Update app.py
Browse files
app.py
CHANGED
|
@@ -11,18 +11,12 @@ import io
|
|
| 11 |
@st.cache_resource
|
| 12 |
def load_models():
|
| 13 |
try:
|
| 14 |
-
# Updated to 3.1 with parameters
|
| 15 |
diarization = Pipeline.from_pretrained(
|
| 16 |
"pyannote/speaker-diarization-3.1",
|
| 17 |
use_auth_token=st.secrets["hf_token"]
|
| 18 |
-
)
|
| 19 |
-
"onset": 0.3,
|
| 20 |
-
"offset": 0.3,
|
| 21 |
-
"min_duration_on": 0.1,
|
| 22 |
-
"min_duration_off": 0.1
|
| 23 |
-
})
|
| 24 |
|
| 25 |
-
transcriber = whisper.load_model("
|
| 26 |
|
| 27 |
summarizer = tf_pipeline(
|
| 28 |
"summarization",
|
|
@@ -78,7 +72,7 @@ def process_audio(audio_file, max_duration=600):
|
|
| 78 |
|
| 79 |
return {
|
| 80 |
"diarization": diarization_result,
|
| 81 |
-
"transcription": transcription,
|
| 82 |
"summary": summary[0]["summary_text"]
|
| 83 |
}
|
| 84 |
|
|
@@ -86,26 +80,24 @@ def process_audio(audio_file, max_duration=600):
|
|
| 86 |
st.error(f"Error processing audio: {str(e)}")
|
| 87 |
return None
|
| 88 |
|
| 89 |
-
def format_speaker_segments(diarization_result
|
| 90 |
formatted_segments = []
|
| 91 |
-
audio_duration = transcription.get('duration', 0)
|
| 92 |
|
| 93 |
for turn, _, speaker in diarization_result.itertracks(yield_label=True):
|
| 94 |
-
|
| 95 |
-
if turn.start > audio_duration or turn.end > audio_duration:
|
| 96 |
-
continue
|
| 97 |
-
|
| 98 |
-
# Only add segments with meaningful duration
|
| 99 |
-
if (turn.end - turn.start) >= 0.1: # 100ms minimum
|
| 100 |
formatted_segments.append({
|
| 101 |
'speaker': speaker,
|
| 102 |
-
'start': turn.start,
|
| 103 |
-
'end': turn.end
|
| 104 |
-
'duration': turn.end - turn.start
|
| 105 |
})
|
| 106 |
|
| 107 |
return formatted_segments
|
| 108 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 109 |
def main():
|
| 110 |
st.title("Multi-Speaker Audio Analyzer")
|
| 111 |
st.write("Upload an audio file (MP3/WAV) up to 5 minutes long for best performance")
|
|
@@ -129,30 +121,21 @@ def main():
|
|
| 129 |
|
| 130 |
with tab1:
|
| 131 |
st.write("Speaker Timeline:")
|
|
|
|
| 132 |
|
| 133 |
-
segments = format_speaker_segments(
|
| 134 |
-
results["diarization"],
|
| 135 |
-
results["transcription"]
|
| 136 |
-
)
|
| 137 |
-
|
| 138 |
-
# Display segments with proper time formatting
|
| 139 |
for segment in segments:
|
| 140 |
col1, col2 = st.columns([2,8])
|
| 141 |
|
| 142 |
with col1:
|
| 143 |
speaker_num = int(segment['speaker'].split('_')[1])
|
| 144 |
-
colors = ['π΅', 'π΄'] #
|
| 145 |
speaker_color = colors[speaker_num % len(colors)]
|
| 146 |
st.write(f"{speaker_color} {segment['speaker']}")
|
| 147 |
|
| 148 |
with col2:
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
|
| 152 |
-
ss_end = segment['end'] % 60
|
| 153 |
-
|
| 154 |
-
time_str = f"{mm_start:02d}:{ss_start:05.2f} β {mm_end:02d}:{ss_end:05.2f}"
|
| 155 |
-
st.write(time_str)
|
| 156 |
|
| 157 |
st.markdown("---")
|
| 158 |
|
|
|
|
| 11 |
@st.cache_resource
|
| 12 |
def load_models():
|
| 13 |
try:
|
|
|
|
| 14 |
diarization = Pipeline.from_pretrained(
|
| 15 |
"pyannote/speaker-diarization-3.1",
|
| 16 |
use_auth_token=st.secrets["hf_token"]
|
| 17 |
+
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 18 |
|
| 19 |
+
transcriber = whisper.load_model("small")
|
| 20 |
|
| 21 |
summarizer = tf_pipeline(
|
| 22 |
"summarization",
|
|
|
|
| 72 |
|
| 73 |
return {
|
| 74 |
"diarization": diarization_result,
|
| 75 |
+
"transcription": transcription,
|
| 76 |
"summary": summary[0]["summary_text"]
|
| 77 |
}
|
| 78 |
|
|
|
|
| 80 |
st.error(f"Error processing audio: {str(e)}")
|
| 81 |
return None
|
| 82 |
|
| 83 |
+
def format_speaker_segments(diarization_result):
|
| 84 |
formatted_segments = []
|
|
|
|
| 85 |
|
| 86 |
for turn, _, speaker in diarization_result.itertracks(yield_label=True):
|
| 87 |
+
if turn.start is not None and turn.end is not None:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 88 |
formatted_segments.append({
|
| 89 |
'speaker': speaker,
|
| 90 |
+
'start': float(turn.start),
|
| 91 |
+
'end': float(turn.end)
|
|
|
|
| 92 |
})
|
| 93 |
|
| 94 |
return formatted_segments
|
| 95 |
|
| 96 |
+
def format_timestamp(seconds):
|
| 97 |
+
minutes = int(seconds // 60)
|
| 98 |
+
seconds = seconds % 60
|
| 99 |
+
return f"{minutes:02d}:{seconds:05.2f}"
|
| 100 |
+
|
| 101 |
def main():
|
| 102 |
st.title("Multi-Speaker Audio Analyzer")
|
| 103 |
st.write("Upload an audio file (MP3/WAV) up to 5 minutes long for best performance")
|
|
|
|
| 121 |
|
| 122 |
with tab1:
|
| 123 |
st.write("Speaker Timeline:")
|
| 124 |
+
segments = format_speaker_segments(results["diarization"])
|
| 125 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 126 |
for segment in segments:
|
| 127 |
col1, col2 = st.columns([2,8])
|
| 128 |
|
| 129 |
with col1:
|
| 130 |
speaker_num = int(segment['speaker'].split('_')[1])
|
| 131 |
+
colors = ['π΅', 'π΄'] # Two colors for alternating speakers
|
| 132 |
speaker_color = colors[speaker_num % len(colors)]
|
| 133 |
st.write(f"{speaker_color} {segment['speaker']}")
|
| 134 |
|
| 135 |
with col2:
|
| 136 |
+
start_time = format_timestamp(segment['start'])
|
| 137 |
+
end_time = format_timestamp(segment['end'])
|
| 138 |
+
st.write(f"{start_time} β {end_time}")
|
|
|
|
|
|
|
|
|
|
|
|
|
| 139 |
|
| 140 |
st.markdown("---")
|
| 141 |
|