first

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+videos_out/
+results/
+# Python build
+.eggs/
+gradio.egg-info/*
+!gradio.egg-info/requires.txt
+!gradio.egg-info/PKG-INFO
+dist/
+*.pyc
+__pycache__/
+*.py[cod]
+*$py.class
+build/
+
+# JS build
+gradio/templates/frontend
+# Secrets
+.env
+
+# Gradio run artifacts
+*.db
+*.sqlite3
+gradio/launches.json
+flagged/
+# gradio_cached_examples/
+
+# Tests
+.coverage
+coverage.xml
+test.txt
+
+# Demos
+demo/tmp.zip
+demo/files/*.avi
+demo/files/*.mp4
+
+# Etc
+.idea/*
+.DS_Store
+*.bak
+workspace.code-workspace
+*.h5
+.vscode/
+
+# log files
+.pnpm-debug.log
+venv/
+*.db-journal

README.md

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+---
+title: Edit Video By Editing Text
+emoji: ✍️🎥📄
+colorFrom: red
+colorTo: gray
+sdk: gradio
+sdk_version: 3.0.3
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

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+import gradio as gr
+import json
+from difflib import Differ
+import ffmpeg
+import os
+from pathlib import Path
+import time
+
+
+# Set true if you're using huggingface inference API API https://huggingface.co/inference-api
+API_BACKEND = True
+# MODEL = 'facebook/wav2vec2-large-960h-lv60-self'
+# MODEL  = "facebook/wav2vec2-large-960h"
+MODEL = "facebook/wav2vec2-base-960h"
+# MODEL = "patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram"
+if API_BACKEND:
+    from dotenv import load_dotenv
+    import requests
+    import base64
+    import asyncio
+    load_dotenv(Path(".env"))
+
+    HF_TOKEN = os.environ["HF_TOKEN"]
+    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+    API_URL = f'https://api-inference.huggingface.co/models/{MODEL}'
+
+else:
+    import torch
+    from transformers import pipeline
+
+    # is cuda available?
+    cuda = torch.device(
+        'cuda:0') if torch.cuda.is_available() else torch.device('cpu')
+    device = 0 if torch.cuda.is_available() else -1
+    speech_recognizer = pipeline(
+        task="automatic-speech-recognition",
+        model=f'{MODEL}',
+        tokenizer=f'{MODEL}',
+        framework="pt",
+        device=device,
+    )
+
+videos_out_path = Path("./videos_out")
+videos_out_path.mkdir(parents=True, exist_ok=True)
+
+samples_data = sorted(Path('examples').glob('*.json'))
+SAMPLES = []
+for file in samples_data:
+    with open(file) as f:
+        sample = json.load(f)
+    SAMPLES.append(sample)
+VIDEOS = list(map(lambda x: [x['video']], SAMPLES))
+
+total_inferences_since_reboot = 0
+total_cuts_since_reboot = 0
+
+
+async def speech_to_text(video_file_path):
+    """
+    Takes a video path to convert to audio, transcribe audio channel to text and char timestamps
+
+    Using https://huggingface.co/tasks/automatic-speech-recognition pipeline
+    """
+    global total_inferences_since_reboot
+    if(video_file_path == None):
+        raise ValueError("Error no video input")
+
+    video_path = Path(video_file_path)
+    try:
+        # convert video to audio 16k using PIPE to audio_memory
+        audio_memory, _ = ffmpeg.input(video_path).output(
+            '-', format="wav", ac=1, ar='16k').overwrite_output().global_args('-loglevel', 'quiet').run(capture_stdout=True)
+    except Exception as e:
+        raise RuntimeError("Error converting video to audio")
+
+    last_time = time.time()
+    if API_BACKEND:
+        # Using Inference API https://huggingface.co/inference-api
+        # try twice, because the model must be loaded
+        for i in range(10):
+            for tries in range(4):
+                print(f'Transcribing from API attempt {tries}')
+                try:
+                    inference_reponse = query_api(audio_memory)
+                    transcription = inference_reponse["text"].lower()
+                    timestamps = [[chunk["text"].lower(), chunk["timestamp"][0], chunk["timestamp"][1]]
+                                  for chunk in inference_reponse['chunks']]
+
+                    total_inferences_since_reboot += 1
+                    print("\n\ntotal_inferences_since_reboot: ",
+                          total_inferences_since_reboot, "\n\n")
+                    return (transcription, transcription, timestamps)
+                except:
+                    if 'error' in inference_reponse and 'estimated_time' in inference_reponse:
+                        wait_time = inference_reponse['estimated_time']
+                        print("Waiting for model to load....", wait_time)
+                        # wait for loading model
+                        # 5 seconds plus for certanty
+                        await asyncio.sleep(wait_time + 5.0)
+                    elif 'error' in inference_reponse:
+                        raise RuntimeError("Error Fetching API",
+                                           inference_reponse['error'])
+                    else:
+                        break
+            else:
+                raise RuntimeError(inference_reponse, "Error Fetching API")
+    else:
+
+        try:
+            print(f'Transcribing via local model')
+            output = speech_recognizer(
+                audio_memory, return_timestamps="char",  chunk_length_s=10, stride_length_s=(4, 2))
+
+            transcription = output["text"].lower()
+            timestamps = [[chunk["text"].lower(), chunk["timestamp"][0].tolist(), chunk["timestamp"][1].tolist()]
+                          for chunk in output['chunks']]
+            total_inferences_since_reboot += 1
+
+            print("\n\ntotal_inferences_since_reboot: ",
+                  total_inferences_since_reboot, "\n\n")
+            return (transcription, transcription, timestamps)
+        except Exception as e:
+            raise RuntimeError("Error Running inference with local model", e)
+
+
+def cut_timestamps_to_video(video_in, transcription, text_in, timestamps):
+    """
+    Given original video input, text transcript + timestamps,
+    and edit ext cuts video segments into a single video
+    """
+    global total_cuts_since_reboot
+
+    video_path = Path(video_in)
+    video_file_name = video_path.stem
+    if(video_in == None or text_in == None or transcription == None):
+        raise ValueError("Inputs undefined")
+
+    d = Differ()
+    # compare original transcription with edit text
+    diff_chars = d.compare(transcription, text_in)
+    # remove all text aditions from diff
+    filtered = list(filter(lambda x: x[0] != '+', diff_chars))
+
+    # filter timestamps to be removed
+    # timestamps_to_cut = [b for (a,b) in zip(filtered, timestamps_var) if a[0]== '-' ]
+    # return diff tokes and cutted video!!
+
+    # groupping character timestamps so there are less cuts
+    idx = 0
+    grouped = {}
+    for(a, b) in zip(filtered, timestamps):
+        if a[0] != '-':
+            if idx in grouped:
+                grouped[idx].append(b)
+            else:
+                grouped[idx] = []
+                grouped[idx].append(b)
+        else:
+            idx += 1
+
+    # after grouping, gets the lower and upter start and time for each group
+    timestamps_to_cut = [[v[0][1], v[-1][2]] for v in grouped.values()]
+
+    between_str = '+'.join(
+        map(lambda t: f'between(t,{t[0]},{t[1]})', timestamps_to_cut))
+
+    if timestamps_to_cut:
+        video_file = ffmpeg.input(video_in)
+        video = video_file.video.filter(
+            "select", f'({between_str})').filter("setpts", "N/FRAME_RATE/TB")
+        audio = video_file.audio.filter(
+            "aselect", f'({between_str})').filter("asetpts", "N/SR/TB")
+
+        output_video = f'./videos_out/{video_file_name}.mp4'
+        ffmpeg.concat(video, audio, v=1, a=1).output(
+            output_video).overwrite_output().global_args('-loglevel', 'quiet').run()
+    else:
+        output_video = video_in
+
+    tokens = [(token[2:], token[0] if token[0] != " " else None)
+              for token in filtered]
+
+    total_cuts_since_reboot += 1
+    print("\n\ntotal_cuts_since_reboot: ", total_cuts_since_reboot, "\n\n")
+    return (tokens, output_video)
+
+
+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
+
+
+# ---- Gradio Layout -----
+video_in = gr.Video(label="Video file")
+text_in = gr.Textbox(label="Transcription", lines=10, interactive=True)
+video_out = gr.Video(label="Video Out")
+diff_out = gr.HighlightedText(label="Cuts Diffs", combine_adjacent=True)
+examples = gr.components.Dataset(
+    components=[video_in], samples=VIDEOS, type="index")
+
+demo = gr.Blocks(enable_queue=True, css='''
+#cut_btn, #reset_btn { align-self:stretch; }
+#\\31 3 { max-width: 540px; }
+.output-markdown {max-width: 65ch !important;}
+''')
+demo.encrypt = False
+with demo:
+    transcription_var = gr.Variable()
+    timestamps_var = gr.Variable()
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown('''
+            # Edit Video By Editing Text
+            This project is a quick proof of concept of a simple video editor where the edits
+            are made by editing the audio transcription.
+            Using the [Huggingface Automatic Speech Recognition Pipeline](https://huggingface.co/tasks/automatic-speech-recognition)
+            with a fine tuned [Wav2Vec2 model using Connectionist Temporal Classification (CTC)](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self)
+            you can predict not only the text transcription but also the [character or word base timestamps](https://huggingface.co/docs/transformers/v4.19.2/en/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline.__call__.return_timestamps)
+            ''')
+
+    with gr.Row():
+
+        examples.render()
+
+        def load_example(id):
+            video = SAMPLES[id]['video']
+            transcription = SAMPLES[id]['transcription'].lower()
+            timestamps = SAMPLES[id]['timestamps']
+
+            return (video, transcription, transcription, timestamps)
+
+        examples.click(
+            load_example,
+            inputs=[examples],
+            outputs=[video_in, text_in, transcription_var, timestamps_var],
+            queue=False)
+    with gr.Row():
+        with gr.Column():
+            video_in.render()
+            transcribe_btn = gr.Button("Transcribe Audio")
+            transcribe_btn.click(speech_to_text, [video_in], [
+                text_in, transcription_var, timestamps_var])
+
+    with gr.Row():
+        gr.Markdown('''
+        ### Now edit as text
+        After running the video transcription, you can make cuts to the text below (only cuts, not additions!)''')
+
+    with gr.Row():
+        with gr.Column():
+            text_in.render()
+            with gr.Row():
+                cut_btn = gr.Button("Cut to video", elem_id="cut_btn")
+                # send audio path and hidden variables
+                cut_btn.click(cut_timestamps_to_video, [
+                    video_in, transcription_var, text_in, timestamps_var], [diff_out, video_out])
+
+                reset_transcription = gr.Button(
+                    "Reset to last trascription", elem_id="reset_btn")
+                reset_transcription.click(
+                    lambda x: x, transcription_var, text_in)
+        with gr.Column():
+            video_out.render()
+            diff_out.render()
+    with gr.Row():
+        gr.Markdown('''
+        #### Video Credits
+
+        1. [Cooking](https://vimeo.com/573792389)
+        1. [Shia LaBeouf "Just Do It"](https://www.youtube.com/watch?v=n2lTxIk_Dr0)
+        1. [Mark Zuckerberg & Yuval Noah Harari in Conversation](https://www.youtube.com/watch?v=Boj9eD0Wug8)
+        ''')
+
+if __name__ == "__main__":
+    demo.launch(debug=True)

examples/.gitattributes

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+ShiaLaBeouf.mp4 filter=lfs diff=lfs merge=lfs -text
+cooking.mp4 filter=lfs diff=lfs merge=lfs -text
+zuckyuval.mp4 filter=lfs diff=lfs merge=lfs -text

examples/ShiaLaBeouf.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7c5d388aa4121e081e5b448113e0aa7886a1c6ccd0cbf5ab8805516f17e26f46
+size 3561536

examples/a-shia.json

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+{"video":"./examples/ShiaLaBeouf.mp4", "transcription":"do it just do it don't let your dreams be dreams yesterday you said to morrow so just do it make your dreams come true just do it some people dream success while you're going to wake up and work hard at it nothing is impossible you should get to the point where any one else would quit and you're not going to stop there no what are you waiting for do it tus do it yes you can trust to it if you're tired of starting over stop giving up","timestamps":[["d",2.42,2.44],["o",2.5,2.52],[" ",2.68,2.7],["i",2.76,2.78],["t",2.86,2.88],[" ",4.46,4.5],["j",4.52,4.54],["u",4.6,4.62],["s",4.72,4.74],["t",4.8,4.82],[" ",5.18,5.22],["d",5.24,5.26],["o",5.32,5.34],[" ",5.48,5.52],["i",5.56,5.58],["t",5.66,5.68],[" ",7.22,7.26],["d",7.28,7.3],["o",7.34,7.36],["n",7.4,7.42],["'",7.44,7.48],["t",7.48,7.5],[" ",7.56,7.6],["l",7.6,7.62],["e",7.66,7.68],["t",7.74,7.76],[" ",7.8,7.82],["y",7.84,7.86],["o",7.88,7.9],["u",7.9,7.92],["r",7.94,7.96],[" 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examples/b-zuckyuval.json

@@ -0,0 +1 @@
+{"video":"./examples/zuckyuval.mp4","transcription":"THE PEOPLE THAT LIVE IN THE PAST ARE DEAD THEY DON'T CARE THE QUESTION IS WHAT HAPPENS TO US AND THE PEOPLE IN THE FUTURE YEA AL RIGHT SO ALL THE QUESTIONS THAT YOU'VE OUTLINED WERE WHERE SHOULD WE START HERE AND I THINK ONE OF THE BIG TOPICS TAT WE'VE TALKED ABOUT IS AROUND AH THIS DUALISM AROUND WHETHER WITH ALL THE TECHNOLOGY AND PROGRESS THAT HAS BEEN MADE OURE PEOPLE COMING TOGETHER AND ARE WE BECOMING MORE UNIFIED  OR A IS OUR IS OUR WORLD BECOMING MORE FRAGMENTED NAN I SOM CURIOUS OT TO START OFF BY HOW YOU'RE THINKING ABOUT THAT AND NOTHING PROBABLYA BIG ARRY WE COUD PRICE SPEND MOST OF THE TIME ON THAT TOPIC YE I MEAN IF YOU LOOK AT IT AT THE LONG SPAN OF HISTORY THEN IT'S OBVIOUS THAT HUMANITY IS BECOMING MORE AND MORE CONNECTED AH IF THOUSANDS OF YEARS AGO PLANET EARTH WAS ACTUALLY A GALAXY OF A LOT OF ISOLATED WORLDS WITH ALMOST NO CONNECTION BETWEEN THEM SO GRADUALLY PEOPLE CAME TOGETHER AND BECAME MORE AND MORE 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examples/cooking.mp4

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examples/zuckyuval.mp4

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packages.txt

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+libsndfile1
+ffmpeg

requirements.txt

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+torch
+transformers
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+datasets
+librosa
+ffmpeg-python
+python-dotenv