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import gradio as gr
#final
import gradio as gr
#import json
#from difflib import Differ
import ffmpeg
#import os
from pathlib import Path
#import time
API_URL = "https://api-inference.huggingface.co/models/facebook/wav2vec2-base-960h"
headers = {"Authorization": "Bearer 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"}
#convert video to audio
video_path = Path("/content/gdrive/My Drive/AI/videoedit/ShiaLaBeouf.mp4")
audio_memory, _ = ffmpeg.input(video_path).output('-', format="wav", ac=1, ar='16k').overwrite_output().global_args('-loglevel', 'quiet').run(capture_stdout=True)
#calling the hosted model
def query_api(audio_bytes: bytes):
"""
Query for Huggingface Inference API for Automatic Speech Recognition task
"""
payload = json.dumps({
"inputs": base64.b64encode(audio_bytes).decode("utf-8"),
"parameters": {
"return_timestamps": "char",
"chunk_length_s": 10,
"stride_length_s": [4, 2]
},
"options": {"use_gpu": False}
}).encode("utf-8")
response = requests.request(
"POST", API_URL, headers=headers, data=payload)
json_reponse = json.loads(response.content.decode("utf-8"))
return json_reponse
#Getting transcripts using wav2Vec2 huggingface hosted accelerated inference
#sending audio file in request along with stride and chunk length information
model_response = query_api(audio_memory)
#model response has both - transcripts as well as character timestamps or chunks
transcription = model_response["text"].lower()
chnk = model_response["chunks"]
#creating lists from chunks to consume downstream easily
timestamps = [[chunk["text"].lower(), chunk["timestamp"][0], chunk["timestamp"][1]]
for chunk in chnk]
#getting word timestams from character timestamps
def get_word_timestamps(timestamps):
words, word = [], []
letter_timestamp, word_timestamp, words_timestamp = [], [], []
for idx,entry in enumerate(timestamps):
word.append(entry[0])
letter_timestamp.append(entry[1])
if entry[0] == ' ':
words.append(''.join(word))
word_timestamp.append(letter_timestamp[0])
word_timestamp.append(timestamps[idx-1][2])
words_timestamp.append(word_timestamp)
word, word_timestamp, letter_timestamp = [], [], []
words = [word.strip() for word in words]
return words, words_timestamp
words, words_timestamp = get_word_timestamps(timestamps)
#words = [word.strip() for word in words]
print(f"Total words in the audio transcript is:{len(words)}, transcript word list is :{words}")
print(f"Total Word timestamps derived fromcharacter timestamp are :{len(words_timestamp)}, Word timestamps are :{words_timestamp}")
#creating list from input gif transcript
gif = "don't let your dreams be dreams"
giflist = gif.split()
#getting index of gif words in main transcript
def get_gif_word_indexes(total_words_list, gif_words_list):
if not gif_words_list:
return
# just optimization
lengthgif_words_list = len(gif_words_list)
firstgif_words_list = gif_words_list[0]
for idx, item in enumerate(total_words_list):
if item == firstgif_words_list:
if total_words_list[idx:idx+lengthgif_words_list] == gif_words_list:
yield tuple(range(idx, idx+lengthgif_words_list))
#getting gif indexes from the generator
giflist_indxs = list(list(get_gif_word_indexes(words, giflist))[0])
#getting start and end timestamps for gif transcript
def get_gif_timestamps(giflist_indxs):
#giflist_indxs = list(list(get_gif_word_indexes(words, giflist))[0])
min_idx = min(giflist_indxs)
max_idx = max(giflist_indxs)
gif_words_timestamp = words_timestamp[min_idx : max_idx+1]
start_seconds, end_seconds = gif_words_timestamp[0][0], gif_words_timestamp[-1][-1]
return start_seconds, end_seconds
#getting start and end timestamps for a gif video
start_seconds, end_seconds = get_gif_timestamps(giflist_indxs)
#extracting the video and building and serving a .gif image
def generate_gif(start_seconds, end_seconds):
final_clip = video.subclip(start_seconds, end_seconds)
#final_clip.write_videofile("/content/gdrive/My Drive/AI/videoedit/gif1.mp4")
final_clip.write_gif("/content/gdrive/My Drive/AI/videoedit/gif1.gif",)
final_clip.close()
return
generate_gif(start_seconds, end_seconds)
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