FlaxSpeaker: ls_conformer_base_ge2e (conformer - base)

Disclaimer & Open-Source Reproduction Notice

This is not an officially supported Google product.

This model checkpoint and the accompanying FlaxSpeaker library are an independent, clean-room open-source reproduction in JAX/Flax of speaker recognition algorithms published in:

• GE2E: Generalized End-to-End Loss for Speaker Verification (arXiv:1710.10467)
• Dr-Vectors: Decision Residual Networks and an Improved Loss for Speaker Recognition (arXiv:2104.01989)
• Conformer-ATP: Attentive Temporal Pooling for Conformer-based Streaming Language Identification in Long-form Speech (arXiv:2202.12163)
• PFAS: Parameter-Free Attentive Scoring for Speaker Verification (arXiv:2203.05642v3)

The experiments in those original papers were conducted using Google's proprietary internal infrastructure and internal datasets, not this open-source library or public dataset split.

Model Summary

Property	Value
Model ID	ls_conformer_base_ge2e
Hugging Face Repository	wq2012/ls_conformer_base_ge2e
Hugging Face Collection	FlaxSpeaker Collection
GitHub Repository	wq2012/FlaxSpeaker
PyPI Package	flaxspeaker
Backbone Architecture	conformer (base)
Temporal Pooling	atp
Training Loss	ge2e_softmax
Verification Scoring	cosine
Output Embedding Dim	256
Encoder Parameters	4,551,558
Training Domain	librispeech
Audio Frontend	80-dim log_mel @ 16000 Hz + Energy VAD + CMVN

Model Files Included

• model.safetensors: Primary model weights in SafeTensors format
• flax_model.msgpack: Native Flax Msgpack serialized parameters
• config.json: FlaxSpeakerConfig model & training architecture configuration
• preprocessor_config.json: WhisperFeatureExtractor-compatible frontend configuration
• eval_results.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs)

Quickstart Usage

Python API (from Hugging Face Hub or Local Directory)

from flaxspeaker import load_pretrained

model = load_pretrained("wq2012/ls_conformer_base_ge2e")
emb = model.extract_embedding("path/to/utterance.flac")
score = model.verify("path/to/enroll.flac", "path/to/test.flac")
print(f"Embedding shape: {emb.shape}, Verification score: {score:.4f}")

Command-Line Inference

python scripts/inference.py --model_dir wq2012/ls_conformer_base_ge2e \
    --enroll_audio path/to/enroll.flac --test_audio path/to/test.flac

Multi-Domain Evaluation Results

1-Utterance Verification Across Evaluation Domains

Evaluation Split	Trials	EER (%)	95% Bootstrap CI (%)	minDCF@0.01	minDCF@0.10	ROC-AUC
LibriSpeech (English Read)	3,000	10.77%	[9.67, 11.92]	0.8133	0.4893	0.9606
CN-Celeb2 (Chinese Multi-Genre)	3,000	28.87%	[27.47, 30.80]	0.9673	0.9167	0.7941
VoxCeleb1 (In-the-Wild Celebrity)	2,000	24.65%	[22.77, 26.43]	0.9500	0.9310	0.8426
Universal Pooled (All 3 Domains)	8,000	21.26%	[20.48, 22.16]	0.9527	0.8123	0.8802
Universal Macro-Average (3 Domains)	8,000	21.43%	—	0.9102	0.7790	0.8658

3-Utterance Multi-Enrollment Verification Across Evaluation Domains

Evaluation Split	Trials	3-Utt EER (%)	95% Bootstrap CI (%)	minDCF@0.01	ROC-AUC
LibriSpeech (3-Utt)	1,000	6.60%	[4.80, 8.30]	0.4060	0.9827
CN-Celeb2 (3-Utt)	1,000	23.90%	[21.45, 26.85]	0.9680	0.8481
VoxCeleb1 (3-Utt)	1,000	19.90%	[17.80, 22.60]	0.8720	0.8849
Universal Pooled (3-Utt)	3,000	17.00%	[15.77, 18.30]	0.9420	0.9165
Universal Macro-Average (3-Utt)	3,000	16.80%	—	0.7487	0.9052

Citation

@inproceedings{wan2018generalized,
  title={Generalized End-to-End Loss for Speaker Verification},
  author={Wan, Li and Wang, Quan and Papir, Alan and Moreno, Ignacio Lopez},
  booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={4879--4883},
  year={2018}
}

@inproceedings{pelecanos2021dr,
  title={Dr-Vectors: Decision Residual Networks and an Improved Loss for Speaker Recognition},
  author={Pelecanos, Jason and Wang, Quan and Moreno, Ignacio Lopez},
  booktitle={Interspeech},
  pages={4603--4607},
  year={2021}
}

@inproceedings{wang2022attentive,
  title={Attentive Temporal Pooling for Conformer-based Streaming Language Identification in Long-form Speech},
  author={Wang, Quan and Yu, Yang and Pelecanos, Jason and Huang, Yiling and Moreno, Ignacio Lopez},
  booktitle={The Speaker and Language Recognition Workshop (Odyssey)},
  year={2022}
}

@inproceedings{pelecanos2022parameter,
  title={Parameter-Free Attentive Scoring for Speaker Verification},
  author={Pelecanos, Jason and Wang, Quan and Huang, Yiling and Moreno, Ignacio Lopez},
  booktitle={The Speaker and Language Recognition Workshop (Odyssey)},
  year={2022}
}