semvec/pypi-packages huggingface dataset:
voicecut 0.2.0
A cli tool for cutting a long audio into segments by silence.
Requires Python: >=3.10
Keywords: utility, audio

# voicecut

[![PyPI version](https://badge.fury.io/py/voicecut.svg)](https://pypi.org/project/voicecut/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A cli utility for splitting a long audio file into shorter chunks based on moments of silence. It is useful for preprocessing audio before performing speech-to-text on it.

## Installation

Through uv (recomended)
```bash
uv tool install voicecut
```

Through pip

```bash
pip install voicecut
```

## Usage

Split audio files from the command line:

```bash
voicecut audio.mp3 [OPTIONS]
```

**Options:**
- `--segment-length FLOAT`: Target segment length in seconds (default: 600)
- `--segment-delta FLOAT`: Allowed deviation from segment length in seconds (default: 30)
- `--silence-thresh-mode {mean, max, auto}`: Mode to determine the silence threshold. 'mean' uses the audio's average dBFS, 'max' uses the audio's peak dBFS, and 'abs' treats --silence-thresh-delta as an absolute dBFS value. For 'mean' and 'max', the computed value is adjusted by --silence-thresh-delta; for 'abs' the delta is used directly as the threshold.
- `--silence-thresh-delta FLOAT`: Silence threshold delta in dB (default: -4)
- `--min-silence-len FLOAT`: Minimum silence length in seconds (default: 0.5)
- `--output-dir PATH`: Output directory for split segments (default: current directory)

**Example:**
```bash
voicecut audio.mp3 --segment-length 600 --output-dir ./segments
```

## Development
It is recomended to use [uv](https://docs.astral.sh/uv/) toolset for development.

## Testing
There are unittests available in the `tests/` directory.
```
uv run pytest
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
otl-normalizer 0.3.0
Normalized text representation of OpenType layout subtables

# otl-normalizer

This crate converts OpenType layout subtables to a normalized representation,
suitable for comparison in a text diff.

It is part of the [`fontc`] project, and is used to test font compilation as
well as to compare the output of different compiler toolchains.


[`fontc`]: https://github.com/googlefonts/fontc
tmesh 0.1.17
TensorMesh CLI tool suite
Requires Python: >=3.9

# Documentation

[docs.tmesh.ai](docs.tmesh.ai)

# Installation

```python
pip install tmesh
```

# CLI Tree

## 1. tmesh-cli benchmark

Format: 
`tmesh-cli benchmark --endpoint "<YOUR_OPEN_AI_API_ENDPOINT>" --api-key "<OPTIONAL_API_KEY>" [--model "<OPTIONAL_MODEL>"]`

Example: 

`tmesh-cli benchmark --endpoint "http://89.169.111.29:30080/" --api-key "vllm_sk_555a1b7ff3e0f617b1240375ea411c2a5f08d2666fcdc718075f66c9"`

The model at the endpoint will automatically be discovered (or you can manually pass it in) and the benchmark will run ad infinitum. 

### Discovery and Configuration: 

```text
endpoint: http://localhost:8000/v1/
found model: Qwen/Qwen3-30B-A3B-Instruct-2507
offload_size: 100
Workload Specifications:
Model: Qwen/Qwen3-30B-A3B-Instruct-2507
Number of Contexts: 30
Number of Questions per Context: 30
Max Inflight Requests (Load-Balancing): 10
Input Length: 32000
Output Length: 100
```

Continually send M long contexts that will always have N randomly generated questions appended to them.

Hardcoded Configurations:

- 32k token context length
- 100 token output length
- TensorMesh SaaS configurations in: `src/benchmark/model_configs.json`

Discovered Configurations:

- Model name (must be one of the ones supported by LMIgnite inside of model_configs.json)
- Number of contexts
- Number of questions per context
- Maximum Inflight Requests

### Refreshing Logs every 5 seconds

```text
Elapsed Time: 165.0652596950531
Total Number of Requests Processed: 280
QPS: 1.696298788232491
Global Average TTFT: 3.77425986954144
Global Average ITL: 0.01485872107813842
Global Average Prefill Throughput: 22871.74402415523
Global Average Decode Throughput: 104.11856198232987
Requests Processed in Interval: 0
Interval Average TTFT: 2.5995567440986633
Interval Average ITL: 0.012132209007956864
Interval Average Prefill Throughput: 25740.820827727515
Interval Average Decode Throughput: 90.82104365217528
```

## 2.
brainaccess 3.6.1
BrainAccess Python API
Requires Python: >=3.8
Keywords: BCI, EEG, brainaccess, neuroscience

# BrainAccess Python API

Welcome to the official Python API for BrainAccess devices.

## Quick Links

- Main Website: https://www.brainaccess.ai/
- Documentation: https://www.brainaccess.ai/software/brainaccess-sdk/