metadata

language: ru
tags:
  - audio-classification
  - audio
  - emotion
  - emotion-recognition
  - emotion-classification
  - speech
license: mit
datasets:
  - Aniemore/resd
model-index:
  - name: >-
      XLS-R Wav2Vec2 For Russian Speech Emotion Classification by Nikita
      Davidchuk
    results:
      - task:
          name: Audio Emotion Recognition
          type: audio-emotion-recognition
        dataset:
          name: Russian Emotional Speech Dialogs
          type: Aniemore/resd
          args: ru
        metrics:
          - name: accuracy
            type: accuracy
            value: 72%

Prepare and importing

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, AutoModel, Wav2Vec2FeatureExtractor

import librosa
import numpy as np


def speech_file_to_array_fn(path, sampling_rate):
    speech_array, _sampling_rate = torchaudio.load(path)
    resampler = torchaudio.transforms.Resample(_sampling_rate)
    speech = resampler(speech_array).squeeze().numpy()
    return speech


def predict(path, sampling_rate):
    speech = speech_file_to_array_fn(path, sampling_rate)
    inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
    inputs = {key: inputs[key].to(device) for key in inputs}

    with torch.no_grad():
        logits = model_(**inputs).logits

    scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
    outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
    return outputs

Evoking:

TRUST = True

config = AutoConfig.from_pretrained('Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition', trust_remote_code=TRUST)
model_ = AutoModel.from_pretrained("Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition", trust_remote_code=TRUST)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_.to(device)

Use case

result = predict("/path/to/russian_audio_speech.wav", 16000)
print(result)

# outputs
[{'Emotion': 'anger', 'Score': '0.0%'},
 {'Emotion': 'disgust', 'Score': '100.0%'},
 {'Emotion': 'enthusiasm', 'Score': '0.0%'},
 {'Emotion': 'fear', 'Score': '0.0%'},
 {'Emotion': 'happiness', 'Score': '0.0%'},
 {'Emotion': 'neutral', 'Score': '0.0%'},
 {'Emotion': 'sadness', 'Score': '0.0%'}]

Results

	precision	recall	f1-score	support
anger	0.97	0.86	0.92	44
disgust	0.71	0.78	0.74	37
enthusiasm	0.51	0.80	0.62	40
fear	0.80	0.62	0.70	45
happiness	0.66	0.70	0.68	44
neutral	0.81	0.66	0.72	38
sadness	0.79	0.59	0.68	32
accuracy			0.72	280
macro avg	0.75	0.72	0.72	280
weighted avg	0.75	0.72	0.73	280

Citations

@misc{Aniemore,
  author = {Артем Аментес, Илья Лубенец, Никита Давидчук},
  title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека},
  year = {2022},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.com/aniemore/Aniemore}},
  email = {[email protected]}
}