Update app.py
Browse files
app.py
CHANGED
@@ -11,18 +11,12 @@ import io
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@st.cache_resource
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def load_models():
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try:
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# Updated to 3.1 with parameters
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diarization = Pipeline.from_pretrained(
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"pyannote/speaker-diarization-3.1",
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use_auth_token=st.secrets["hf_token"]
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)
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"onset": 0.3,
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"offset": 0.3,
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"min_duration_on": 0.1,
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"min_duration_off": 0.1
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})
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transcriber = whisper.load_model("
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summarizer = tf_pipeline(
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"summarization",
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@@ -78,7 +72,7 @@ def process_audio(audio_file, max_duration=600):
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return {
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"diarization": diarization_result,
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"transcription": transcription,
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"summary": summary[0]["summary_text"]
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}
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@@ -86,26 +80,24 @@ def process_audio(audio_file, max_duration=600):
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st.error(f"Error processing audio: {str(e)}")
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return None
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def format_speaker_segments(diarization_result
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formatted_segments = []
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audio_duration = transcription.get('duration', 0)
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for turn, _, speaker in diarization_result.itertracks(yield_label=True):
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if turn.start > audio_duration or turn.end > audio_duration:
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continue
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# Only add segments with meaningful duration
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if (turn.end - turn.start) >= 0.1: # 100ms minimum
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formatted_segments.append({
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'speaker': speaker,
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'start': turn.start,
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'end': turn.end
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'duration': turn.end - turn.start
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})
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return formatted_segments
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def main():
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st.title("Multi-Speaker Audio Analyzer")
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st.write("Upload an audio file (MP3/WAV) up to 5 minutes long for best performance")
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@@ -129,30 +121,21 @@ def main():
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with tab1:
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st.write("Speaker Timeline:")
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segments = format_speaker_segments(
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results["diarization"],
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results["transcription"]
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)
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# Display segments with proper time formatting
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for segment in segments:
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col1, col2 = st.columns([2,8])
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with col1:
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speaker_num = int(segment['speaker'].split('_')[1])
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colors = ['π΅', 'π΄'] #
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speaker_color = colors[speaker_num % len(colors)]
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st.write(f"{speaker_color} {segment['speaker']}")
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with col2:
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ss_end = segment['end'] % 60
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time_str = f"{mm_start:02d}:{ss_start:05.2f} β {mm_end:02d}:{ss_end:05.2f}"
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st.write(time_str)
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st.markdown("---")
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@st.cache_resource
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def load_models():
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try:
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diarization = Pipeline.from_pretrained(
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"pyannote/speaker-diarization-3.1",
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use_auth_token=st.secrets["hf_token"]
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)
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transcriber = whisper.load_model("small")
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summarizer = tf_pipeline(
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"summarization",
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return {
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"diarization": diarization_result,
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"transcription": transcription,
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"summary": summary[0]["summary_text"]
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}
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st.error(f"Error processing audio: {str(e)}")
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return None
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def format_speaker_segments(diarization_result):
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formatted_segments = []
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for turn, _, speaker in diarization_result.itertracks(yield_label=True):
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if turn.start is not None and turn.end is not None:
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formatted_segments.append({
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'speaker': speaker,
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'start': float(turn.start),
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'end': float(turn.end)
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})
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return formatted_segments
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def format_timestamp(seconds):
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minutes = int(seconds // 60)
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seconds = seconds % 60
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return f"{minutes:02d}:{seconds:05.2f}"
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def main():
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st.title("Multi-Speaker Audio Analyzer")
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st.write("Upload an audio file (MP3/WAV) up to 5 minutes long for best performance")
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with tab1:
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st.write("Speaker Timeline:")
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segments = format_speaker_segments(results["diarization"])
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for segment in segments:
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col1, col2 = st.columns([2,8])
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with col1:
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speaker_num = int(segment['speaker'].split('_')[1])
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colors = ['π΅', 'π΄'] # Two colors for alternating speakers
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speaker_color = colors[speaker_num % len(colors)]
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st.write(f"{speaker_color} {segment['speaker']}")
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with col2:
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start_time = format_timestamp(segment['start'])
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end_time = format_timestamp(segment['end'])
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st.write(f"{start_time} β {end_time}")
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st.markdown("---")
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