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| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from transformers import pipeline | |
| import librosa | |
| class EmotionRecognizer: | |
| def __init__(self): | |
| self.classifier = pipeline( | |
| "audio-classification", | |
| model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition", | |
| device=0 if torch.cuda.is_available() else -1 | |
| ) | |
| self.sample_rate = 16000 | |
| def process_audio(self, audio_input): | |
| try: | |
| # Extract audio data and sample rate from gradio input | |
| sample_rate, audio_data = audio_input | |
| # Convert stereo to mono if necessary | |
| if len(audio_data.shape) > 1: | |
| audio_data = np.mean(audio_data, axis=1) | |
| # Convert to float32 and normalize | |
| audio_data = audio_data.astype(np.float32) | |
| if np.max(np.abs(audio_data)) > 1.0: | |
| audio_data = audio_data / np.max(np.abs(audio_data)) | |
| # Resample if necessary | |
| if sample_rate != self.sample_rate: | |
| audio_data = librosa.resample( | |
| y=audio_data, | |
| orig_sr=sample_rate, | |
| target_sr=self.sample_rate | |
| ) | |
| # Ensure the audio isn't too short | |
| if len(audio_data) < self.sample_rate: | |
| # Pad audio if it's too short | |
| audio_data = np.pad(audio_data, (0, self.sample_rate - len(audio_data))) | |
| elif len(audio_data) > 10 * self.sample_rate: | |
| # Take first 10 seconds if audio is too long | |
| audio_data = audio_data[:10 * self.sample_rate] | |
| # Make prediction | |
| result = self.classifier({"array": audio_data, "sampling_rate": self.sample_rate}) | |
| # Format results | |
| emotions_text = "\n".join([ | |
| f"{pred['label']}: {pred['score']*100:.2f}%" | |
| for pred in result | |
| ]) | |
| # Prepare plot data | |
| plot_data = { | |
| "labels": [pred['label'] for pred in result], | |
| "values": [pred['score'] * 100 for pred in result] | |
| } | |
| return emotions_text, plot_data | |
| except Exception as e: | |
| print(f"Error details: {str(e)}") | |
| return f"Error processing audio: {str(e)}", None | |
| def create_interface(): | |
| recognizer = EmotionRecognizer() | |
| def process_audio_file(audio): | |
| if audio is None: | |
| return "Please provide an audio input.", None | |
| return recognizer.process_audio(audio) | |
| with gr.Blocks() as interface: | |
| gr.Markdown("# Audio Emotion Recognition") | |
| gr.Markdown("Record or upload audio to analyze the emotional content. The model works best with clear speech in English.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| audio_input = gr.Audio( | |
| label="Upload or Record Audio", | |
| type="numpy", | |
| sources=["microphone", "upload"], | |
| ) | |
| analyze_btn = gr.Button("Analyze Emotion") | |
| gr.Markdown("Note: Audio will be automatically converted to mono and resampled if needed.") | |
| with gr.Column(): | |
| output_text = gr.Textbox( | |
| label="Results", | |
| lines=5 | |
| ) | |
| output_plot = gr.BarPlot( | |
| title="Emotion Confidence Scores", | |
| x_title="Emotions", | |
| y_title="Confidence (%)" | |
| ) | |
| analyze_btn.click( | |
| fn=process_audio_file, | |
| inputs=[audio_input], | |
| outputs=[output_text, output_plot] | |
| ) | |
| return interface | |
| if __name__ == "__main__": | |
| demo = create_interface() | |
| demo.launch(share=True) |