Some more advanced diarization

tavern_talk/diarization.py

@@ -1,40 +1,67 @@
-# instantiate the pipeline
-from pyannote.audio import Pipeline
 import torch
-
+from pyannote.audio import Pipeline
-audio_path = "short_transcript.wav"
+import whisper
-
-pipeline = Pipeline.from_pretrained(
-    "pyannote/speaker-diarization-3.1",
-    use_auth_token="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",
-)
-
-
-# run the pipeline on an audio file
-diarization = pipeline(audio_path, min_speakers=6, max_speakers=7)
-
-# dump the diarization output to disk using RTTM format
-with open("short_transcript.rttm", "w") as rttm:
-    diarization.write_rttm(rttm)
-
-
-import matplotlib.pyplot as plt
 import librosa
+import numpy as np
+import matplotlib.pyplot as plt
 import librosa.display
+from pyannote.core import Segment, Annotation
 
-# Load the audio file and compute its waveform
+# Load Whisper model for transcription
+whisper_model = whisper.load_model("large")
+
+
+# Transcribe audio using Whisper
+def transcribe_audio(audio_path):
+    result = whisper_model.transcribe(audio_path)
+    segments = result["segments"]
+    return [(segment["start"], segment["end"], segment["text"]) for segment in segments]
+
+
+# Initialize Pyannote Pipeline for diarization
+pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
+pipeline.to(torch.device("cuda"))
+
+
+# Perform diarization
+def perform_diarization(audio_path) -> Pipeline:
+    diarization = pipeline(audio_path, min_speakers=5, max_speakers=7)
+
+    # dump the diarization output to disk using RTTM format
+    with open("diarization.rttm", "w") as rttm:
+        diarization.write_rttm(rttm)
+
+    print("Finished diarization")
+
+    return diarization
+
+
+# Load audio and perform both transcription and diarization
+audio_path = "mid_audio.wav"
+transcription_segments = transcribe_audio(audio_path)
+diarization: Pipeline = perform_diarization(audio_path)
+
+# Print speaker and corresponding text
+print("\nSpeaker and Text Segments:")
+for segment in transcription_segments:
+    start, end, text = segment
+    for spk_segment, _, speaker_label in diarization.itertracks(yield_label=True):
+        if spk_segment.start < end and spk_segment.end > start:
+            print(f"Speaker {speaker_label}: {text}")
+            break
+
+# Load audio for plotting
 audio, sr = librosa.load(audio_path, sr=None)
 
-# Plot the audio waveform
+# Plot the audio waveform and speaker segments
-plt.figure(figsize=(10, 6))
+plt.figure(figsize=(12, 6))
 librosa.display.waveshow(audio, sr=sr, alpha=0.5, color="gray")
 plt.xlabel("Time (s)")
 plt.ylabel("Amplitude")
-plt.title("Speaker Diarization Results")
+plt.title("Speaker Diarization with Transcription")
 
-# Plot speaker segments
+# Plot speaker segments and add transcription text
 for segment, _, label in diarization.itertracks(yield_label=True):
-    # Get start and end times of each speaker segment
     start, end = segment.start, segment.end
     plt.plot([start, end], [0.9, 0.9], label=f"Speaker {label}")