import gradio as gr from transformers import pipeline import whisper from collections import Counter import matplotlib.pyplot as plt # Load models emotion_classifier = pipeline("audio-classification", model="superb/hubert-large-superb-er") whisper_model = whisper.load_model("base") def create_emotion_chart(labels, scores): emoji_map = { "hap": "๐Ÿ˜Š Happy", "sad": "๐Ÿ˜” Sad", "neu": "๐Ÿ˜ Neutral", "ang": "๐Ÿ˜  Angry", "fea": "๐Ÿ˜จ Fear", "dis": "๐Ÿคข Disgust", "sur": "๐Ÿ˜ฎ Surprise" } color_map = { "hap": "#facc15", "sad": "#60a5fa", "neu": "#a1a1aa", "ang": "#ef4444", "fea": "#818cf8", "dis": "#14b8a6", "sur": "#f472b6" } display_labels = [emoji_map.get(label, label) for label in labels] colors = [color_map.get(label, "#60a5fa") for label in labels] fig, ax = plt.subplots(figsize=(5, 3.5)) bars = ax.barh(display_labels, scores, color=colors, edgecolor="black", height=0.5) for bar, score in zip(bars, scores): ax.text(bar.get_width() + 0.02, bar.get_y() + bar.get_height() / 2, f"{score:.2f}", va='center', fontsize=10) ax.set_xlim(0, 1) ax.set_title("๐ŸŽญ Emotion Confidence Scores", fontsize=13, pad=10) ax.invert_yaxis() ax.set_facecolor("#f9fafb") fig.patch.set_facecolor("#f9fafb") for spine in ax.spines.values(): spine.set_visible(False) ax.tick_params(axis='x', colors='gray') ax.tick_params(axis='y', colors='gray') return fig def generate_next_moves(dominant_emotion, conf_score, transcript=""): suggestions = [] harsh_words = ["bad", "ugly", "terrible", "hate", "worst"] positive_tone_negative_words = any(word in transcript.lower() for word in harsh_words) if "happiness" in dominant_emotion else False if 'sadness' in dominant_emotion: suggestions.append("Your tone feels low โ€” try lifting the pitch slightly to bring more warmth.") suggestions.append("Even if the words are positive, a brighter tone helps convey enthusiasm.") elif 'happiness' in dominant_emotion and conf_score >= 80: suggestions.append("Nice energy! Try modulating your tone even more for emphasis in key moments.") suggestions.append("Experiment with subtle emotional shifts as you speak for more depth.") elif 'neutral' in dominant_emotion: suggestions.append("Add inflection to break a monotone pattern โ€” especially at the ends of sentences.") suggestions.append("Highlight your message by stressing emotionally important words.") elif conf_score < 50: suggestions.append("Try exaggerating vocal ups and downs when reading to unlock more expression.") suggestions.append("Slow down slightly and stretch certain words to vary your delivery.") else: suggestions.append("Keep practicing tone variation โ€” youโ€™re building a solid base.") if positive_tone_negative_words: suggestions.append("Your tone was upbeat, but the word choices were harsh โ€” aim to align both for better impact.") return "\n- " + "\n- ".join(suggestions) def generate_personacoach_report(emotions, transcript): report = "## ๐Ÿ“ **Your PersonaCoach Report**\n---\n\n" report += "### ๐Ÿ—’๏ธ **What You Said:**\n" report += f"> _{transcript.strip()}_\n\n" label_map = { 'hap': '๐Ÿ˜Š happiness', 'sad': '๐Ÿ˜” sadness', 'neu': '๐Ÿ˜ neutral', 'ang': '๐Ÿ˜  anger', 'fea': '๐Ÿ˜จ fear', 'dis': '๐Ÿคข disgust', 'sur': '๐Ÿ˜ฎ surprise' } for e in emotions: e['emotion'] = label_map.get(e['label'], e['label']) scores = [s['score'] for s in emotions] top_score = max(scores) conf_score = int(top_score * 100) meaningful_emotions = [(e['emotion'], e['score']) for e in emotions if e['score'] >= 0.2] emotion_labels = [e[0] for e in meaningful_emotions] dominant_emotion = emotion_labels[0] if emotion_labels else "neutral" report += f"### ๐ŸŽฏ **Tone Strength:**\n- Your tone scored **{conf_score}/100** in clarity.\n\n" report += "### ๐Ÿ—ฃ๏ธ **Emotion & Delivery:**\n" if meaningful_emotions: emotions_str = ", ".join([f"**{label}** ({score:.2f})" for label, score in meaningful_emotions]) report += f"- Emotionally, your voice showed: {emotions_str}\n" else: report += "- Your tone wasnโ€™t clearly expressive. Try reading with a bit more emphasis or emotion.\n" filler_words = ["um", "uh", "like", "you know", "so", "actually", "basically", "literally"] words = transcript.lower().split() total_words = len(words) filler_count = sum(words.count(fw) for fw in filler_words) filler_ratio = filler_count / total_words if total_words > 0 else 0 report += "\n### ๐Ÿ’ฌ **Pausing Style (e.g., 'um', 'like', 'you know'):**\n" report += f"- You used **{filler_count}** hesitation phrases out of **{total_words}** words.\n" if filler_ratio > 0.06: report += "- Try pausing instead of using fillers โ€” it builds stronger presence.\n" elif filler_ratio > 0.03: report += "- A few slipped in. Practice holding space with silence instead.\n" else: report += "- Great fluency โ€” you stayed focused and controlled.\n" report += "\n### โœ… **What You're Doing Well:**\n" if top_score >= 0.75 and filler_ratio < 0.03: report += "- Confident tone and smooth delivery โ€” keep it up!\n" else: report += "- Youโ€™re on track. Keep refining tone and pacing.\n" report += "\n### ๐Ÿงญ **Next Moves:**\n" report += generate_next_moves(dominant_emotion, conf_score, transcript) + "\n" return report def analyze_audio(audio_path): result = whisper_model.transcribe(audio_path) transcript = result['text'] emotion_results = emotion_classifier(audio_path) labels = [r['label'] for r in emotion_results] scores = [r['score'] for r in emotion_results] fig = create_emotion_chart(labels, scores) report = generate_personacoach_report(emotion_results, transcript) return transcript, fig, report with gr.Blocks(title="SPEAK: PersonaCoach", theme=gr.themes.Soft()) as app: gr.Markdown("""

๐ŸŽค SPEAK: PersonaCoach

Your smart voice reflection tool โ€” assess tone, confidence, and delivery

""", elem_id="header") with gr.Row(): with gr.Column(scale=4): audio_input = gr.Audio(type="filepath", label="๐ŸŽง Upload Your Voice (.wav)", elem_id="upload-audio") with gr.Column(scale=1, min_width=120): analyze_btn = gr.Button("๐Ÿ” Analyze", size="sm", elem_id="analyze-btn") gr.Markdown("## ๐Ÿง  Results", elem_id="results-header") with gr.Row(equal_height=True): with gr.Column(scale=2): feedback_output = gr.Markdown(label="๐Ÿ“„ PersonaCoach Feedback", elem_id="report-section") with gr.Column(scale=1): emotion_plot = gr.Plot(label="๐ŸŽญ Emotion Chart", elem_id="chart") analyze_btn.click( fn=analyze_audio, inputs=audio_input, outputs=[gr.Textbox(visible=False), emotion_plot, feedback_output] ) app.launch()