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metadata
license: other
license_name: model-license
license_link: https://github.com/alibaba-damo-academy/FunASR
frameworks:
  - Pytorch
tasks:
  - emotion-recognition
widgets:
  - enable: true
    version: 1
    task: emotion-recognition
    examples:
      - inputs:
          - data: git://example/test.wav
    inputs:
      - type: audio
        displayType: AudioUploader
        validator:
          max_size: 10M
        name: input
    output:
      displayType: Prediction
      displayValueMapping:
        labels: labels
        scores: scores
    inferencespec:
      cpu: 8
      gpu: 0
      gpu_memory: 0
      memory: 4096
    model_revision: master
    extendsParameters:
      extract_embedding: false

EMOTION2VEC+

emotion2vec+: speech emotion recognition foundation model
emotion2vec+ seed model

Guides

emotion2vec+ is a series of foundational models for speech emotion recognition (SER). We aim to train a "whisper" in the field of speech emotion recognition, overcoming the effects of language and recording environments through data-driven methods to achieve universal, robust emotion recognition capabilities. The performance of emotion2vec+ significantly exceeds other highly downloaded open-source models on Hugging Face.

This version (emotion2vec_plus_seed) is a seed model trained on academic data, and currently supports the following categories: 0: angry 1: disgusted 2: fearful 3: happy 4: neutral 5: other 6: sad 7: surprised 8: unknown

Model Card

GitHub Repo: emotion2vec

Model ⭐Model Scope 🤗Hugging Face Fine-tuning Data (Hours)
emotion2vec Link Link /
emotion2vec+ seed Link Link 201
emotion2vec+ base Link Link 4788
emotion2vec+ large Link Link 42526

Data Iteration

We offer 3 versions of emotion2vec+, each derived from the data of its predecessor. If you need a model focusing on spech emotion representation, refer to emotion2vec: universal speech emotion representation model.

  • emotion2vec+ seed: Fine-tuned with academic speech emotion data from EmoBox
  • emotion2vec+ base: Fine-tuned with filtered large-scale pseudo-labeled data to obtain the base size model (~90M)
  • emotion2vec+ large: Fine-tuned with filtered large-scale pseudo-labeled data to obtain the large size model (~300M)

The iteration process is illustrated below, culminating in the training of the emotion2vec+ large model with 40k out of 160k hours of speech emotion data. Details of data engineering will be announced later.

Installation

pip install -U funasr modelscope

Usage

input: 16k Hz speech recording

granularity:

  • "utterance": Extract features from the entire utterance
  • "frame": Extract frame-level features (50 Hz)

extract_embedding: Whether to extract features; set to False if using only the classification model

Inference based on ModelScope 

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

inference_pipeline = pipeline(
    task=Tasks.emotion_recognition,
    model="iic/emotion2vec_plus_seed")

rec_result = inference_pipeline('https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav', granularity="utterance", extract_embedding=False)
print(rec_result)

Inference based on FunASR 

from funasr import AutoModel

model = AutoModel(model="iic/emotion2vec_plus_seed")

wav_file = f"{model.model_path}/example/test.wav"
res = model.generate(wav_file, output_dir="./outputs", granularity="utterance", extract_embedding=False)
print(res)

Note: The model will automatically download.

Supports input file list, wav.scp (Kaldi style):

wav_name1 wav_path1.wav
wav_name2 wav_path2.wav
...

Outputs are emotion representation, saved in the output_dir in numpy format (can be loaded with np.load())

Note

This repository is the Huggingface version of emotion2vec, with identical model parameters as the original model and Model Scope version.

Original repository: https://github.com/ddlBoJack/emotion2vec

Model Scope repository: https://www.modelscope.cn/models/iic/emotion2vec_plus_large/summary

Hugging Face repository: https://huggingface.co/emotion2vec

FunASR repository: https://github.com/alibaba-damo-academy/FunASR

Citation 

@article{ma2023emotion2vec,
  title={emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation},
  author={Ma, Ziyang and Zheng, Zhisheng and Ye, Jiaxin and Li, Jinchao and Gao, Zhifu and Zhang, Shiliang and Chen, Xie},
  journal={arXiv preprint arXiv:2312.15185},
  year={2023}
}