wq2012

v0.5.0: SOTA upgrade, MUSAN+RIR+speed shards, BatchNorm ECAPA, AS-Norm, TFLite exports, and full-data flagships

4b876d6 verified 3 days ago

11.4 kB

---
license: apache-2.0
library_name: flaxspeaker
pipeline_tag: feature-extraction
tags:
- audio
- speaker-verification
- speaker-recognition
- speaker-embeddings
- jax
- flax
- safetensors
- conformer
- ge2e_softmax
- cosine
datasets:
- librispeech_asr
- cnceleb
- voxceleb
metrics:
- eer
- min_dcf
- roc_auc
---
# FlaxSpeaker: `ls_conformer_base_ge2e` (`conformer` - `base`)
## Disclaimer & Reproduction Notice
> **This is not an officially supported Google product.**
>
> This checkpoint and the accompanying `FlaxSpeaker` library are a from-scratch re-implementation in JAX/Flax, written using only the published papers (no proprietary code or data), of speaker recognition algorithms from:
> - **GE2E**: *Generalized End-to-End Loss for Speaker Verification* ([arXiv:1710.10467](https://arxiv.org/abs/1710.10467))
> - **Dr-Vectors**: *Decision Residual Networks and an Improved Loss for Speaker Recognition* ([arXiv:2104.01989](https://arxiv.org/abs/2104.01989))
> - **ATP** (pooling head repurposed from a *language identification* paper): *Attentive Temporal Pooling for Conformer-based Streaming Language Identification in Long-form Speech* ([arXiv:2202.12163](https://arxiv.org/abs/2202.12163))
> - **PFAS**: *Parameter-Free Attentive Scoring for Speaker Verification* ([arXiv:2203.05642](https://arxiv.org/abs/2203.05642))
>
> The experiments in those papers were run on Google-internal infrastructure and data, not with this library. **This is a converged small-corpus benchmark checkpoint (2,600 steps / ~14.37 epochs on 5,788 utterances from 232 speakers), not a full-corpus reproduction of the papers' large-scale results** -- see *Training budget* and *Deviations from the source papers* below.
## Training Budget (read this first)
This model was trained to convergence for **2,600 gradient steps** (`S_safe = X + 2 epochs` safety margin, warmup `260` steps) with mini-batches of 32 utterances (8 speakers x 4 utterances), i.e. **~14.37 epochs** over the 5,788-utterance LibriSpeech training pool (232 speakers), on a CPU workstation with seed 42. Across the training trajectory, in-domain LibriSpeech EER progressed from step `400`: **19.77%** (1-utt) / **16.60%** (3-utt) -> step `2200` (knee): **12.60%** (1-utt) / **8.80%** (3-utt) -> step `2600` (final): **12.13%** (1-utt) / **8.90%** (3-utt) (Universal Pooled final: **20.32%** 1-utt / **16.73%** 3-utt). While training loss and validation EER have reached their small-corpus convergence plateau on this 232-speaker open split (<= 25 utterances/speaker), absolute EERs remain bounded by training speaker diversity compared to industrial benchmarks trained on thousands of speakers (see below).

**Training-crop provenance.** This checkpoint was trained with `U(90, 180)`-frame variable-length crops and an exact validity mask (the current pipeline default); the shipped `config.json` / YAML (`variable_length_training: true`) describes the released weights. Step-0 loss fingerprint at the released seed (`scripts/fingerprint_released_checkpoints.py`, `pretrained_models/step0_fingerprints.json`): archived `2.143`; current code with fixed 180-frame crops `2.1324`, with `U(90, 180)` crops `2.143` (match: `variable`). Evaluating the released weights with the current code reproduces the archived metrics in `eval_results.json`. At the converged `S_safe` budget, the seed-to-seed standard deviation of pooled 1-utt EER across seeds 0-2 is 0.08-0.41 % absolute (`pretrained_models/r2_seed_and_control_results.json`), while the 8,000-trial 95 % bootstrap CI half-width is ~0.8 %.

For orientation only: published ECAPA-TDNN systems trained on VoxCeleb2-dev reach about 0.9% EER on the full VoxCeleb1-O list (Desplanques et al., 2020), and sub-1M-parameter systems about 2% (Wang et al., EfficientTDNN, 2022). The checkpoints released here are trained to convergence (including a +2-epoch safety margin) on the self-contained 381-speaker (<=25 utterances/speaker) benchmark corpus rather than VoxCeleb2-dev (5,994 speakers, ~1.1M utterances), and are intended for controlled relative comparisons within the FlaxSpeaker benchmark, not as state-of-the-art models trained on thousand-speaker corpora.

## Model Summary

| Property | Value |
| :--- | :--- |
| **Model ID** | `ls_conformer_base_ge2e` |
| **Hugging Face Repository** | [`wq2012/ls_conformer_base_ge2e`](https://huggingface.co/wq2012/ls_conformer_base_ge2e) |
| **Hugging Face Collection** | [FlaxSpeaker Collection](https://huggingface.co/collections/wq2012/flaxspeaker) |
| **GitHub Repository** | [`wq2012/FlaxSpeaker`](https://github.com/wq2012/FlaxSpeaker) |
| **PyPI Package** | [`flaxspeaker`](https://pypi.org/project/flaxspeaker/) |
| **Backbone Architecture** | `conformer` (`base`) |
| **Temporal Pooling** | `atp` |
| **Training Objective** | GE2E softmax (Wan et al. 2018, Eq. 6; exact leave-one-out centroids) (`ge2e_softmax`) |
| **Verification Scorer** | Cosine similarity (`cosine`) |
| **Output Representation Dim** | `256` |
| **Inference Parameters (encoder + scoring head; excludes training-only classifier weights)** | `4,551,558` |
| **Training Data** | LibriSpeech only (232 speakers, 5,788 utterances) |
| **Training Steps / Batch** | `2,600` steps x 32 utterances (~14.37 epochs), seed 42 |
| **Audio Frontend** | `80`-dim natural-log Mel @ `16000` Hz + frame-level energy VAD + CMVN (`flaxspeaker.AudioFrontend`; **not** a Hugging Face `AutoFeatureExtractor`) |

## Model Files Included

- `model.safetensors`: model weights (encoder + GE2E `w,b` / Dr-Vector / PFAS heads) in SafeTensors format
- `flax_model.msgpack`: native Flax Msgpack serialized parameters
- `config.json`: `FlaxSpeakerConfig` (full `ExperimentConfig`, incl. the frontend/VAD settings)
- `eval_results.json`: multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs, VoxCeleb1-O 10-speaker subset)

## Quickstart Usage

### Python API (from Hugging Face Hub or Local Directory)

Requires `flaxspeaker >= 0.5.0` (`pip install -U flaxspeaker`).

```python
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

```bash
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

Trial lists are 50/50 target/impostor over held-out speakers (LibriSpeech 40, CN-Celeb2 30, VoxCeleb1 10 speakers); minDCF@0.01 is close to its trivial ceiling of 1.0 for every model under this trial mix.

| Evaluation Split | Trials | Raw EER (%) | AS-Norm EER (%) | 95% Bootstrap CI (%) | minDCF@0.01 | minDCF@0.10 | ROC-AUC |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **LibriSpeech (English Read)** | 3,000 | **12.13%** | **11.60%** | [11.07, 13.47] | 0.8953 | 0.6520 | 0.9467 |
| **CN-Celeb2 (Chinese Multi-Genre)** | 3,000 | **27.30%** | **26.87%** | [25.87, 28.90] | 0.9287 | 0.9220 | 0.8093 |
| **VoxCeleb1 (In-the-Wild Celebrity)** | 2,000 | **21.10%** | **20.70%** | [19.45, 22.80] | 0.9620 | 0.8850 | 0.8752 |
| **Universal Pooled (All 3 Domains)** | 8,000 | **20.32%** | **20.26%** | [19.46, 21.19] | 0.9520 | 0.8237 | 0.8826 |
| **Universal Macro-Average (3 Domains)** | — | **20.18%** | **19.72%** | — | 0.9287 | 0.8197 | 0.8771 |
| **VoxCeleb1-O 10-speaker subset** (see caveat) | 2,500 | **23.45%** | **24.25%** | [21.87, 25.73] | 0.9877 | 0.9670 | 0.8449 |

Caveat: VoxCeleb1-O **10-speaker subset**: the official `veri_test2.txt` list filtered to trials whose enrollment and test speakers are both among the 10 held-out evaluation speakers (2,500 trials, ~72% target). **Not comparable to published VoxCeleb1-O numbers**; the other 30 of the 40 official test speakers are used as training data by `vox_*` and `univ_*` models.

### 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 | **8.90%** | [6.90, 10.40] | 0.8240 | 0.9711 |
| **CN-Celeb2 (3-Utt)** | 1,000 | **22.50%** | [19.60, 25.05] | 0.9200 | 0.8488 |
| **VoxCeleb1 (3-Utt)** | 1,000 | **18.50%** | [15.89, 20.80] | 0.9180 | 0.9058 |
| **Universal Pooled (3-Utt)** | 3,000 | **16.73%** | [15.27, 18.00] | 0.8987 | 0.9148 |
| **Universal Macro-Average (3-Utt)** | — | **16.63%** | — | 0.8873 | 0.9086 |

Single seed; the 95% bootstrap CIs resample trials (not speakers). Differences below about 1% absolute EER between FlaxSpeaker checkpoints are within the seed-to-seed variation measured in `pretrained_models/r2_seed_and_control_results.json` (see the GitHub README) and should not be interpreted as rankings.

## Deviations from the Source Papers

- **Training regime**: 2,600 gradient steps x 32 utterances (N = 8 speakers x M = 4, ~14.37 epochs) on <= 25 utterances/speaker from 232 speakers (5,788 utterances); the source papers train for ~500k steps with N = 16-64 / M = 8-10 on thousands of speakers.
- **Augmentation**: corpus-backed MUSAN environmental noise, music, and multi-speaker babble + RIRS_NOISES room impulse responses + 3-way polyphase speed perturbation (`0.9x`, `1.0x`, `1.1x`) + SpecAugment.
- **Conformer**: parallel linear + Conv1D stem, no positional encoding, 4 blocks x 256-D, 8 heads, kernel 15, mask-aware GroupNorm(2), no dropout, multi-layer feature aggregation; the Google papers use 12 x 512 blocks, 4-8 heads, kernel 32, relative positional encoding and dropout.
- **ATP pooling**: 4 heads with a tanh bottleneck (128-D) and `subspace_heads = True`; the papers use a single head with a linear-sigmoid weight (`attention_dim = 0`). Note that arXiv:2202.12163 is a language-identification paper whose pooling head is repurposed here.
- **Frontend**: 80-D natural-log Mel + frame-level energy VAD (26 dB dynamic range) + CMVN; evaluation processes up to 3000 frames (30.0 s).
## Citation
```bibtex
@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}
}
```

	---
	license: apache-2.0
	library_name: flaxspeaker
	pipeline_tag: feature-extraction
	tags:
	- audio
	- speaker-verification
	- speaker-recognition
	- speaker-embeddings
	- jax
	- flax
	- safetensors
	- conformer
	- ge2e_softmax
	- cosine
	datasets:
	- librispeech_asr
	- cnceleb
	- voxceleb
	metrics:
	- eer
	- min_dcf
	- roc_auc
	---

	# FlaxSpeaker: `ls_conformer_base_ge2e` (`conformer` - `base`)

	## Disclaimer & Reproduction Notice

	> This is not an officially supported Google product.
	>
	> This checkpoint and the accompanying `FlaxSpeaker` library are a from-scratch re-implementation in JAX/Flax, written using only the published papers (no proprietary code or data), of speaker recognition algorithms from:
	> - GE2E: Generalized End-to-End Loss for Speaker Verification ([arXiv:1710.10467](https://arxiv.org/abs/1710.10467))
	> - Dr-Vectors: Decision Residual Networks and an Improved Loss for Speaker Recognition ([arXiv:2104.01989](https://arxiv.org/abs/2104.01989))
	> - ATP (pooling head repurposed from a language identification paper): Attentive Temporal Pooling for Conformer-based Streaming Language Identification in Long-form Speech ([arXiv:2202.12163](https://arxiv.org/abs/2202.12163))
	> - PFAS: Parameter-Free Attentive Scoring for Speaker Verification ([arXiv:2203.05642](https://arxiv.org/abs/2203.05642))
	>
	> The experiments in those papers were run on Google-internal infrastructure and data, not with this library. This is a converged small-corpus benchmark checkpoint (2,600 steps / ~14.37 epochs on 5,788 utterances from 232 speakers), not a full-corpus reproduction of the papers' large-scale results -- see Training budget and Deviations from the source papers below.

	## Training Budget (read this first)

	This model was trained to convergence for 2,600 gradient steps (`S_safe = X + 2 epochs` safety margin, warmup `260` steps) with mini-batches of 32 utterances (8 speakers x 4 utterances), i.e. ~14.37 epochs over the 5,788-utterance LibriSpeech training pool (232 speakers), on a CPU workstation with seed 42. Across the training trajectory, in-domain LibriSpeech EER progressed from step `400`: 19.77% (1-utt) / 16.60% (3-utt) -> step `2200` (knee): 12.60% (1-utt) / 8.80% (3-utt) -> step `2600` (final): 12.13% (1-utt) / 8.90% (3-utt) (Universal Pooled final: 20.32% 1-utt / 16.73% 3-utt). While training loss and validation EER have reached their small-corpus convergence plateau on this 232-speaker open split (<= 25 utterances/speaker), absolute EERs remain bounded by training speaker diversity compared to industrial benchmarks trained on thousands of speakers (see below).

	Training-crop provenance. This checkpoint was trained with `U(90, 180)`-frame variable-length crops and an exact validity mask (the current pipeline default); the shipped `config.json` / YAML (`variable_length_training: true`) describes the released weights. Step-0 loss fingerprint at the released seed (`scripts/fingerprint_released_checkpoints.py`, `pretrained_models/step0_fingerprints.json`): archived `2.143`; current code with fixed 180-frame crops `2.1324`, with `U(90, 180)` crops `2.143` (match: `variable`). Evaluating the released weights with the current code reproduces the archived metrics in `eval_results.json`. At the converged `S_safe` budget, the seed-to-seed standard deviation of pooled 1-utt EER across seeds 0-2 is 0.08-0.41 % absolute (`pretrained_models/r2_seed_and_control_results.json`), while the 8,000-trial 95 % bootstrap CI half-width is ~0.8 %.

	For orientation only: published ECAPA-TDNN systems trained on VoxCeleb2-dev reach about 0.9% EER on the full VoxCeleb1-O list (Desplanques et al., 2020), and sub-1M-parameter systems about 2% (Wang et al., EfficientTDNN, 2022). The checkpoints released here are trained to convergence (including a +2-epoch safety margin) on the self-contained 381-speaker (<=25 utterances/speaker) benchmark corpus rather than VoxCeleb2-dev (5,994 speakers, ~1.1M utterances), and are intended for controlled relative comparisons within the FlaxSpeaker benchmark, not as state-of-the-art models trained on thousand-speaker corpora.

	## Model Summary

	\| Property \| Value \|
	\| :--- \| :--- \|
	\| Model ID \| `ls_conformer_base_ge2e` \|
	\| Hugging Face Repository \| [`wq2012/ls_conformer_base_ge2e`](https://huggingface.co/wq2012/ls_conformer_base_ge2e) \|
	\| Hugging Face Collection \| [FlaxSpeaker Collection](https://huggingface.co/collections/wq2012/flaxspeaker) \|
	\| GitHub Repository \| [`wq2012/FlaxSpeaker`](https://github.com/wq2012/FlaxSpeaker) \|
	\| PyPI Package \| [`flaxspeaker`](https://pypi.org/project/flaxspeaker/) \|
	\| Backbone Architecture \| `conformer` (`base`) \|
	\| Temporal Pooling \| `atp` \|
	\| Training Objective \| GE2E softmax (Wan et al. 2018, Eq. 6; exact leave-one-out centroids) (`ge2e_softmax`) \|
	\| Verification Scorer \| Cosine similarity (`cosine`) \|
	\| Output Representation Dim \| `256` \|
	\| Inference Parameters (encoder + scoring head; excludes training-only classifier weights) \| `4,551,558` \|
	\| Training Data \| LibriSpeech only (232 speakers, 5,788 utterances) \|
	\| Training Steps / Batch \| `2,600` steps x 32 utterances (~14.37 epochs), seed 42 \|
	\| Audio Frontend \| `80`-dim natural-log Mel @ `16000` Hz + frame-level energy VAD + CMVN (`flaxspeaker.AudioFrontend`; not a Hugging Face `AutoFeatureExtractor`) \|

	## Model Files Included

	- `model.safetensors`: model weights (encoder + GE2E `w,b` / Dr-Vector / PFAS heads) in SafeTensors format
	- `flax_model.msgpack`: native Flax Msgpack serialized parameters
	- `config.json`: `FlaxSpeakerConfig` (full `ExperimentConfig`, incl. the frontend/VAD settings)
	- `eval_results.json`: multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs, VoxCeleb1-O 10-speaker subset)

	## Quickstart Usage

	### Python API (from Hugging Face Hub or Local Directory)

	Requires `flaxspeaker >= 0.5.0` (`pip install -U flaxspeaker`).

	```python
	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

	```bash
	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

	Trial lists are 50/50 target/impostor over held-out speakers (LibriSpeech 40, CN-Celeb2 30, VoxCeleb1 10 speakers); minDCF@0.01 is close to its trivial ceiling of 1.0 for every model under this trial mix.

	\| Evaluation Split \| Trials \| Raw EER (%) \| AS-Norm EER (%) \| 95% Bootstrap CI (%) \| minDCF@0.01 \| minDCF@0.10 \| ROC-AUC \|
	\| :--- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \|
	\| LibriSpeech (English Read) \| 3,000 \| 12.13% \| 11.60% \| [11.07, 13.47] \| 0.8953 \| 0.6520 \| 0.9467 \|
	\| CN-Celeb2 (Chinese Multi-Genre) \| 3,000 \| 27.30% \| 26.87% \| [25.87, 28.90] \| 0.9287 \| 0.9220 \| 0.8093 \|
	\| VoxCeleb1 (In-the-Wild Celebrity) \| 2,000 \| 21.10% \| 20.70% \| [19.45, 22.80] \| 0.9620 \| 0.8850 \| 0.8752 \|
	\| Universal Pooled (All 3 Domains) \| 8,000 \| 20.32% \| 20.26% \| [19.46, 21.19] \| 0.9520 \| 0.8237 \| 0.8826 \|
	\| Universal Macro-Average (3 Domains) \| — \| 20.18% \| 19.72% \| — \| 0.9287 \| 0.8197 \| 0.8771 \|
	\| VoxCeleb1-O 10-speaker subset (see caveat) \| 2,500 \| 23.45% \| 24.25% \| [21.87, 25.73] \| 0.9877 \| 0.9670 \| 0.8449 \|

	Caveat: VoxCeleb1-O 10-speaker subset: the official `veri_test2.txt` list filtered to trials whose enrollment and test speakers are both among the 10 held-out evaluation speakers (2,500 trials, ~72% target). Not comparable to published VoxCeleb1-O numbers; the other 30 of the 40 official test speakers are used as training data by `vox_` and `univ_` models.

	### 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 \| 8.90% \| [6.90, 10.40] \| 0.8240 \| 0.9711 \|
	\| CN-Celeb2 (3-Utt) \| 1,000 \| 22.50% \| [19.60, 25.05] \| 0.9200 \| 0.8488 \|
	\| VoxCeleb1 (3-Utt) \| 1,000 \| 18.50% \| [15.89, 20.80] \| 0.9180 \| 0.9058 \|
	\| Universal Pooled (3-Utt) \| 3,000 \| 16.73% \| [15.27, 18.00] \| 0.8987 \| 0.9148 \|
	\| Universal Macro-Average (3-Utt) \| — \| 16.63% \| — \| 0.8873 \| 0.9086 \|

	Single seed; the 95% bootstrap CIs resample trials (not speakers). Differences below about 1% absolute EER between FlaxSpeaker checkpoints are within the seed-to-seed variation measured in `pretrained_models/r2_seed_and_control_results.json` (see the GitHub README) and should not be interpreted as rankings.

	## Deviations from the Source Papers

	- Training regime: 2,600 gradient steps x 32 utterances (N = 8 speakers x M = 4, ~14.37 epochs) on <= 25 utterances/speaker from 232 speakers (5,788 utterances); the source papers train for ~500k steps with N = 16-64 / M = 8-10 on thousands of speakers.
	- Augmentation: corpus-backed MUSAN environmental noise, music, and multi-speaker babble + RIRS_NOISES room impulse responses + 3-way polyphase speed perturbation (`0.9x`, `1.0x`, `1.1x`) + SpecAugment.
	- Conformer: parallel linear + Conv1D stem, no positional encoding, 4 blocks x 256-D, 8 heads, kernel 15, mask-aware GroupNorm(2), no dropout, multi-layer feature aggregation; the Google papers use 12 x 512 blocks, 4-8 heads, kernel 32, relative positional encoding and dropout.
	- ATP pooling: 4 heads with a tanh bottleneck (128-D) and `subspace_heads = True`; the papers use a single head with a linear-sigmoid weight (`attention_dim = 0`). Note that arXiv:2202.12163 is a language-identification paper whose pooling head is repurposed here.
	- Frontend: 80-D natural-log Mel + frame-level energy VAD (26 dB dynamic range) + CMVN; evaluation processes up to 3000 frames (30.0 s).

	## Citation

	```bibtex
	@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}
	}
	```