Spaces:
Sleeping
Sleeping
File size: 17,910 Bytes
9b6561b 068543b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 |
from langchain.tools import tool, Tool
import re
import os
from langchain_groq import ChatGroq
import requests
import cv2
from moviepy.editor import ImageClip, AudioFileClip, concatenate_videoclips
from langchain.pydantic_v1 import BaseModel, Field
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
# from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
# import bitsandbytes as bnb
# import torch.nn as nn
# import torch
import pyttsx3
# from agents import get_agents_and_tasks
# from langchain_google_genai import ChatGoogleGenerativeAI
# from langchain.chat_models import ChatOpenAI
# # llm2 = ChatOpenAI(model='gpt-3.5-turbo')
# # llm3 = ChatOpenAI(model='gpt-3.5-turbo')
# llm1 = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=2048)
# # llm2 = ChatGroq(model='mixtral-8x7b-32768', temperature=0.6, max_tokens=2048, api_key='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')
# llm2 = ChatGoogleGenerativeAI(model='gemini-pro', temperature=0.0)
# # llm2 = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=2048, api_key='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')
# # llm3 = ChatGoogleGenerativeAI(model='gemini-pro')
# llm4 = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=2048, api_key='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')
# groq_api_key=os.environ.get('GROQ_API_KEY')
# llm = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=1024, api_key=groq_api_key)
# pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16).to('cuda')
# pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
# def quantize_model_to_4bit(model):
# replacements = []
# # Collect layers to be replaced
# for name, module in model.named_modules():
# if isinstance(module, nn.Linear):
# replacements.append((name, module))
# # Replace layers
# for name, module in replacements:
# # Split the name to navigate to the parent module
# *path, last = name.split('.')
# parent = model
# for part in path:
# parent = getattr(parent, part)
# # Create and assign the quantized layer
# quantized_layer = bnb.nn.Linear4bit(module.in_features, module.out_features, bias=module.bias is not None)
# quantized_layer.weight.data = module.weight.data
# if module.bias is not None:
# quantized_layer.bias.data = module.bias.data
# setattr(parent, last, quantized_layer)
# return model
# pipe.unet = quantize_model_to_4bit(pipe.unet)
# pipe.enable_model_cpu_offload()
def generate_speech(text, speech_dir='./outputs/audio', lang='en', speed=170, voice='default', num=0):
"""
Generates speech for given script.
"""
engine = pyttsx3.init()
# Set language and voice
voices = engine.getProperty('voices')
if voice == 'default':
voice_id = voices[1].id
else:
# Try to find the voice with the given name
voice_id = None
for v in voices:
if voice in v.name:
voice_id = v.id
break
if not voice_id:
raise ValueError(f"Voice '{voice}' not found.")
engine.setProperty('voice', voice_id)
engine.setProperty('rate', speed)
os.remove(os.path.join(os.path.dirname(os.path.abspath(__file__)), speech_dir, f'speech_{num}.mp3')) if os.path.exists(os.path.join(speech_dir, f'speech_{num}.mp3')) else None
engine.save_to_file(text, os.path.join(os.path.dirname(os.path.abspath(__file__)), speech_dir, f'speech_{num}.mp3'))
engine.runAndWait()
# class VideoGeneration(BaseModel):
# images_dir : str = Field(description='Path to images directory, such as "outputs/images"')
# speeches_dir : str = Field(description='Path to speeches directory, such as "outputs/speeches"')
# @tool(args_schema=VideoGeneration)
# def create_video_from_images_and_audio(images_dir, speeches_dir, zoom_factor=1.2):
# """Creates video using images and audios with zoom-in effect"""
# images_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), images_dir)
# speeches_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), speeches_dir)
# images_paths = os.listdir(images_dir)
# audio_paths = os.listdir(speeches_dir)
# # print(images_paths, audio_paths)
# clips = []
# for i in range(min(len(images_paths), len(audio_paths))):
# # Load the image
# img_clip = ImageClip(os.path.join(images_dir, images_paths[i]))
# # Load the audio file
# audioclip = AudioFileClip(os.path.join(speeches_dir, audio_paths[i]))
# # Set the duration of the video clip to the duration of the audio file
# videoclip = img_clip.set_duration(audioclip.duration)
# # Apply zoom-in effect to the video clip
# zoomed_clip = apply_zoom_in_effect(videoclip, zoom_factor)
# # Add audio to the zoomed video clip
# zoomed_clip = zoomed_clip.set_audio(audioclip)
# clips.append(zoomed_clip)
# # Concatenate all video clips
# final_clip = concatenate_videoclips(clips)
# # Write the result to a file
# final_clip.write_videofile(os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4"), codec='libx264', fps=24)
# return os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4")
# def apply_zoom_in_effect(clip, zoom_factor=1.2):
# width, height = clip.size
# duration = clip.duration
# def zoom_in_effect(get_frame, t):
# frame = get_frame(t)
# zoom = 1 + (zoom_factor - 1) * (t / duration)
# new_width, new_height = int(width * zoom), int(height * zoom)
# resized_frame = cv2.resize(frame, (new_width, new_height))
# # Calculate the position to crop the frame to the original size
# x_start = (new_width - width) // 2
# y_start = (new_height - height) // 2
# cropped_frame = resized_frame[y_start:y_start + height, x_start:x_start + width]
# return cropped_frame
# return clip.fl(zoom_in_effect, apply_to=['mask'])
# Example usage
# image_paths = "outputs/images"
# audio_paths = "outputs/audio"
# video_path = create_video_from_images_and_audio(image_paths, audio_paths)
# print(f"Video created at: {video_path}")
# class ImageGeneration(BaseModel):
# text : str = Field(description='description of sentence used for image generation')
# num : int = Field(description='sequence of description passed this tool. Used in image saving path. Example 1,2,3,4,5 and so on')
# class SpeechGeneration(BaseModel):
# text : str = Field(description='description of sentence used for image generation')
# num : int = Field(description='sequence of description passed this tool. Used in image saving path. Example 1,2,3,4,5 and so on')
import os
import cv2
from moviepy.editor import ImageClip, AudioFileClip, concatenate_videoclips, VideoFileClip
from PIL import Image, ImageDraw, ImageFont
import numpy as np
from groq import Groq
class VideoGeneration(BaseModel):
images_dir: str = Field(description='Path to images directory, such as "outputs/images"')
speeches_dir: str = Field(description='Path to speeches directory, such as "outputs/speeches"')
def split_text_into_chunks(text, chunk_size):
words = text.split()
return [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
def add_text_to_video(input_video, output_video, text, duration=1, fontsize=40, fontcolor=(255, 255, 255),
outline_thickness=2, outline_color=(0, 0, 0), delay_between_chunks=0.1,
font_path=os.path.join(os.path.dirname(os.path.abspath(__file__)),'Montserrat-Bold.ttf')):
chunks = split_text_into_chunks(text, 3) # Adjust chunk size as needed
cap = cv2.VideoCapture(input_video)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
fps = int(cap.get(cv2.CAP_PROP_FPS))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
out = cv2.VideoWriter(output_video, fourcc, fps, (width, height))
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
chunk_duration_frames = duration * fps
delay_frames = int(delay_between_chunks * fps)
font = ImageFont.truetype(font_path, fontsize)
current_frame = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(frame_pil)
chunk_index = current_frame // (chunk_duration_frames + delay_frames)
if current_frame % (chunk_duration_frames + delay_frames) < chunk_duration_frames and chunk_index < len(chunks):
chunk = chunks[chunk_index]
text_width, text_height = draw.textsize(chunk, font=font)
text_x = (width - text_width) // 2
text_y = height - 400 # Position text at the bottom
if text_width > width:
words = chunk.split()
half = len(words) // 2
line1 = ' '.join(words[:half])
line2 = ' '.join(words[half:])
text_size_line1 = draw.textsize(line1, font=font)
text_size_line2 = draw.textsize(line2, font=font)
text_x_line1 = (width - text_size_line1[0]) // 2
text_x_line2 = (width - text_size_line2[0]) // 2
text_y = height - 250 - text_size_line1[1] # Adjust vertical position for two lines
for dx in range(-outline_thickness, outline_thickness + 1):
for dy in range(-outline_thickness, outline_thickness + 1):
if dx != 0 or dy != 0:
draw.text((text_x_line1 + dx, text_y + dy), line1, font=font, fill=outline_color)
draw.text((text_x_line2 + dx, text_y + text_size_line1[1] + dy), line2, font=font, fill=outline_color)
draw.text((text_x_line1, text_y), line1, font=font, fill=fontcolor)
draw.text((text_x_line2, text_y + text_size_line1[1]), line2, font=font, fill=fontcolor)
else:
for dx in range(-outline_thickness, outline_thickness + 1):
for dy in range(-outline_thickness, outline_thickness + 1):
if dx != 0 or dy != 0:
draw.text((text_x + dx, text_y + dy), chunk, font=font, fill=outline_color)
draw.text((text_x, text_y), chunk, font=font, fill=fontcolor)
frame = cv2.cvtColor(np.array(frame_pil), cv2.COLOR_RGB2BGR)
out.write(frame)
current_frame += 1
cap.release()
out.release()
cv2.destroyAllWindows()
def apply_zoom_in_effect(clip, zoom_factor=1.2):
width, height = clip.size
duration = clip.duration
def zoom_in_effect(get_frame, t):
frame = get_frame(t)
zoom = 1 + (zoom_factor - 1) * (t / duration)
new_width, new_height = int(width * zoom), int(height * zoom)
resized_frame = cv2.resize(frame, (new_width, new_height))
x_start = (new_width - width) // 2
y_start = (new_height - height) // 2
cropped_frame = resized_frame[y_start:y_start + height, x_start:x_start + width]
return cropped_frame
return clip.fl(zoom_in_effect, apply_to=['mask'])
@tool(args_schema=VideoGeneration)
def create_video_from_images_and_audio(images_dir, speeches_dir, zoom_factor=1.2):
"""Creates video using images and audios.
Args:
images_dir: path to images folder, example 'outputs/images'
speeches_dir: path to speeches folder, example 'outputs/speeches'"""
client = Groq()
images_paths = sorted(os.listdir(os.path.join(os.path.dirname(os.path.abspath(__file__)),images_dir)))
audio_paths = sorted(os.listdir(os.path.join(os.path.dirname(os.path.abspath(__file__)),speeches_dir)))
clips = []
temp_files = []
for i in range(min(len(images_paths), len(audio_paths))):
img_clip = ImageClip(os.path.join(os.path.dirname(os.path.abspath(__file__)),images_dir, images_paths[i]))
audioclip = AudioFileClip(os.path.join(os.path.dirname(os.path.abspath(__file__)),speeches_dir, audio_paths[i]))
videoclip = img_clip.set_duration(audioclip.duration)
zoomed_clip = apply_zoom_in_effect(videoclip, zoom_factor)
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)),speeches_dir, audio_paths[i]), "rb") as file:
transcription = client.audio.transcriptions.create(
file=(audio_paths[i], file.read()),
model="whisper-large-v3",
response_format="verbose_json",
)
caption = transcription.text
temp_video_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), f"outputs/final_video/temp_zoomed_{i}.mp4")
zoomed_clip.write_videofile(temp_video_path, codec='libx264', fps=24)
temp_files.append(temp_video_path)
final_video_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), f"outputs/final_video/temp_captioned_{i}.mp4")
add_text_to_video(temp_video_path, final_video_path, caption, duration=1, fontsize=60)
temp_files.append(final_video_path)
final_clip = VideoFileClip(final_video_path)
final_clip = final_clip.set_audio(audioclip)
clips.append(final_clip)
final_clip = concatenate_videoclips(clips)
final_clip.write_videofile(os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4"), codec='libx264', fps=24)
# Close all video files properly
for clip in clips:
clip.close()
# Remove all temporary files
for temp_file in temp_files:
try:
os.remove(temp_file)
except Exception as e:
print(f"Error removing file {temp_file}: {e}")
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4")
# Example usage
# image_paths = "outputs/images"
# audio_paths = "outputs/speeches"
# video_path = create_video_from_images_and_audio(image_paths, audio_paths)
# print(f"Video created at: {video_path}")
class WikiInputs(BaseModel):
"""Inputs to the wikipedia tool."""
query: str = Field(description="query to look up in Wikipedia, should be 3 or less words")
api_wrapper = WikipediaAPIWrapper(top_k_results=3)#, doc_content_chars_max=100)
wiki_tool = WikipediaQueryRun(
name="wiki-tool",
description="{query:'input here'}",
args_schema=WikiInputs,
api_wrapper=api_wrapper,
return_direct=True,
)
wiki = Tool(
name = 'wikipedia',
func = wiki_tool.run,
description= "{query:'input here'}"
)
# wiki_tool.run("latest news in India")
# @tool
def process_script(script):
"""Used to process the script into dictionary format"""
dict = {}
dict['text_for_image_generation'] = re.findall(r'<image>(.*?)</?image>', script)
dict['text_for_speech_generation'] = re.findall(r'<narration>.*?</?narration>', script)
return dict
@tool#(args_schema=ImageGeneration)
def image_generator(script):
"""Generates images for the given script.
Saves it to images_dir and return path
Args:
script: a complete script containing narrations and image descriptions"""
images_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), './outputs/images')
# if num==1:
for filename in os.listdir(images_dir):
file_path = os.path.join(images_dir, filename)
if os.path.isfile(file_path):
os.remove(file_path)
dict = process_script(script)
for i, text in enumerate(dict['text_for_image_generation']):
# image = pipe(text, num_inference_steps=12, guidance_scale=2, width=720, height=1280, verbose=0).images[0]
# image.save(os.path.join(images_dir, f'image{i}.jpg'))
response = requests.post(
f"https://api.stability.ai/v2beta/stable-image/generate/core",
headers={
"authorization": os.environ.get('STABILITY_AI_API_KEY'),
"accept": "image/*"
},
files={"none": ''},
data={
"prompt": text,
"output_format": "png",
'aspect_ratio': "9:16",
},
)
if response.status_code == 200:
with open(os.path.join(images_dir, f'image_{i}.png'), 'wb') as file:
file.write(response.content)
else:
raise Exception(str(response.json()))
return f'images generated.'#f'image generated for "{text}" and saved to directory {images_dir} as image{num}.jpg'
@tool
def speech_generator(script):
"""Generates speech for given text
Saves it to speech_dir and return path
Args:
script: a complete script containing narrations and image descriptions"""
speech_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), './outputs/speeches')
# if num==1:
for filename in os.listdir(speech_dir):
file_path = os.path.join(speech_dir, filename)
if os.path.isfile(file_path):
os.remove(file_path)
dict = process_script(script)
print(dict)
for i, text in enumerate(dict['text_for_speech_generation']):
generate_speech(text, speech_dir, num=i)
return f'speechs generated.'#f'speech generated for "{text}" and saved to directory {speech_dir} as speech{num}.mp3' |