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--- |
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language: el |
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datasets: |
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- aesdd |
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tags: |
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- audio |
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- speech |
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- speech-emotion-recognition |
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license: apache-2.0 |
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--- |
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# Emotion Recognition in Greek (el) Speech using HuBERT |
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## How to use |
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### Requirements |
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```bash |
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# requirement packages |
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!pip install git+https://github.com/huggingface/datasets.git |
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!pip install git+https://github.com/huggingface/transformers.git |
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!pip install torchaudio |
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!pip install librosa |
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``` |
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```bash |
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!git clone https://github.com/m3hrdadfi/soxan.git . |
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``` |
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### Prediction |
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```python |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torchaudio |
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from transformers import AutoConfig, Wav2Vec2FeatureExtractor |
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from src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification |
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import librosa |
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import IPython.display as ipd |
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import numpy as np |
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import pandas as pd |
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``` |
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```python |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model_name_or_path = "m3hrdadfi/hubert-large-greek-speech-emotion-recognition" |
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config = AutoConfig.from_pretrained(model_name_or_path) |
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path) |
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sampling_rate = feature_extractor.sampling_rate |
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model = HubertForSpeechClassification.from_pretrained(model_name_or_path).to(device) |
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``` |
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```python |
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def speech_file_to_array_fn(path, sampling_rate): |
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speech_array, _sampling_rate = torchaudio.load(path) |
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resampler = torchaudio.transforms.Resample(_sampling_rate) |
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speech = resampler(speech_array).squeeze().numpy() |
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return speech |
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def predict(path, sampling_rate): |
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speech = speech_file_to_array_fn(path, sampling_rate) |
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inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True) |
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inputs = {key: inputs[key].to(device) for key in inputs} |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0] |
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outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)] |
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return outputs |
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``` |
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```python |
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path = "/path/to/disgust.wav" |
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outputs = predict(path, sampling_rate) |
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``` |
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```bash |
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[ |
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{'Emotion': 'anger', 'Score': '0.0%'}, |
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{'Emotion': 'disgust', 'Score': '99.2%'}, |
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{'Emotion': 'fear', 'Score': '0.1%'}, |
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{'Emotion': 'happiness', 'Score': '0.3%'}, |
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{'Emotion': 'sadness', 'Score': '0.5%'} |
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] |
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``` |
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## Evaluation |
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The following tables summarize the scores obtained by model overall and per each class. |
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| Emotions | precision | recall | f1-score | accuracy | |
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|:---------:|:---------:|:------:|:--------:|:--------:| |
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| anger | 0.96 | 0.96 | 0.96 | | |
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| disgust | 1.00 | 0.96 | 0.98 | | |
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| fear | 1.00 | 0.83 | 0.91 | | |
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| happiness | 1.00 | 0.96 | 0.98 | | |
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| sadness | 0.81 | 1.00 | 0.89 | | |
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| | | | Overal | 0.94 | |
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## Questions? |
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Post a Github issue from [HERE](https://github.com/m3hrdadfi/soxan/issues). |
