GGUF quantizations of the CED family (Consistent Ensemble Distillation, Xiaomi) - SOTA-tier audio-tagging models that classify everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark,...) into the 527-class AudioSet ontology. These files run with ced.cpp, a standalone C++/ggml port (no Python, no PyTorch at inference), and with LocalAI via the ced backend. Converted from the mispeech/ced- checkpoints (Apache-2.0). CED is a plain AST/DeiT Vision Transformer over a log-mel spectrogram; the port is numerically equal to the PyTorch reference. One self-contained GGUF per size + quant (config, 527 labels, and the mel filterbank/window are all embedded). Pick by your accuracy/size…
Open-weight model · Audio classification
hubert-large-superb-er
by Superb superb/hubert-large-superb-er
This is a ported version of The base model is hubert-large-ll60k, which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Parameters—
Context—
Weights1.3 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads6.5k
Model Card
By Superb, published under apache-2.0, revision ef1a2ebfd7cf.
This is a ported version of The base model is hubert-large-ll60k, which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to SUPERB: Speech processing Universal PERformance Benchmark Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalanced emotion classes to leave the final four classes with a similar amount of data points and cross-validate on five folds of the standard splits. For the original model's training and evaluation instructions refer to the…
Read Superb's full model card
Hubert-Large for Emotion Recognition
Model description
This is a ported version of S3PRL's Hubert for the SUPERB Emotion Recognition task.
The base model is hubert-large-ll60k, which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
For more information refer to SUPERB: Speech processing Universal PERformance Benchmark
Task and dataset description
Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalanced emotion classes to leave the final four classes with a similar amount of data points and cross-validate on five folds of the standard splits.
For the original model's training and evaluation instructions refer to the S3PRL downstream task README.
Usage examples
You can use the model via the Audio Classification pipeline:
from datasets import load_dataset
from transformers import pipeline
dataset = load_dataset("anton-l/superb_demo", "er", split="session1")
classifier = pipeline("audio-classification", model="superb/hubert-large-superb-er")
labels = classifier(dataset[0]["file"], top_k=5)
Or use the model directly:
import torch
import librosa
from datasets import load_dataset
from transformers import HubertForSequenceClassification, Wav2Vec2FeatureExtractor
def map_to_array(example):
speech, _ = librosa.load(example["file"], sr=16000, mono=True)
example["speech"] = speech
return example
# load a demo dataset and read audio files
dataset = load_dataset("anton-l/superb_demo", "er", split="session1")
dataset = dataset.map(map_to_array)
model = HubertForSequenceClassification.from_pretrained("superb/hubert-large-superb-er")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-large-superb-er")
# compute attention masks and normalize the waveform if needed
inputs = feature_extractor(dataset[:4]["speech"], sampling_rate=16000, padding=True, return_tensors="pt")
logits = model(**inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)
labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()]
Eval results
The evaluation metric is accuracy.
| s3prl | transformers | |
|---|---|---|
| session1 | 0.6762 |
N/A |
BibTeX entry and citation info
@article{yang2021superb,
title={SUPERB: Speech processing Universal PERformance Benchmark},
author={Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y and Liu, Andy T and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and others},
journal={arXiv preprint arXiv:2105.01051},
year={2021}
}
Configuration
- Architecture
- HubertForSequenceClassification
- Layers
- 24
- Hidden size
- 1,024
- Feed-forward size
- 4,096
- Attention heads
- 16
- Vocabulary size
- 32
- Stored precision
- float32
- Model type
- hubert
Identity and Version
- Repository
- superb/hubert-large-superb-er
- Publisher
- Superb
- Task
- Audio classification
- Modality
- Audio
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- en
- Revision
- ef1a2ebfd7cfc424dc7f0fbcdc406e8b794d63bb
- First published
- 2022-03-02
- Last updated
- 2021-11-04
Files and Weights
5 files, 1.3 GB in total. The weights are 1 file totalling 1.3 GB in bin.
Weights1 file · 1.3 GB
Configuration2 files · 2.0 KB
Documentation1 file · 3.5 KB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| pytorch_model.bin | Weights | 1.3 GB | f9e5f9386285 |
| config.json | Configuration | 1.8 KB | — |
| preprocessor_config.json | Configuration | 212 B | — |
| README.md | Documentation | 3.5 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 1.3 GB
Download from Superb
Released by Superb through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2105.01051
- Trained on (disclosed) superb
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 1.3 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About hubert-large-superb-er
Can I use hubert-large-superb-er commercially?
Yes. hubert-large-superb-er is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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