Update README.md

README.md

@@ -131,14 +131,269 @@ The TALI dataset consists of the following modalities:
 4. Video
   1. YouTube Content Video
      
-### Dataset Variants
-The TALI dataset comes in three variants that differ in the training set size:
+## Usage:
+To get started with TALI, you can load the dataset via Hugging Face's `datasets` library through our helper functions. The reason we don't use `datasets` directly is because we found huggingface_hub downloads much faster and reliable. For a full set of possible configurations look at [examples.py](examples.py). Here's a basic usage example:
 
-- TALI-small: Contains about 1.3 million 30-second video clips, aligned with 120K WiT entries.
-- TALI-base: Contains about 6.5 million 30-second video clips, aligned with 120K WiT entries.
-- TALI-big: Contains about 13 million 30-second video clips, aligned with 120K WiT entries.
+First install the tali package:
 
-The validation and test sets remain consistent across all three variants at about 80K Videos aligned to 8K wikipedia entries (10 subclips for each Wikipedia entry) each.
+### Installation
+
+For the default install use:
+
+```bash
+pip install git+https://github.com/AntreasAntoniou/TALI
+```
+
+For the dev install use:
+
+```bash
+pip install git+https://github.com/AntreasAntoniou/TALI[dev]
+```
+
+Then use the dataset using:
+
+### Examples
+Import relevant helper functions
+```python
+  import pathlib
+  from enum import Enum
+  
+  import torch
+  from tqdm.auto import tqdm
+  
+  from tali.data import (
+      SubModalityTypes,
+      TALIBaseTransform,
+      TALIBaseTransformConfig,
+      VideoFramesFormat,
+      default_transforms,
+      load_dataset_via_hub,
+  )
+```
+
+#### TALI with default transforms (CLIP and Whisper) and no streaming
+
+```python
+  def tali_with_transforms_no_streaming(
+    dataset_storage_path: pathlib.Path | str,
+):
+    if isinstance(dataset_storage_path, str):
+        dataset_storage_path = pathlib.Path(dataset_storage_path)
+
+    dataset = load_dataset_via_hub(
+        dataset_storage_path, dataset_name="Antreas/TALI"
+    )["train"]
+
+    (
+        image_transforms,
+        text_transforms,
+        audio_transforms,
+        video_transforms,
+    ) = default_transforms()
+
+    preprocessing_transform = TALIBaseTransform(
+        cache_dir=dataset_storage_path / "cache",
+        text_tokenizer=text_transforms,
+        image_tokenizer=image_transforms,
+        audio_tokenizer=audio_transforms,
+        video_tokenizer=video_transforms,
+        config=TALIBaseTransformConfig(
+            root_filepath=dataset_storage_path,
+            modality_list=[
+                SubModalityTypes.youtube_content_video,
+                SubModalityTypes.youtube_content_audio,
+                SubModalityTypes.youtube_random_video_frame,
+                SubModalityTypes.youtube_subtitle_text,
+                SubModalityTypes.youtube_description_text,
+                SubModalityTypes.youtube_title_text,
+                SubModalityTypes.wikipedia_caption_image,
+                SubModalityTypes.wikipedia_caption_text,
+                SubModalityTypes.wikipedia_main_body_text,
+                SubModalityTypes.wikipedia_title_text,
+            ],
+            video_frames_format=VideoFramesFormat.PIL,
+        ),
+    )
+
+    for sample in tqdm(dataset):
+        sample = preprocessing_transform(sample)
+        print(list(sample.keys()))
+        for key, value in sample.items():
+            if hasattr(value, "shape"):
+                print(key, value.shape)
+            elif isinstance(value, torch.Tensor):
+                print(key, value.shape)
+            elif hasattr(value, "__len__"):
+                print(key, len(value))
+            print(key, type(value))
+
+        break
+
+    
+```
+
+#### TALI with no transforms and no streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats
+
+```python
+def tali_without_transforms_no_streaming(
+    dataset_storage_path: pathlib.Path | str,
+):
+    if isinstance(dataset_storage_path, str):
+        dataset_storage_path = pathlib.Path(dataset_storage_path)
+
+    dataset = load_dataset_via_hub(
+        dataset_storage_path, dataset_name="Antreas/TALI"
+    )["train"]
+
+    preprocessing_transform = TALIBaseTransform(
+        cache_dir=dataset_storage_path / "cache",
+        text_tokenizer=None,
+        image_tokenizer=None,
+        audio_tokenizer=None,
+        video_tokenizer=None,
+        config=TALIBaseTransformConfig(
+            root_filepath=dataset_storage_path,
+            modality_list=[
+                SubModalityTypes.youtube_content_video,
+                SubModalityTypes.youtube_content_audio,
+                SubModalityTypes.youtube_random_video_frame,
+                SubModalityTypes.youtube_subtitle_text,
+                SubModalityTypes.youtube_description_text,
+                SubModalityTypes.youtube_title_text,
+                SubModalityTypes.wikipedia_caption_image,
+                SubModalityTypes.wikipedia_caption_text,
+                SubModalityTypes.wikipedia_main_body_text,
+                SubModalityTypes.wikipedia_title_text,
+            ],
+            video_frames_format=VideoFramesFormat.PIL,
+        ),
+    )
+
+    for sample in tqdm(dataset):
+        sample = preprocessing_transform(sample)
+        print(list(sample.keys()))
+        for key, value in sample.items():
+            if hasattr(value, "shape"):
+                print(key, value.shape)
+            elif isinstance(value, torch.Tensor):
+                print(key, value.shape)
+            elif hasattr(value, "__len__"):
+                print(key, len(value))
+            print(key, type(value))
+
+        break
+```
+
+#### TALI with default transforms and streaming
+
+```python
+  def tali_with_transforms_streaming(
+    dataset_storage_path: pathlib.Path | str,
+):
+    if isinstance(dataset_storage_path, str):
+        dataset_storage_path = pathlib.Path(dataset_storage_path)
+
+    dataset = load_dataset_via_hub(
+        dataset_storage_path, dataset_name="Antreas/TALI", streaming=True
+    )["train"]
+
+    (
+        image_transforms,
+        text_transforms,
+        audio_transforms,
+        video_transforms,
+    ) = default_transforms()
+
+    preprocessing_transform = TALIBaseTransform(
+        cache_dir=dataset_storage_path / "cache",
+        text_tokenizer=text_transforms,
+        image_tokenizer=image_transforms,
+        audio_tokenizer=audio_transforms,
+        video_tokenizer=video_transforms,
+        config=TALIBaseTransformConfig(
+            root_filepath=dataset_storage_path,
+            modality_list=[
+                SubModalityTypes.youtube_content_video,
+                SubModalityTypes.youtube_content_audio,
+                SubModalityTypes.youtube_random_video_frame,
+                SubModalityTypes.youtube_subtitle_text,
+                SubModalityTypes.youtube_description_text,
+                SubModalityTypes.youtube_title_text,
+                SubModalityTypes.wikipedia_caption_image,
+                SubModalityTypes.wikipedia_caption_text,
+                SubModalityTypes.wikipedia_main_body_text,
+                SubModalityTypes.wikipedia_title_text,
+            ],
+            video_frames_format=VideoFramesFormat.PIL,
+        ),
+    )
+
+    for sample in tqdm(dataset):
+        sample = preprocessing_transform(sample)
+        print(list(sample.keys()))
+        for key, value in sample.items():
+            if hasattr(value, "shape"):
+                print(key, value.shape)
+            elif isinstance(value, torch.Tensor):
+                print(key, value.shape)
+            elif hasattr(value, "__len__"):
+                print(key, len(value))
+            print(key, type(value))
+
+        break
+
+```
+
+#### TALI with no transforms and streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats
+```python
+  def tali_without_transforms_streaming(
+    dataset_storage_path: pathlib.Path | str,
+):
+    if isinstance(dataset_storage_path, str):
+        dataset_storage_path = pathlib.Path(dataset_storage_path)
+
+    dataset = load_dataset_via_hub(
+        dataset_storage_path, dataset_name="Antreas/TALI", streaming=True
+    )["train"]
+
+    preprocessing_transform = TALIBaseTransform(
+        cache_dir=dataset_storage_path / "cache",
+        text_tokenizer=None,
+        image_tokenizer=None,
+        audio_tokenizer=None,
+        video_tokenizer=None,
+        config=TALIBaseTransformConfig(
+            root_filepath=dataset_storage_path,
+            modality_list=[
+                SubModalityTypes.youtube_content_video,
+                SubModalityTypes.youtube_content_audio,
+                SubModalityTypes.youtube_random_video_frame,
+                SubModalityTypes.youtube_subtitle_text,
+                SubModalityTypes.youtube_description_text,
+                SubModalityTypes.youtube_title_text,
+                SubModalityTypes.wikipedia_caption_image,
+                SubModalityTypes.wikipedia_caption_text,
+                SubModalityTypes.wikipedia_main_body_text,
+                SubModalityTypes.wikipedia_title_text,
+            ],
+            video_frames_format=VideoFramesFormat.PIL,
+        ),
+    )
+
+    for sample in tqdm(dataset):
+        sample = preprocessing_transform(sample)
+        print(list(sample.keys()))
+        for key, value in sample.items():
+            if hasattr(value, "shape"):
+                print(key, value.shape)
+            elif isinstance(value, torch.Tensor):
+                print(key, value.shape)
+            elif hasattr(value, "__len__"):
+                print(key, len(value))
+            print(key, type(value))
+
+        break
+```
 
 
 ### Dataset Statistics