Create README.md

README.md

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+---
+language: fa
+datasets:
+- shemo
+tags:
+- audio
+- speech
+- speech-emotion-recognition
+license: apache-2.0
+---
+
+# Emotion Recognition in Persian (fa) Speech using HuBERT
+
+
+## How to use
+
+### Requirements
+
+```bash
+# 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
+```
+
+```bash
+!git clone https://github.com/m3hrdadfi/soxan.git .
+```
+
+### Prediction
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchaudio
+from transformers import AutoConfig, Wav2Vec2FeatureExtractor
+from src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification
+
+import librosa
+import IPython.display as ipd
+import numpy as np
+import pandas as pd
+```
+
+```python
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model_name_or_path = "m3hrdadfi/hubert-base-persian-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 = HubertForSpeechClassification.from_pretrained(model_name_or_path).to(device)
+```
+
+```python
+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
+```
+
+```python
+path = "/path/to/sadness.wav"
+outputs = predict(path, sampling_rate)
+```
+
+```bash
+[
+{'Label': 'Anger', 'Score': '0.0%'},
+{'Label': 'Fear', 'Score': '0.0%'},
+{'Label': 'Happiness', 'Score': '0.0%'},
+{'Label': 'Neutral', 'Score': '0.0%'},
+{'Label': 'Sadness', 'Score': '99.9%'},
+{'Label': 'Surprise', 'Score': '0.0%'}]
+]
+```
+
+
+## Evaluation
+The following tables summarize the scores obtained by model overall and per each class.
+
+
+|  Emotions | precision | recall | f1-score | accuracy |
+|:---------:|:---------:|:------:|:--------:|:--------:|
+|   Anger   |    0.96   |  0.96  |   0.96   |          |
+|    Fear   |    1.00   |  0.50  |   0.67   |          |
+| Happiness |    0.79   |  0.87  |   0.83   |          |
+|  Neutral  |    0.93   |  0.94  |   0.93   |          |
+|  Sadness  |    0.87   |  0.94  |   0.91   |          |
+|  Surprise |    0.97   |  0.75  |   0.85   |          |
+|           |           |        |  Overal  |   0.92   |
+
+
+## Questions?
+Post a Github issue from [HERE](https://github.com/m3hrdadfi/soxan/issues).