semvec/pypi-packages huggingface dataset:
vadslice 1.1.0
Slices audio clips into chunks of a specific number of seconds at the closest moment of silence (decided by silero-vad). Uses onnx which avoids the pytorch dependency.
Requires Python: >=3.12

# vadslice

Slices audio clips of speech into chunks of certain length in seconds at silence points so it doesn't cut in the middle of someone speaking. It uses VAD (voice activity detection from [silero-vad](https://github.com/snakers4/silero-vad)) to determine these points of silence within the clip.

Uses ONNX runtime instead of PyTorch, avoiding the heavy PyTorch dependency while maintaining the same VAD accuracy.

It's extremely fast. 


## Installation

Requires Python 3.12+.

```bash
uv add vadslice
```

or pip:

```bash
pip install vadslice
```

## Usage

### CLI

```bash
vadslice <size> <input_file> <output_dir> [--format wav|mp3|ogg|m4a] [--silence-flush SECONDS]
```

Example:
```bash
vadslice 30 audio.mp3 ./chunks --format mp3

# Flush a chunk early if there's a 5+ second silence gap
vadslice 30 audio.mp3 ./chunks --silence-flush 5
```

Chunks are saved to `output_dir/input_file_stem/chunk_XXXX_offset_XX.XXs.ext`. `--format` is the output format.

Input: Supports any audio format that PyAV can decode (MP3, WAV, OGG, M4A, FLAC, etc.)

### Python API

```python
from vadslice import slicer

parts = slicer("audio.mp3", slice_length_s=30.0)

# Optionally flush chunks on long silence gaps
parts = slicer("audio.mp3", slice_length_s=30.0, silence_flush_s=5.0)

for i, part in enumerate(parts):
    print(f"Part {i}: Offset: {part.offset_s}s, Size: {len(part.part)} bytes")
```

The function `slicer` returns a list of `AudioPart` objects, each containing:
- `part`: WAV bytes (16 kHz mono) for that slice
- `offset_s`: Start time offset in seconds from the original audio

.
pytest-relay-run 0.0.1
A pytest wrapper using for pytest-relay with pytest-relay-ws to control pytest executions.
Requires Python: >=3.10


# Job messages

All published messages follow this model:

```json
{
  "type": "job",
  "payload": {
    "id": "<job-id>",
    "state": "<job-state>",
    "returncode": "<int|null>",
    "text": "<process-output-as-str|null>"
  }
}
```

Each job transitions through a series of well-defined states, represented by the (`JobState`)[pytest_relay_run/src/pytest_relay_run/api/model.py]:

| Value           | Meaning                                                                       |
| --------------- | ----------------------------------------------------------------------------- |
| `"created"`     | The job was instantiated but has not yet started running pytest.              |
| `"collected"`   | Test collection was performed (using `--collect-only`), and no execution ran. |
| `"in-progress"` | The job’s pytest process is currently executing tests.                        |
| `"terminating"` | A termination signal (SIGINT) was sent, graceful shutdown is in progress.     |
| `"done"`        | The pytest process has completed (successfully or with errors).               |

The following transitions trigger a `JobState` message:

- **created** upon job creation (if not `collect_only`).
- **collected** as result for jobs with `--collect-only`.
- **in-progress** when a job subprocess starts executing.
- **done** after a job subprocess completes and output is captured.
- **terminating** when `stop()` is called and SIGINT is sent.

The states `collected` and `done` are final states, i.e., the job is complete.
rshogi-avx2 0.1.1
Python bindings for the rshogi core library (AVX2 build).
Requires Python: >=3.9

# rshogi-avx2

AVX2 enabled build of the Python bindings for the `rshogi-core` crate.

This package provides the same `rshogi` module as the standard package,
but it is compiled with `-C target-feature=+avx2` for x86_64 CPUs.

## Installation

```bash
python -m pip install rshogi-avx2
```

## Notes

- `rshogi` and `rshogi-avx2` are mutually exclusive; install only one.
- This build requires an AVX2-capable CPU on x86_64.