README.md

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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
The data is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
See the [DATA_LICENSE](https://github.com/microsoft/NOTSOFAR1-Challenge/blob/main/DATA_LICENSE) file for details.
# 🤝 Contribute

Please refer to our [contributing guide](CONTRIBUTING.md) for more information on how to contribute!