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mscoco_omni/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt 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
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz 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
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl 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
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* 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
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

mscoco_omni/README.md

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+---
+license: cc-by-4.0
+---
+#
+**catalog.jsonl** contains the captions and filenames for each image id. There are around 170 text-image-video-audio (omni) tuples in it.
+# How to run
+1. Specify GOOGLE_API_KEY
+2. run the following to get setup ready
+   ```bash
+   bash setup.sh
+   ```
+3. then only need to run this file only in the future
+   ```bash
+   python geminiAPI.py
+   ```
+4. If runs successfully, a file called **mscoco_cmret.jsonl** will be generated, please provide this file to me.

mscoco_omni/audios.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2c9e6f5a737cfb84d861a3b6fe409e2a46ec054bdf059b347eb33c10cfbf344e
+size 126042220

mscoco_omni/geminiAPI.py

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+from google import genai
+from google.genai import types
+from tqdm import tqdm
+from pycocotools.coco import COCO
+import pandas as pd
+import matplotlib.pyplot as plt
+import time
+import os
+import wave
+import subprocess
+from PIL import Image
+
+import time
+from google.genai.errors import ClientError
+
+def prRed(s): print("\033[1;31m {}\033[0m".format(s))
+def prGreen(s): print("\033[92m {}\033[00m".format(s))
+def prYellow(s): print("\033[93m {}\033[00m".format(s))
+def prBlue(s): print("\033[94m {}\033[00m".format(s))
+def prOrange(s): print("\033[38;5;214m {}\033[00m".format(s))
+def prPurple(s): print("\033[95m {}\033[00m".format(s))
+def prCyan(s): print("\033[96m {}\033[00m".format(s))
+def prLightGray(s): print("\033[97m {}\033[00m".format(s))
+def prBlack(s): print("\033[90m {}\033[00m".format(s))
+
+def get_id_with_filename(file_name):
+    img_id = os.path.basename(file_name).split('.')[0]
+    img_id = img_id.split('_')[-1]
+    return int(img_id)
+
+def get_filename_with_id(id, format='jpg'):
+    return f'COCO_val2014_{str(id).zfill(12)}.{format}'
+
+def plot_grouped_imgs(loi, ncols=5, figsize=(20, 20)):
+    nrows = (len(loi) + ncols - 1) // ncols
+    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)
+    for i, img_path in enumerate(loi):
+        row = i // ncols
+        col = i % ncols
+        axes[row, col].imshow(Image.open(img_path))
+        axes[row, col].set_title(os.path.basename(img_path))
+        axes[row, col].axis('off')
+    plt.tight_layout()
+    plt.show()
+    return fig
+
+def generate_video_with_retry(client, image, text, max_retries=20):
+    for attempt in range(max_retries):
+        prYellow(f'Attempt {attempt} ...')
+        try:
+            return generate_video_from_image_and_text(client, image, text)
+        except ClientError as e:
+            if e.code != 429:
+                raise
+            if attempt < max_retries - 1:
+                wait_time = min(2 ** attempt, 64)  # Exponential backoff: 1s, 2s, 4s
+                prRed(f"Connection error: {e}. Retrying in {wait_time}s... (attempt {attempt + 1}/{max_retries})")
+                time.sleep(wait_time)
+            else:
+                prRed(f"Failed after {max_retries} attempts")
+                raise
+
+def generate_audio_with_retry(client, text, max_retries=20):
+    for attempt in range(max_retries):
+        prYellow(f'Attempt {attempt+1} ...')
+        try:
+            return generate_audio_from_text(client, text)
+        except ClientError as e:
+            if e.code != 429:
+                raise
+            if attempt < max_retries - 1:
+                wait_time = min(2 ** attempt, 64)
+                prRed(f"Connection error: {e}. Retrying in {wait_time}s... (attempt {attempt + 1}/{max_retries})")
+                time.sleep(wait_time)
+            else:
+                prRed(f"Failed after {max_retries} attempts")
+                raise
+
+
+
+def load_image(image_path):
+    with open(image_path, "rb") as f:
+        image_bytes = f.read()
+    return types.Image(mime_type="image/jpeg", image_bytes=image_bytes)
+
+def generate_prompt(client, prompt):
+    transcript = client.models.generate_content(
+                    model="gemini-2.5-flash",
+                    contents="""Generate a short transcript based on the following description: """ + prompt).text
+    return transcript
+
+def generate_image_from_text(client, text):
+    response = client.models.generate_content(
+        model="gemini-2.5-flash-image",
+        contents=[text],
+        config={"response_modalities":['IMAGE']}
+    )
+    return response.candidates[0].content.parts[0].as_image()
+
+def wave_file(filename, pcm, channels=1, rate=24000, sample_width=2):
+    """Save PCM audio data to a .wav file.
+    """
+    with wave.open(filename, "wb") as wf:
+        wf.setnchannels(channels)
+        wf.setsampwidth(sample_width)
+        wf.setframerate(rate)
+        wf.writeframes(pcm)
+
+def generate_audio_from_text(client, text):
+    response = client.models.generate_content(
+    model="gemini-2.5-flash-preview-tts",
+    contents=text,
+    config=types.GenerateContentConfig(
+        response_modalities=["AUDIO"],
+        speech_config=types.SpeechConfig(
+            voice_config=types.VoiceConfig(
+                prebuilt_voice_config=types.PrebuiltVoiceConfig(
+                voice_name='Kore',
+                )
+            )
+        ),
+    )
+    )
+
+    data = response.candidates[0].content.parts[0].inline_data.data
+    return data
+
+def generate_video_from_image_and_text(client, image, text):
+    operation = client.models.generate_videos(
+        model="veo-3.1-fast-generate-preview",
+        prompt=text,
+        image=image,
+        config=types.GenerateVideosConfig(
+            aspect_ratio="16:9",
+            resolution="720p",
+            duration_seconds="8",
+        )
+    )
+
+    # Poll the operation status until the video is ready.
+    while not operation.done:
+        prCyan("Waiting for video generation to complete...")
+        time.sleep(10)
+        operation = client.operations.get(operation)
+
+    if operation.result and operation.result.generated_videos:
+        generated_video = operation.result.generated_videos[0]
+        return generated_video.video
+    elif operation.response and operation.response.generated_videos:
+        generated_video = operation.response.generated_videos[0]
+        return generated_video.video
+    else:
+        # Handle cases where the model might fail (e.g. safety filters)
+        prRed(f"Generation failed: {operation}")
+        if not operation.response:
+            raise RuntimeError(f"Unknown reasons for {operation} to be None")
+        raise RuntimeError(f"Generation failed or was filtered: {operation.response.rai_media_filtered_reasons}")
+
+# def main():
+#     with open('allowed_ids.json', 'r') as f:
+#         allowed_ids = json.load(f)
+
+#     num = 100
+#     data = load_dataset('json', data_files='MSCOCO_t2i_test.jsonl', split='train')
+#     data = data.shuffle(seed=42).select(range(num))
+
+#     client = genai.Client()
+#     ttv_prompt_instruct = "Generate a video based on the following image and description: "
+#     tts_prompt_instruct = "Read aloud the following sentence in a natural and expressive way, in a warm and friendly tone: "
+
+#     save_dir = './output/'
+#     os.makedirs(save_dir, exist_ok=True)
+
+#     infos = []
+#     for item in tqdm(data):
+#         image_path = item['tgt_img_path'][0]
+#         save_name = os.path.basename(image_path).split('.')[0]
+#         save_name_mp4 = f'{save_name}.mp4'
+#         save_name_wav = f'{save_name}.wav'
+#         caption = item['qry_text']
+#         print(f"Processing {save_name} with caption: {caption}")
+
+#         if save_name not in allowed_ids:
+#             print(f"🛑 {save_name} not in allowed IDs, skipping this item.")
+#             continue
+
+#         existing_objects = list(set([obj.split('.')[0] for obj in os.listdir(save_dir)]))
+#         if save_name in existing_objects:
+#             continue
+
+#         image = load_image(os.path.join(data_dir, image_path))
+#         try:
+#             video_prompt = ttv_prompt_instruct + caption
+#             # transcript = generate_prompt(client, video_prompt)
+#             # print(f"Generated transcript: {transcript}")
+#             video_data = generate_video_from_image_and_text(client, image, video_prompt)
+#             video_filename = os.path.join(save_dir, save_name_mp4)
+#             client.files.download(file=video_data)
+#             video_data.save(video_filename)
+#             print(f"Generated video saved to {video_filename}")
+
+#             audio_prompt = tts_prompt_instruct + caption
+#             audio_data = generate_audio_from_text(client, audio_prompt)
+#             audio_filename = os.path.join(save_dir, save_name_wav)
+#             wave_file(audio_filename, audio_data)
+#             print(f"Generated audio saved to {audio_filename}")
+#         except (TypeError, RuntimeError) as e:
+#             print(f"🛑 Error processing {save_name}: {e}, skipping this item.")
+#             continue
+
+#         # always update negative videos, even if the video already exists
+#         raw_neg_objs = [os.path.basename(img_path).split('.')[0] for img_path in item['tgt_img_path'][1:]]
+#         neg_obj_list = list(set(raw_neg_objs) & set(existing_objects))
+#         infos.append({
+#             "id": save_name,
+#             "qry_text": caption,
+#             "qry_image_path": image_path, 
+#             "negatives": neg_obj_list,
+#         })
+#     results = pd.DataFrame(infos)
+#     results.to_json('MSCOCO_ti2v.jsonl', orient='records', lines=True)
+#     print("✅ All done! Results saved to MSCOCO_ti2v.jsonl")
+
+def main():
+    coco_caps = COCO("annotations/captions_val2014.json")
+
+    client = genai.Client()
+    ttv_prompt_instruct = "Generate a video based on the following image and description: "
+    tts_prompt_instruct = "Read aloud the following sentence in a natural and expressive way, in a warm and friendly tone: "
+
+    data_dir = 'val2014'
+    video_save_dir = 'videos'
+    audio_save_dir = 'audios'
+    os.makedirs(video_save_dir, exist_ok=True)
+    os.makedirs(audio_save_dir, exist_ok=True)
+
+    infos = pd.read_json('mscoco_cmret_all.jsonl', lines=True)
+    infos = infos[['image_id', 'file_name', 'hard_negatives']].set_index('image_id', drop=False)
+    records = []
+    with open('invalid.log') as f:
+        invalids = f.read().splitlines()
+    prBlue(f"Invalid ids: {invalids}")
+
+    for info in tqdm(infos.itertuples(), total=len(infos)):
+        image_id = info.image_id
+        file_name = info.file_name
+        negatives = info.hard_negatives
+
+        captions = [ann['caption'] for ann in coco_caps.loadAnns(coco_caps.getAnnIds(imgIds=image_id))]
+        qry_text = captions[-1] # we use different caption as query text, to make it harder
+
+        prBlue(f"Processing {file_name}\nQuery text: {qry_text}")
+
+        image = load_image(os.path.join(data_dir, file_name))
+        generated_negatives = []
+        for cand_id in tqdm([image_id]+negatives[:10], leave=True, desc="Waiting for API responses ... "):
+            if str(cand_id) in invalids:
+                prYellow(f"{cand_id} contains restricted content, skipping ... ")
+                continue
+            
+            captions = [ann['caption'] for ann in coco_caps.loadAnns(coco_caps.getAnnIds(imgIds=cand_id))]
+            caption = captions[0] # use first caption to generate video and audio
+            image = load_image(os.path.join(data_dir, get_filename_with_id(cand_id)))
+            cand_video_name = get_filename_with_id(cand_id, format='mp4')
+            cand_audio_name = get_filename_with_id(cand_id, format='wav')
+            
+            try:
+                video_save_path = os.path.join(video_save_dir, cand_video_name)
+                if os.path.exists(video_save_path):
+                    prGreen(f"Video {cand_id} already exists, skipping generation.")
+                else:
+                    prYellow(f"Generating video ...Caption: {caption}")
+                    video_prompt = ttv_prompt_instruct + caption
+                    video_data = generate_video_with_retry(client, image, video_prompt, max_retries=20) # change max_retries if needed
+                    client.files.download(file=video_data)
+                    video_data.save(video_save_path)
+                    prGreen(f"Generated video saved to {video_save_path}")
+
+                audio_save_path = os.path.join(audio_save_dir, cand_audio_name)
+                if os.path.exists(audio_save_path):
+                    prGreen(f"Audio {cand_id} already exists, skipping generation.")
+                else:
+                    prYellow(f"Generating audio ...Caption: {caption}")
+                    audio_prompt = tts_prompt_instruct + caption
+                    audio_data = generate_audio_with_retry(client, audio_prompt, max_retries=20) # change max_retries if needed
+                    wave_file(audio_save_path, audio_data)
+                    prGreen(f"Generated audio saved to {audio_save_path}")
+                generated_negatives.append(cand_id)
+
+
+            except (TypeError, RuntimeError) as e:
+                invalids.append(cand_id)
+                with open('invalid.log', 'a') as f:
+                    f.write(f'{cand_id}\n')
+                prRed(f"🛑 Error processing {cand_video_name}: {e}, skipping this item.")
+                continue
+
+        records.append({
+            "image_id": image_id,
+            "qry_text": qry_text,
+            "hard_negatives": [idx for idx in generated_negatives if idx != image_id]
+        })
+    records = pd.DataFrame(records)
+    records.to_json('mscoco_cmret.jsonl', orient='records', lines=True)
+
+if __name__ == "__main__":
+    if not os.path.exists("annotations/instances_val2014.json"):
+        subprocess.run(["wget", "http://images.cocodataset.org/annotations/annotations_trainval2014.zip"])
+        subprocess.run(["unzip", "annotations_trainval2014.zip", "-d", "annotations"])
+        subprocess.run(["rm", "annotations_trainval2014.zip"])
+        subprocess.run(["wget", "http://images.cocodataset.org/zips/val2014.zip"])
+        subprocess.run(["unzip", "val2014.zip", "-d", "."])
+        subprocess.run(["rm", "val2014.zip"])
+    main()
+    # client = genai.Client()
+    # video = generate_video_with_retry(client, load_image('val2014/COCO_val2014_000000176744.jpg'), "Generate a video based on the following image and description: Croweded area on the beach with many kites being flown in the air.")
+    # client.files.download(file=video)
+    # video.save('mscoco_omini/COCO_val2014_000000176744.mp4')
+
+    # audio = generate_audio_with_retry(client, "Read aloud the following sentence in a natural and expressive way, in a warm and friendly tone: Croweded area on the beach with many kites being flown in the air.")
+    # wave_file('mscoco_omini/COCO_val2014_000000176744.wav', audio)

mscoco_omni/invalid.log

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mscoco_omni/mscoco_cmret_all.jsonl

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+{"image_id":2295,"file_name":"COCO_val2014_000000002295.jpg","objects":["bowl","dining table","cat"],"hard_negatives":[347113,99269,554291,183967,157876,40414,397133,60623,374628,19358,452684,350341,309852,26942,383406,311553,280819,269089]}
+{"image_id":3067,"file_name":"COCO_val2014_000000003067.jpg","objects":["sandwich","apple","cat"],"hard_negatives":[715,339442,229191,425526,411832,563723,254838,360209,21161,566644,374628]}
+{"image_id":11291,"file_name":"COCO_val2014_000000011291.jpg","objects":["tv","person","wine glass","bottle"],"hard_negatives":[300403,123677,242051,180751,209530,33052,502671,273329,225913,263323,137260,480000,368402,203849,195645,31442,363840,367398]}
+{"image_id":17182,"file_name":"COCO_val2014_000000017182.jpg","objects":["chair","book","apple"],"hard_negatives":[451468,177496,248919,156278,484351,303626,208995,374628,386912,165547,472795,26942,280819,143975,362373,65267,436470,400538,38034]}
+{"image_id":18046,"file_name":"COCO_val2014_000000018046.jpg","objects":["bus","horse"],"hard_negatives":[367680,23201,301937,26201,237326,190494,427655,224104,454798,432317,436127,95702,228477,122871,313718,395634,495117,548713,138246,37675]}
+{"image_id":19589,"file_name":"COCO_val2014_000000019589.jpg","objects":["book","bottle","vase","bowl","chair","couch"],"hard_negatives":[65267,463290,314757,366096,143247,445009,173434,491856,528062,303006,497444,292169,370899,267135,33052,471816,498489,185614]}
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mscoco_omni/requirements.txt

@@ -0,0 +1,6 @@
+google
+wave
+tadm
+pandas
+matplotlib
+pycocotools

mscoco_omni/setup.sh

@@ -0,0 +1,14 @@
+# download coco source images
+wget "http://images.cocodataset.org/annotations/annotations_trainval2014.zip"
+unzip "annotations_trainval2014.zip" -d "annotations"
+rm "annotations_trainval2014.zip"
+wget "http://images.cocodataset.org/zips/val2014.zip"
+unzip "val2014.zip", -d .
+rm "val2014.zip"
+
+pip install -r requirements.txt
+tar -xzf videos.tar.gz
+tar -xzf audios.tar.gz
+
+echo "Specify GOOGLE_API_KEY first ..."
+export GOOGLE_API_KEY=''

mscoco_omni/videos.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f448f4bdc2d46aaa1a12d0825074a346b68f6c74a805febf515f9ac912658ef
+size 5374313848