---
license: cc-by-nc-sa-4.0
language:
- en
pipeline_tag: feature-extraction
tags:
- audio
- speaker-verification
- speaker-embedding
- voice-authentication
- tflite
- litert
- quantized
- dynamic-range-quantization
base_model:
- facebook/w2v-bert-2.0
- zl389/w2v-bert-2.0_SV
---

# Speaker Embedding W2V-BERT TFLite Package

This repository contains a two-stage TFLite/LiteRT speaker embedding package for
local CPU testing: deterministic waveform feature extraction followed by a
dynamic-range quantized W2V-BERT feature-to-embedding model.

## Files

| File | Purpose |
| --- | --- |
| `speaker_feature_extractor.tflite` | TFLite waveform-to-`input_features` preprocessing. |
| `speaker_feature_extractor.tflite.manifest.json` | Feature extractor conversion/runtime manifest. |
| `speaker_features_to_embedding.tflite` | TFLite `input_features`-to-embedding flatbuffer. |
| `speaker_features_to_embedding.tflite.manifest.json` | Conversion/runtime manifest. |
| `artifact_manifest.json` | Clean upload metadata, checksum, and source lineage. |

## Source lineage

This is a converted/quantized derivative of a speaker verification stack built
from:

- Base model: [`facebook/w2v-bert-2.0`](https://huggingface.co/facebook/w2v-bert-2.0)
- Speaker checkpoint / adapter: [`zl389/w2v-bert-2.0_SV`](https://huggingface.co/zl389/w2v-bert-2.0_SV)
- Original implementation repo: <https://github.com/ZXHY-82/w2v-BERT-2.0_SV>

Direct upstream artifact links used by this project:

- W2V-BERT base weights: <https://huggingface.co/facebook/w2v-bert-2.0/resolve/main/model.safetensors?download=true>
- Speaker checkpoint: <https://huggingface.co/zl389/w2v-bert-2.0_SV/resolve/main/model_lmft_0.14.pth?download=true>

GitHub repositories cannot be attached to the Hugging Face `base_model` field in
the same way as Hub models, so the implementation source is listed explicitly
above and in `artifact_manifest.json`.

## Runtime contract

This package is a two-flatbuffer pipeline rather than a single direct
waveform-to-embedding flatbuffer.

- Sample rate: `16000 Hz`
- Stage 1 input: waveform `[1, 64000]`, `float32`
- Stage 1 output: `input_features` `[1, 199, 160]`, `float32`
- Stage 1 preprocessing: SeamlessM4T/Kaldi fbank feature extraction equivalent
  to the W2V-BERT feature extractor configuration from `facebook/w2v-bert-2.0`
- Stage 2 input: `input_features` `[1, 199, 160]`, `float32`
- Stage 2 output: speaker embedding `[1, 256]`, `float32`
- Stage 2 quantization mode: dynamic-range
- Recommended postprocessing: L2-normalize each chunk embedding, average chunk
  embeddings for longer audio, then L2-normalize the pooled vector.

## Limitations

- This is an experimental converted artifact, not an upstream official release.
- It is packaged as two TFLite files; callers must run stage 1 before stage 2.
- Quantization can shift speaker similarity scores. Recalibrate thresholds for
  your deployment.
- The upstream speaker assets are marked in this project as CC BY-NC-SA 4.0 /
  research/testing unless legal review approves broader use.