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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) | |