Create README.md

README.md

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+~~~
+# requirement packages
+!pip install git+https://github.com/huggingface/datasets.git
+!pip install git+https://github.com/huggingface/transformers.git
+!pip install torchaudio
+!pip install librosa
+!git clone https://github.com/m3hrdadfi/soxan
+cd soxan
+~~~
+
+
+# prediction
+~~
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchaudio
+from transformers import AutoConfig, Wav2Vec2FeatureExtractor
+
+import librosa
+import IPython.display as ipd
+import numpy as np
+import pandas as pd
+~~~
+
+~~~
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model_name_or_path = "Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition"
+config = AutoConfig.from_pretrained(model_name_or_path)
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
+sampling_rate = feature_extractor.sampling_rate
+model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
+~~~
+
+~~~
+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
+~~~
+
+# prediction
+~~~
+# path for a sample
+path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav' 
+outputs = predict(path, sampling_rate)
+~~~
+
+~~~
+[{'Emotion': 'anger', 'Score': '98.3%'},
+ {'Emotion': 'disgust', 'Score': '0.0%'},
+ {'Emotion': 'fear', 'Score': '0.4%'},
+ {'Emotion': 'happiness', 'Score': '0.7%'},
+ {'Emotion': 'sadness', 'Score': '0.5%'}]
+ ~~~