| import gradio as gr |
| import torch |
| from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2Processor |
| import librosa |
| import numpy as np |
|
|
| |
| model_name = "facebook/wav2vec2-large-xlsr-53" |
| model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name, num_labels=7) |
| processor = Wav2Vec2Processor.from_pretrained(model_name) |
|
|
| |
| def recognize_emotion(audio): |
| |
| audio_input, _ = librosa.load(audio, sr=16000) |
| |
| |
| inputs = processor(audio_input, sampling_rate=16000, return_tensors="pt", padding=True) |
| |
| |
| with torch.no_grad(): |
| logits = model(**inputs).logits |
| |
| |
| emotion_map = { |
| 0: "Neutral", |
| 1: "Happy", |
| 2: "Angry", |
| 3: "Sad", |
| 4: "Surprised", |
| 5: "Fearful", |
| 6: "Disgusted" |
| } |
| |
| |
| predicted_class = torch.argmax(logits, dim=-1).item() |
| emotion = emotion_map[predicted_class] |
| |
| return emotion |
|
|
| |
| iface = gr.Interface( |
| fn=recognize_emotion, |
| inputs=gr.inputs.Audio(source="microphone", type="filepath"), |
| outputs="text", |
| title="Speech Emotion Recognition", |
| description="Identify the emotion in the speech: Happy, Sad, Angry, Surprised, Neutral, Fearful, or Disgusted." |
| ) |
|
|
| |
| iface.launch() |
|
|
