semvec/pypi-packages huggingface dataset:
proscenium 0.0.14
Declare Collaborative, Asynchronous Human To Agent Interactions
Requires Python: >=3.11

# Proscenium

[![PyPI version](https://img.shields.io/pypi/v/proscenium)](https://pypi.org/project/proscenium/)
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[![License](https://img.shields.io/github/license/The-AI-Alliance/proscenium)](https://github.com/The-AI-Alliance/proscenium/tree/main?tab=Apache-2.0-1-ov-file#readme)
[![Issues](https://img.shields.io/github/issues/The-AI-Alliance/proscenium)](https://github.com/The-AI-Alliance/proscenium/issues)
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Proscenium is a small, experimental library for
declaring collaborative, asynchronous human to agent interactions
in enterprise AI applications.
It was started in February 2025 and is still in early development.

See the [website](https://the-ai-alliance.github.io/proscenium/) for quickstart info, goals, and other links.

To find the Proscenium community, see the [discussions](https://github.com/The-AI-Alliance/proscenium/discussions)
dvtk 0.2.1
Lightweight package to validate/measure audio files in local or S3 directories.
Requires Python: >=3.10

# Usage

## 1. Install

```bash
pip install dvtk
```

## 2. Run

Run dvtk to validate audio files in local directories.
```bash
dvtk \
  --paths /path/to/directory/ \
  --ext .wav \
  --metrics dvtk.default_metrics:sr,duration,max_amplitude,dnsmos_speech \
  --output dv_result.csv \
  --parallel \
  --max-workers 8
```

Run dvtk to validate audio files in S3 directories.
```bash
dvtk \
  --paths s3://bucket-name/path/to/directory/ \
  --ext .wav \
  --metrics dvtk.default_metrics:sr,duration,max_amplitude,dnsmos_speech \
  --output dv_result.csv \
  --parallel \
  --max-workers 8
```

Remarks
- You can specify multiple paths and extensions (e.g., `--ext .wav .flac`).
- You can specify the AWS profile to use (`--aws-profile`).
- More default metrics are available in `dvtk.default_metrics` (e.g., reference-free PESQ, reference-free SI-SDR for speech).


### Using Custom Metrics

1. Create a small python module (e.g., `mymetrics.py`) in your working directory:

```python
# mymetrics.py
import numpy as np

def custom_metric(audio: np.ndarray, sr: int) -> float:
    ...  # compute the custom metric
    return metric  # float
```

2. Run the CLI:
```bash
dvtk \
  --paths /path/to/directory/ \
  --ext .wav \
  --metrics dvtk.default_metrics:duration,max_amplitude,dnsmos_speech \
            mymetrics:custom_metric \
  --output dv_result.csv \
  --parallel \
  --max-workers 8
```


[TODOs](https://chatgpt.com/share/684056bd-6d9c-8012-9174-938f44271299)
- [ ] Support more storage backends (e.g., Google Cloud Storage)
- [ ] On-disk caching or cehckpointing
- [ ] Progress bar