initial

.gitignore

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+/.venv/

main.py

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+#!/usr/bin/env python
+
+import argparse
+
+from pyannote.audio import Pipeline
+
+def srt_timestamp(milis):
+    h = milis // (1000 * 60 * 60)
+    milis %= 1000 * 60 * 60
+    m = milis // (1000 * 60)
+    milis %= 1000 * 60
+    s = milis // 1000
+    milis %= 1000
+    return f"{h:02}:{m:02}:{s:02},{milis:03}"
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("input")
+    parser.add_argument("-o", "--output", default="output.srt")
+    args = parser.parse_args()
+
+    pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-community-1",
+                                        token="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")
+    output = pipeline(args.input, num_speakers=2)
+    for turn, speaker in output.speaker_diarization:
+        print(f"{speaker} speaks between t={turn.start:.3f}s and t={turn.end:.3f}s")
+
+    with open(args.output, "w") as srt_file:
+        for i, (turn, speaker) in enumerate(output.speaker_diarization, 1):
+            srt_file.write(f"{i}\n"
+                           f"{srt_timestamp(int(turn.start * 1000))} --> "
+                           f"{srt_timestamp(int(turn.end * 1000))}\n"
+                           f"{speaker}\n\n")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+pyannote-audio==4.0.4
+openai-whisper==20250625