Delete app.py

app.py

@@ -1,152 +0,0 @@
-import tempfile
-import shutil
-import numpy as np
-import librosa
-from fastapi import FastAPI, UploadFile, File, Form
-import uvicorn
-import gradio as gr
-from sklearn.preprocessing import StandardScaler
-
-app = FastAPI()
-
-CHUNK_DURATION = 30  # seconds
-SIMILARITY_THRESHOLD = 0.75
-
-@app.get("/")
-def home():
-    return {"message": "MFCC Speaker Diarization Server Running"}
-
-# 🔹 Feature extraction
-def extract_features(y, sr):
-    features = []
-
-    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
-    features.extend(np.mean(mfcc, axis=1))
-
-    spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)
-    features.append(np.mean(spectral_centroid))
-
-    spectral_bandwidth = librosa.feature.spectral_bandwidth(y=y, sr=sr)
-    features.append(np.mean(spectral_bandwidth))
-
-    zcr = librosa.feature.zero_crossing_rate(y)
-    features.append(np.mean(zcr))
-
-    rms = librosa.feature.rms(y=y)
-    features.append(np.mean(rms))
-
-    return np.array(features)
-
-# 🔹 Split audio into small segments
-def split_audio(y, sr, frame_sec=1.0):
-    frame_len = int(sr * frame_sec)
-    segments = []
-    times = []
-
-    for i in range(0, len(y), frame_len):
-        segment = y[i:i+frame_len]
-        if len(segment) < frame_len:
-            continue
-
-        energy = np.mean(np.abs(segment))
-        if energy > 0.01:
-            segments.append(segment)
-            times.append((i/sr, (i+frame_len)/sr))
-
-    return segments, times
-
-# 🔹 Core diarization logic (shared by API + UI)
-def process_audio_file(file_path):
-
-    y, sr = librosa.load(file_path, sr=None)
-    total_duration = len(y) / sr
-
-    all_segments = []
-    speaker_embeddings = []
-    speaker_labels = []
-    speaker_count = 0
-
-    current_time = 0
-
-    while current_time < total_duration:
-
-        start_sample = int(current_time * sr)
-        end_sample = int(min((current_time + CHUNK_DURATION) * sr, len(y)))
-
-        chunk = y[start_sample:end_sample]
-
-        segments, times = split_audio(chunk, sr)
-
-        for seg, (start, end) in zip(segments, times):
-
-            feat = extract_features(seg, sr)
-
-            if len(speaker_embeddings) > 0:
-                scaler = StandardScaler()
-                X = np.vstack(speaker_embeddings + [feat])
-                X = scaler.fit_transform(X)
-                feat_norm = X[-1]
-                existing = X[:-1]
-            else:
-                feat_norm = feat
-                existing = []
-
-            assigned = False
-            for i, emb in enumerate(existing):
-                similarity = np.dot(feat_norm, emb) / (
-                    np.linalg.norm(feat_norm) * np.linalg.norm(emb)
-                )
-                if similarity > SIMILARITY_THRESHOLD:
-                    speaker_id = speaker_labels[i]
-                    assigned = True
-                    break
-
-            if not assigned:
-                speaker_count += 1
-                speaker_id = f"SPEAKER_{speaker_count}"
-                speaker_embeddings.append(feat)
-                speaker_labels.append(speaker_id)
-
-            all_segments.append({
-                "speaker": speaker_id,
-                "start": round(current_time + start, 2),
-                "end": round(current_time + end, 2)
-            })
-
-        current_time += CHUNK_DURATION
-
-    return {"segments": all_segments}
-
-# 🔹 FastAPI endpoint
-@app.post("/transcribe")
-async def transcribe(audio: UploadFile = File(...), lang: str = Form("en")):
-    temp = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
-    with temp as buffer:
-        shutil.copyfileobj(audio.file, buffer)
-
-    result = process_audio_file(temp.name)
-    return result
-
-# 🔹 Gradio UI function
-def gradio_process(audio_file):
-    if audio_file is None:
-        return {"error": "No file uploaded"}
-
-    return process_audio_file(audio_file)
-
-# 🔹 Build Gradio Interface
-gradio_ui = gr.Interface(
-    fn=gradio_process,
-    inputs=gr.Audio(type="filepath", label="Upload Audio"),
-    outputs=gr.JSON(label="Speaker Segments"),
-    title="Speaker Diarization (CPU)",
-    description="Upload audio and get speaker labels with timestamps"
-)
-
-# 🔹 Mount Gradio into FastAPI
-from fastapi.middleware.wsgi import WSGIMiddleware
-app.mount("/ui", WSGIMiddleware(gradio_ui))
-
-# 🔹 Run
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)