安装环境

modelscope>=1.11.1
funasr>=1.0.5

用法

基于modelscope进行推理

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

inference_pipeline = pipeline(
    task=Tasks.emotion_recognition,
    model="iic/emotion2vec_base_finetuned", model_revision="v2.0.4")

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)

基于FunASR进行推理

from funasr import AutoModel

model = AutoModel(model="iic/emotion2vec_base_finetuned", model_revision="v2.0.4")

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

注：模型会自动下载

支持输入文件列表，wav.scp（kaldi风格）：

wav_name1 wav_path1.wav
wav_name2 wav_path2.wav
...

输出为情感表征向量，保存在output_dir中，格式为numpy格式（可以用np.load()加载）

说明

本仓库为emotion2vec的modelscope版本，模型参数完全一致。

原始仓库地址: https://github.com/ddlBoJack/emotion2vec

modelscope版本仓库：https://github.com/alibaba-damo-academy/FunASR

相关论文以及引用信息

@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}
}