app.py

import gradio as gr
import ffmpeg
from pathlib import Path
import os
import json
import base64
import requests
import ast

API_URL = "https://api-inference.huggingface.co/models/facebook/wav2vec2-base-960h"
#headers = {"Authorization": "Bearer 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"}
HF_TOKEN = os.environ["HF_TOKEN"]
headers = {"Authorization": f"Bearer {HF_TOKEN}"}


def generate_transcripts(in_video): #generate_gifs(in_video, gif_transcript):
    print("********* Inside generate_transcripts() **********")
    #convert video to audio
    print(f" input video is : {in_video}")
    
    video_path = Path("./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)
    
    #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 words and word timestamps
    words, words_timestamp = get_word_timestamps(timestamps)
    print(f"Total words in the audio transcript is:{len(words)}, transcript word list is :{words}, type of words is :{type(words)} ") 
    print(f"Total Word timestamps derived fromcharacter timestamp are :{len(words_timestamp)}, Word timestamps are :{words_timestamp}")
    
    return transcription, words, words_timestamp
    
    
def generate_gifs(gif_transcript, words, words_timestamp):
    print("********* Inside generate_gifs() **********")
    
    #creating list from input gif transcript 
    gif = "don't let your dreams be dreams"
    #gif = gif_transcript
    giflist = gif.split()
    
    #getting gif indexes from the generator
    # Converting string to list
    words = ast.literal_eval(words)
    print(f"words is :{words}")
    print(f"type of words is :{type(words)}")
    print(f"length of words is :{len(words)}")
    print(f"giflist is :{giflist}")
    #print(f"haystack and needle function returns value as : {list(get_gif_word_indexes(words, giflist))}")
    #indx_tmp = [num for num in get_gif_word_indexes(words, giflist)]
    #print(f"index temp is : {indx_tmp}")
    giflist_indxs = list(list(get_gif_word_indexes(words, giflist))[0])

    #getting start and end timestamps for a gif video
    start_seconds, end_seconds = get_gif_timestamps(giflist_indxs, words_timestamp)
    
    #generated .gif image
    generate_gif(start_seconds, end_seconds)
    #("./gifimage.gif")
    html_out = "<img src='./gifimage.gif' />"
            
    return html_out

    
#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 word timestamps 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


#getting index of gif words in main transcript
def get_gif_word_indexes(total_words_list, gif_words_list):  
    if not gif_words_list:
        print("THIS IS 1")
        return
    # just optimization
    COUNT=0
    lengthgif_words_list = len(gif_words_list)
    print("THIS IS 2")
    firstgif_words_list = gif_words_list[0]
    print("THIS IS 3")
    
    print(f"total_words_list is :{total_words_list}")
    print(f"length of total_words_list is :{len(total_words_list)}")
    print(f"gif_words_list is :{gif_words_list}")
    print(f"length of gif_words_list is :{len(gif_words_list)}")
    
    for idx, item in enumerate(total_words_list):
        COUNT+=1
        #print("COUNT IS :", COUNT)
        if item == firstgif_words_list:
            print("THIS IS 5")
            if total_words_list[idx:idx+lengthgif_words_list] == gif_words_list:
                print("THIS IS 6")
                print(f"value 1 is: {range(idx, idx+lengthgif_words_list)}")
                print(f"value of tuple is : {tuple(range(idx, idx+lengthgif_words_list))}")
                yield tuple(range(idx, idx+lengthgif_words_list))


#getting start and end timestamps for gif transcript
def get_gif_timestamps(giflist_indxs, words_timestamp):
  #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


#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("./gifimage.gif",)
  final_clip.close()
  return 


sample_video = ['./ShiaLaBeouf.mp4']
sample_vid = gr.Video(label='Video file')  #for displaying the example
examples = gr.components.Dataset(components=[sample_vid], samples=[sample_video], type='values')


demo = gr.Blocks()

with demo:
    with gr.Row():
        input_video = gr.Video(label="Upload a Video", visible=True)  #for incoming video
        text_transcript = gr.Textbox(label="Transcripts", lines = 10, interactive = True )  #to generate and display transcriptions for input video
        text_words = gr.Textbox(visible=False)
        text_wordstimestamps = gr.Textbox(visible=False)
        text_gif_transcript = gr.Textbox(label="Transcripts", placeholder="Copy paste transcripts here to create GIF image" , lines = 3, interactive = True ) #to copy paste required gif transcript
        out_gif = gr.HTML(label="Generated GIF from transcript selected", show_label=True)
        
        examples.render()
        def load_examples(video):  #to load sample video into input_video upon clicking on it
            print("****** inside load_example() ******")
            print("in_video is : ", video[0])
            return video[0]
        
        examples.click(load_examples, examples, input_video) 

    with gr.Row():
        button_transcript = gr.Button("Generate transcripts")
        button_gifs = gr.Button("Create Gif")
        
        #def load_gif():
        #    print("****** inside load_gif() ******")
        #    #created embedding  width='560' height='315' 
        #    html_out = "<img src='./gifimage.gif' />"
        #    print(f"html output is : {html_out}")
        #    return 

    button_transcript.click(generate_transcripts, input_video, [text_transcript, text_words, text_wordstimestamps ])
    button_gifs.click(generate_gifs, [text_gif_transcript, text_words, text_wordstimestamps], out_gif )
    
demo.launch(debug=True)