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import spaces
import gradio as gr
import os
import torch
from pyannote.audio import Pipeline
# Configuration
HF_TOKEN = os.environ.get('HF_TOKEN')
# 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}")
@spaces.GPU
def diarize_audio(audio_path, min_speakers=1, max_speakers=5):
if not diarization_pipeline:
return {"error": "Diarization not available. Check HF_TOKEN."}
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 - Speaker Diarization") as demo:
gr.Markdown("# STTR - Speaker Diarization")
gr.Markdown("### Identify who speaks when (pyannote 3.1)")
audio_in = gr.Audio(type="filepath", label="Upload 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 Speakers", variant="primary")
output = gr.JSON(label="Speaker Segments")
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))) |