Metadata-Version: 2.1
Name: miya-speechless
Version: 0.0.18
Summary: Speechless repo for sales call analysis
License: MIT
Author: Michal Pleva (MIYA Consulting)
Requires-Python: >=3.10,<3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Requires-Dist: llvmlite (==0.38.1)
Requires-Dist: noisereduce (>=3.0.3,<4.0.0)
Requires-Dist: numba (>=0.55.0,<0.56.0)
Requires-Dist: onnx (>=1.17.0,<2.0.0)
Requires-Dist: onnxruntime (>=1.20.0,<2.0.0)
Requires-Dist: openai (>=1.51.2,<2.0.0)
Requires-Dist: pvfalcon (>=1.0.2,<2.0.0)
Requires-Dist: pyannote-audio (>=3.3.2,<4.0.0)
Requires-Dist: pydub (>=0.25.1,<0.26.0)
Requires-Dist: python-dotenv (>=1.0.1,<2.0.0)
Requires-Dist: pytubefix (>=7.4.0,<8.0.0)
Requires-Dist: silero-vad (>=5.1.2,<6.0.0)
Requires-Dist: streamlit (>=1.39.0,<2.0.0)
Requires-Dist: streamlit-audiorec (>=0.1.3,<0.2.0)
Requires-Dist: twine (>=5.1.1,<6.0.0)
Description-Content-Type: text/markdown


# miya-speechless

## Poetry Installation Instructions

To install the dependencies and manage the project, we use Poetry. Follow the steps below to set up your environment with Poetry.

### Step 1: Install Poetry

You can install Poetry by running the official installation script:

```bash
curl -sSL https://install.python-poetry.org | python3 -
```

Alternatively, you can install it via pip:

```bash
pip install poetry
```

### Step 2: Verify Poetry Installation

After installing, verify that Poetry is available by running:

```bash
poetry --version
```

### Step 3: Install Dependencies

Once Poetry is installed, you can install the project dependencies by running the following command in the project root:

```bash
poetry install
```

This will create a virtual environment and install all dependencies specified in the `pyproject.toml` file.

### Step 4: Run the Project or Tests

You can now run the project or the tests using the Poetry environment:

To activate the Poetry environment:

```bash
poetry shell
```

To run the tests:

```bash
poetry run pytest
```

### Step 4: Convert the model to onnx format
To convert the model to onnx format, run the following command:

```bash
poetry poetry run python export_to_onnx.py --checkpoint /path/to/checkpoint --onnx_model /path/to/onnx_model
```

### Step 5: Run the streamlit app
Place the onnx model in the models directory.

To start the streamlit app, run the following command:

```bash
 poetry run streamlit run app/app.py
```

In the application, you can set the following parameters:
- overlap: Set the overlap between the transcript and the diaretization (default: 0.1)
- onset_threshold: Set the onset threshold for speaker start (default: 0.1)
- offset_threshold: Set the offset threshold for speaker stop (default: 0.1)

