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Create app.py
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app.py
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import gradio as gr
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import soundfile as sf
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import wave
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from pyannote.audio import Pipeline
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import torch
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pipeline = Pipeline.from_pretrained(
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"pyannote/speaker-diarization-3.0",
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use_auth_token=os.getenv("HF_AUTH_TOKEN")
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pipeline.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
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def process_audio(audio_file):
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diarization = pipeline(audio_file)
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with open("audio.rttm", "w") as rttm:
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diarization.write_rttm(rttm)
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speaker_durations = {}
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first_speaker = None
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with open("audio.rttm", "r") as file:
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for line in file:
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parts = line.strip().split()
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speaker = parts[7]
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start_time = float(parts[3])
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duration = float(parts[4])
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if first_speaker is None:
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first_speaker = speaker
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if speaker not in speaker_durations:
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speaker_durations[speaker] = 0
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speaker_durations[speaker] += duration
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total_duration = sum(speaker_durations.values())
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first_speaker_duration = speaker_durations.get(first_speaker, 0)
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percentage_first_speaker = (first_speaker_duration / total_duration) * 100 if total_duration > 0 else 0
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return percentage_first_speaker
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def record_and_process(audio):
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if audio is None:
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return "No audio was recorded. Please try again."
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sample_rate, audio_data = audio
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file_path = "audio.wav"
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sf.write(file_path, audio_data, sample_rate)
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percentage = process_audio(file_path)
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return f"Percentage of time spoken by the first speaker: {percentage:.2f}%"
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interface = gr.Interface(
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fn=record_and_process,
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inputs=gr.Audio(type="numpy"),
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outputs="text",
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title="See How Much You Talk in a Conversation",
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description=(
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"Make sure you are the first person to speak!<br>"
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"You can also use a sample audio file for testing: "
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"<a href='https://www.uclass.psychol.ucl.ac.uk/Release2/Conversation/AudioOnly/wav/M_0025_11y10m_1.wav' "
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"target='_blank'>sample audio</a>.<br>"
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"<u>Your voice never leaves your device.</u>"
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),
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allow_flagging="never"
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)
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interface.launch()
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