Add dataset README

README.md

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-license: cc-by-4.0
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+
+
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+
+# Introduction 
+Welcome to the "NOTSOFAR-1: Distant Meeting Transcription with a Single Device" Challenge.
+
+This repo contains the baseline system code for the NOTSOFAR-1 Challenge.
+
+- For more information about NOTSOFAR, visit [CHiME's official challenge website](https://www.chimechallenge.org/current/task2/index)
+- [Register](https://www.chimechallenge.org/current/task2/submission) to participate.
+- [Baseline system description](https://www.chimechallenge.org/current/task2/baseline).
+- Contact us: join the `chime-8-notsofar` channel on the [CHiME Slack](https://join.slack.com/t/chime-fey5388/shared_invite/zt-1oha0gedv-JEUr1mSztR7~iK9AxM4HOA), or open a [GitHub issue](https://github.com/microsoft/NOTSOFAR1-Challenge/issues). 
+
+### 📊 Baseline Results on NOTSOFAR dev-set-1
+
+Values are presented in `tcpWER / tcORC-WER (session count)` format.
+<br>
+As mentioned in the [official website](https://www.chimechallenge.org/current/task2/index#tracks), 
+systems are ranked based on the speaker-attributed 
+[tcpWER](https://github.com/fgnt/meeteval/blob/main/doc/tcpwer.md)
+, while the speaker-agnostic [tcORC-WER](https://github.com/fgnt/meeteval) serves as a supplementary metric for analysis.
+<br>
+We include analysis based on a selection of hashtags from our [metadata](https://www.chimechallenge.org/current/task2/data#metadata), providing insights into how different conditions affect system performance.
+
+
+
+|                      | Single-Channel        | Multi-Channel         |
+|----------------------|-----------------------|-----------------------|
+| All Sessions         | **46.8** / 38.5 (177) | **32.4** / 26.7 (106) |
+| #NaturalMeeting      | 47.6 / 40.2 (30)      | 32.3 / 26.2 (18)      |
+| #DebateOverlaps      | 54.9 / 44.7 (39)      | 38.0 / 31.4 (24)      |
+| #TurnsNoOverlap      | 32.4 / 29.7 (10)      | 21.2 / 18.8 (6)       |
+| #TransientNoise=high | 51.0 / 43.7 (10)      | 33.6 / 29.1 (5)       |
+| #TalkNearWhiteboard  | 55.4 / 43.9 (40)      | 39.9 / 31.2 (22)      |
+
+
+
+
+
+
+
+# Project Setup
+The following steps will guide you through setting up the project on your machine. <br>
+
+### Windows Users
+This project is compatible with **Linux** environments. Windows users can refer to [Docker](#docker) or 
+[Devcontainer](#devcontainer) sections. <br>
+Alternatively, install WSL2 by following the [WSL2 Installation Guide](https://learn.microsoft.com/en-us/windows/wsl/install), then install Ubuntu 20.04 from the [Microsoft Store](https://www.microsoft.com/en-us/p/ubuntu-2004-lts/9n6svws3rx71?activetab=pivot:overviewtab). <br>
+
+## Cloning the Repository
+
+Clone the `NOTSOFAR1-Challenge` repository from GitHub. Open your terminal and run the following command:
+
+```bash
+sudo apt-get install git
+cd path/to/your/projects/directory
+git clone https://github.com/microsoft/NOTSOFAR1-Challenge.git
+```
+
+
+## Setting up the environment
+
+### Conda
+
+#### Step 1: Install Conda
+
+Conda is a package manager that is used to install Python and other dependencies.<br>
+To install Miniconda, which is a minimal version of Conda, run the following commands:
+
+```bash
+miniconda_dir="$HOME/miniconda3"
+script="Miniconda3-latest-Linux-$(uname -m).sh"
+wget --tries=3 "https://repo.anaconda.com/miniconda/${script}"
+bash "${script}" -b -p "${miniconda_dir}"
+export PATH="${miniconda_dir}/bin:$PATH"
+````
+*** You may change the `miniconda_dir` variable to install Miniconda in a different directory.
+
+
+#### Step 2: Create a Conda Environment 
+
+Conda Environments are used to isolate Python dependencies. <br> 
+To set it up, run the following commands:
+
+```bash
+source "/path/to/conda/dir/etc/profile.d/conda.sh"
+conda create --name notsofar python=3.10 -y
+conda activate notsofar 
+cd /path/to/NOTSOFAR1-Challenge
+python -m pip install --upgrade pip
+pip install --upgrade setuptools wheel Cython fasttext-wheel
+pip install -r requirements.txt
+conda install ffmpeg -c conda-forge -y
+```
+
+### PIP
+
+#### Step 1: Install Python 3.10
+
+Python 3.10 is required to run the project. To install it, run the following commands:
+
+```bash
+sudo apt update && sudo apt upgrade
+sudo add-apt-repository ppa:deadsnakes/ppa -y
+sudo apt update
+sudo apt install python3.10
+```
+
+#### Step 2: Set Up the Python Virtual Environment
+
+Python virtual environments are used to isolate Python dependencies. <br> 
+To set it up, run the following commands:
+
+```bash
+sudo apt-get install python3.10-venv
+python3.10 -m venv /path/to/virtualenvs/NOTSOFAR
+source /path/to/virtualenvs/NOTSOFAR/bin/activate
+```
+
+#### Step 3: Install Python Dependencies
+
+Navigate to the cloned repository and install the required Python dependencies:
+
+```bash
+cd /path/to/NOTSOFAR1-Challenge
+python -m pip install --upgrade pip
+pip install --upgrade setuptools wheel Cython fasttext-wheel
+sudo apt-get install python3.10-dev ffmpeg build-essential
+pip install -r requirements.txt
+```
+
+### Docker
+
+Refer to the `Dockerfile` in the project's root for dependencies setup. To use Docker, ensure you have Docker installed on your system and configured to use Linux containers.
+
+### Devcontainer
+With the provided `devcontainer.json` you can run and work on the project in a [devctonainer](https://containers.dev/) using, for example, the [Dev Containers VSCode Extension](https://code.visualstudio.com/docs/devcontainers/containers).
+
+
+# Running evaluation - the inference pipeline
+The following command will download the **entire dev-set** of the recorded meeting dataset and run the inference pipeline
+according to selected configuration. The default is configured to `--config-name dev_set_1_mc_debug` for quick debugging, 
+running on a single session with the Whisper 'tiny' model.
+```bash
+cd /path/to/NOTSOFAR1-Challenge
+python run_inference.py
+```
+
+To run on all multi-channel or single-channel dev-set sessions, use the following commands respectively:
+```bash
+python run_inference.py --config-name full_dev_set_mc
+python run_inference.py --config-name full_dev_set_sc
+```
+The first time `run_inference.py` runs, it will automatically download these required models and datasets from blob storage:
+
+
+1. The development set of the meeting dataset (dev-set) will be stored in the `artifacts/meeting_data` directory.
+2. The CSS models required to run the inference pipeline will be stored in the `artifacts/css_models` directory. 
+
+Outputs will be written to the `artifacts/outputs` directory.
+
+
+
+The `session_query` argument found in the yaml config file (e.g. `configs/inference/inference_v1.yaml`) offers more control over filtering meetings.
+Note that to submit results on the dev-set, you must evaluate on the full set (`full_dev_set_mc` or `full_dev_set_sc`) and no filtering must be performed.
+
+
+# Integrating your own models 
+The inference pipeline is modular, designed for easy research and extension.
+Begin by exploring the following components:
+- **Continuous Speech Separation (CSS)**: See `css_inference` in `css.py` . We provide a model pre-trained on NOTSOFAR's simulated training dataset, as well as inference and training code. For more information, refer to the [CSS section](#running-css-continuous-speech-separation-training).
+- **Automatic Speech Recognition (ASR)**: See `asr_inference` in `asr.py`. The baseline implementation relies on [Whisper](https://github.com/openai/whisper). 
+- **Speaker Diarization**: See `diarization_inference` in `diarization.py`. The baseline implementation relies on the [NeMo toolkit](https://github.com/NVIDIA/NeMo).
+
+### Training datasets
+For training and fine-tuning your models, NOTSOFAR offers the **simulated training set** and the training portion of the
+**recorded meeting dataset**. Refer to the `download_simulated_subset` and `download_meeting_subset` functions in 
+[utils/azure_storage.py](https://github.com/microsoft/NOTSOFAR1-Challenge/blob/main/utils/azure_storage.py#L109), 
+or the [NOTSOFAR-1 Datasets](#notsofar-1-datasets---download-instructions) section.
+
+
+# Running CSS (continuous speech separation) training
+
+## 1. Local training on a data sample for development and debugging
+The following command will run CSS training on the 10-second simulated training data sample in `sample_data/css_train_set`.
+```bash
+cd /path/to/NOTSOFAR1-Challenge
+python run_training_css_local.py
+```
+
+## 2. Training on the full simulated training dataset
+
+### Step 1: Download the simulated training dataset
+You can use the `download_simulated_subset` function in 
+[utils/azure_storage.py](https://github.com/microsoft/NOTSOFAR1-Challenge/blob/main/utils/azure_storage.py)
+to download the training dataset from blob storage.
+You have the option to download either the complete dataset, comprising almost 1000 hours, or a smaller, 200-hour subset.
+
+Examples:
+```python
+ver='v1.5'  # this should point to the lateset and greatest version of the dataset.
+
+# Option 1: Download the training and validation sets of the entire 1000-hour dataset. 
+train_set_path = download_simulated_subset(
+    version=ver, volume='1000hrs', subset_name='train', destination_dir=os.path.join(my_dir, 'train'))
+
+val_set_path = download_simulated_subset(
+    version=ver, volume='1000hrs', subset_name='val', destination_dir=os.path.join(my_dir, 'val'))
+
+
+# Option 2: Download the training and validation sets of the smaller 200-hour dataset.
+train_set_path = download_simulated_subset(
+    version=ver, volume='200hrs', subset_name='train', destination_dir=os.path.join(my_dir, 'train'))
+
+val_set_path = download_simulated_subset(
+    version=ver, volume='200hrs', subset_name='val', destination_dir=os.path.join(my_dir, 'val'))
+```
+
+### Step 2: Run CSS training
+Once you have downloaded the training dataset, you can run CSS training on it using the `run_training_css` function in `css/training/train.py`.
+The `main` function in `run_training_css.py` provides an entry point with `conf`, `data_root_in`, and `data_root_out` arguments that you can use to configure the run.
+
+It is important to note that the setup and provisioning of a compute cloud environment for running this training process is the responsibility of the user. Our code is designed to support **PyTorch's Distributed Data Parallel (DDP)** framework. This means you can leverage multiple GPUs across several nodes efficiently.
+
+### Step 3: Customizing the CSS model
+To add a new CSS model, you need to do the following:
+1. Have your model implement the same interface as our baseline CSS model class `ConformerCssWrapper` which is located
+in `css/training/conformer_wrapper.py`. Note that in addition to the `forward` method, it must also implement the 
+`separate`, `stft`, and `istft` methods. The latter three methods will be used in the inference pipeline and to 
+calculate the loss when training.
+2. Create a configuration dataclass for your model. Add it as a member of the `TrainCfg` dataclass in 
+`css/training/train.py`.
+3. Add your model to the `get_model` function in `css/training/train.py`.
+
+
+
+# NOTSOFAR-1 Datasets - Download Instructions
+This section is for those specifically interested in downloading the NOTSOFAR datasets.<br>
+The NOTSOFAR-1 Challenge provides two datasets: a recorded meeting dataset and a simulated training dataset. <br>
+The datasets are stored in Azure Blob Storage, to download them, you will need to setup [AzCopy](https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10#download-azcopy).
+
+You can use either the python utilities in `utils/azure_storage.py` or the `AzCopy` command to download the datasets as described below.
+
+
+
+### Meeting Dataset for Benchmarking and Training
+
+The NOTSOFAR-1 Recorded Meeting Dataset is a collection of 315 meetings, each averaging 6 minutes, recorded across 30 conference rooms with 4-8 attendees, featuring a total of 35 unique speakers. This dataset captures a broad spectrum of real-world acoustic conditions and conversational dynamics.
+
+### Download
+
+To download the dataset, you can call the python function `download_meeting_subset` within `utils/azure_storage.py`.
+
+Alternatively, using AzCopy CLI, set these arguments and run the following command:
+
+- `subset_name`: name of split to download (`dev_set` / `eval_set` / `train_set`).
+- `version`: version to download (`240103g` / etc.). Use the latest version. 
+- `datasets_path` - path to the directory where you want to download the benchmarking dataset (destination directory must exist). <br>
+
+Train, dev, and eval sets for the NOTSOFAR challenge are released in stages. 
+See release timeline on the [NOTSOFAR page](https://www.chimechallenge.org/current/task2/index#dates).
+See doc in `download_meeting_subset` function in 
+[utils/azure_storage.py](https://github.com/microsoft/NOTSOFAR1-Challenge/blob/main/utils/azure_storage.py#L109) 
+for latest available versions.
+
+```bash
+azcopy copy https://notsofarsa.blob.core.windows.net/benchmark-datasets/<subset_name>/<version>/MTG <datasets_path>/benchmark --recursive
+```
+
+Example:
+```bash
+azcopy copy https://notsofarsa.blob.core.windows.net/benchmark-datasets/dev_set/240415.2_dev/MTG . --recursive
+````
+
+
+### Simulated Training Dataset
+
+The NOTSOFAR-1 Training Dataset is a 1000-hour simulated training dataset, synthesized with enhanced authenticity for real-world generalization, incorporating 15,000 real acoustic transfer functions.
+
+### Download
+
+
+To download the dataset, you can call the python function `download_simulated_subset` within `utils/azure_storage.py`.
+Alternatively, using AzCopy CLI, set these arguments and run the following command:
+
+- `version`: version of the train data to download (`v1.1` / `v1.2` / `v1.3` / `1.4` / `1.5` / etc.).
+See doc in `download_simulated_subset` function in `utils/azure_storage.py` for latest available versions.
+- `volume` - volume of the train data to download (`200hrs` / `1000hrs`)
+- `subset_name`: train data type to download (`train` / `val`)
+- `datasets_path` - path to the directory where you want to download the simulated dataset (destination directory must exist). <br>
+
+
+```bash
+azcopy copy https://notsofarsa.blob.core.windows.net/css-datasets/<version>/<volume>/<subset_name> <datasets_path>/benchmark --recursive 
+```
+
+Example:
+```bash
+azcopy copy https://notsofarsa.blob.core.windows.net/css-datasets/v1.5/200hrs/train . --recursive
+```
+
+
+## Data License
+This public data is currently licensed for use exclusively in the NOTSOFAR challenge event. 
+We appreciate your understanding that it is not yet available for academic or commercial use. 
+However, we are actively working towards expanding its availability for these purposes. 
+We anticipate a forthcoming announcement that will enable broader and more impactful use of this data. Stay tuned for updates. 
+Thank you for your interest and patience.
+
+
+# 🤝 Contribute
+
+Please refer to our [contributing guide](CONTRIBUTING.md) for more information on how to contribute!
+