Create app.py

app.py

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+import gradio as gr
+import requests
+import base64
+import os
+import json
+import numpy as np
+import scipy.io.wavfile as wavfile
+import tempfile
+import torch
+from google import genai
+from google.genai import types
+from gradio_client import Client, handle_file
+from pyannote.audio import Pipeline
+
+# Configuration
+SEAMLESS_SPACE = "tgpro1/sttr"
+GEMINI_API_KEY = os.environ.get('GEMINI_API_KEY')
+HF_TOKEN = os.environ.get('HF_TOKEN')
+
+LANGUAGES = {
+    "Darija": "ar-SA",
+    "Arabic": "ar-SA", 
+    "French": "fr-FR",
+    "English": "en-US",
+    "Spanish": "es-ES",
+    "German": "de-DE",
+    "Italian": "it-IT",
+    "Portuguese": "pt-PT",
+    "Chinese": "zh-CN",
+    "Japanese": "ja-JP",
+    "Korean": "ko-KR",
+    "Russian": "ru-RU",
+}
+
+# Pyannote Diarization
+diarization_pipeline = None
+try:
+    if HF_TOKEN:
+        diarization_pipeline = Pipeline.from_pretrained(
+            "pyannote/speaker-diarization-3.1",
+            use_auth_token=HF_TOKEN
+        )
+        if torch.cuda.is_available():
+            diarization_pipeline.to(torch.device("cuda"))
+            print("Pyannote: LOADED (GPU)")
+        else:
+            print("Pyannote: LOADED (CPU)")
+except Exception as e:
+    print(f"Pyannote Error: {e}")
+
+def diarize_audio(audio_path, min_speakers=1, max_speakers=5):
+    if not diarization_pipeline:
+        return {"error": "Diarization not available"}
+    try:
+        diarization = diarization_pipeline(audio_path, min_speakers=int(min_speakers), max_speakers=int(max_speakers))
+        speakers = []
+        for turn, _, speaker in diarization.itertracks(yield_label=True):
+            speakers.append({"speaker": speaker, "start": round(turn.start, 2), "end": round(turn.end, 2)})
+        return {"segments": speakers, "num_speakers": len(set(s["speaker"] for s in speakers))}
+    except Exception as e:
+        return {"error": str(e)}
+
+with gr.Blocks(title="STTR") as demo:
+    gr.Markdown("# STTR - Speaker Diarization")
+    with gr.Tab("Diarization"):
+        audio_in = gr.Audio(type="filepath", label="Audio")
+        with gr.Row():
+            min_spk = gr.Slider(1, 10, value=1, step=1, label="Min Speakers")
+            max_spk = gr.Slider(1, 10, value=5, step=1, label="Max Speakers")
+        btn = gr.Button("Analyze", variant="primary")
+        output = gr.JSON(label="Result")
+        btn.click(diarize_audio, [audio_in, min_spk, max_spk], output, api_name="/diarize")
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))