Metadata-Version: 2.4
Name: simulstream
Version: 0.3.0
Summary: A server to run simultaneous/streaming experiments and demo
Author-email: Marco Gaido <mgaido@fbk.eu>, FBK HLT-MT <mt@fbk.eu>
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<div align="center">
  <img src="https://github.com/hlt-mt/simulstream/blob/main/docs/source/_static/logo.png?raw=true" width="200" alt="simulstream logo">
</div>


# simulstream

``simulstream`` is a Python library for simultaneous/streaming speech recognition and translation.
It enables both the simulation with existing files to score systems, like in the
[SimulEval](https://github.com/facebookresearch/SimulEval) project, and the possibility to run
demos on a browser.

``simulstream`` provides a WebSocket server and utilities for running streaming speech processing
experiments and demos. It supports real-time transcription and translation through streaming audio
input. By streaming, we mean that the library by default assumes that the input is an unbounded
speech signal, rather than many short speech segments as in simultaneous speech processing.
The simultaneous setting can be easily addressed by pre-segmenting the audio into many small
segments and feed each segment to ``simulstream``.

The repository is tested using Python 3.11. Although it may work also with other Python versions,
we do not ensure compatibility with them. Check out the [Usage](#Usage) section for instructions on
how to use the repository and the [Installation](#Installation) section for further information
about how to install the project.


## Installation

You can install the latest stable version from PyPI:

```shell
pip install simulstream
```

Or, to install from source:

```shell
git clone https://github.com/hlt-mt/simulstream.git
cd simulstream
pip install .
```

Please notice that these commands will only install the basic functionalities of the repository,
i.e. the WebSocket server and client. Additionally, you have to install the dependencies required
by the `speech processor` that you want to use. The repository comes with examples of speech
processors that rely on e.g. Transformers models or on the NVIDIA Canary model. You can install
the required dependencies for them by specifying the corresponding selector when installing
the repository. For instance, using Canary speech processors requires the ``canary`` selector.
But you can create your custom speech processor and install the corresponding dependencies.

Also the evaluation comes with additional dependencies, which can be installed with the ``eval``
selector. Be careful as the metrics include COMET, which has dependencies on Transformers and
other libraries that can run on conflict with those required by your speech processor.

As an example, if you want to install the ``simulstream`` package with Canary speech processors
and the evaluation package, run:

```shell
pip install -e .[canary,eval]
```

For development (with docs and testing tools):

```shell
pip install -e .[dev]
```

## Usage

The package is based on a WebSocket client-server interaction. The server waits for connections
by the clients. The clients can send configurations messages in JSON format (e.g. to specify
the input/output desired language) and audio chunks, encoded as 16-bit integers. The server
processes the input audio, generates the transcript/translation by means of the configured
speech processor and sends it to the client in JSON format. In addition, if configured to do so,
the server writes logs that can be used to compute metrics in a JSONL file.

To have everything up and running, first run the server and once the server is up and running
execute one or more clients. The repository contains the code for two types of clients: a web
interface client that can be used for demos and a python command-line client that can be used
for testing or evaluation of the speech processor performance.

Below, you can find a simple illustration of the overall architecture with the two scenarios:

<img src="docs/source/_static/architecture.png" alt="Overall simulstream architecture">

To streamline the evaluation of speech processors in terms of quality and latency, there is also
the possibility to run the inference with a configured speech processor without setting up a
client-server interaction. For more information, refer to the [Evaluation section](#Evaluation).

### WebSocket Server

Run the WebSocket server with YAML configuration files:

```shell
simulstream_server --server-config config/server.yaml \
    --speech-processor-config config/speech_processor.yaml
```

The repository contains examples of YAML files both for the server and for some speech processors.
They can be edited and adapted.

#### Customize with Your Speech Processor

If you want to implement your own ``speech_processor``, you need to create a subclass
of ``simulstream.server.speech_processors.base.SpeechProcessor`` and add the class
to the ``PYTHONPATH``. Then reference the new class in the YAML file configuration
of the speech processor. Refer to the dedicated documentation for more
details on how to implement a speech processor in the project documentation and in the
docstring of both [``simulstream.server.speech_processors.base.SpeechProcessor``](simulstream/server/speech_processors/__init__.py)
and [`simulstream.server.speech_processors.base.BaseSpeechProcessor`](simulstream/server/speech_processors/base.py).

As reference, you can check the processors implemented in this repository, available in the
[`simulstream.server.speech_processors`](simulstream/server/speech_processors) module.
Notice that each speech processor can have additional dependencies that  are not installed by
default with this codebase (see [Installation](#installation)).


### HTTP Server Web Client

For a demo, you can create an HTTP web server that servers a web interface interacting with the
WebSocket server. This can be done by:

```shell
simulstream_demo_http_server --config config/server.yaml -p 8001 --directory webdemo
```

In case you are not running the command from the root folder of this repository, you should change
accordingly the ``--directory`` parameter to point to the ``webdemo`` folder containing the HTML
files of the HTTP demo. You can of course replace ``8001`` with any other port number you prefer.
The web interface can then be accessed from the local laptop at ``http://localhost:8001/`` or from
any other terminal connected  to the same network using the IP address of your workstation instead
of ``localhost``. Be careful not to use the same port specified for the WebSocket server if they
are running on the same machine. If running the HTTP server from a machine different from the one
where the WebSocket server runs, ensure that the HTTP server can connect to the WebSocket server
through the address specified in the ``config/server.yaml`` file.


### Python Client


If you want to process a set of audio files (e.g. a test set) with your system, send the audios
with the provided client:

```shell
simulstream_wavs_client --uri ws://localhost:8080/ \
    --wav-list-file PATH_TO_TXT_FILE_WITH_A_LIST_OF_WAV_FILES.txt \
    --tgt-lang it --src-lang en
```

Tne ``--uri`` should contain the address of the WebSocket client, so it should correspond to what
is specified in the server YAML config. The file specified in ``--wav-list-file`` should be a TXT
files containing for each line the path to a WAV file.

Before running the command, ensure that the logging of metrics is enabled in the configuration file
of the WebSocket server (i.e., ``metrics.enabled = True``) and that metrics are logged to an
empty/non-existing file, to ensure that the resulting file will contain only the logs related to
the files sent by the client. Then, compute the relevant scores as described below
([Evaluation](#Evaluation)).


### Evaluation

To evaluate a speech processor, as a first thing you need to run the inference on a test set.
This can be done either through the Python client (see section above) or through the dedicated
command that performs the inference without setting up a client-server interaction. This second
option can be followed by running the dedicated command:

```
simulstream_inference --speech-processor-config config/speech_processor.yaml \
    --wav-list-file PATH_TO_TXT_FILE_WITH_A_LIST_OF_WAV_FILES.txt \
    --tgt-lang it --src-lang en --metrics-log-file metrics.jsonl
```

Once you have generated the JSONL file containing the inference log (e.g. ``metrics.jsonl``), you
can score your speech processor by running:

```shell
simulstream_score_latency --scorer stream_laal \
    --eval-config config/speech_processor.yaml \
    --log-file metrics.jsonl \
    --reference REFERENCES_FILE.tgt \
    --audio-definition YAML_AUDIO_REFERENCES_DEFINITION.yaml

simulstream_score_quality --scorer comet \
    --eval-config config/speech_processor.yaml \
    --log-file metrics.jsonl \
    --references REFERENCES_FILE.tgt \
    --transcripts TRANSCRIPTS_FILE.src \
    --audio-definition YAML_AUDIO_REFERENCES_DEFINITION.yaml

simulstream_stats --eval-config config/speech_processor.yaml \
    --log-file metrics.jsonl
```

Each of them will output different metrics. ``simulstream_score_latency`` provides the metric for
the latency of the system by leveraging a sentence-based TXT reference file and a YAML file, which
for each line in the reference TXT contains the WAV file to which it belong, its ``offset``
(i.e. start time) and ``duration``. The exact definition of how the latency is defined depends on
the selected metric (``--scorer``).

Similarly, ``simulstream_score_quality`` evaluated the quality
of the generated outputs against one (or more) reference (and transcript, only for metrics
requiring them) file(s). Here, the `YAML_AUDIO_REFERENCES_DEFINITION.yaml` has the same number of entries (sentence definitions
in terms of wav file origin, offset and duration) as `REFERENCES_FILE.tgt` and `TRANSCRIPTS_FILE.src`.

As an alternative, `simulstream_score_quality` can be run without the `--audio-definition` specification, by using a list of 
files as arguments of `--references` and `--transcripts`. In this case, the name of the files (trimmed of the extension) 
**must be the same** of the audio files used (i.e. the names present in `metrics.jsonl`). For instance:

```
simulstream_score_quality --scorer comet \
    --eval-config config/speech_processor.yaml \
    --log-file metrics.jsonl \
    --references AUDIO1.tgt,AUDIO2.tgt,AUDIO3.tgt \
    --transcripts AUDIO1.src,AUDIO2.src,AUDIO3.src
```

Lastly, ``simulstream_stats`` computes statistics like the computational cost and flickering ratio.


## Contributing

Contributions from interested researchers and developers are extremely appreciated.

You can create an ***issue*** in case of problems with the code, questions, or feature requests.
You are also more than welcome to create a ***pull request*** that addresses any ***issue***.

## Licence

``simulstream`` is licensed under [Apache Version 2.0](LICENSE). 

## Credits
If you use this library, please cite:


```
@article{gaido-et-al-2025-simulstream,
    title={{simulstream: Open-Source Toolkit for Evaluation and Demonstration of Streaming Speech-to-Text Translation Systems}},
    author={Gaido, Marco and Papi, Sara and Cettolo, Mauro and Negri, Matteo and Bentivogli, Luisa},
    journal = "arXiv",
    year={2025}
}
```
