File size: 5,749 Bytes
c76626b 604f346 c76626b b4c8497 c76626b 604f346 c76626b ff826f2 c76626b ff826f2 c76626b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
---
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
---
<div align="center">
<h1>
EMOTION2VEC+
</h1>
<p>
emotion2vec+: speech emotion recognition foundation model <br>
<b>emotion2vec+ base model</b>
</p>
<p>
<img src="logo.png" style="width: 200px; height: 200px;">
</p>
<p>
</p>
</div>
# 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_base) uses a large-scale pseudo-labeled data for finetuning to obtain a base size model (~90M), 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](https://github.com/ddlBoJack/emotion2vec)
|Model|⭐Model Scope|🤗Hugging Face|Fine-tuning Data (Hours)|
|:---:|:-------------:|:-----------:|:-------------:|
|emotion2vec|[Link](https://www.modelscope.cn/models/iic/emotion2vec_base/summary)|[Link](https://huggingface.co/emotion2vec/emotion2vec_base)|/|
emotion2vec+ seed|[Link](https://modelscope.cn/models/iic/emotion2vec_plus_seed/summary)|[Link](https://huggingface.co/emotion2vec/emotion2vec_plus_seed)|201|
emotion2vec+ base|[Link](https://modelscope.cn/models/iic/emotion2vec_plus_base/summary)|[Link](https://huggingface.co/emotion2vec/emotion2vec_plus_base)|4788|
emotion2vec+ large|[Link](https://modelscope.cn/models/iic/emotion2vec_plus_large/summary)|[Link](https://huggingface.co/emotion2vec/emotion2vec_plus_large)|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](https://huggingface.co/emotion2vec/emotion2vec).
- emotion2vec+ seed: Fine-tuned with academic speech emotion data from [EmoBox](https://github.com/emo-box/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
```python
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
inference_pipeline = pipeline(
task=Tasks.emotion_recognition,
model="iic/emotion2vec_plus_base")
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
```python
from funasr import AutoModel
model = AutoModel(model="iic/emotion2vec_plus_base")
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):
```cat wav.scp
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](https://github.com/ddlBoJack/emotion2vec)
Model Scope repository: [https://www.modelscope.cn/models/iic/emotion2vec_plus_large/summary](https://www.modelscope.cn/models/iic/emotion2vec_plus_large/summary)
Hugging Face repository: [https://huggingface.co/emotion2vec](https://huggingface.co/emotion2vec)
FunASR repository: [https://github.com/alibaba-damo-academy/FunASR](https://github.com/alibaba-damo-academy/FunASR/tree/funasr1.0/examples/industrial_data_pretraining/emotion2vec)
# Citation
```BibTeX
@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}
}
``` |