rado-tech/uzbek-gender-wav2vec2

Uzbek Speaker Gender Recognition (wav2vec2-base)

facebook/wav2vec2-base fine-tuned to predict speaker gender from Uzbek speech. 94.6M parameters, 361 MB.

This is the accuracy-first variant. For real-time or resource-constrained use see rado-tech/uzbek-gender-cnn, which is 126× faster and 1480× smaller at a 0.65-point accuracy cost.

Model details

• Base model: facebook/wav2vec2-base
• Head: Wav2Vec2ForSequenceClassification, 2 labels
• Frozen: the convolutional feature encoder (4.2M parameters). It encodes low-level acoustics that transfer unchanged, and freezing it cuts ~30% of step time.
• Trainable: 90.4M parameters
• Input: raw waveform, 2 seconds at 16 kHz (32,000 samples)
• Output: two logits; index 0 = Ayol (female), 1 = Erkak (male)
• License: Apache 2.0

Training

Setting	Value
Epochs	3
Batch size	16
Learning rate	3e-5 backbone / 1e-3 classifier head
Schedule	linear with 10% warmup
Weight decay	0.01
Precision	mixed (fp16)
Hardware	one NVIDIA T4, ~15 minutes

Discriminative learning rates matter here: a single large rate destroys the pretrained representations, while a single small rate leaves the randomly initialised head undertrained.

Epoch	Train loss / acc	Val loss / acc
1	0.0709 / 0.9795	0.0301 / 0.9920
2	0.0206 / 0.9953	0.0187 / 0.9963
3	0.0130 / 0.9972	0.0200 / 0.9967

Validation accuracy after a single epoch already exceeded what a comparable from-scratch CNN reached in fifteen.

Evaluation

Held-out test split: 3,049 clips from 364 speakers, disjoint from training and validation.

Metric	wav2vec2	CNN baseline
Accuracy	98.75%	98.10%
Errors	38	58
Precision (male)	0.9993	0.9940
Recall (male)	0.9759	0.9681
Recall (female)	0.9993	0.9940
F1	0.9875	0.9809
CPU latency (2 s audio)	504 ms	4 ms

McNemar's test against the CNN: χ² = 16.41 (critical 3.841), so the difference is statistically significant. Notably the two models disagree asymmetrically — wav2vec2 was right where the CNN was wrong 21 times, the reverse happened once.

37 of the 38 errors are shared with the CNN, and 34 of those trace to five contributor accounts with demonstrably wrong labels — one contains recordings from two different people. Excluding two such accounts the model scores 99.73%, though the reportable figure remains 98.75%.

Usage

import torch
from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification

repo = "rado-tech/uzbek-gender-wav2vec2"
extractor = Wav2Vec2FeatureExtractor.from_pretrained(repo)
model = Wav2Vec2ForSequenceClassification.from_pretrained(repo).eval()

# waveform: mono float32 at 16 kHz, ideally a 2-second window
inputs = extractor([waveform], sampling_rate=16_000, return_tensors="pt")
with torch.no_grad():
    probs = torch.softmax(model(**inputs).logits, dim=1)

labels = ["Ayol", "Erkak"]   # female, male
print(labels[probs.argmax(1).item()], probs.max().item())

For longer recordings, split into overlapping 2-second windows, drop silent ones, and average the probabilities — the reference implementation is in the project repository.

Intended use

Research and exploratory analysis of Uzbek speech.

Out of scope. Do not use this model to make decisions about individuals — access control, identity verification, profiling, or content moderation. It predicts an acoustic correlate of vocal tract physiology, not a person's gender identity.

Limitations and biases

• Language. Trained only on Uzbek; accuracy elsewhere is unverified.
• Binary labels. The source dataset labels gender as binary, so the model does too. This reflects the data, not the range of human gender identity.
• Age distribution. About 75% of training speakers are 18–24.
• Latency. 504 ms per 2-second window on CPU makes this unsuitable for real-time streaming; use the CNN variant for that.
• Recording conditions. Crowdsourced phone and laptop audio. Studio recordings and heavy background noise are out of distribution.

Training data

DavronSherbaev/uzbekvoice-filtered, Apache 2.0. A 30,000-clip subset (34.8 hours) from 3,505 speakers, filtered on community quality signals, capped at 40 clips per speaker, and split so no speaker appears in more than one partition.

Citation

@software{uzbek_gender_wav2vec2,
  title   = {Uzbek Speaker Gender Recognition (wav2vec2)},
  year    = {2026},
  note    = {facebook/wav2vec2-base fine-tuned on uzbekvoice-filtered},
  license = {Apache-2.0}
}