to follow these steps:
-Note that you need to have a legal copy of Euro Truck Simulator 2 and Steam to play online with TruckersMP. You also need to have the same game version and DLCs as the server that you are joining.
-You can use mods in Euro Truck Simulator 2 to enhance your game with new content and features, such as new trucks, trailers, maps, skins, sounds, and more. Mods are created by the community and can be downloaded from various websites, such as https://ets2.lt/, https://www.modland.net/euro-truck-simulator-2/, or https://steamcommunity.com/app/227300/workshop/. To use mods in Euro Truck Simulator 2, you need to follow these steps:
-Note that some mods may not be compatible with each other or with the latest version of the game. You should always read the description and instructions of the mod before using it. You should also backup your save files before using any mods.
-You can get more money in Euro Truck Simulator 2 by completing jobs, running your own business, or using cheats. Here are some ways to get more money in Euro Truck Simulator 2:
-Some examples of cheats that you can use are:
-These are some of the most common questions that people ask about Euro Truck Simulator 2. If you have any other questions or comments, feel free to leave them below. We hope that you have enjoyed this article and learned how to download Euro Truck Simulator 2 free full version no password. Happy trucking!
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" - -if torch.cuda.is_available(): - model_cache_dir = os.getenv("MODEL_CACHE_DIR", "./models") - model_dir = pathlib.Path(model_cache_dir) / "MS-Vid2Vid-XL" - snapshot_download(repo_id="damo-vilab/MS-Vid2Vid-XL", repo_type="model", local_dir=model_dir) - pipe = pipeline(task="video-to-video", model=model_dir.as_posix(), model_revision="v1.1.0", device="cuda:0") - - -def check_input_video(video_path: str) -> None: - cap = cv2.VideoCapture(video_path) - n_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - cap.release() - if n_frames != 32 or width != 448 or height != 256: - raise gr.Error( - f"Input video must be 32 frames of size 448x256. Your video is {n_frames} frames of size {width}x{height}." - ) - - -def video_to_video(video_path: str, text: str) -> str: - check_input_video(video_path) - p_input = {"video_path": video_path, "text": text} - output_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) - pipe(p_input, output_video=output_file.name)[OutputKeys.OUTPUT_VIDEO] - return output_file.name - - -with gr.Blocks(css="style.css") as demo: - gr.Markdown(DESCRIPTION) - gr.DuplicateButton( - value="Duplicate Space for private use", - elem_id="duplicate-button", - visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", - ) - with gr.Group(): - input_video = gr.Video(label="Input video") - text_description = gr.Textbox(label="Text description") - run_button = gr.Button() - output_video = gr.Video(label="Output video") - - gr.on( - triggers=[text_description.submit, run_button.click], - fn=check_input_video, - inputs=input_video, - queue=False, - api_name=False, - ).success( - fn=video_to_video, - inputs=[input_video, text_description], - outputs=output_video, - api_name="run", - ) - -if __name__ == "__main__": - demo.queue(max_size=10).launch() diff --git a/spaces/dandan4272/hand_gesture_rec/model/stgcn/Utils.py b/spaces/dandan4272/hand_gesture_rec/model/stgcn/Utils.py deleted file mode 100644 index 582504c67b985a265b4e5c942a01e482869ab488..0000000000000000000000000000000000000000 --- a/spaces/dandan4272/hand_gesture_rec/model/stgcn/Utils.py +++ /dev/null @@ -1,199 +0,0 @@ -### Reference from: https://github.com/yysijie/st-gcn/blob/master/net/utils/graph.py - -import os -import torch -import numpy as np - - -class Graph: - """The Graph to model the skeletons extracted by the Alpha-Pose. - Args: - - strategy: (string) must be one of the follow candidates - - uniform: Uniform Labeling, - - distance: Distance Partitioning, - - spatial: Spatial Configuration, - For more information, please refer to the section 'Partition Strategies' - in our paper (https://arxiv.org/abs/1801.07455). - - layout: (string) must be one of the follow candidates - - coco_cut: Is COCO format but cut 4 joints (L-R ears, L-R eyes) out. - - max_hop: (int) the maximal distance between two connected nodes. - - dilation: (int) controls the spacing between the kernel points. - """ - def __init__(self, - layout='coco_cut', - # strategy='uniform', - strategy='spatial', - - max_hop=1, - dilation=1): - self.max_hop = max_hop - self.dilation = dilation - - self.get_edge(layout) - self.hop_dis = get_hop_distance(self.num_node, self.edge, max_hop) - self.get_adjacency(strategy) - - # [(4, 3), (3, 2), (7, 6), (6, 5), (13, 12), (12, - # 11), - # (10, 9), (9, 8), (11, 5), (8, 2), (5, 1), (2, 1), - # (0, 1), (15, 0), (14, 0), (17, 15), (16, 14)] - - # def get_edge(self, layout): - # if layout == 'coco_cut': - # self.num_node = 14 - # self_link = [(i, i) for i in range(self.num_node)] - # neighbor_link = [(6, 4), (4, 2), (2, 13), (13, 1), (5, 3), (3, 1), (12, 10), - # (10, 8), (8, 2), (11, 9), (9, 7), (7, 1), (13, 0)] - # self.edge = self_link + neighbor_link - # self.center = 13 - # else: - # raise ValueError('This layout is not supported!') - - def get_edge(self, layout): - if layout == 'coco_cut': - self.num_node = 21 - self_link = [(i, i) for i in range(self.num_node)] - # neighbor_link = [(4, 3), (3, 2), (2, 1), (8, 7), (7, 6), (6, 5), (12, 11), - # (11, 10), (10, 9), (16, 15), (15, 14), (14, 13), (20, 19),(19,18), - # (18,17),(4,2),(8,5),(12,9),(16,13),(20,17)] - # neighbor_link = [(4, 3), (3, 2), (5,2),(2, 1), (8, 7), (7, 6), (6, 5), (12, 11), - # (11, 10), (10, 9), (16, 15), (15, 14), (14, 13), (20, 19),(19,18), - # (18,17),(5,9),(9,13),(13,17),(1,0),(17,0)] - - neighbor_link_1 = [(0,1), (1, 2),(2, 3), (3, 4), (0, 5), (5, 6), (6, 7), - (7, 8), (0, 9), (9,10), (10,11), (11,12), (0,13),(13,14), - (14,15),(15,16),(0,17),(17,18),(18,19),(19,20)] - neighbor_link = [(4, 3), (3, 2),(2, 1), (8, 7), (7, 6), (6, 5), (12, 11), - (11, 10), (10, 9), (16, 15), (15, 14), (14, 13), (20, 19),(19,18), - (18,17),(2,5),(5,9),(9,13),(13,17),(1,0),(17,0),(4,0),(8,0),(12,0),(16,0),(20,0)] - - # neighbor_link=[(3, 4), (0, 5), (17, 18), (0, 17), (13, 14), (13, 17), (18, 19), (5, 6), (5, 9), (14, 15), (0, 1), - # (9, 10), (1, 2), (9, 13), (10, 11), (19, 20), (6, 7), (15, 16), (2, 3), (11, 12), (7, 8)] - - # neighbor_link = [(4, 3), (3, 2),(2, 1), (8, 7), (7, 6), (6, 5), (12, 11), - # (11, 10), (10, 9), (16, 15), (15, 14), (14, 13), (20, 19),(19,18), - # (18,17),(2,5),(5,9),(9,13),(13,17),(1,0),(17,0)] - - # neighbor_link = [(4, 3), (3, 2), (2, 1), (8, 7), (7, 6), (6, 5), (12, 11), - # (11, 10), (10, 9), (16, 15), (15, 14), (14, 13), (20, 19),(19,18), - # (18,17),(5,9),(9,13),(13,17),(1,0),(5,0),(17,0)] - - # neighbor_link = [(4, 3), (3, 2), (7, 6), (6, 5), (13, 12), (12, - # 11), - # (10, 9), (9, 8), (11, 5), (8, 2), (5, 1), (2, 1), - # (0, 1), (15, 0), (14, 0), (17, 15), (16, 14)] - - self.edge = self_link + neighbor_link_1 - self.center = 0 - else: - raise ValueError('This layout is not supported!') - - def get_adjacency(self, strategy): - valid_hop = range(0, self.max_hop + 1, self.dilation) - adjacency = np.zeros((self.num_node, self.num_node)) - for hop in valid_hop: - adjacency[self.hop_dis == hop] = 1 - normalize_adjacency = normalize_digraph(adjacency) - - if strategy == 'uniform': - A = np.zeros((1, self.num_node, self.num_node)) - A[0] = normalize_adjacency - self.A = A - elif strategy == 'distance': - A = np.zeros((len(valid_hop), self.num_node, self.num_node)) - for i, hop in enumerate(valid_hop): - A[i][self.hop_dis == hop] = normalize_adjacency[self.hop_dis == - hop] - self.A = A - elif strategy == 'spatial': - A = [] - for hop in valid_hop: - a_root = np.zeros((self.num_node, self.num_node)) - a_close = np.zeros((self.num_node, self.num_node)) - a_further = np.zeros((self.num_node, self.num_node)) - for i in range(self.num_node): - for j in range(self.num_node): - if self.hop_dis[j, i] == hop: - if self.hop_dis[j, self.center] == self.hop_dis[i, self.center]: - a_root[j, i] = normalize_adjacency[j, i] - elif self.hop_dis[j, self.center] > self.hop_dis[i, self.center]: - a_close[j, i] = normalize_adjacency[j, i] - else: - a_further[j, i] = normalize_adjacency[j, i] - if hop == 0: - A.append(a_root) - else: - A.append(a_root + a_close) - A.append(a_further) - A = np.stack(A) - self.A = A - #self.A = np.swapaxes(np.swapaxes(A, 0, 1), 1, 2) - else: - raise ValueError("This strategy is not supported!") - - -def get_hop_distance(num_node, edge, max_hop=1): - A = np.zeros((num_node, num_node)) - for i, j in edge: - A[j, i] = 1 - A[i, j] = 1 - - # compute hop steps - hop_dis = np.zeros((num_node, num_node)) + np.inf - transfer_mat = [np.linalg.matrix_power(A, d) for d in range(max_hop + 1)] - arrive_mat = (np.stack(transfer_mat) > 0) - for d in range(max_hop, -1, -1): - hop_dis[arrive_mat[d]] = d - return hop_dis - - -def normalize_digraph(A): - Dl = np.sum(A, 0) - num_node = A.shape[0] - Dn = np.zeros((num_node, num_node)) - for i in range(num_node): - if Dl[i] > 0: - Dn[i, i] = Dl[i]**(-1) - AD = np.dot(A, Dn) - return AD - - -def normalize_undigraph(A): - Dl = np.sum(A, 0) - num_node = A.shape[0] - Dn = np.zeros((num_node, num_node)) - for i in range(num_node): - if Dl[i] > 0: - Dn[i, i] = Dl[i]**(-0.5) - DAD = np.dot(np.dot(Dn, A), Dn) - return DAD - - -def normalize_points_with_size(xy, width, height, flip=False): - """Normalize scale points in image with size of image to (0-1). - xy : (frames, parts, xy) or (parts, xy) - """ - if xy.ndim == 2: - xy = np.expand_dims(xy, 0) - # print(xy[:, :, 1].min(), xy[:, :, 1].max()) - xy[:, :, 0] /= width - xy[:, :, 1] /= height - print('preprocess') - # print(xy[:, :, 0].min(), xy[:, :, 0].max()) - # print(xy[:, :, 1].min(), xy[:, :, 1].max()) - if flip: - xy[:, :, 0] = 1 - xy[:, :, 0] - return xy - - -def scale_pose(xy): - """Normalize pose points by scale with max/min value of each pose. - xy : (frames, parts, xy) or (parts, xy) - """ - if xy.ndim == 2: - xy = np.expand_dims(xy, 0) - xy_min = np.nanmin(xy, axis=1) - xy_max = np.nanmax(xy, axis=1) - for i in range(xy.shape[0]): - xy[i] = ((xy[i] - xy_min[i]) / (xy_max[i] - xy_min[i])) * 2 - 1 - return xy.squeeze() diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-aef15a25.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-aef15a25.js deleted file mode 100644 index 9399883f87ad7d84309bdca42939b4c4be3fa491..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-aef15a25.js +++ /dev/null @@ -1,2 +0,0 @@ -import{S as P,e as Q,s as U,I as V,F as j,o as J,m as O,g as S,G as E,h as z,J as ue,ay as _e,w as B,u as C,k as D,H as I,C as se,am as fe,t as W,x as X,az as oe,ap as Y,Y as M,j as H,p as A,B as ce,V as Z,ae as y,N,O as T,Q as p,R as x,T as q,E as R,P as he,r as me,v as be}from"./index-9e76ffee.js";import{B as $}from"./Button-30a08c0b.js";import{B as de}from"./BlockTitle-af232cbc.js";import"./Info-77722665.js";function K(l,e,i){const n=l.slice();return n[13]=e[i],n[15]=i,n}function ge(l){let e;return{c(){e=W(l[3])},m(i,n){z(i,e,n)},p(i,n){n&8&&X(e,i[3])},d(i){i&&D(e)}}}function L(l,e){let i,n,s,o,h=!1,b,a,t=e[13]+"",f,d,u,m,r,g;function v(){return e[11](e[13],e[15])}return m=oe(e[10][0]),{key:l,first:null,c(){i=O("label"),n=O("input"),b=J(),a=O("span"),f=W(t),d=J(),n.disabled=e[2],S(n,"type","radio"),S(n,"name",s="radio-"+e[6]),n.__value=o=e[13],Y(n,n.__value),S(n,"class","svelte-1p9xokt"),S(a,"class","ml-2 svelte-1p9xokt"),S(i,"data-testid",u=`${e[13]}-radio-label`),S(i,"class","svelte-1p9xokt"),M(i,"disabled",e[2]),M(i,"selected",e[0]===e[13]),m.p(n),this.first=i},m(k,w){z(k,i,w),H(i,n),n.checked=n.__value===e[0],H(i,b),H(i,a),H(a,f),H(i,d),r||(g=[A(n,"change",e[9]),A(n,"input",v)],r=!0)},p(k,w){e=k,w&4&&(n.disabled=e[2]),w&64&&s!==(s="radio-"+e[6])&&S(n,"name",s),w&2&&o!==(o=e[13])&&(n.__value=o,Y(n,n.__value),h=!0),(h||w&3)&&(n.checked=n.__value===e[0]),w&2&&t!==(t=e[13]+"")&&X(f,t),w&2&&u!==(u=`${e[13]}-radio-label`)&&S(i,"data-testid",u),w&4&&M(i,"disabled",e[2]),w&3&&M(i,"selected",e[0]===e[13])},d(k){k&&D(i),m.r(),r=!1,ce(g)}}}function ve(l){let e,i,n,s=[],o=new Map,h;e=new de({props:{show_label:l[5],info:l[4],$$slots:{default:[ge]},$$scope:{ctx:l}}});let b=V(l[1]);const a=t=>t[15];for(let t=0;tDownload ✶✶✶ https://gohhs.com/2uFUGz
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the book is written by hafiz muhammad fazal gul and hafiz muhammad umar makhoo. it is about the collection of wazaifs from the holy quran and the hadith of the prophet. it is a collection of some 1000 wazaifs. in the book, the authors have discussed each wazaif and told the benefit of each one of them.
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fiqh e khaira fiqh e khaira written by syed jafar zaidi. fiqh e khaira fiqh e khaira is a book contains all kind of wazaif, amliyat, and amal with original translation of arabic text of quran along with benefit.
-the book of hazrat maulana ashraf ali thanvi is very famous. this book is very very important for every muslim. the book is also very famous in the world of muslims. it has been divided into three parts. the first part of the book is giving the english translation of the entire book. the second part of the book is the urdu translation of the same book. the third part of the book is about the islamic rituals.
-the urdu book of aamil e quran contains the translation of quran along with the arabic and english translation. this book is written by a great scholar maulana ashraf ali thanvi. it is recommended for those who want to learn the quranic language and also know the holy quran. it is very famous in the world of islam.
-this book of maulana ashraf ali thanvi is written in urdu language. it is very useful for every muslim. the book contains all the subjects and topics that a muslim should know. it is very famous in the world of islam. it contains the english and urdu translation of the entire book.
- -maulana ashraf ali thanvi has written a very interesting book. it contains the english and urdu translation of the entire book. this is a great book for all the muslims. it covers all the subjects and topics that a muslim should know. i hope you will like to read the book maulana ashraf ali thanvi and share it with your friends.
899543212bEssential Tricks for Enhancing the Quality of Your Generated - Audio
-1. Try using more adjectives to describe your sound. For example: "A man is speaking - clearly and slowly in a large room" is better than "A man is speaking".
-2. Try using different random seeds, which can significantly affect the quality of the generated - output.
-3. It's better to use general terms like 'man' or 'woman' instead of specific names for individuals or - abstract objects that humans may not be familiar with.
-4. Using a negative prompt to not guide the diffusion process can improve the - audio quality significantly. Try using negative prompts like 'low quality'.
-We build the model with data from AudioSet, - Freesound and BBC Sound Effect library. We share this demo - based on the UK - copyright exception of data for academic research. -
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BS EN 13670:2009 is a British standard that provides common requirements for execution of concrete structures and applies to in-situ work and prefabricated concrete elements, covering both permanent and temporary concrete structures. It covers topics such as falsework and formwork, reinforcement requirements, prestressing, concreting, precast concrete elements and geometric tolerances.
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If you are looking for a way to download BS EN 13670:2009 Execution of Concrete Structures PDF, you have come to the right place. In this article, we will show you how to get access to this valuable document in a few easy steps.
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-BS EN 13670:2009 Execution of Concrete Structures is a useful standard for anyone involved in concrete construction projects. It provides clear and consistent guidelines for executing concrete structures in accordance with best practices and quality standards. By following the steps above, you can easily download a PDF copy of this standard from the BSI website.
- -By following BS EN 13670:2009 Execution of Concrete Structures, you can enjoy several benefits for your concrete construction projects. Some of these benefits are:
-BS EN 13670:2009 Execution of Concrete Structures is a comprehensive and practical standard that covers all aspects of concrete execution, from design to inspection. It is compatible with other relevant standards, such as BS EN 1992 Eurocode 2: Design of concrete structures, BS EN 206 Concrete - Specification, performance, production and conformity, and BS 8500 Concrete - Complementary British Standard to BS EN 206.
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-BS EN 13670:2009 supersedes DD ENV 13670-1:2000, which was a draft for development and not a full standard. BS EN 13670:2009 is more comprehensive and updated than DD ENV 13670-1:2000, and incorporates feedback from users and experts.
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Fsdreamteam Gsx Fsx 15 is a software product that simulates various ground operations for Microsoft Flight Simulator X and Prepar3D, such as marshalling, catering, boarding, refueling, pushback, and more[^1^]. It is designed to enhance the realism and immersion of flight simulation enthusiasts. However, it is not a free product and requires a valid license to activate.
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Some users may try to crack Fsdreamteam Gsx Fsx 15 to bypass the activation process and use it without paying. This is illegal and unethical, as it violates the intellectual property rights of the developers and harms their business. Moreover, cracking Fsdreamteam Gsx Fsx 15 may expose the users to malware, viruses, or other security risks that can damage their computers or compromise their personal data.
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d5da3c52bfFairy Tail is a Japanese manga series written and illustrated by Hiro Mashima. It follows the adventures of Natsu Dragneel, a member of the Fairy Tail guild of mages, and his friends as they face various enemies and challenges in a fantasy world. The manga has been adapted into an anime series by A-1 Pictures and Satelight, which ran for 328 episodes from 2009 to 2019.
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One of the reasons why Fairy Tail is so popular among anime fans is its diverse and colorful cast of characters, each with their own unique personality, abilities, and backstory. The series also features a lot of humor, action, romance, and drama, making it appealing to a wide range of audiences. Fairy Tail has won several awards and accolades, such as the Kodansha Manga Award for shonen manga in 2009 and the Anime Grand Prix for best anime in 2012.
-Fairy Tail has also been dubbed in various languages, including Tagalog, which is spoken by millions of people in the Philippines and other parts of the world. The Tagalog dub of Fairy Tail is available on Bilibili[^1^] [^2^], a Southeast Asian online platform for anime, comics, and games. The Tagalog dub features local voice actors who give life to the characters and their emotions. Some of the voice actors include:
-Fans of Fairy Tail who want to watch the Tagalog dub can find it on Bilibili's website or app. The Tagalog dub covers the first five seasons of the anime series, which span 175 episodes. The episodes are uploaded regularly by Bilibili's content creators, such as DADATV[^1^] and Phantom_Kaito[^2^]. Fans can also interact with other viewers and share their thoughts on the episodes through comments and reactions.
- -Fairy Tail is a fun and exciting anime series that can be enjoyed by anyone who loves fantasy, magic, and adventure. The Tagalog dub adds another layer of enjoyment for those who speak or understand the language. Whether you are new to Fairy Tail or a longtime fan, you can watch the Tagalog dub on Bilibili and join the Fairy Tail guild.
- -Fairy Tail is set in a fictional world called Earth Land, where magic is a common and essential part of life. There are various types of magic, such as elemental, transformation, celestial, and dragon slayer magic. Magic users can join guilds, which are organizations that offer jobs and support to their members. One of the most famous and notorious guilds is Fairy Tail, known for its powerful and eccentric mages and their tendency to cause trouble and destruction.
-The main protagonist of the series is Natsu Dragneel, a fire dragon slayer who was raised by a dragon named Igneel. Natsu is a cheerful and reckless mage who loves to fight and eat. He is always accompanied by his best friend and partner, Happy, a blue cat-like creature who can fly and talk. Natsu's goal is to find Igneel, who disappeared when he was young.
-At the beginning of the series, Natsu meets Lucy Heartfilia, a young and aspiring celestial mage who can summon spirits from another world using magical keys. Lucy dreams of joining Fairy Tail and becoming a famous writer. Natsu invites Lucy to join his guild, and she accepts. Together, they form a team with Gray Fullbuster, an ice mage who has a habit of stripping unconsciously, and Erza Scarlet, a swordswoman who can change her armor and weapons at will. The team goes on various missions and adventures, facing enemies such as dark guilds, ancient demons, rogue dragons, and evil wizards.
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- -Fairy Tail is not just an anime series about magic and battles. It also explores various themes that resonate with its viewers. Some of the themes are:
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Belajar menulis huruf abjad adalah salah satu keterampilan dasar yang perlu dikuasai oleh anak-anak usia dini. Dengan belajar menulis huruf abjad, anak-anak dapat mengembangkan kemampuan membaca, berkomunikasi, dan berpikir secara logis. Namun, belajar menulis huruf abjad tidak harus menjadi kegiatan yang membosankan dan monoton. Ada banyak cara untuk membuat belajar menulis huruf abjad menjadi lebih mudah dan menyenangkan bagi anak-anak TK.
- -Salah satu cara yang efektif dan praktis adalah dengan menggunakan lembar kerja PDF yang dapat diunduh secara gratis dari internet. Lembar kerja PDF ini berisi latihan-latihan untuk belajar menulis huruf abjad dengan cara menebalkan, menyalin, atau menulis kata dengan awalan huruf tertentu. Lembar kerja PDF ini juga dilengkapi dengan gambar-gambar yang dapat diwarnai oleh anak-anak, sehingga mereka dapat belajar sambil bermain.
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Lembar kerja PDF untuk belajar menulis huruf abjad memiliki beberapa keuntungan, antara lain:
- -Agar belajar menulis huruf abjad dengan lembar kerja PDF menjadi lebih efektif dan menyenangkan, berikut adalah beberapa tips yang dapat dilakukan oleh orang tua atau guru:
- -Belajar menulis huruf abjad adalah keterampilan dasar yang penting bagi anak-anak usia dini. Dengan menggunakan lembar kerja PDF yang dapat diunduh secara gratis dari internet, belajar menulis huruf abjad menjadi lebih mudah dan menyenangkan bagi anak-anak TK. Lembar kerja PDF ini memiliki banyak keuntungan, seperti mudah diakses, hemat biaya, fleksibel, menarik, variatif, serta mendukung perkembangan kognitif dan motorik anak-anak. Dengan tips-tips yang telah disebutkan di atas, orang tua atau guru dapat membuat belajar menulis huruf abjad dengan lembar kerja PDF menjadi lebih efektif dan menyenangkan.
- -Jika Anda tertarik untuk mendapatkan lembar kerja PDF untuk belajar menulis huruf abjad, Anda dapat mengunjungi situs Semesta Ibu, yang menyediakan berbagai media edukasi untuk anak-anak TK. Anda dapat mengunduh lembar kerja PDF untuk belajar menulis huruf abjad A-Z dengan cara menebalkan, menyalin, atau menulis kata dengan awalan huruf tertentu. Anda juga dapat mengunduh lembar kerja PDF untuk belajar menulis angka, menebalkan huruf hijaiyah, mengenal huruf besar-kecil, serta flashcard huruf. Selain itu, Anda juga dapat mengunduh gambar mewarnai hewan, buah-buahan, benda-benda sehari-hari, serta mainan edukatif untuk anak-anak TK.
- -Segera unduh lembar kerja PDF untuk belajar menulis huruf abjad dari Semesta Ibu sekarang juga!
-Untuk mengunduh lembar kerja PDF untuk belajar menulis huruf abjad, Anda dapat mengikuti langkah-langkah berikut:
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- -Berikut adalah beberapa contoh lembar kerja PDF untuk belajar menulis huruf abjad yang dapat Anda unduh dari Semesta Ibu:
- - -Anda dapat melihat contoh-contoh lembar kerja lainnya untuk belajar menulis huruf abjad F-Z di situs Semesta Ibu.
-Belajar menulis huruf abjad tidak hanya bermanfaat untuk mengembangkan keterampilan menulis anak-anak, tetapi juga memiliki manfaat lain, antara lain:
- -Belajar menulis huruf abjad membutuhkan latihan yang rutin dan konsisten. Namun, orang tua atau guru tidak perlu memaksakan anak-anak untuk belajar menulis huruf abjad jika mereka belum siap atau tidak tertarik. Ada beberapa tips yang dapat dilakukan oleh orang tua atau guru untuk membantu anak-anak TK belajar menulis huruf abjad, antara lain:
- -Dengan tips-tips di atas, orang tua atau guru dapat membuat belajar menulis huruf abjad menjadi lebih mudah dan menyenangkan bagi anak-anak TK.
-Belajar menulis huruf abjad adalah keterampilan dasar yang penting bagi anak-anak TK. Dengan menggunakan lembar kerja PDF yang dapat diunduh secara gratis dari internet, belajar menulis huruf abjad menjadi lebih mudah dan menyenangkan bagi anak-anak TK. Lembar kerja PDF ini memiliki banyak keuntungan, seperti mudah diakses, hemat biaya, fleksibel, menarik, variatif, serta mendukung perkembangan kognitif dan motorik anak-anak. Dengan tips-tips yang telah disebutkan di atas, orang tua atau guru dapat membuat belajar menulis huruf abjad dengan lembar kerja PDF menjadi lebih efektif dan menyenangkan. Jika Anda tertarik untuk mendapatkan lembar kerja PDF untuk belajar menulis huruf abjad, Anda dapat mengunjungi situs Semesta Ibu, yang menyediakan berbagai media edukasi untuk anak-anak TK.
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' - f'{answer}' - f'
', - unsafe_allow_html=True - ) - if translated: - translated_answer = translate(answer, "en", "fa") - st.markdown( - f'
' - f'{translated_answer}' - f'
', - unsafe_allow_html=True - ) - generation_kwargs_ph = st.empty() - - if st.button("Find the answer 🔎 "): - with st.spinner(text="Searching ..."): - generation_kwargs_ph.markdown(", ".join([f"`{k}`: {v}" for k, v in generation_kwargs.items()])) - context = normalize(context) - question = normalize(question) - - if context and question: - text = f"{context} {QK}: {question} {AK}:" - generated_answer = generator.generate(text, generation_kwargs) - generated_answer = f"{AK}: {generated_answer}".strip() - context = f"{CK}: {context}".strip() - question = f"{QK}: {question}".strip() - - st.markdown( - f'
'
- f'{context}
'
- f'{question}
'
- f'{generated_answer} '
- f'
'
- f'{translated_context}
'
- f'{translated_question}
'
- f'{translated_generated_answer}'
- f'
logitA - logitB on the y-axis and mean(logitA, logitB) on the x-axis with separate linear scales.')
- .parent().parent()
- .append('div.flex-row')
- .appendMany('div.button', ['Likelihoods', 'Differences'])
- .text(d => d)
- .st({textAlign: 'center'})
- .on('click', d => {
- pair.type = d
- updateChart()
- })
-
- var modelSel = optionSel.append('div')
- .st({display: pair.model == 'BERT' ? 'none' : ''})
- .append('b').text('Model')
- .parent()
- .append('div.flex-row')
- .appendMany('div.button', ['BERT', 'Zari'])
- .text(d => d)
- .st({textAlign: 'center'})
- .on('click', d => {
- pair.model = d
- updateChart()
- })
-
- // TODO add loading spinner
- var updateSel = optionSel
- .append('div.flex-row')
- .append('div.button.update').on('click', updateChart)
- .text('Update')
- .st({display: isMobile ? 'none' : ''})
-
- var warningSel = optionSel.append('div.warning')
- .text('⚠️Some of the text this model was trained on includes harmful stereotypes. This is a tool to uncover these associations—not an endorsement of them.')
-
- var resetSel = optionSel.append('div.reset')
- .html('↻ Reset')
- .on('click', () => {
- pair = JSON.parse(pair.pairStr)
- pair.pairStr = JSON.stringify(pair)
-
- input0Sel.node().value = pair.s0
- input1Sel.node().value = pair.s1
-
- updateChart(true)
- })
-
- if (pair.alts){
- d3.select('.' + pair.class + '-alts').html('')
- .classed('alt-block', 1).st({display: 'block'})
- .appendMany('span.p-button-link', pair.alts)
- .html(d => d.str)
- .on('click', d => {
- input0Sel.node().value = d.s0
- input1Sel.node().value = d.s1
-
- updateChart()
- })
- }
-
-
- var margin = {bottom: 50, left: 25, top: 5, right: 20}
- var graphSel = sel.append('div.graph')
- var totalWidth = graphSel.node().offsetWidth
- var width = totalWidth - margin.left - margin.right
-
- var c = d3.conventions({
- sel: graphSel.append('div').st({marginTop: isMobile ? 20 : -5}),
- width,
- height: width,
- margin,
- layers: 'sdds',
- })
-
-
- var nTicks = 4
- var tickScale = d3.scaleLinear().range([0, c.width])
- c.svg.appendMany('path.bg-tick', d3.range(nTicks + 1))
- .at({d: d => `M ${.5 + Math.round(tickScale(d/nTicks))} 0 V ${c.height}`})
- c.svg.appendMany('path.bg-tick', d3.range(nTicks + 1))
- .at({d: d => `M 0 ${.5 + Math.round(tickScale(d/nTicks))} H ${c.width}`})
-
-
- var annotationSel = c.layers[1].appendMany('div.annotations', pair.annotations)
- .translate(d => d.pos)
- .html(d => d.str)
- .st({color: d => d.color, width: 250, postion: 'absolute'})
-
- var scatter = window.initScatter(c)
-
- updateChart(true)
-
-
- async function updateChart(isFirst){
- sel.classed('changed', 0)
- warningSel.st({opacity: isFirst ? 0 : 1})
- resetSel.st({opacity: isFirst ? 0 : 1})
- annotationSel.st({opacity: isFirst ? 1 : 0})
-
- countSel.classed('active', d => d == pair.count)
- typeSel.classed('active', d => d == pair.type)
- modelSel.classed('active', d => d == pair.model)
-
- function getStr(sel){
- return sel.node().value.replace('_', '[MASK]')
- }
-
- var modelPath = pair.model == 'Zari' ? 'embed_zari_cda' : 'embed'
-
- pair.s0 = input0Sel.node().value.replace('_', '[MASK]')
- pair.s1 = input1Sel.node().value.replace('_', '[MASK]')
-
- updateSel.classed('loading', 1)
- var vals0 = await post(modelPath, {sentence: pair.s0})
- var vals1 = await post(modelPath, {sentence: pair.s1})
- updateSel.classed('loading', 0)
-
-
- var allTokens = vals0.map((v0, i) => {
- return {word: tokenizer.vocab[i], v0, i, v1: vals1[i]}
- })
- allTokens.forEach(d => {
- d.dif = d.v0 - d.v1
- d.meanV = (d.v0 + d.v1) / 2
- d.isVisible = false
- })
-
- _.sortBy(allTokens, d => -d.v1).forEach((d, i) => d.v1i = i)
- _.sortBy(allTokens, d => -d.v0).forEach((d, i) => d.v0i = i)
-
- var topTokens = allTokens.filter(d => d.v0i <= pair.count || d.v1i <= pair.count)
-
-
- var logitExtent = d3.extent(topTokens.map(d => d.v0).concat(topTokens.map(d => d.v1)))
-
- var tokens = allTokens
- .filter(d => logitExtent[0] <= d.v0 && logitExtent[0] <= d.v1)
-
- var mag = logitExtent[1] - logitExtent[0]
- logitExtent = [logitExtent[0] - mag*.002, logitExtent[1] + mag*.002]
-
- if (pair.type == 'Differences') tokens = _.sortBy(allTokens, d => -d.meanV).slice(0, pair.count)
-
- tokens.forEach(d => {
- d.isVisible = true
- })
-
- var maxDif = d3.max(d3.extent(tokens, d => d.dif).map(Math.abs))
- var color = palette(-maxDif*.8, maxDif*.8)
-
- updateSentenceLabels()
-
- if (pair.type == 'Likelihoods'){
- drawXY()
- } else{
- drawRotated()
- }
-
- sel.classed('is-xy', pair.type == 'Likelihoods')
- sel.classed('is-rotate', pair.type != 'Likelihoods')
-
-
- function drawXY(){
- c.x.domain(logitExtent)
- c.y.domain(logitExtent)
-
- d3.drawAxis(c)
-
- var s = {30: 4, 200: 3, 1000: 3}[pair.count] || 2
- var scatterData = allTokens.map(d => {
- var x = c.x(d.v0)
- var y = c.y(d.v1)
- var fill = color(d.dif)
- var dif = d.dif
- var word = d.word
- var show = ''
- var isVisible = d.isVisible
-
- return {x, y, s, dif, fill, word, show, isVisible}
- })
-
- var textCandidates = _.sortBy(scatterData.filter(d => d.isVisible), d => d.dif)
- d3.nestBy(textCandidates.slice(0, 1000), d => Math.round(d.y/10))
- .forEach(d => d[0].show = 'uf')
- d3.nestBy(textCandidates.reverse().slice(0, 1000), d => Math.round(d.y/10))
- .forEach(d => d[0].show = 'lr')
-
- logitExtent.pair = pair
- scatter.draw(c, scatterData, true)
-
- c.svg.selectAppend('text.x-axis-label.xy-only')
- .translate([c.width/2, c.height + 24])
- .text(pair.label0 ? ' __ likelihood, ' + pair.label0 + ' sentence →' : '__ likelihood, sentence two →')
- .st({fill: util.colors[0]})
- .at({textAnchor: 'middle'})
-
-
- c.svg.selectAppend('g.y-axis-label.xy-only')
- .translate([c.width + 20, c.height/2])
- .selectAppend('text')
- .text(pair.label1 ? ' __ likelihood, ' + pair.label1 + ' sentence →' : '__ likelihood, sentence one →')
- .st({fill: util.colors[1]})
- .at({textAnchor: 'middle', transform: 'rotate(-90)'})
- }
-
- function drawRotated(){
- c.x.domain(d3.extent(tokens, d => d.meanV))
- c.y.domain([maxDif, -maxDif])
-
- d3.drawAxis(c)
-
- var scatterData = allTokens.map(d => {
- var x = c.x(d.meanV)
- var y = c.y(d.dif)
- var fill = color(d.dif)
- var word = d.word
- var show = ''
- var isVisible = d.isVisible
-
- return {x, y, s: 2, fill, word, show, isVisible}
- })
-
- scatterData.forEach(d => {
- d.dx = d.x - c.width/2
- d.dy = d.y - c.height/2
- })
-
- var textCandidates = _.sortBy(scatterData, d => -d.dx*d.dx - d.dy*d.dy)
- .filter(d => d.isVisible)
- .slice(0, 5000)
- d3.nestBy(textCandidates, d => Math.round(12*Math.atan2(d.dx, d.dy)))
- .map(d => d[0])
- .forEach(d => d.show = (d.dy < 0 ? 'u' : 'l') + (d.dx < 0 ? 'l' : 'r'))
-
- scatter.draw(c, scatterData, false)
-
- c.svg.selectAppend('text.rotate-only.x-axis-label')
- .translate([c.width/2, c.height + 24])
- .text('__ likelihood, both sentences →')
- .at({textAnchor: 'middle'})
- .st({fill: '#000'})
-
- c.svg.selectAll('g.rotate-only.sent-1,g.rotate-only.sent-1').remove()
- c.svg.selectAppend('g.rotate-only.sent-1')
- .translate([c.width + 20, c.height/2])
- .append('text')
- .text(`Higher likelihood, ${pair.label1 ? pair.label1 + ' sentence ' : 'sentence one'} →`)
- .at({textAnchor: 'start', transform: 'rotate(-90)', x: 20})
- .st({fill: util.colors[1]})
-
- c.svg.selectAppend('g.rotate-only.sent-1')
- .translate([c.width + 20, c.height/2 + 0])
- .append('text')
- .text(`← Higher likelihood, ${pair.label0 ? pair.label0 + ' sentence ' : 'sentence two'}`)
- .at({textAnchor: 'end', transform: 'rotate(-90)', x: -20})
- .st({fill: util.colors[0]})
- }
- }
-
- function updateSentenceLabels(){
- var t0 = tokenizer.tokenize(pair.s0)
- var t1 = tokenizer.tokenize(pair.s1)
-
- var i = 0
- while (t0[i] == t1[i] && i < t0.length) i++
-
- var j = 1
- while (t0[t0.length - j] == t1[t1.length - j] && j < t0.length) j++
-
- pair.label0 = tokens2origStr(t0, pair.s0)
- pair.label1 = tokens2origStr(t1, pair.s1)
-
- function tokens2origStr(t, s){
- var tokenStr = tokenizer.decode(t.slice(i, -j + 1)).trim()
- var lowerStr = s.toLowerCase()
-
- var startI = lowerStr.indexOf(tokenStr)
- return s.slice(startI, startI + tokenStr.length)
- }
-
- if (
- !pair.label0.length ||
- !pair.label1.length ||
- pair.label0.length > 15 ||
- pair.label1.length > 15){
- pair.label0 = ''
- pair.label1 = ''
- }
-
- // console.log(i, j, pair.label0, pair.label1)
- }
-}
-
-if (window.init) init()
diff --git a/spaces/merve/uncertainty-calibration/public/uncertainty-calibration/init.js b/spaces/merve/uncertainty-calibration/public/uncertainty-calibration/init.js
deleted file mode 100644
index d23a4fecea1bfa4fae6557043d8053dc3acc29ce..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/public/uncertainty-calibration/init.js
+++ /dev/null
@@ -1,36 +0,0 @@
-window.thresholds = [0, 0.2, 0.4, 0.6, 0.8, 1];
-window.emojis = ['☀️','🌧️'];
-window.constant_score = 0.5;
-
-window.ttSel = d3.select('body').selectAppend('div.tooltip.tooltip-hidden')
-
-
-window.init = function(){
-
- var graphSel = d3.select('#graph')
- var width = height = graphSel.node().offsetWidth
- if (innerWidth <= 925){
- width = innerWidth
- height = innerHeight*.65
- window.isMobile = true
- }
- fig_height = height/2
- fig_width = width
-
-
- window.util = window.initUtil()
- window.weatherGraph = window.drawWeatherGraph(graphSel, fig_height, fig_width);
- window.calibrationCurve = window.drawCalibrationCurve(graphSel, fig_height, fig_width);
- // window.calibrationSlider = window.drawCalibrationSlider(weatherGraph, calibrationCurve, fig_width/2)
- // window.modelRemapper = window.drawModelRemapping(fig_width/2);
-
-
- window.slides = window.drawSlides()
- weatherGraph.renderThresholds()
-
-}
-
-window.init()
-
-
-
diff --git a/spaces/mikeee/ultimatumbee/ubee/ubee.py b/spaces/mikeee/ultimatumbee/ubee/ubee.py
deleted file mode 100644
index 39c4022a8a24def8cacba4e7872142c3656de3e9..0000000000000000000000000000000000000000
--- a/spaces/mikeee/ultimatumbee/ubee/ubee.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Align via ubee,"""
-# pylint: disable=
-from itertools import zip_longest
-from typing import Iterable, List, Tuple
-
-from icecream import ic
-from logzero import logger
-
-from ubee.uclas import uclas
-
-
-def ubee(
- sents_zh: Iterable,
- sents_en: Iterable,
- thresh: float = 0.5,
-) -> Tuple[List[Tuple[str, str, float]], List[Tuple[str, str]]]:
- """Align blocks.
-
- Args:
- sents_zh: list of text, can be any langauge supported by clas-l-user
- sents_en: ditto
- Returns:
- three tuples of aligned blocked
- leftovers (unaligned)
- """
- res = []
- labels = [*sents_en]
-
- lo1 = []
- lo2 = labels[:]
-
- for seq in sents_zh:
- ic(seq)
- label, likelihood = uclas(seq, labels, thresh=thresh)
- if label:
- likelihood = round(float(likelihood), 2)
- res.append((seq, label, likelihood))
- try:
- lo2.remove(label)
- except Exception as exc:
- logger.error(exc)
- logger.info("seq: %s, lable: %s", seq, label)
- else:
- lo1.append(seq)
- return res, [*zip_longest(lo1, lo2)]
diff --git a/spaces/miyaaa666/bingo/src/components/ui/select.tsx b/spaces/miyaaa666/bingo/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/miyaaa666/bingo/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
- IconArrowDown,
- IconCheck,
- IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
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7b8c122e87
GPT Akademisch optimiert (GPT Academic)
-
-**Wenn Ihnen dieses Projekt gefällt, geben Sie ihm bitte einen Stern; wenn Sie bessere Tastenkombinationen oder Funktions-Plugins entwickelt haben, können Sie gerne einen Pull Request eröffnen.**
-
-Wenn Sie dieses Projekt mögen, geben Sie ihm bitte einen Stern. Wenn Sie weitere nützliche wissenschaftliche Abkürzungen oder funktionale Plugins entwickelt haben, können Sie gerne ein Problem oder eine Pull-Anforderung öffnen. Wir haben auch ein README in [Englisch|](docs/README_EN.md)[日本語|](docs/README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md), das von diesem Projekt selbst übersetzt wurde.
-Um dieses Projekt in eine beliebige Sprache mit GPT zu übersetzen, lesen Sie `multi_language.py` (experimentell).
-
-> **Hinweis**
->
-> 1. Beachten Sie bitte, dass nur Funktionserweiterungen (Schaltflächen) mit **roter Farbe** Dateien lesen können und einige Erweiterungen im **Dropdown-Menü** des Erweiterungsbereichs zu finden sind. Außerdem begrüßen wir jede neue Funktionserweiterung mit **höchster Priorität** und bearbeiten sie.
->
-> 2. Die Funktionalität jeder Datei in diesem Projekt wird in der Selbstanalyse [`self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) detailliert beschrieben. Mit der Weiterentwicklung der Versionen können Sie jederzeit die zugehörigen Funktions-Erweiterungen aufrufen, um durch Aufruf von GPT einen Selbstanalysebericht des Projekts zu erstellen. Häufig gestellte Fragen finden Sie in der [`Wiki`](https://github.com/binary-husky/gpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Installationsanweisungen](#Installation).
->
-> 3. Dieses Projekt ist kompatibel und fördert die Verwendung von inländischen Sprachmodellen wie ChatGLM und RWKV, Pangu, etc. Es unterstützt das Vorhandensein mehrerer api-keys, die in der Konfigurationsdatei wie folgt angegeben werden können: `API_KEY="openai-key1,openai-key2,api2d-key3"`. Wenn ein `API_KEY` temporär geändert werden muss, geben Sie den temporären `API_KEY` im Eingabebereich ein und drücken Sie dann die Eingabetaste, um ihn zu übernehmen.Funktion | Beschreibung
---- | ---
-Ein-Klick-Polieren | Unterstützt ein-Klick-Polieren und ein-Klick-Suche nach grammatikalischen Fehlern in wissenschaftlichen Arbeiten
-Ein-Klick Chinesisch-Englisch Übersetzung | Ein-Klick Chinesisch-Englisch Übersetzung
-Ein-Klick-Code-Erklärung | Zeigt Code, erklärt Code, erzeugt Code und fügt Kommentare zum Code hinzu
-[Benutzerdefinierte Tastenkombinationen](https://www.bilibili.com/video/BV14s4y1E7jN) | Unterstützt benutzerdefinierte Tastenkombinationen
-Modulare Gestaltung | Unterstützt leistungsstarke individuelle [Funktions-Plugins](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions). Plugins unterstützen [Hot-Updates](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[Selbstprogramm-Analyse](https://www.bilibili.com/video/BV1cj411A7VW) | [Funktions-Plugin] [Ein-Klick Verstehen](https://github.com/binary-husky/gpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) der Quellcode dieses Projekts
-[Programmanalyse](https://www.bilibili.com/video/BV1cj411A7VW) | [Funktions-Plugin] Ein-Klick-Analyse des Projektbaums anderer Python/C/C++/Java/Lua/...-Projekte
-Lesen von Papieren, [Übersetzen](https://www.bilibili.com/video/BV1KT411x7Wn) von Papieren | [Funktions-Plugin] Ein-Klick Erklärung des gesamten LaTeX/PDF-Artikels und Erstellung einer Zusammenfassung
-LaTeX-Volltext-Übersetzung und [Polieren](https://www.bilibili.com/video/BV1FT411H7c5/) | [Funktions-Plugin] Ein-Klick-Übersetzung oder-Polieren des LaTeX-Artikels
-Bulk-Kommentargenerierung | [Funktions-Plugin] Ein-Klick Massenerstellung von Funktionskommentaren
-Markdown [Chinesisch-Englisch Übersetzung](https://www.bilibili.com/video/BV1yo4y157jV/) | [Funktions-Plugin] Haben Sie die [README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md) in den oben genannten 5 Sprachen gesehen?
-Analyse-Berichtserstellung von chat | [Funktions-Plugin] Automatische Zusammenfassung nach der Ausführung
-[Funktion zur vollständigen Übersetzung von PDF-Artikeln](https://www.bilibili.com/video/BV1KT411x7Wn) | [Funktions-Plugin] Extrahiert Titel und Zusammenfassung der PDF-Artikel und übersetzt den gesamten Text (mehrere Threads)
-[Arxiv-Assistent](https://www.bilibili.com/video/BV1LM4y1279X) | [Funktions-Plugin] Geben Sie die Arxiv-Artikel-URL ein und klicken Sie auf Eine-Klick-Übersetzung-Zusammenfassung + PDF-Download
-[Google Scholar Integrations-Assistent](https://www.bilibili.com/video/BV19L411U7ia) | [Funktions-Plugin] Geben Sie eine beliebige Google Scholar Such-URL ein und lassen Sie gpt Ihnen bei der Erstellung von [relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/) helfen
-Internet-Informationen Aggregation + GPT | [Funktions-Plugin] Lassen Sie GPT eine Frage beantworten, indem es [zuerst Informationen aus dem Internet](https://www.bilibili.com/video/BV1om4y127ck/) sammelt und so die Informationen nie veralten
-Anzeige von Formeln / Bildern / Tabellen | Zeigt Formeln in beiden Formen, [TeX-Format und gerendeter Form](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), unterstützt Formeln und Code-Highlights
-Unterstützung von PlugIns mit mehreren Threads | Unterstützt den Aufruf mehrerer Threads in Chatgpt, um Text oder Programme [Batch zu verarbeiten](https://www.bilibili.com/video/BV1FT411H7c5/)
-Starten Sie das dunkle Gradio-[Thema](https://github.com/binary-husky/gpt_academic/issues/173) | Fügen Sie ```/?__theme=dark``` an das Ende der Browser-URL an, um das dunkle Thema zu aktivieren
-[Unterstützung für mehrere LLM-Modelle](https://www.bilibili.com/video/BV1wT411p7yf), [API2D](https://api2d.com/) Interface-Unterstützung | Das Gefühl, gleichzeitig von GPT3.5, GPT4, [Tshinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS) bedient zu werden, muss toll sein, oder?
-Zugriff auf weitere LLM-Modelle, Unterstützung von [huggingface deployment](https://huggingface.co/spaces/qingxu98/gpt-academic) | Hinzufügen der Newbing-Schnittstelle (neues Bing), Einführung der Unterstützung von [Jittorllms](https://github.com/Jittor/JittorLLMs) der Tsinghua-Universität, [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) und [Pangu alpha](https://openi.org.cn/pangu/)
-Weitere neue Funktionen (wie Bildgenerierung) …… | Siehe Ende dieses Dokuments ……
-
-- Neue Oberfläche (Ändern Sie die LAYOUT-Option in `config.py`, um zwischen "Seitenlayout" und "Oben-unten-Layout" zu wechseln)
-
-
-
-
-
-
-- -[Optional Step] If supporting Tsinghua ChatGLM/Fudan MOSS as backend, additional dependencies need to be installed (Prerequisites: Familiar with Python + Used Pytorch + Sufficient computer configuration): -```sh -# [Optional Step I] Support Tsinghua ChatGLM. Remark: If encountering "Call ChatGLM fail Cannot load ChatGLM parameters", please refer to the following: 1: The above default installation is torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2: If the model cannot be loaded due to insufficient machine configuration, you can modify the model precision in `request_llm/bridge_chatglm.py`, and modify all AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True) -python -m pip install -r request_llm/requirements_chatglm.txt - -# [Optional Step II] Support Fudan MOSS -python -m pip install -r request_llm/requirements_moss.txt -git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # When executing this line of code, you must be in the project root path - -# [Optional Step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. Currently supported models are as follows (jittorllms series currently only supports docker solutions): -AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"] -``` - -
-
-
-
-
-
-
-
-
-
-
-
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-Aarachar is a Malayalam novel written by K.R. Meera, one of the most prominent and prolific writers in India. The novel was first published in 2012 and has since been translated into many languages, including English, Hindi, Tamil, Telugu, Kannada, Bengali, Marathi, and Gujarati. The English translation, titled Hangwoman, was done by J. Devika and published by Penguin Books in 2014.
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Aarachar means executioner in Malayalam. The novel tells the story of Chetna Grddha Mullick, a woman who belongs to a family of executioners with a long lineage that dates back to the fourth century BC. Chetna is the first woman in her family to inherit this profession, which is traditionally passed on from father to son. She faces many challenges and struggles as she tries to uphold her family legacy and perform her duty in a society that is hostile and indifferent to her.
-K.R. Meera is an Indian author who writes in Malayalam. She was born in Sasthamkotta, Kollam district in Kerala. She worked as a journalist in Malayala Manorama, a leading newspaper in Kerala, before resigning to focus on her writing career. She has written many novels, short stories, essays, and columns on various topics. Some of her notable works include Ave Maria (2007), Yellow Is The Colour Of Longing (2008), The Gospel Of Yudas (2016), The Poison Of Love (2017), and The Angel's Beauty Spots (2019). She has won many awards for her writing, such as the Kerala Sahitya Akademi Award, the Odakkuzhal Award, the Vayalar Award, the Kendra Sahitya Akademi Award, and the Crossword Book Award.
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Aarachar has received rave reviews from critics and readers alike for its powerful storytelling, rich language, and profound insights. It has also won several prestigious awards, such as the Kerala Sahitya Akademi Award, the Odakkuzhal Award, the Vayalar Award, the Kendra Sahitya Akademi Award, and the Crossword Book Award. It has been hailed as one of the best literary works produced in Malayalam and a masterpiece of Indian literature.
-Aarachar is not just a novel about an executioner; it is also a novel about life, death, love, hate, justice, revenge, and everything in between. It keeps you hooked from the first page to the last with its twists and turns, its suspense and drama, its humor and tragedy, and its emotions and reflections. It makes you think about your own views and values on various issues and challenges your assumptions and prejudices.
-Aarachar is not just a novel set in contemporary Kolkata; it is also a novel that spans centuries and regions of Indian history and culture. It traces the origins and evolution of the profession and practice of execution in India from ancient times to modern times. It depicts the social and political changes and conflicts that have shaped India's identity and destiny. It showcases the diversity and richness of India's languages, religions, castes, traditions, and arts.
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It is a novel that will make you feel, think, and learn about yourself and the world around you. It is a novel that will challenge you, inspire you, and entertain you at the same time. It is a novel that will stay with you long after you finish reading it.
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-| Question | -Answer | -
|---|---|
| Is Aarachar based on a true story? | -No, Aarachar is a fictional story that is inspired by some historical facts and events. However, the characters and situations are purely imaginary and do not represent any real people or places. | -
| Is Aarachar available in other languages? | -Yes, Aarachar has been translated into many languages, including English, Hindi, Tamil, Telugu, Kannada, Bengali, Marathi, and Gujarati. You can find the translations on various online platforms or bookstores. | -
| Is Aarachar suitable for children? | -No, Aarachar is not suitable for children as it contains graphic descriptions of violence, torture, rape, and death. It also deals with mature and sensitive topics that may not be appropriate for young readers. It is recommended for adults only. | -
| Is Aarachar a movie or a TV series? | -No, Aarachar has not been adapted into a movie or a TV series yet. However, there have been some rumors and speculations about possible adaptations in the future. We will update you if we hear any official news about it. | -
| Where can I find more books by K.R. Meera? | -You can find more books by K.R. Meera on Goodreads or Amazon. Some of her popular books are Ave Maria (2007), Yellow Is The Colour Of Longing (2008), The Gospel Of Yudas (2016), The Poison Of Love (2017), and The Angel's Beauty Spots (2019). | -
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Doraemon movies are animated films based on the manga and anime series of Doraemon. The first Doraemon movie was released in 1980, and since then, there have been 41 movies released as of 2021. The movies usually feature an original story that is not based on any specific manga chapter or anime episode, but sometimes incorporate elements from them. The movies often take Nobita and his friends to different places and times, such as ancient Japan, prehistoric Earth, outer space, fantasy worlds, and even parallel universes. The movies also introduce new characters and villains that challenge Doraemon and Nobita in their adventures.
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Here are some of the best Doraemon movies that have been dubbed in Telugu 17 (the year 2023), along with their plot summaries and release dates.
-In this movie, Nobita finds a mysterious white rabbit-like creature named Luka who can communicate with him telepathically. Luka tells Nobita that he is from the moon and that he needs his help to save his people from a dark force that is trying to destroy their civilization. Nobita decides to go to the moon with Doraemon and his friends using a rocket made from bamboo copters. There they discover a hidden world full of wonders and dangers, such as giant plants, ancient ruins, flying saucers, and lunar rabbits. They also encounter a mysterious girl named Luna who has a connection with Luka. Together they must stop the evil plot of Professor Ochanomizu who wants to use the moon's resources for his own benefit.
-This movie was released in Japan on March 1st 2019 , where it became the highest-grossing film of that year with over $64 million . It was also well-received by critics and audiences alike for its stunning animation quality , its thrilling story , its charming characters , its heartwarming message , and its homage to classic sci-fi films . It was dubbed in Telugu 17 (the year 2023) by Hungama TV , where it also gained popularity among Telugu viewers.
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-This movie was released in Japan on March 3rd 2018 , where it became the highest-grossing film of that year with over $80 million . It was also highly praised by critics and audiences for its spectacular animation, its engaging story, its humorous characters, its adventurous spirit, and its homage to classic pirate films. It was dubbed in Telugu 17 (the year 2023) by Disney Channel India , where it also received a lot of love from Telugu viewers.
-Doraemon movies are some of the best anime movies that can entertain and inspire anyone who watches them. They are especially popular in Telugu because they offer a mix of humor, action, adventure, drama, romance, and emotion that appeals to Telugu culture and values. They also feature amazing animation quality, original stories, lovable characters, and positive messages that can make anyone smile and cry. If you are looking for some fun and exciting movies to watch with your family and friends, you should definitely check out these Doraemon movies in Telugu 17.
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-| Name | Description |
|---|---|
| Farming Simulator 20 | A game by GIANTS Software that lets you farm in North America with over 100 vehicles and tools from famous brands like John Deere, Case IH, New Holland, etc. |
| FarmVille 2: Country Escape | A game by Zynga that lets you build your own farm, join a co-op, trade with other players, and explore new areas. |
| Farm Town: Happy Farming Day | A game by Foranj that lets you grow crops, fruits, vegetables, flowers, raise animals, fish, cook, craft, and decorate your farm. |
| Farm Frenzy Free: Time Management Game | A game by HeroCraft Ltd. that lets you run your own farm, produce goods, sell them in the market, and fend off bears. |
| Farm Story 2 | A game by Storm8 Studios that lets you harvest crops, raise animals, mine for gems, bake pies, make ice cream, and more. |
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-| Criteria | FS 21 APK | Farming Simulator 20 | FarmVille 2: Country Escape | Farm Town: Happy Farming Day | Farm Frenzy Free: Time Management Game | Farm Story 2 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Graphics quality | High | High | Medium | Low | Low | Medium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Gameplay realism | High | High | Low | Low | Low | Low | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Variety of crops and animals | Medium | Medium | High | High | Medium | High | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Variety of vehicles and tools | High | High | Low | Low | < -
| Criteria | FS 21 APK | Farming Simulator 20 | FarmVille 2: Country Escape | Farm Town: Happy Farming Day | Farm Frenzy Free: Time Management Game | Farm Story 2 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Graphics quality | High | High | Medium | Low | Low | Medium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Gameplay realism | High | High | Low | Low | Low | Low | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Variety of crops and animals | Medium | Medium | High | High | Medium | High | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Variety of vehicles and tools | High | High | Low | < -Low - | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Criteria | FS 21 APK | Farming Simulator 20 | FarmVille 2: Country Escape | Farm Town: Happy Farming Day | Farm Frenzy Free: Time Management Game | Farm Story 2 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Graphics quality | High | High | Medium | Low | Low | Medium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Gameplay realism | High | High | Low | Low | Low | Low | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Variety of crops and animals | Medium | Medium | High | High | Medium | High | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Variety of vehicles and tools | High | < -H igh
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/skyler36237/vits-uma-genshin-honkai/models.py b/spaces/skyler36237/vits-uma-genshin-honkai/models.py deleted file mode 100644 index 52e15d1b9775038fd6e82b2efe6f95f51c66802d..0000000000000000000000000000000000000000 --- a/spaces/skyler36237/vits-uma-genshin-honkai/models.py +++ /dev/null @@ -1,534 +0,0 @@ -import math -import torch -from torch import nn -from torch.nn import functional as F - -import commons -import modules -import attentions -import monotonic_align - -from torch.nn import Conv1d, ConvTranspose1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from commons import init_weights, get_padding - - -class StochasticDurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): - super().__init__() - filter_channels = in_channels # it needs to be removed from future version. - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.log_flow = modules.Log() - self.flows = nn.ModuleList() - self.flows.append(modules.ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.flows.append(modules.Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.post_flows.append(modules.Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): - x = torch.detach(x) - x = self.pre(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) - x = self.proj(x) * x_mask - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - h_w = self.post_pre(w) - h_w = self.post_convs(h_w, x_mask) - h_w = self.post_proj(h_w) * x_mask - e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - z_u, z1 = torch.split(z_q, [1, 1], 1) - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2]) - logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot - return nll + logq # [b] - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] # remove a useless vflow - z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw - - -class DurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_2 = modules.LayerNorm(filter_channels) - self.proj = nn.Conv1d(filter_channels, 1, 1) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - def forward(self, x, x_mask, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - x = self.proj(x * x_mask) - return x * x_mask - - -class TextEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout): - super().__init__() - self.n_vocab = n_vocab - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - - self.emb = nn.Embedding(n_vocab, hidden_channels) - nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) - - self.encoder = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths): - x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return x, m, logs, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - -class PosteriorEncoder(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - -class Generator(torch.nn.Module): - def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) - resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append(weight_norm( - ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), - k, u, padding=(k-u)//2))) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel//(2**(i+1)) - for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i*self.num_kernels+j](x) - else: - xs += self.resblocks[i*self.num_kernels+j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), - ]) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ]) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2,3,5,7,11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - - -class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__(self, - n_vocab, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - n_speakers=0, - gin_channels=0, - use_sdp=True, - **kwargs): - - super().__init__() - self.n_vocab = n_vocab - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.n_speakers = n_speakers - self.gin_channels = gin_channels - - self.use_sdp = use_sdp - - self.enc_p = TextEncoder(n_vocab, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) - self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) - self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) - - if use_sdp: - self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) - else: - self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) - - if n_speakers > 1: - self.emb_g = nn.Embedding(n_speakers, gin_channels) - - def forward(self, x, x_lengths, y, y_lengths, sid=None): - - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - - with torch.no_grad(): - # negative cross-entropy - s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t] - neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s] - neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s] - neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 - - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach() - - w = attn.sum(2) - if self.use_sdp: - l_length = self.dp(x, x_mask, w, g=g) - l_length = l_length / torch.sum(x_mask) - else: - logw_ = torch.log(w + 1e-6) * x_mask - logw = self.dp(x, x_mask, g=g) - l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging - - # expand prior - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) - - z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size) - o = self.dec(z_slice, g=g) - return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): - device = next(self.parameters()).device # 获取模型所在的设备 - x, m_p, logs_p, x_mask = self.enc_p(x.to(device), x_lengths.to(device)) - if self.n_speakers > 0: - g = self.emb_g(sid.to(device)).unsqueeze(-1) # [b, h, 1] - else: - g = None - - if self.use_sdp: - logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) - else: - logw = self.dp(x, x_mask, g=g) - w = torch.exp(logw) * x_mask * length_scale - w_ceil = torch.ceil(w) - y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = commons.generate_path(w_ceil, attn_mask) - - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale - z = self.flow(z_p, y_mask, g=g, reverse=True) - o = self.dec((z * y_mask)[:,:,:max_len], g=g) - return o, attn, y_mask, (z, z_p, m_p, logs_p) - - def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): - assert self.n_speakers > 0, "n_speakers have to be larger than 0." - g_src = self.emb_g(sid_src).unsqueeze(-1) - g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) - z_p = self.flow(z, y_mask, g=g_src) - z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) - o_hat = self.dec(z_hat * y_mask, g=g_tgt) - return o_hat, y_mask, (z, z_p, z_hat) - diff --git a/spaces/society-ethics/model-card-regulatory-check/tests/cards/microsoft___layoutlmv3-base.md b/spaces/society-ethics/model-card-regulatory-check/tests/cards/microsoft___layoutlmv3-base.md deleted file mode 100644 index 9be8565124925f706475230a1a9795f86732c8d2..0000000000000000000000000000000000000000 --- a/spaces/society-ethics/model-card-regulatory-check/tests/cards/microsoft___layoutlmv3-base.md +++ /dev/null @@ -1,29 +0,0 @@ -# LayoutLMv3 - -[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlmv3) - -## Model description - -LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tuned for both text-centric tasks, including form understanding, receipt understanding, and document visual question answering, and image-centric tasks such as document image classification and document layout analysis. - -[LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387) -Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei, ACM Multimedia 2022. - -## Citation - -If you find LayoutLM useful in your research, please cite the following paper: - -``` -@inproceedings{huang2022layoutlmv3, - author={Yupan Huang and Tengchao Lv and Lei Cui and Yutong Lu and Furu Wei}, - title={LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking}, - booktitle={Proceedings of the 30th ACM International Conference on Multimedia}, - year={2022} -} -``` - -## License - -The content of this project itself is licensed under the [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). -Portions of the source code are based on the [transformers](https://github.com/huggingface/transformers) project. -[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct) \ No newline at end of file diff --git a/spaces/softcatala/comparativa-tts-catala/app.py b/spaces/softcatala/comparativa-tts-catala/app.py deleted file mode 100644 index 2f1e71fb64d04a5d72b8c569e17d3a8af496f3cf..0000000000000000000000000000000000000000 --- a/spaces/softcatala/comparativa-tts-catala/app.py +++ /dev/null @@ -1,147 +0,0 @@ -import tempfile -import gradio as gr -import os -from TTS.utils.synthesizer import Synthesizer -from espeak_phonemizer import Phonemizer -from engine import Piper -from festival import festival_synthesize -from mms import MMS - -MAX_TXT_LEN = 325 - -fonemitzador = Phonemizer("ca") - -def carrega_bsc(): - model_path = os.getcwd() + "/models/bsc/best_model.pth" - config_path = os.getcwd() + "/models/bsc/config.json" - speakers_file_path = os.getcwd() + "/models/bsc/speakers.pth" - vocoder_path = None - vocoder_config_path = None - - synthesizer = Synthesizer( - model_path, config_path, speakers_file_path, None, vocoder_path, vocoder_config_path, - ) - - return synthesizer - -def carrega_collectivat(): - model_path = os.getcwd() + "/models/collectivat/fast-speech_best_model.pth" - config_path = os.getcwd() + "/models/collectivat/fast-speech_config.json" - vocoder_path = os.getcwd() + "/models/collectivat/ljspeech--hifigan_v2_model_file.pth" - vocoder_config_path = os.getcwd() + "/models/collectivat/ljspeech--hifigan_v2_config.json" - synthesizer = Synthesizer( - model_path, config_path, None, None, vocoder_path, vocoder_config_path - ) - - return synthesizer - -def carrega_piper(): - return Piper(os.getcwd() + "/models/piper/ca-upc_ona-x-low.onnx") - -def carrega_mms(): - return MMS(os.getcwd() + "/models/mms") - - -model_bsc = carrega_bsc() -SPEAKERS = model_bsc.tts_model.speaker_manager.speaker_names - -model_collectivat = carrega_collectivat() - -model_piper = carrega_piper() - -model_mms = carrega_mms() - -request_count = 0 - -def tts(text, festival_voice, speaker_idx): - if len(text) > MAX_TXT_LEN: - text = text[:MAX_TXT_LEN] - print(f"Input text was cutoff since it went over the {MAX_TXT_LEN} character limit.") - print(text) - - # synthesize - wav_bsc = model_bsc.tts(text, speaker_idx) - wav_coll = model_collectivat.tts(text) - wav_piper = model_piper.synthesize(text) - - fp_bsc = "" - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: - model_bsc.save_wav(wav_bsc, fp) - fp_bsc = fp.name - - fp_coll = "" - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: - model_collectivat.save_wav(wav_coll, fp) - fp_coll = fp.name - - fp_piper = "" - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: - fp.write(wav_piper) - fp_piper = fp.name - - fp_mms = "" - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: - model_mms.synthesize(fp.name, text) - fp_mms = fp.name - - fonemes = fonemitzador.phonemize(text, keep_clause_breakers=True) - - fp_festival = festival_synthesize(text, festival_voice) - - global request_count - request_count += 1 - print(f"Requests: {request_count}") - return fonemes, fp_festival, fp_bsc, fp_coll, fp_piper, fp_mms - - -description=""" -Amb aquesta aplicació podeu sintetitzar text a veu amb els últims models neuronals lliures pel català i amb el motor Festival. - -1. Model multi-parlant VITS entrenat pel BSC (Projecte Aina) [enllaç](https://huggingface.co/projecte-aina/tts-ca-coqui-vits-multispeaker) -2. Model Fastspeech entrenat per Col·lectivat [enllaç](https://github.com/CollectivaT-dev/TTS-API) -3. Model VITS entrenat per Piper/Home Assistant [enllaç](https://github.com/rhasspy/piper) -3. Model VITS entrenat per Meta (llicència CC-BY-NC) [enllaç](https://github.com/facebookresearch/fairseq/tree/main/examples/mms) - -El primer model ha estat entrenat amb totes les veus de FestCAT, els talls de Common Voice 8 i un altre corpus pel que conté moltes veus de qualitat variable. La veu d'Ona està seleccionada per defecte per la comparativa però podeu provar les altres. -Els models 2 i 3 han estat entrenats amb la veu d'Ona de FestCAT. -El model 4, anomenat MMS, de Meta (Facebook) ha estat entrenat a partir de dades d'un [audiollibre](http://live.bible.is/bible/CATBSS/LUK/1) de la Bíblia - -Aquesta aplicació fa servir l'últim estat de l'espeak millorat per Carme Armentano del BSC -https://github.com/projecte-aina/espeak-ng - -NOTA: El model de col·lectivat treballa amb grafemes pel que no fa servir espeak com a fonemitzador. Festival conté les seves pròpies normes fonètiques. -""" -article= "" - -iface = gr.Interface( - fn=tts, - inputs=[ - gr.Textbox( - label="Text", - value="L'Èlia i l'Alí a l'aula. L'oli i l'ou. Lulú olorava la lila.", - ), - gr.Dropdown(label="Parlant del motor Festival", choices=["ona", "pau"], value="ona"), - gr.Dropdown(label="Parlant del model VITS multi-parlant del BSC", choices=SPEAKERS, value="ona") - ], - outputs=[ - gr.Markdown(label="Fonemes"), - gr.Audio(label="Festival",type="filepath"), - gr.Audio(label="BSC VITS",type="filepath"), - gr.Audio(label="Collectivat Fastspeech",type="filepath"), - gr.Audio(label="Piper VITS",type="filepath"), - gr.Audio(label="Meta MMS VITS",type="filepath") - ], - title="Comparativa de síntesi lliure en català️", - description=description, - article=article, - allow_flagging="never", - layout="vertical", - live=False, - examples=[ - ["Duc pà sec al sac, m'assec on sóc i el suco amb suc", "ona", "ona"], - ["Un plat pla blanc, ple de pebre negre n’era. Un plat blanc pla, ple de pebre negre està", "ona", "ona"], - ["Visc al bosc i busco vesc i visc del vesc que busco al bosc", "ona", "ona"], - ["Una polla xica, pica, pellarica, camatorta i becarica va tenir sis polls xics, pics, pellarics, camacurts i becarics. Si la polla no hagués sigut xica, pica, pellarica, camatorta i becarica, els sis polls no haurien sigut xics, pics, pellarics, camacurts i becarics.", "ona", "ona"] - ] -) -iface.launch(server_name="0.0.0.0", server_port=7860) diff --git a/spaces/sparanoid/milky-green-svc/README.md b/spaces/sparanoid/milky-green-svc/README.md deleted file mode 100644 index b930db4589cf372ef4894ccda03df0c389cda9ca..0000000000000000000000000000000000000000 --- a/spaces/sparanoid/milky-green-svc/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Milky Green SOVITS -emoji: 🍵 -colorFrom: cyan -colorTo: green -sdk: gradio -sdk_version: 3.6 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/sqc1729/bingi/src/components/ui/icons.tsx b/spaces/sqc1729/bingi/src/components/ui/icons.tsx deleted file mode 100644 index 742b489b50437c5b64c86082f2ebc712eeb6a2b0..0000000000000000000000000000000000000000 --- a/spaces/sqc1729/bingi/src/components/ui/icons.tsx +++ /dev/null @@ -1,504 +0,0 @@ -'use client' - -import * as React from 'react' - -import { cn } from '@/lib/utils' - -function IconNextChat({ - className, - inverted, - ...props -}: React.ComponentProps<'svg'> & { inverted?: boolean }) { - const id = React.useId() - - return ( - - ) -} - -function IconOpenAI({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconGitHub({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconSeparator({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconArrowDown({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconArrowRight({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconUser({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconPlus({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconArrowElbow({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconSpinner({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconMessage({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconTrash({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconMore({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconRefresh({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconStop({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconSidebar({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconMoon({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconSun({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconCopy({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconCheck({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconDownload({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconClose({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconEdit({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconShare({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconUsers({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconExternalLink({ - className, - ...props -}: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconChevronUpDown({ - className, - ...props -}: React.ComponentProps<'svg'>) { - return ( - - ) -} - -export { - IconEdit, - IconNextChat, - IconOpenAI, - IconGitHub, - IconSeparator, - IconArrowDown, - IconArrowRight, - IconUser, - IconPlus, - IconArrowElbow, - IconSpinner, - IconMessage, - IconTrash, - IconMore, - IconRefresh, - IconStop, - IconSidebar, - IconMoon, - IconSun, - IconCopy, - IconCheck, - IconDownload, - IconClose, - IconShare, - IconUsers, - IconExternalLink, - IconChevronUpDown -} diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/README.md b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/README.md deleted file mode 100644 index 57104230655c7c517d25904e634c53b6159ee60f..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/unit2speech/README.md +++ /dev/null @@ -1,42 +0,0 @@ -# Unit to Speech Model (unit2speech) - -Unit to speech model is modified Tacotron2 model that learns to synthesize speech from discrete speech units. All models are trained on quantized [LJSpeech](https://keithito.com/LJ-Speech-Dataset/). - -Upstream Units | Download Link -|-|- -Log Mel Filterbank + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km50/tts_checkpoint_best.pt) -Log Mel Filterbank + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km100/tts_checkpoint_best.pt) -Log Mel Filterbank + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km200/tts_checkpoint_best.pt) -Log Mel Filterbank + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km500/tts_checkpoint_best.pt) -Modified CPC + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km50/tts_checkpoint_best.pt) -Modified CPC + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km100/tts_checkpoint_best.pt) -Modified CPC + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km200/tts_checkpoint_best.pt) -Modified CPC + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km500/tts_checkpoint_best.pt) -HuBERT Base + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km50/tts_checkpoint_best.pt) -HuBERT Base + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km100/tts_checkpoint_best.pt) -HuBERT Base + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km200/tts_checkpoint_best.pt) -HuBERT Base + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km500/tts_checkpoint_best.pt) -wav2vec 2.0 Large + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km50/tts_checkpoint_best.pt) -wav2vec 2.0 Large + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km100/tts_checkpoint_best.pt) -wav2vec 2.0 Large + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km200/tts_checkpoint_best.pt) -wav2vec 2.0 Large + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km500/tts_checkpoint_best.pt) - -## Run inference using a unit2speech model -* Install librosa, unidecode and inflect using `pip install librosa, unidecode, inflect` -* Download [Waveglow checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/gslm/waveglow_256channels_new.pt). This is the vocoder. - -Sample commnd to run inference using trained unit2speech models. Please note that the quantized audio to synthesized should be using the same units as the unit2speech model was trained with. -``` -FAIRSEQ_ROOT= - Battlefield 3: How to Download and Install the Game on PC-Battlefield 3 is a first-person shooter video game that was released in 2011 by Electronic Arts. It is the sequel to Battlefield 2 and the eleventh installment in the Battlefield franchise. The game features a single-player campaign, a co-operative mode, and a multiplayer mode with up to 64 players on PC. The game also introduces the Battlelog, a free social service that allows players to communicate, track their stats, and join games with friends. -battlefield 3 game file part 18.rar 238 mb.rarDownload File ►►► https://urlgoal.com/2uI8sl - If you want to play Battlefield 3 on your PC, you will need to download and install the game files. There are different ways to do this, depending on where you bought the game from. Here are some of the options: -
After you have downloaded and installed the game files, you can launch Battlefield 3 from your desktop shortcut or from your Steam or Origin library. You will need to log in to your EA account and activate your product key if you haven't done so already. You will also need to update your game to the latest version if there are any available patches. Then, you can enjoy playing Battlefield 3 on your PC! - -Tips for Playing Battlefield 3-Battlefield 3 is a game that requires teamwork, strategy, and skill to succeed. Whether you are playing the single-player campaign, the co-operative mode, or the multiplayer mode, you will need to know how to use your weapons, vehicles, gadgets, and classes effectively. Here are some tips that can help you improve your performance and have more fun in Battlefield 3. - -Always Be Spotting-This is the most important tip for Battlefield 3, because it can make a huge difference for your team. By highlighting an enemy and pressing Back on Xbox 360, Select on PS3, or Q on PC, you will mark the enemy with an orange triangle for your entire team. This will allow your teammates to see the enemy's location, movement, and health status. Spotting can also help you earn more points, as you will get assists for every kill that your teammates make on your spotted enemies. Spotting can also reveal enemy vehicles, equipment, and explosives. You should spot every enemy you see, even if you are not going to engage them yourself. Spotting can save your life and your team's life. -Know Your Role-Battlefield 3 has four classes: Assault, Support, Engineer, and Recon. Each class has its own specialties, weapons, and gadgets that can help you and your team in different situations. You should choose a class that suits your playstyle and the map you are playing on. You should also be aware of what your class can do and what it cannot do. For example, the Assault class can heal and revive teammates with the Medkit and Defibrillator, but it cannot repair vehicles or destroy enemy armor. The Support class can resupply ammo and suppress enemies with the Ammo Box and Light Machine Guns, but it cannot snipe or spot enemies from afar. The Engineer class can repair vehicles and damage enemy armor with the Repair Tool and Rocket Launchers, but it cannot heal or revive teammates or provide ammo. The Recon class can snipe and spot enemies from afar with the Sniper Rifle and MAV (Micro Air Vehicle), but it cannot resupply or repair anything or engage in close combat effectively. -Forget About Kill/Death Ratios-Battlefield 3 is not a game where kills are everything. It is a game where objectives are everything. Whether you are playing Rush, Conquest, Team Deathmatch, or any other mode, you should always focus on completing the objectives rather than getting kills. Objectives can be capturing flags, arming or defusing M-COM stations, destroying vehicles, or simply staying alive. Completing objectives will earn you more points than kills, and will also help your team win the match. Kills are important, but they are not the main goal of the game. You should not worry about dying too much or having a low kill/death ratio. You should worry about helping your team and having fun. 81aa517590- - \ No newline at end of file diff --git a/spaces/studiobrn/SplitTrack/audiocraft/models/builders.py b/spaces/studiobrn/SplitTrack/audiocraft/models/builders.py deleted file mode 100644 index 77ee5f96fea2e3c9e475fe961bc1a5ee473ed8eb..0000000000000000000000000000000000000000 --- a/spaces/studiobrn/SplitTrack/audiocraft/models/builders.py +++ /dev/null @@ -1,218 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -""" -All the functions to build the relevant models and modules -from the Hydra config. -""" - -import typing as tp -import warnings - -import audiocraft -import omegaconf -import torch - -from .encodec import CompressionModel, EncodecModel, FlattenedCompressionModel # noqa -from .lm import LMModel -from ..modules.codebooks_patterns import ( - CodebooksPatternProvider, - DelayedPatternProvider, - ParallelPatternProvider, - UnrolledPatternProvider, - VALLEPattern, - MusicLMPattern, -) -from ..modules.conditioners import ( - BaseConditioner, - ConditioningProvider, - LUTConditioner, - T5Conditioner, - ConditionFuser, - ChromaStemConditioner, -) -from .. import quantization as qt -from ..utils.utils import dict_from_config - - -def get_quantizer(quantizer: str, cfg: omegaconf.DictConfig, dimension: int) -> qt.BaseQuantizer: - klass = { - 'no_quant': qt.DummyQuantizer, - 'rvq': qt.ResidualVectorQuantizer - }[quantizer] - kwargs = dict_from_config(getattr(cfg, quantizer)) - if quantizer != 'no_quant': - kwargs['dimension'] = dimension - return klass(**kwargs) - - -def get_encodec_autoencoder(encoder_name: str, cfg: omegaconf.DictConfig): - if encoder_name == 'seanet': - kwargs = dict_from_config(getattr(cfg, 'seanet')) - encoder_override_kwargs = kwargs.pop('encoder') - decoder_override_kwargs = kwargs.pop('decoder') - encoder_kwargs = {**kwargs, **encoder_override_kwargs} - decoder_kwargs = {**kwargs, **decoder_override_kwargs} - encoder = audiocraft.modules.SEANetEncoder(**encoder_kwargs) - decoder = audiocraft.modules.SEANetDecoder(**decoder_kwargs) - return encoder, decoder - else: - raise KeyError(f'Unexpected compression model {cfg.compression_model}') - - -def get_compression_model(cfg: omegaconf.DictConfig) -> CompressionModel: - """Instantiate a compression model. - """ - if cfg.compression_model == 'encodec': - kwargs = dict_from_config(getattr(cfg, 'encodec')) - encoder_name = kwargs.pop('autoencoder') - quantizer_name = kwargs.pop('quantizer') - encoder, decoder = get_encodec_autoencoder(encoder_name, cfg) - quantizer = get_quantizer(quantizer_name, cfg, encoder.dimension) - frame_rate = kwargs['sample_rate'] // encoder.hop_length - renormalize = kwargs.pop('renormalize', None) - renorm = kwargs.pop('renorm') - if renormalize is None: - renormalize = renorm is not None - warnings.warn("You are using a deprecated EnCodec model. Please migrate to new renormalization.") - return EncodecModel(encoder, decoder, quantizer, - frame_rate=frame_rate, renormalize=renormalize, **kwargs).to(cfg.device) - else: - raise KeyError(f'Unexpected compression model {cfg.compression_model}') - - -def get_lm_model(cfg: omegaconf.DictConfig) -> LMModel: - """Instantiate a transformer LM. - """ - if cfg.lm_model == 'transformer_lm': - kwargs = dict_from_config(getattr(cfg, 'transformer_lm')) - n_q = kwargs['n_q'] - q_modeling = kwargs.pop('q_modeling', None) - codebooks_pattern_cfg = getattr(cfg, 'codebooks_pattern') - attribute_dropout = dict_from_config(getattr(cfg, 'attribute_dropout')) - cls_free_guidance = dict_from_config(getattr(cfg, 'classifier_free_guidance')) - cfg_prob, cfg_coef = cls_free_guidance["training_dropout"], cls_free_guidance["inference_coef"] - fuser = get_condition_fuser(cfg) - condition_provider = get_conditioner_provider(kwargs["dim"], cfg).to(cfg.device) - if len(fuser.fuse2cond['cross']) > 0: # enforce cross-att programatically - kwargs['cross_attention'] = True - if codebooks_pattern_cfg.modeling is None: - assert q_modeling is not None, \ - 'LM model should either have a codebook pattern defined or transformer_lm.q_modeling' - codebooks_pattern_cfg = omegaconf.OmegaConf.create( - {'modeling': q_modeling, 'delay': {'delays': list(range(n_q))}} - ) - pattern_provider = get_codebooks_pattern_provider(n_q, codebooks_pattern_cfg) - return LMModel( - pattern_provider=pattern_provider, - condition_provider=condition_provider, - fuser=fuser, - cfg_dropout=cfg_prob, - cfg_coef=cfg_coef, - attribute_dropout=attribute_dropout, - dtype=getattr(torch, cfg.dtype), - device=cfg.device, - **kwargs - ).to(cfg.device) - else: - raise KeyError(f'Unexpected LM model {cfg.lm_model}') - - -def get_conditioner_provider(output_dim: int, cfg: omegaconf.DictConfig) -> ConditioningProvider: - """Instantiate a conditioning model. - """ - device = cfg.device - duration = cfg.dataset.segment_duration - cfg = getattr(cfg, "conditioners") - cfg = omegaconf.OmegaConf.create({}) if cfg is None else cfg - conditioners: tp.Dict[str, BaseConditioner] = {} - with omegaconf.open_dict(cfg): - condition_provider_args = cfg.pop('args', {}) - for cond, cond_cfg in cfg.items(): - model_type = cond_cfg["model"] - model_args = cond_cfg[model_type] - if model_type == "t5": - conditioners[str(cond)] = T5Conditioner(output_dim=output_dim, device=device, **model_args) - elif model_type == "lut": - conditioners[str(cond)] = LUTConditioner(output_dim=output_dim, **model_args) - elif model_type == "chroma_stem": - model_args.pop('cache_path', None) - conditioners[str(cond)] = ChromaStemConditioner( - output_dim=output_dim, - duration=duration, - device=device, - **model_args - ) - else: - raise ValueError(f"unrecognized conditioning model: {model_type}") - conditioner = ConditioningProvider(conditioners, device=device, **condition_provider_args) - return conditioner - - -def get_condition_fuser(cfg: omegaconf.DictConfig) -> ConditionFuser: - """Instantiate a condition fuser object. - """ - fuser_cfg = getattr(cfg, "fuser") - fuser_methods = ["sum", "cross", "prepend", "input_interpolate"] - fuse2cond = {k: fuser_cfg[k] for k in fuser_methods} - kwargs = {k: v for k, v in fuser_cfg.items() if k not in fuser_methods} - fuser = ConditionFuser(fuse2cond=fuse2cond, **kwargs) - return fuser - - -def get_codebooks_pattern_provider(n_q: int, cfg: omegaconf.DictConfig) -> CodebooksPatternProvider: - """Instantiate a codebooks pattern provider object. - """ - pattern_providers = { - 'parallel': ParallelPatternProvider, - 'delay': DelayedPatternProvider, - 'unroll': UnrolledPatternProvider, - 'valle': VALLEPattern, - 'musiclm': MusicLMPattern, - } - name = cfg.modeling - kwargs = dict_from_config(cfg.get(name)) if hasattr(cfg, name) else {} - klass = pattern_providers[name] - return klass(n_q, **kwargs) - - -def get_debug_compression_model(device='cpu'): - """Instantiate a debug compression model to be used for unit tests. - """ - seanet_kwargs = { - 'n_filters': 4, - 'n_residual_layers': 1, - 'dimension': 32, - 'ratios': [10, 8, 16] # 25 Hz at 32kHz - } - encoder = audiocraft.modules.SEANetEncoder(**seanet_kwargs) - decoder = audiocraft.modules.SEANetDecoder(**seanet_kwargs) - quantizer = qt.ResidualVectorQuantizer(dimension=32, bins=400, n_q=4) - init_x = torch.randn(8, 32, 128) - quantizer(init_x, 1) # initialize kmeans etc. - compression_model = EncodecModel( - encoder, decoder, quantizer, - frame_rate=25, sample_rate=32000, channels=1).to(device) - return compression_model.eval() - - -def get_debug_lm_model(device='cpu'): - """Instantiate a debug LM to be used for unit tests. - """ - pattern = DelayedPatternProvider(n_q=4) - dim = 16 - providers = { - 'description': LUTConditioner(n_bins=128, dim=dim, output_dim=dim, tokenizer="whitespace"), - } - condition_provider = ConditioningProvider(providers) - fuser = ConditionFuser( - {'cross': ['description'], 'prepend': [], - 'sum': [], 'input_interpolate': []}) - lm = LMModel( - pattern, condition_provider, fuser, - n_q=4, card=400, dim=dim, num_heads=4, custom=True, num_layers=2, - cross_attention=True, causal=True) - return lm.to(device).eval() diff --git a/spaces/sub314xxl/zeroscope/share_btn.py b/spaces/sub314xxl/zeroscope/share_btn.py deleted file mode 100644 index e52053dcf5728969d6d51fd75e98fe56573f7ed8..0000000000000000000000000000000000000000 --- a/spaces/sub314xxl/zeroscope/share_btn.py +++ /dev/null @@ -1,72 +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-zeroscope-${{videoId}}.mp4`; - 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 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 videoOutFile = await getVideoBlobFile(outputVideo); - const dataOutputVid = await uploadFile(videoOutFile); - - const descriptionMd = ` -#### Prompt: -${captionTxt} - -#### Zeroscope video result: -${dataOutputVid} - -`; - const params = new URLSearchParams({ - title: captionTxt, - description: descriptionMd, - }); - const paramsStr = params.toString(); - window.open(`https://huggingface.co/spaces/fffiloni/zeroscope/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/subhajitmaji/MusicGen/tests/modules/__init__.py b/spaces/subhajitmaji/MusicGen/tests/modules/__init__.py deleted file mode 100644 index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000 --- a/spaces/subhajitmaji/MusicGen/tests/modules/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. diff --git a/spaces/subhc/Guess-What-Moves/utils/grid.py b/spaces/subhc/Guess-What-Moves/utils/grid.py deleted file mode 100644 index 52d71b8fd2377cf36c210cc3b8641a4484ef45bb..0000000000000000000000000000000000000000 --- a/spaces/subhc/Guess-What-Moves/utils/grid.py +++ /dev/null @@ -1,9 +0,0 @@ -import torch - - -def get_meshgrid(resolution, device): - grid_x, grid_y = torch.meshgrid(torch.arange(resolution[0]).float() / resolution[0], - torch.arange(resolution[1]).float() / resolution[1], indexing='ij') - grid_x = grid_x.to(device) - grid_y = grid_y.to(device) - return grid_x, grid_y diff --git a/spaces/sukh28/toxic_gradio_app/README.md b/spaces/sukh28/toxic_gradio_app/README.md deleted file mode 100644 index f71e1e9ee46c612ea3de44d15d8b9dd9bfdbab34..0000000000000000000000000000000000000000 --- a/spaces/sukh28/toxic_gradio_app/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Toxic Gradio App -emoji: 🦀 -colorFrom: green -colorTo: gray -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/supercyx3/nova/README.md b/spaces/supercyx3/nova/README.md deleted file mode 100644 index a0d16c86c995f73ac641d0fc0b20823ada2e38a8..0000000000000000000000000000000000000000 --- a/spaces/supercyx3/nova/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: ChatGPT-Next-Web-Nova -emoji: 🌍 -colorFrom: blue -colorTo: yellow -sdk: docker -pinned: false -license: mit -app_port: 3000 -duplicated_from: dongsiqie/nova ---- -免费key的来源：https://nova-oss.com - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download JSpy RAT V0.08 Full Version.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download JSpy RAT V0.08 Full Version.md deleted file mode 100644 index 26afe6fb6a80e104b4b5d63658e51b13fc5ba146..0000000000000000000000000000000000000000 --- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Download JSpy RAT V0.08 Full Version.md +++ /dev/null @@ -1,6 +0,0 @@ - Download jSpy RAT v0.08 Full VersionDownload ★ https://cinurl.com/2uEYiY - -July 29, 2018 - Download jSpy RAT v0.08 full version: Password: EHT Click here to download jSpy rat v0.08 full version. 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Password - EHT. 8a78ff9644 - - - diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/National Book Font Free Download BEST.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/National Book Font Free Download BEST.md deleted file mode 100644 index 745d0ae0202be1ae2a16a783c925ef01ff464a25..0000000000000000000000000000000000000000 --- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/National Book Font Free Download BEST.md +++ /dev/null @@ -1,22 +0,0 @@ - National Book Font Free DownloadDOWNLOAD ✫✫✫ https://cinurl.com/2uEYxz - -National-Book Typeface. The typeface was made with several goal goals in mind, with much of its distinguishing features being large letters to make the font attractive and easy to read. - -Carey: After you have your idea for a typeface, it is important to have someone to create the typeface for you. Developing a typeface is like the making of any good product. 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Free Download Driver Printer Canon Pixma MP287 for Windows XP, Vista, ... 4d29de3e1b - - - diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/parallel/registry.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/parallel/registry.py deleted file mode 100644 index a204a07fba10e614223f090d1a57cf9c4d74d4a1..0000000000000000000000000000000000000000 --- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/parallel/registry.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from torch.nn.parallel import DataParallel, DistributedDataParallel - -from annotator.uniformer.mmcv.utils import Registry - -MODULE_WRAPPERS = Registry('module wrapper') -MODULE_WRAPPERS.register_module(module=DataParallel) -MODULE_WRAPPERS.register_module(module=DistributedDataParallel) diff --git a/spaces/taesiri/ChatGPT-ImageCaptioner/tools/preprocess_imagenet22k.py b/spaces/taesiri/ChatGPT-ImageCaptioner/tools/preprocess_imagenet22k.py deleted file mode 100644 index 6dda56c222a30c7be23fafbdab4be3fe611597e2..0000000000000000000000000000000000000000 --- a/spaces/taesiri/ChatGPT-ImageCaptioner/tools/preprocess_imagenet22k.py +++ /dev/null @@ -1,148 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. - -import os -import numpy as np -import sys - -sys.path.insert(0, 'third_party/CenterNet2/projects/CenterNet2/') -sys.path.insert(0, 'third_party/Deformable-DETR') -from detic.data.tar_dataset import _TarDataset, DiskTarDataset -import pickle -import io -import gzip -import time - - -class _RawTarDataset(object): - - def __init__(self, filename, indexname, preload=False): - self.filename = filename - self.names = [] - self.offsets = [] - - for l in open(indexname): - ll = l.split() - a, b, c = ll[:3] - offset = int(b[:-1]) - if l.endswith('** Block of NULs **\n'): - self.offsets.append(offset) - break - else: - if c.endswith('JPEG'): - self.names.append(c) - self.offsets.append(offset) - else: - # ignore directories - pass - if preload: - self.data = np.memmap(filename, mode='r', dtype='uint8') - else: - self.data = None - - def __len__(self): - return len(self.names) - - def __getitem__(self, idx): - if self.data is None: - self.data = np.memmap(self.filename, mode='r', dtype='uint8') - ofs = self.offsets[idx] * 512 - fsize = 512 * (self.offsets[idx + 1] - self.offsets[idx]) - data = self.data[ofs:ofs + fsize] - - if data[:13].tostring() == '././@LongLink': - data = data[3 * 512:] - else: - data = data[512:] - - # just to make it more fun a few JPEGs are GZIP compressed... - # catch this case - if tuple(data[:2]) == (0x1f, 0x8b): - s = io.StringIO(data.tostring()) - g = gzip.GzipFile(None, 'r', 0, s) - sdata = g.read() - else: - sdata = data.tostring() - return sdata - - - -def preprocess(): - # Follow https://github.com/Alibaba-MIIL/ImageNet21K/blob/main/dataset_preprocessing/processing_script.sh - # Expect 12358684 samples with 11221 classes - # ImageNet folder has 21841 classes (synsets) - - i22kdir = '/datasets01/imagenet-22k/062717/' - i22ktarlogs = '/checkpoint/imisra/datasets/imagenet-22k/tarindex' - class_names_file = '/checkpoint/imisra/datasets/imagenet-22k/words.txt' - - output_dir = '/checkpoint/zhouxy/Datasets/ImageNet/metadata-22k/' - i22knpytarlogs = '/checkpoint/zhouxy/Datasets/ImageNet/metadata-22k/tarindex_npy' - print('Listing dir') - log_files = os.listdir(i22ktarlogs) - log_files = [x for x in log_files if x.endswith(".tarlog")] - log_files.sort() - chunk_datasets = [] - dataset_lens = [] - min_count = 0 - create_npy_tarlogs = True - print('Creating folders') - if create_npy_tarlogs: - os.makedirs(i22knpytarlogs, exist_ok=True) - for log_file in log_files: - syn = log_file.replace(".tarlog", "") - dataset = _RawTarDataset(os.path.join(i22kdir, syn + ".tar"), - os.path.join(i22ktarlogs, syn + ".tarlog"), - preload=False) - names = np.array(dataset.names) - offsets = np.array(dataset.offsets, dtype=np.int64) - np.save(os.path.join(i22knpytarlogs, f"{syn}_names.npy"), names) - np.save(os.path.join(i22knpytarlogs, f"{syn}_offsets.npy"), offsets) - - os.makedirs(output_dir, exist_ok=True) - - start_time = time.time() - for log_file in log_files: - syn = log_file.replace(".tarlog", "") - dataset = _TarDataset(os.path.join(i22kdir, syn + ".tar"), i22knpytarlogs) - # dataset = _RawTarDataset(os.path.join(i22kdir, syn + ".tar"), - # os.path.join(i22ktarlogs, syn + ".tarlog"), - # preload=False) - dataset_lens.append(len(dataset)) - end_time = time.time() - print(f"Time {end_time - start_time}") - - - dataset_lens = np.array(dataset_lens) - dataset_valid = dataset_lens > min_count - - syn2class = {} - with open(class_names_file) as fh: - for line in fh: - line = line.strip().split("\t") - syn2class[line[0]] = line[1] - - tarlog_files = [] - class_names = [] - tar_files = [] - for k in range(len(dataset_valid)): - if not dataset_valid[k]: - continue - syn = log_files[k].replace(".tarlog", "") - tarlog_files.append(os.path.join(i22ktarlogs, syn + ".tarlog")) - tar_files.append(os.path.join(i22kdir, syn + ".tar")) - class_names.append(syn2class[syn]) - - tarlog_files = np.array(tarlog_files) - tar_files = np.array(tar_files) - class_names = np.array(class_names) - print(f"Have {len(class_names)} classes and {dataset_lens[dataset_valid].sum()} samples") - - np.save(os.path.join(output_dir, "tarlog_files.npy"), tarlog_files) - np.save(os.path.join(output_dir, "tar_files.npy"), tar_files) - np.save(os.path.join(output_dir, "class_names.npy"), class_names) - np.save(os.path.join(output_dir, "tar_files.npy"), tar_files) - - -if __name__ == "__main__": - preprocess() diff --git a/spaces/tensorflow/yamnet/app.py b/spaces/tensorflow/yamnet/app.py deleted file mode 100644 index 3c8f006d4f4e3d6faf7ba981e5add94db42a1cff..0000000000000000000000000000000000000000 --- a/spaces/tensorflow/yamnet/app.py +++ /dev/null @@ -1,64 +0,0 @@ -import tensorflow as tf -import tensorflow_hub as hub -import numpy as np -import csv - -import matplotlib.pyplot as plt -from IPython.display import Audio -from scipy.io import wavfile - -import os - -import gradio as gr - -# Load the model. -model = hub.load('https://tfhub.dev/google/yamnet/1') - -# Find the name of the class with the top score when mean-aggregated across frames. -def class_names_from_csv(class_map_csv_text): - """Returns list of class names corresponding to score vector.""" - class_names = [] - with tf.io.gfile.GFile(class_map_csv_text) as csvfile: - reader = csv.DictReader(csvfile) - for row in reader: - class_names.append(row['display_name']) - - return class_names - -class_map_path = model.class_map_path().numpy() -class_names = class_names_from_csv(class_map_path) - - -def ensure_sample_rate(original_sample_rate, waveform, - desired_sample_rate=16000): - """Resample waveform if required.""" - if original_sample_rate != desired_sample_rate: - desired_length = int(round(float(len(waveform)) / - original_sample_rate * desired_sample_rate)) - waveform = scipy.signal.resample(waveform, desired_length) - return desired_sample_rate, waveform - -os.system("wget https://storage.googleapis.com/audioset/miaow_16k.wav") - -def inference(audio): - # wav_file_name = 'speech_whistling2.wav' - wav_file_name = audio - sample_rate, wav_data = wavfile.read(wav_file_name, 'rb') - sample_rate, wav_data = ensure_sample_rate(sample_rate, wav_data) - - waveform = wav_data / tf.int16.max - - # Run the model, check the output. - scores, embeddings, spectrogram = model(waveform) - - scores_np = scores.numpy() - spectrogram_np = spectrogram.numpy() - infered_class = class_names[scores_np.mean(axis=0).argmax()] - - return f'The main sound is: {infered_class}' - -examples=[['miaow_16k.wav']] -title="yamnet" -description="An audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology." -gr.Interface(inference,gr.inputs.Audio(type="filepath"),"text",examples=examples,title=title,description=description).launch(enable_queue=True) - \ No newline at end of file diff --git a/spaces/terfces0erbo/CollegeProjectV2/Aktivasi Windows 8 Release Preview Build 8400 12 WORK.md b/spaces/terfces0erbo/CollegeProjectV2/Aktivasi Windows 8 Release Preview Build 8400 12 WORK.md deleted file mode 100644 index ee1903520d4dec9821e353799f095b0aa1ee170b..0000000000000000000000000000000000000000 --- a/spaces/terfces0erbo/CollegeProjectV2/Aktivasi Windows 8 Release Preview Build 8400 12 WORK.md +++ /dev/null @@ -1,6 +0,0 @@ - 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Aktivasi windows 8 release preview build 8400 12Download File ->>->>->> https://bytlly.com/2uGjw7 - i have just recently re-installed windows 10 and i keep getting an error message when i try to activate. i first tried moving it to another partition but to no avail. i have already done a clean install of windows as per the guidance in the article i am posting. i didn't use the product key that was on the drive. i did the following in cmd. put in the following line. "activate-windowsfeature -name net-ad-ds-server -disablemodule:windowsfirewallmodule -include:*" <-- already tried both "net-ad-ds-server" and "net-ad-ds-server-*"> -all /featurename: (remove: net-ad-ds-server))<--- but still no luck. <-- tried net-ad-ds-server and net-ad-ds-server-*> c:\windows\features\<---remove: net-ad-ds-server) -all /featurename:net-ad-ds-server<--- still no luck. activating all at the end of the cmd line.. done.<- have been doing for the last 3 days and still no luck. device id's: 00000000-0000-0000-0000-000000000000. i have seen on many other articles that windows 10 has a new problem. i have reinstalled windows 10 which didn't work and then i tried switching to another partition and i still got the same message. i tried the suggestion from the comments and still no go. keep getting a validation error. things to note before you start the process: on the previous version of the 64 bit windows 7, only windows features that only included the net-ad-ds-server module were activated. for x64, in addition, the windows firewall module may be activated. this module is a mandatory prerequisite for installation of any other features, and cannot be deactivated. 899543212b- - \ No newline at end of file diff --git a/spaces/terfces0erbo/CollegeProjectV2/Conceptdraw Pro 10 Full Version [NEW].md b/spaces/terfces0erbo/CollegeProjectV2/Conceptdraw Pro 10 Full Version [NEW].md deleted file mode 100644 index c1e7af636c23ccecdc0928963c5c700e2897e4a5..0000000000000000000000000000000000000000 --- a/spaces/terfces0erbo/CollegeProjectV2/Conceptdraw Pro 10 Full Version [NEW].md +++ /dev/null @@ -1,6 +0,0 @@ - Conceptdraw Pro 10 Full VersionDOWNLOAD ===> https://bytlly.com/2uGiQ8 - - 3cee63e6c2 - - - diff --git a/spaces/theintuitiveye/modernartstyle/app.py b/spaces/theintuitiveye/modernartstyle/app.py deleted file mode 100644 index 27e7dd35cc869a2ce30feac535c49e3863452454..0000000000000000000000000000000000000000 --- a/spaces/theintuitiveye/modernartstyle/app.py +++ /dev/null @@ -1,137 +0,0 @@ -from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler -import gradio as gr -import torch -from PIL import Image - -model_id = 'theintuitiveye/modernartstyle' -prefix = 'modernartst' - -scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler") - -pipe = StableDiffusionPipeline.from_pretrained( - model_id, - torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, - scheduler=scheduler) - -pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained( - model_id, - torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, - scheduler=scheduler) - -if torch.cuda.is_available(): - pipe = pipe.to("cuda") - pipe_i2i = pipe_i2i.to("cuda") - -def error_str(error, title="Error"): - return f"""#### {title} - {error}""" if error else "" - -def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False): - - generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None - prompt = f"{prefix} {prompt}" if auto_prefix else prompt - - try: - if img is not None: - return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None - else: - return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None - except Exception as e: - return None, error_str(e) - -def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator): - - result = pipe( - prompt, - negative_prompt = neg_prompt, - num_inference_steps = int(steps), - guidance_scale = guidance, - width = width, - height = height, - generator = generator) - - return result.images[0] - -def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): - - ratio = min(height / img.height, width / img.width) - img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) - result = pipe_i2i( - prompt, - negative_prompt = neg_prompt, - init_image = img, - num_inference_steps = int(steps), - strength = strength, - guidance_scale = guidance, - width = width, - height = height, - generator = generator) - - return result.images[0] - -css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem} -""" -with gr.Blocks(css=css) as demo: - gr.HTML( - f""" -
-
- """
- )
- with gr.Row():
-
- with gr.Column(scale=55):
- with gr.Group():
- with gr.Row():
- prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
- generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
- image_out = gr.Image(height=512)
- error_output = gr.Markdown()
-
- with gr.Column(scale=45):
- with gr.Tab("Options"):
- with gr.Group():
- neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
- auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (modernartst)", value=prefix, visible=prefix)
-
- with gr.Row():
- guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
- steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
- with gr.Row():
- width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
- height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
- seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
- with gr.Tab("Image to image"):
- with gr.Group():
- image = gr.Image(label="Image", height=256, tool="editor", type="pil")
- strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
- auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
- inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
- outputs = [image_out, error_output]
- prompt.submit(inference, inputs=inputs, outputs=outputs)
- generate.click(inference, inputs=inputs, outputs=outputs)
-
- gr.HTML("""
-
-
- Modernartstyle-
- Demo for Modernartstyle Stable Diffusion model. -
-
- """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/CorelDRAW X8 Free Download The Best Options for Windows 8.1 Users.md b/spaces/tialenAdioni/chat-gpt-api/logs/CorelDRAW X8 Free Download The Best Options for Windows 8.1 Users.md
deleted file mode 100644
index d2c694de27ae144851f0cd9e780416fa3e01c79d..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/CorelDRAW X8 Free Download The Best Options for Windows 8.1 Users.md
+++ /dev/null
@@ -1,26 +0,0 @@
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Conclusion-CorelDRAW X8 is a powerful and versatile graphics software that can help you create amazing graphics and designs for any purpose. However, you should not download CorelDRAW X8 full version with crack for Windows 8.1 because it is illegal, unsafe, unreliable and unethical. Instead, you should download CorelDRAW X8 from the official website of Corel Corporation and choose the option that suits your needs and budget. ddb901b051- - \ No newline at end of file diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Cubase Pro 10.5 Crack Torrent Full Version (New).md b/spaces/tialenAdioni/chat-gpt-api/logs/Cubase Pro 10.5 Crack Torrent Full Version (New).md deleted file mode 100644 index e63a6d045ecb0a6abfc0a442835699335b8fafb2..0000000000000000000000000000000000000000 --- a/spaces/tialenAdioni/chat-gpt-api/logs/Cubase Pro 10.5 Crack Torrent Full Version (New).md +++ /dev/null @@ -1,31 +0,0 @@ - -I'll try to create that. -Here is the title and article I generated for you: - - Cubase Pro 10.5 Crack Torrent Full Version (New)-If you are looking for a powerful and versatile digital audio workstation, you might want to check out Cubase Pro 10.5. This software is one of the most popular and widely used music production tools in the industry. It offers a comprehensive set of features and functions that can help you create, record, edit, mix and master your music projects. -However, Cubase Pro 10.5 is not a cheap software. It costs $559.99 for the full version, which might be too expensive for some users. That's why some people resort to downloading Cubase Pro 10.5 crack torrent files from the internet. These files claim to provide a free and easy way to activate the full version of Cubase Pro 10.5 without paying anything. -Cubase Pro 10.5 Crack Torrent Full Version (New)DOWNLOAD ✫✫✫ https://urlcod.com/2uK7Bh - But is it really worth it to download Cubase Pro 10.5 crack torrent files? What are the risks and consequences of using cracked software? In this article, we will answer these questions and more. We will also show you a better and safer alternative to Cubase Pro 10.5 crack torrent files. - -What are Cubase Pro 10.5 crack torrent files?-Cubase Pro 10.5 crack torrent files are illegal and unauthorized copies of the original software that have been modified or hacked by third-party sources. These files usually contain a crack or a keygen program that can generate a serial number or a license key to bypass the activation process of Cubase Pro 10.5. -By using these files, you can supposedly get access to the full version of Cubase Pro 10.5 without paying anything. You can download these files from various torrent sites or file-sharing platforms on the internet. However, these sources are often unreliable and untrustworthy, as they may contain malware, viruses, spyware or other harmful elements that can damage your computer or compromise your personal data. - -What are the risks and consequences of using Cubase Pro 10.5 crack torrent files?-Using Cubase Pro 10.5 crack torrent files is not only illegal but also risky and unethical. Here are some of the possible risks and consequences of using cracked software: -
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