semvec/pypi-packages huggingface dataset:
stapesai-ssi 0.0.1
This project builds upon Whisper and VAD systems to provide plug and play solutions (FastAPI router) that can be easily included in any AI Assistant type project to have Streaming ASR in their application.
License: MIT
Requires Python: <4.0,>=3.10

@mayank https://www.markepear.com/blog/github-search-engine-optimization

https://www.codemotion.com/magazine/dev-life/github-project/

usecase 1 - use in existing reposall models will be loaded from the existing repo and then the server will be started

```bash
pip install ssi[fastapi]
```

```python
from fastapi import FastAPI
from stapesai_ssi.fastapi import StreamingWSRouter
from stapesai_ssi.types import StreamingDataChunk, NewClientConnected

app = FastAPI()

def asr_callback(data: StreamingDataChunk):
    print(data)

def new_client_callback(data: NewClientConnected):
    print(data)

streaming_ws_router = StreamingWSRouter(
    asr_callback=asr_callback,
    new_client_callback=new_client_callback,
    endpoint="/ws/transcribe"
)

app.include_router(streaming_ws_router)
```

usecase 2 - deploy as a docker server and then use in existing repos
here SSI server is deployed as a docker container and then ssi_client can be used in existing repos

```bash
pip install ssi[client]
```

```python
from fastapi import FastAPI
from stapesai_ssi.clients import StreamingClient
from stapesai_ssi.types import StreamingDataChunk

app = FastAPI()

def callback(data: StreamingDataChunk):
    print(data)

ssi_client = StreamingClient(
    server_host="localhost",
    server_port=8000,
    callback=callback,
)

app.include_router(ssi_client.fastapi_proxy_router)
```

usecase 3 - CLI (for local system as well as simple socket server)
bc dimak nhi chal rha abhi