Same architecture as Aniemore/hubert-emotion-russian-resd, retrained on a mix of 27,939 clips spanning eight languages and three speaking registers instead of one acted Russian corpus. The point of the change is spontaneous speech: the previous release was trained only on acted dialogue, where every class is equally frequent and every utterance is performed, and real speech is neither.
Quantized builds ship in the same repository under int8/, fp8/ and int4/.
Results
test set
what it is
macro-F1
UA
WA
previous release
RESD test
acted Russian, 7 balanced classes
0.6167
0.6158
0.6214
0.7522
Dusha podcast test
spontaneous Russian, majority neutral
0.3147
0.5898
0.5942
0.1477
CAMEO test
7 non-Russian languages
0.5293
0.5363
0.6350
0.2404
Panel results
Read the first two rows together. The acted score goes down and the spontaneous score goes up; both follow from the same change, and which one matters is a deployment question. If your audio is read or performed speech, the previous release may still suit you better.
About the CAMEO row
CAMEO ships no train/test partition, and the usual way to make one — a random split over clips — puts nearly every test speaker into training as well: three of its twelve constituent corpora contain a single speaker each, so no clip-level split of them can be speaker-disjoint even in principle. The number above is reported for completeness. Treat it as an in-domain figure, not as evidence of cross-lingual transfer.
Per class, across the panel — recall and F1 for every class on every test set
Per-class recall and F1
Each card leads with the macro-F1 for that set; the rows are the detail behind it. Both per-class numbers are shown because they disagree in a way that matters: recall rewards a class the model over-predicts, so on the spontaneous set the minority classes reach decent recall at poor F1 — most clips called sad there are not sad. If you are going to act on one class, read its F1.
Each set keeps its own class list: the spontaneous corpus has four classes and the other two have seven, and there is no correspondence between positive and any single one of happiness/enthusiasm to line them up with.
Per-class recall on spontaneous speech
class
this model
previous release
angry
0.6287
0.5329
neutral
0.5848
0.1601
positive
0.8445
0.5854
sad
0.3010
0.2136
neutral carries most of real speech and is the class the previous release missed.
Quantized variants
subfolder
scheme
weights
vs fp32
macro-F1
UA
WA
(root)
fp32
1206 MiB
1.0x
0.6167
0.6158
0.6214
int8
W8A16
351 MiB
3.4x smaller
0.6195
0.6190
0.6250
fp8
W8A16-float
343 MiB
3.5x smaller
0.6190
0.6194
0.6250
int4
W4A16_ASYM
209 MiB
5.8x smaller
0.6194
0.6204
0.6250
Quality after quantization
Weights on disk
Weight-only, round-to-nearest, no calibration. Every variant lands within the seed spread of the fp32 parent on RESD test, so the choice is about download size rather than about quality.
Training data
corpus
clips
language
register
RESD
948
Russian
acted dialogue
Dusha crowd
6,800
Russian
acted, crowd-sourced
CAMEO
6,800
7 languages
12 corpora, no Russian
Dusha podcast
6,060
Russian
spontaneous podcast speech
IEMOCAP
4,735
English
elicited dyadic sessions
ASVP-ESD
2,596
multilingual
mixed register
total
27,939
8 languages
3 registers
A slice is held out of every corpus in the mix, in the same proportion, and model selection is on that held-out split — never on any of the test sets above. Labels are unified to seven classes; four-class corpora are mapped upward and scored on the classes they actually contain.
Usage
python
1import torch, librosa
2from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
34repo ="Aniemore/hubert-emotion-v1-crosslingual"5model = AutoModelForAudioClassification.from_pretrained(repo).eval()6fe = AutoFeatureExtractor.from_pretrained(repo)78# Resample to 16 kHz. Do not skip it: RESD itself ships at 44.1 kHz,9# and handing the model 44.1 kHz audio while telling the extractor it10# is 16 kHz stretches time 2.8x and silently changes the answer.11wav, _ = librosa.load("clip.wav", sr=16000, mono=True)12x = fe(wav, sampling_rate=16000, return_tensors="pt", padding=True)13with torch.no_grad():14 probs = model(**x).logits.softmax(-1)[0]15print({model.config.id2label[i]:round(p.item(),3)for i, p inenumerate(probs)})
For a quantized build, name the subfolder — only that subfolder is downloaded:
Scores are the mean of two seeds; the seed spread on the 280-clip RESD split is ±0.03–0.05, so differences smaller than that are not differences.
The spontaneous set has four classes where the model has seven, so its numbers are computed over a mapped label space and are not comparable to seven-class figures.
Spontaneous scores are at zero decision bias. Calibrating the neutral threshold on your own development split will move them.
This is a bimodal checkpoint: it takes a transcript alongside the audio. The transcripts used in training and evaluation are corpus-provided and clean, which ASR output is not.
