README.md

---
license: apache-2.0
library_name: naptrap
pipeline_tag: feature-extraction
tags:
  - audio
  - speech
  - keyword-spotting
  - wake-word
  - speech-embedding
  - pytorch
  - safetensors
---

# Google speech_embedding/1, converted to PyTorch

This is a PyTorch/safetensors conversion of Google's
[`speech_embedding/1`](https://www.kaggle.com/models/google/speech-embedding/TensorFlow1/speech-embedding/1),
a small pretrained convolutional speech encoder (Lin et al., 2020). It maps
16 kHz audio to one **96-dimensional embedding every 80 ms**. Those embeddings
describe speech sounds; the model does not produce transcripts.

It is the frozen audio feature extractor used by the keyword models in
[naptrap](https://github.com/androiddrew/naptrap), such as
[`androiddrew/naptrap-phonmatchnet-en`](https://huggingface.co/androiddrew/naptrap-phonmatchnet-en).

> This conversion is not affiliated with or endorsed by Google. The original
> model is published by Google under the Apache License 2.0. This conversion
> keeps that license; see `LICENSE` and `NOTICE`.

## Usage

```bash
pip install "naptrap[hub]"
```

```python
from huggingface_hub import snapshot_download
from naptrap import GoogleSpeechEmbedding, load_wav

encoder = GoogleSpeechEmbedding.from_pretrained(
    snapshot_download("androiddrew/google-speech-embedding-pytorch", revision="v1")
)
waveform = load_wav("clip.wav")          # mono 16 kHz, 0.5–2 s
embeddings = encoder(waveform)           # torch.float32 [len(waveform) // 1280, 96]
```

The runtime needs only PyTorch; TensorFlow is not used.

## Preprocessing contract

| Step | Value |
|---|---|
| Input | mono 16 kHz float32 in [-1, 1]; naptrap accepts 0.5–2 s clips |
| Padding | 11,120 leading zero samples, no trimming |
| STFT | 400-sample Hann window, hop 160, 512-point FFT, no centering, magnitude |
| Mel | 32 bins, natural `log(mel + 0.001)` |
| Output | `floor(samples / 1280)` frames × 96 |

The keyword models were trained on features computed this way, one clip at a
time. Slicing features computed over a longer recording gives different values.

## Conversion and accuracy

All 96 original variables (329,448 parameters plus BatchNorm statistics) and
the original window and mel-matrix constants are kept. The network is
re-implemented with standard PyTorch operations. `conversion/` documents each
step; `config.json` and `NOTICE` list the modifications.

The results match the TensorFlow original to within floating-point
differences between FFT implementations. They are not bit-identical. Across
CPU runtimes from Python 3.8/PyTorch 1.13 to Python 3.14/PyTorch 2.10 (see
`qualification/`):

- the maximum feature difference on real speech was 7.2e-5;
- on 231 downstream keyword-model cases, the probability difference was at most
  2.45e-6, and every accept/reject decision matched.

Pure-tone stress inputs show larger, documented FFT differences.

## Training data and limitations

Google reports training the original on roughly 200 million two-second YouTube
clips covering about 5,000 keywords (Lin et al., 2020). No clip-level
inventory is available. This repository adds no training. Behaviour on
non-English speech, far-field audio or unusual microphones is unknown.

## Citation

```bibtex
@article{lin2020training,
  title   = {Training Keyword Spotters with Limited and Synthesized Speech Data},
  author  = {Lin, James and Kilgour, Kevin and Roblek, Dominik and Sharifi, Matthew},
  journal = {arXiv preprint arXiv:2002.01322},
  year    = {2020}
}
```