semvec/pypi-packages huggingface dataset:
pinch-sdk 0.1.1
Pinch Python SDK for real-time speech translation
License: MIT
Requires Python: >=3.10
Keywords: audio, pinch, realtime, speech, stt, translation, tts, voice

## Pinch Python SDK

### Requirements

- Python **3.10+**
- A Pinch API key in `PINCH_API_KEY`
  - Docs: `https://www.startpinch.com/docs`
  - Create/manage API keys (Portal): `https://portal.startpinch.com/dashboard/developers`

### Authentication (`PINCH_API_KEY`)

The SDK reads the API key from:

1) `PINCH_API_KEY` environment variable (recommended)
2) A local `.env` file in your current working directory (optional)

Example `.env` file (optional):

```env
PINCH_API_KEY=your_key_here
```

### Install (pip)

```bash
python3 -m pip install pinch-sdk
```

Optional (only needed for WAV resampling support for non-16k/48k inputs):

```bash
python3 -m pip install "pinch-sdk[audio]"
```

### Install (uv)

In a uv-managed project:

```bash
uv add pinch-sdk
```

### Usage

Create a small script (for example `translate.py`) in your own project:

```python
import asyncio

from pinch import PinchClient

async def main() -> None:
    client = PinchClient()  # reads PINCH_API_KEY

    await client.translate_file(
        input_wav_path="input.wav",
        output_wav_path="output.wav",
        transcript_path="transcript.txt",
        # defaults:
        # source_language="en-US"
        # target_language="es-ES"
        # audio_output_enabled=True
    )

if __name__ == "__main__":
    asyncio.run(main())
```

Run (pip environment):

```bash
export PINCH_API_KEY="..."
python3 translate.py
```

Run (uv environment):

```bash
export PINCH_API_KEY="..."
uv run python translate.py
```

Outputs:

- `output.wav` (if `audio_output_enabled=True`)
- `transcript.txt`

### Input audio notes

- Input must be **16-bit PCM WAV**
- Streaming API supports sample rates: **16000 Hz** (recommended) and **48000 Hz**
- WAV helpers support **16000 Hz** and **48000 Hz** out of the box
- Other sample rates require resampling deps: `python3 -m pip install "pinch-sdk[audio]"`

### Repo example (for this git checkout)

This repository includes an example script and a default input WAV.

Run with pip:

```bash
python3 -m pip install -e .
export PINCH_API_KEY="..."
python3 examples/translate.py
```

Run with uv:

```bash
uv sync
export PINCH_API_KEY="..."
uv run python examples/translate.py
```

Outputs (generated):

- `examples/output.wav`
- `examples/transcript.txt`

Notes:
- The example reads `examples/input.wav` by default.
- The example does not print transcripts to stdout; it only writes `examples/transcript.txt`.
fast-graph-gp 0.1.2
Graph Random Features for Scalable Gaussian Processes (GRF-GP)
License: MIT
Requires Python: >=3.8
Keywords: gaussian-processes, graph-kernels, machine-learning, pytorch

# Fast-Graph-GP



Fast-Graph-GP is the package for performing fast Gaussian Process (GP) inference on graphs. Internally, it uses **Graph Random Features** (GRFs) to compute a **unbiased** & **sparse** estimate of a family of well-known graph node kernels. It further uses **path-wise conditioning** to leverage the sparsity of the kernel approximation, enabling you to perform GP model train / inference in $\mathcal{O}(N^{3/2})$ time and $\mathcal{O}(N)$ space complexity.


## Examples

For a detailed example of training and using a Graph GP model, refer to the [example notebook](examples/basic_usage.ipynb).

## Installation

Install Fast-Graph-GP via pip:

```bash
pip install fast-graph-gp
```

## Citing Us

If you use Fast-Graph-GP, please cite the following papers:

    @article{zhang2025graph,
    title={Graph random features for scalable Gaussian processes},
    author={Zhang, Matthew and Lin, Jihao Andreas and Choromanski, Krzysztof and Weller, Adrian and Turner, Richard E and Reid, Isaac},
    journal={arXiv preprint arXiv:2509.03691},
    year={2025}
    }