app.py

import torch
import spaces
import gradio as gr
import os
from pyannote.audio import Pipeline

# instantiate the pipeline
try:
    pipeline = Pipeline.from_pretrained(
        "pyannote/speaker-diarization-3.1",
        use_auth_token=os.environ["api"]
    )
    # Move the pipeline to the GPU
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    pipeline.to(device)
except Exception as e:
    print(f"Error initializing pipeline: {e}")
    pipeline = None


def save_audio(audio):
    if pipeline is None:
        return "Error: Pipeline not initialized"

    # Read the uploaded audio file as bytes
    with open(audio, "rb") as f:
        audio_data = f.read()

    # Save the uploaded audio file to a temporary location
    with open("temp.wav", "wb") as f:
        f.write(audio_data)

    return "temp.wav"

@spaces.GPU(duration=90)
def diarize_audio(temp_file, num_speakers, min_speakers, max_speakers):
    if pipeline is None:
        return "Error: Pipeline not initialized"

    try:
        params = {}
        if num_speakers > 0:
            params["num_speakers"] = num_speakers
        if min_speakers > 0:
            params["min_speakers"] = min_speakers
        if max_speakers > 0:
            params["max_speakers"] = max_speakers

        diarization = pipeline(temp_file, **params)
    except Exception as e:
        return f"Error processing audio: {e}"

    # Remove the temporary file
    os.remove(temp_file)

    # Return the diarization output
    return str(diarization)

with gr.Blocks() as demo:
    gr.Markdown("""
    # 🗣️Pyannote Speaker Diarization 3.1🗣️

    This model takes an audio file as input and outputs the diarization of the speakers in the audio.

    Please upload an audio file and adjust the parameters as needed.
    
    The maximum length of the audio file it can process is around **35-40 minutes**.

    If you find this space helpful, please ❤ it.

    """)
    audio_input = gr.Audio(type="filepath", label="Upload Audio")  
    num_speakers_input = gr.Number(label="Number of Speakers (The maximum number of speakers to detect)", value=0)
    min_speakers_input = gr.Number(label="Minimum Number of Speakers (The maximum number of speakers to detect)", value=0)
    max_speakers_input = gr.Number(label="Maximum Number of Speakers (The maximum number of speakers to detect)", value=0)
    process_button = gr.Button("Process")
    diarization_output = gr.Textbox(label="Diarization Output")

    process_button.click(
        fn=lambda audio, num_speakers, min_speakers, max_speakers: 
            diarize_audio(save_audio(audio), num_speakers, min_speakers, max_speakers),
        inputs=[audio_input, num_speakers_input, min_speakers_input, max_speakers_input],
        outputs=diarization_output
    )

demo.launch()