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--- |
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language: en |
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datasets: |
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- aesdd |
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tags: |
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- audio |
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- audio-classification |
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- speech |
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license: apache-2.0 |
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--- |
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~~~ |
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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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# prediction |
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~~~ |
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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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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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~~~ |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model_name_or_path = "harshit345/xlsr-wav2vec-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 = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device) |
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~~~ |
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~~~ |
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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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# prediction |
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~~~ |
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# path for a sample |
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path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav' |
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outputs = predict(path, sampling_rate) |
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~~~ |
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~~~ |
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[{'Emotion': 'anger', 'Score': '78.3%'}, |
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{'Emotion': 'disgust', 'Score': '11.7%'}, |
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{'Emotion': 'fear', 'Score': '5.4%'}, |
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{'Emotion': 'happiness', 'Score': '4.1%'}, |
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{'Emotion': 'sadness', 'Score': '0.5%'}] |
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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 | 0.82 | 1.00 | 0.81 | | |
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| disgust | 0.85 | 0.96 | 0.85 | | |
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| fear | 0.78 | 0.88 | 0.80 | | |
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| happiness | 0.84 | 0.71 | 0.78 | | |
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| sadness | 0.86 | 1.00 | 0.79 | | |
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| | | | Overall | 0.806 | |
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## |
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Colab Notebook |
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https://colab.research.google.com/drive/1aPPb_ZVS5dlFVZySly8Q80a44La1XjJu?usp=sharing |
