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app.py

@@ -1 +1,115 @@
-import gradio as gr
+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)