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| import gradio as gr | |
| import torch | |
| import torch.nn.functional as F | |
| from transformers import Wav2Vec2Processor, Wav2Vec2ForSequenceClassification | |
| import torchaudio | |
| import numpy as np | |
| # Define emotion labels | |
| emotion_labels = ["angry", "calm", "disgust", "fearful", "happy", "neutral", "sad", "surprised"] | |
| # Load model and processor | |
| model_name = "Dpngtm/wav2vec2-emotion-recognition" | |
| model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name) | |
| processor = Wav2Vec2Processor.from_pretrained(model_name, num_labels=len(emotion_labels)) | |
| # Define device | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| model.eval() | |
| # At the top with other global variables | |
| emotion_icons = { | |
| "angry": "😠", | |
| "calm": "😌", | |
| "disgust": "🤢", | |
| "fearful": "😨", | |
| "happy": "😊", | |
| "neutral": "😐", | |
| "sad": "😢", | |
| "surprised": "😲" | |
| } | |
| def recognize_emotion(audio): | |
| try: | |
| if audio is None: | |
| return {f"{emotion} {emotion_icons[emotion]}": 0 for emotion in emotion_labels} | |
| audio_path = audio if isinstance(audio, str) else audio.name | |
| speech_array, sampling_rate = torchaudio.load(audio_path) | |
| duration = speech_array.shape[1] / sampling_rate | |
| if duration > 60: | |
| return { | |
| "Error": "Audio too long (max 1 minute)", | |
| **{f"{emotion} {emotion_icons[emotion]}": 0 for emotion in emotion_labels} | |
| } | |
| if sampling_rate != 16000: | |
| resampler = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000) | |
| speech_array = resampler(speech_array) | |
| if speech_array.shape[0] > 1: | |
| speech_array = torch.mean(speech_array, dim=0, keepdim=True) | |
| speech_array = speech_array / torch.max(torch.abs(speech_array)) | |
| speech_array = speech_array.squeeze().numpy() | |
| inputs = processor(speech_array, sampling_rate=16000, return_tensors='pt', padding=True) | |
| input_values = inputs.input_values.to(device) | |
| with torch.no_grad(): | |
| outputs = model(input_values) | |
| logits = outputs.logits | |
| probs = F.softmax(logits, dim=-1)[0].cpu().numpy() | |
| confidence_scores = { | |
| f"{emotion} {emotion_icons[emotion]}": round(float(prob),2) | |
| for emotion, prob in zip(emotion_labels, probs) | |
| } | |
| sorted_scores = dict(sorted( | |
| confidence_scores.items(), | |
| key=lambda x: x[1], | |
| reverse=True | |
| )) | |
| return sorted_scores | |
| except Exception as e: | |
| return { | |
| "Error": str(e), | |
| **{f"{emotion} {emotion_icons[emotion]}": 0 for emotion in emotion_labels} | |
| } | |
| # Create a formatted string of supported emotions | |
| supported_emotions = " | ".join([f"{emotion_icons[emotion]} {emotion}" for emotion in emotion_labels]) | |
| interface = gr.Interface( | |
| fn=recognize_emotion, | |
| inputs=gr.Audio( | |
| sources=["microphone", "upload"], | |
| type="filepath", | |
| label="Record or Upload Audio" | |
| ), | |
| outputs=gr.Label( | |
| num_top_classes=len(emotion_labels), | |
| label="Detected Emotion" | |
| ), | |
| title="Speech Emotion Recognition", | |
| description=f""" | |
| ### Supported Emotions: | |
| {supported_emotions} | |
| Maximum audio length: 1 minute""", | |
| theme=gr.themes.Soft( | |
| primary_hue="orange", | |
| secondary_hue="blue" | |
| ), | |
| css=""" | |
| .gradio-container {max-width: 800px} | |
| .label {font-size: 18px} | |
| """ | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch( | |
| share=True, | |
| debug=True, | |
| server_name="0.0.0.0", | |
| server_port=7860 | |
| ) |