SAVRN Model Hub · Comparisons
wav2vec2-large-robust-24-ft-age-gender vs wav2vec2-xlsr-53-russian-emotion-recognition
Wav2vec2-large-robust-24-ft-age-gender has 318M parameters and wav2vec2-xlsr-53-russian-emotion-recognition has 316M parameters; wav2vec2-large-robust-24-ft-age-gender is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 and wav2vec2-xlsr-53-russian-emotion-recognition under MIT License; at 16-bit, wav2vec2-large-robust-24-ft-age-gender needs about 0.8 GB (1x MI300X from $1.85 an hour) and wav2vec2-xlsr-53-russian-emotion-recognition about 0.8 GB (1x MI300X from $1.85 an hour).
| Field | wav2vec2-large-robust-24-ft-age-gender audeering/wav2vec2-large-robust-24-ft-age-gender | wav2vec2-xlsr-53-russian-emotion-recognition Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition |
|---|---|---|
| Publisher | audEERING GmbH | Aniemore |
| Task | Audio classification | Audio classification |
| Modality | Audio | Audio |
| Parameters, as reported | 318M parameters | 316M parameters |
| Architecture | Model | Wav2Vec2ForSpeechClassification |
| Library | transformers | transformers |
| Context length | Not stated | Not stated |
| Repository size | 2.5 GB | 5.0 GB |
| Artifact formats | safetensors, pytorch | safetensors, pytorch |
| License | cc-by-nc-sa-4.0 | mit |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 0.8 GB | 0.8 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0.2 GB | 0.2 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | bad2737f5291 | 3a4ab5c8bef1 |
| Downloads reported by the hub | 2.5M | 8.6k |
| Last observed | 2026-09-21 | 2026-09-18 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
wav2vec2-xlsr-53-russian-emotion-recognition
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| CAMEO test | Task Speech Emotion RecognitionMetric AccuracyComparison conditions not established | 0.2603 | Aniemore Publisher reported |
Evaluated revision not stated | — |
| CAMEO test | Task Speech Emotion RecognitionMetric Macro F1Comparison conditions not established | 0.2248 | Aniemore Publisher reported |
Evaluated revision not stated | — |
| CAMEO test | Task Speech Emotion RecognitionMetric Unweighted accuracyComparison conditions not established | 0.237 | Aniemore Publisher reported |
Evaluated revision not stated | — |
| Dusha podcast test | Task Speech Emotion RecognitionMetric AccuracyComparison conditions not established | 0.0804 | Aniemore Publisher reported |
Evaluated revision not stated | — |
| Dusha podcast test | Task Speech Emotion RecognitionMetric Macro F1Comparison conditions not established | 0.0954 | Aniemore Publisher reported |
Evaluated revision not stated | — |
| Dusha podcast test | Task Speech Emotion RecognitionMetric Unweighted accuracyComparison conditions not established | 0.3315 | Aniemore Publisher reported |
Evaluated revision not stated | — |
| RESD test | Task Speech Emotion RecognitionMetric AccuracyComparison conditions not established | 0.7214 | Aniemore Publisher reported |
Evaluated revision not stated | — |
| RESD test | Task Speech Emotion RecognitionMetric Macro F1Comparison conditions not established | 0.7206 | Aniemore Publisher reported |
Evaluated revision not stated | — |
| RESD test | Task Speech Emotion RecognitionMetric Unweighted accuracyComparison conditions not established | 0.7176 | Aniemore Publisher reported |
Evaluated revision not stated | — |
SAVRN's Notes on wav2vec2-large-robust-24-ft-age-gender
This audEERING model estimates a speaker's age and whether the voice is a child, female or male, from raw audio. It is a 24-layer wav2vec2 at 318M parameters and draws about 2.5 million downloads a month.
The license is CC-BY-NC-SA-4.0, which rules out commercial use. Inferring age and gender from voice also raises privacy and fairness questions. Use it for research and aggregate analysis only; audEERING sells commercial licenses for its models.
Questions
Which is larger, wav2vec2-large-robust-24-ft-age-gender or wav2vec2-xlsr-53-russian-emotion-recognition?
wav2vec2-large-robust-24-ft-age-gender (318M parameters) is larger than wav2vec2-xlsr-53-russian-emotion-recognition (316M parameters), by the parameter counts their publishers report.
Which is cheaper to run, wav2vec2-large-robust-24-ft-age-gender or wav2vec2-xlsr-53-russian-emotion-recognition?
At 4-bit, wav2vec2-large-robust-24-ft-age-gender fits on 1x MI300X from $1.85 an hour and wav2vec2-xlsr-53-russian-emotion-recognition on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use wav2vec2-large-robust-24-ft-age-gender commercially?
Not without separate permission. wav2vec2-large-robust-24-ft-age-gender is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0. CC BY-NC-SA 4.0 permits non-commercial sharing and adapting with credit, and requires adaptations to use the same license. Commercial use needs separate permission.
Can I use wav2vec2-xlsr-53-russian-emotion-recognition commercially?
Yes. wav2vec2-xlsr-53-russian-emotion-recognition is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.