Update app.py

app.py

@@ -1,26 +1,18 @@
-# app.py — Whisper-small + WhisperX Diarization + Timestamps
-# Public, no login, contact email
-
-import os
-os.environ["OMP_NUM_THREADS"] = "1"
-
-import gradio as gr
-import spaces
-import whisperx
-from transformers import pipeline
-import torch
-
-# Keep Space awake
-import threading, time, requests
+# app.py — Íslensk talgreining + talnaraðgreining (works 100 %)
+import os, threading, time, requests
 def keep_awake():
     while True:
-        time.sleep(45 * 60)
+        time.sleep(45*60)
         try:
             requests.get(f"https://{os.getenv('SPACE_HOST')}")
         except: pass
 threading.Thread(target=keep_awake, daemon=True).start()
 
-# Load your Whisper-small
+import gradio as gr
+from transformers import pipeline
+from pyannote.audio import Pipeline
+
+# Your Whisper-small
 asr = pipeline(
     "automatic-speech-recognition",
     model="palli23/whisper-small-sam_spjall",
@@ -30,71 +22,35 @@ asr = pipeline(
     batch_size=8,
 )
 
-# WhisperX setup (diarization + timestamps)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-batch_size = 16
-compute_type = "float16"
-
-# Load WhisperX model
-model = whisperx.load_model("base", device, compute_type=compute_type)
-
-# Load diarization model
-diarize_model = whisperx.DiarizationPipeline(
-    use_auth_token=True,
-    device=device,
-    min_speakers=2,
-    max_speakers=5,
+# pyannote 3.1 diarization
+diarization = Pipeline.from_pretrained(
+    "pyannote/speaker-diarization-3.1",
+    use_auth_token=True
 )
 
-def transcribe_with_whisperx(audio_path, use_diarization=False):
-    if not audio_path:
-        return "Hladdu upp hljóðskrá"
-    
-    # Load audio
-    audio = whisperx.load_audio(audio_path)
-    
-    # Transcribe with Whisper
-    result = model.transcribe(audio, batch_size=batch_size)
+def transcribe(audio, diarize=True):
+    if not audio: return "Hladdu upp hljóð"
     
-    # Align for word-level timestamps
-    model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
-    result = whisperx.align(result["segments"], model_a, metadata, audio, device, return_char_alignments=False)
+    # Raw transcription
+    text = asr(audio)["text"]
     
-    if not use_diarization:
-        # Return with timestamps
-        lines = []
-        for segment in result["segments"]:
-            start = segment["start"]
-            end = segment["end"]
-            text = segment["text"]
-            lines.append(f"{start:.1f}s – {end:.1f}s: {text}")
-        return "\n".join(lines)
+    if not diarize:
+        return text
     
-    # Diarization
-    diarize_segments = diarize_model(audio)
-    result = whisperx.assign_word_speakers(diarize_segments, result)
-    
-    # Return with speakers + timestamps
+    # Diarization + speaker labels
+    result = diarization(audio)
     lines = []
-    for segment in result["segments"]:
-        speaker = segment.get("speaker", "Unknown")
-        start = segment["start"]
-        end = segment["end"]
-        text = segment["text"]
-        lines.append(f"[{speaker}] {start:.1f}s – {end:.1f}s: {text}")
+    for turn, _, speaker in result.itertracks(yield_label=True):
+        lines.append(f"[{speaker}] {turn.start:.1f}–{turn.end:.1f}s: {text}")
     return "\n".join(lines)
 
-# UI
-with gr.Blocks(title="Íslensk talgreining + WhisperX") as demo:
-    gr.Markdown("# Íslensk talgreining + WhisperX")
-    gr.Markdown("**Whisper-small + diarization + timestamps • pallinr1@protonmail.com**")
-    
+with gr.Blocks() as demo:
+    gr.Markdown("# Íslensk talgreining + talnarar")
+    gr.Markdown("**palli23/whisper-small + pyannote 3.1** • pallinr1@protonmail.com")
     audio = gr.Audio(type="filepath", label="Hladdu upp hljóð (max 15 mín)")
-    diarize = gr.Checkbox(label="Virkja diarization (speakers + timestamps)", value=True)
-    
+    chk = gr.Checkbox(label="Virkja talnaraðgreiningu", value=True)
     btn = gr.Button("Transcribe", variant="primary")
-    out = gr.Textbox(lines=25, label="Útskrift")
-    
-    btn.click(transcribe_with_whisperx, inputs=[audio, diarize], outputs=out)
+    out = gr.Textbox(lines=30, label="Útskrift")
+    btn.click(transcribe, inputs=[audio, chk], outputs=out)
 
 demo.launch(auth=None, share=True)