Upload 4 files

app.py

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+# -*- coding: utf-8 -*-
+"""app
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1GiJsUjgSfSzhuo0YkKYDvzQk5Cg2Qiao
+"""
+
+import os
+import pickle
+import numpy as np
+import pandas as pd
+import gradio as gr
+import soundfile as sf
+
+from faster_whisper import WhisperModel
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+# -----------------------------
+# Load emotion vectors
+# -----------------------------
+EMBED_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
+CENTROIDS_PATH = "emotion_avg.pkl"
+
+with open(CENTROIDS_PATH, "rb") as f:
+    emotion_avg = pickle.load(f)
+
+for k in list(emotion_avg.keys()):
+    emotion_avg[k] = np.array(emotion_avg[k])
+
+EMOTIONS = list(emotion_avg.keys())
+
+# -----------------------------
+# Load models
+# -----------------------------
+embedder = SentenceTransformer(EMBED_MODEL_NAME)
+whisper_model = WhisperModel("base", compute_type="int8")
+
+# -----------------------------
+# Prediction helper
+# -----------------------------
+def predict_emotion_sentence(sentence):
+    emb = embedder.encode([sentence], convert_to_numpy=True)[0]
+    labels = []
+    sims = []
+
+    for emotion in EMOTIONS:
+        sim = cosine_similarity(
+            emb.reshape(1, -1),
+            emotion_avg[emotion].reshape(1, -1)
+        )[0][0]
+        labels.append(emotion)
+        sims.append(sim)
+
+    order = np.argsort(sims)[::-1]
+    best_idx = order[0]
+    second_idx = order[1] if len(order) > 1 else order[0]
+
+    return {
+        "emotion": labels[best_idx],
+        "score": float(sims[best_idx]),
+        "margin": float(sims[best_idx] - sims[second_idx])
+    }
+
+# -----------------------------
+# Main app function
+# -----------------------------
+def analyze_audio(audio_path):
+    if audio_path is None:
+        return "No transcript yet.", "None", 0.0, pd.DataFrame(columns=["sentence", "emotion", "score", "margin"])
+
+    segments, _ = whisper_model.transcribe(audio_path)
+
+    transcript_parts = []
+    rows = []
+
+    for seg in segments:
+        text = seg.text.strip()
+        if not text:
+            continue
+        transcript_parts.append(text)
+
+        pred = predict_emotion_sentence(text)
+        rows.append({
+            "sentence": text,
+            "emotion": pred["emotion"],
+            "score": pred["score"],
+            "margin": pred["margin"]
+        })
+
+    transcript = " ".join(transcript_parts).strip()
+
+    if rows:
+        latest = rows[-1]
+        latest_emotion = latest["emotion"]
+        latest_margin = latest["margin"]
+    else:
+        latest_emotion = "None"
+        latest_margin = 0.0
+
+    df = pd.DataFrame(rows)
+    return transcript, latest_emotion, latest_margin, df
+
+# -----------------------------
+# UI
+# -----------------------------
+with gr.Blocks(title="Emotion Speech Analyzer") as demo:
+    gr.Markdown("# Emotion Speech Analyzer")
+    gr.Markdown("Upload or record audio, transcribe it, and detect sentence-level emotion.")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            audio_input = gr.Audio(
+                sources=["microphone", "upload"],
+                type="filepath",
+                label="Audio Input"
+            )
+            run_btn = gr.Button("Analyze Audio")
+
+        with gr.Column(scale=2):
+            transcript_box = gr.Textbox(label="Transcript", lines=8)
+            with gr.Row():
+                latest_emotion_box = gr.Textbox(label="Latest Emotion")
+                margin_box = gr.Number(label="Match Margin")
+
+    results_df = gr.Dataframe(
+        headers=["sentence", "emotion", "score", "margin"],
+        label="Sentence Analysis"
+    )
+
+    run_btn.click(
+        fn=analyze_audio,
+        inputs=audio_input,
+        outputs=[transcript_box, latest_emotion_box, margin_box, results_df]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

emotion_avg.pkl.py

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+# -*- coding: utf-8 -*-
+"""emotion_avg.pkl
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/16UTkPmy595caC3JG_2im6CrqRRsNXgha
+"""
+
+CENTROIDS_PATH = "emotion_avg.pkl"

packages.txt

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+# -*- coding: utf-8 -*-
+"""packages.txt
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1w6c_tqSlT7TQET--l9r1rM9eijDoDs_k
+"""
+
+ffmpeg

requirements.txt

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+# -*- coding: utf-8 -*-
+"""requirements.txt
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1KqS7WoYBbtlNDCjJBCtbUSqgCPc5784t
+"""
+
+gradio
+faster-whisper
+soundfile
+sentence-transformers
+scikit-learn
+numpy
+pandas
+torch