> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ai-coustics.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart audio fixture

> Download a short, attributed speech recording for repeatable SDK setup checks.

Use this recording for the SDK quickstarts. The noisy and clean versions contain the same 56,080 samples: mono PCM16 WAV at 16 kHz, lasting 3.505 s. Each file is about 112 kB.

* [Download noisy speech](/assets/audio/noisy-speech.wav) and save it as `input.wav` for a quickstart.
* [Download the clean reference](/assets/audio/clean-speech.wav) for an aligned comparison.

Or download the noisy file from a terminal:

```bash theme={null}
curl --fail --location \
  https://docs.ai-coustics.com/assets/audio/noisy-speech.wav \
  --output input.wav
```

The recording says: "Concord returned to its place amidst the tents."

## Check the input

| Property              | Value                                                              |
| :-------------------- | :----------------------------------------------------------------- |
| **Encoding**          | Uncompressed signed 16-bit PCM, little-endian WAV                  |
| **Sample rate**       | 16,000 Hz                                                          |
| **Channels**          | One                                                                |
| **Samples**           | 56,080                                                             |
| **Duration**          | 3.505 s                                                            |
| **Noisy WAV SHA-256** | `b67c9d618bca9fa365afa91aecd8080299b44f8d60206cd8f5cc543848fd2223` |
| **Clean WAV SHA-256** | `103c3f15eb3715ebc6243d142244128a3ac39b6bfae315baa6b8dc4a8be14aa8` |

Verify the checksums and keep both source files separate from the enhanced output.

## Attribution and changes

The source is LibriSpeech test-clean utterance `6930-75918-0000`, from *LibriSpeech: An ASR Corpus Based on Public Domain Audio Books* by Vassil Panayotov, Guoguo Chen, Daniel Povey and Sanjeev Khudanpur. [OpenSLR SLR12](https://www.openslr.org/12/) distributes the corpus under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/). These derived fixtures retain that license. No endorsement by the source authors is implied.

Source archive: `test-clean.tar.gz`; member: `LibriSpeech/test-clean/6930/75918/6930-75918-0000.flac`. The source FLAC SHA-256 is `9ce35224156f071ab58eb7feb8a5ceae600f6f9f353da2a6cbf797b6b1ac8a23`.

Changes made for this example: decoded the FLAC to mono samples, exported PCM16 WAV and created a second version with seeded Gaussian white noise. Noise was scaled to a 12 dB signal-to-noise ratio over the whole clean recording before PCM quantization, using NumPy 2.5.3 `default_rng(20260918)` and SoundFile 0.13.1. No speech was cut, reordered or transcribed automatically.

## Use representative audio for evaluation

This short recording with synthetic noise checks setup. Evaluate quality on recordings that represent your microphones, languages, codecs and competing speakers.

After setup, follow [audio evaluation](/guides/evaluate-audio) with recordings representative of your application and review [audio format](/reference/concepts/audio-format) before changing rates or channels.