Some diarization

tavern_talk/diarization.py

@@ -1,82 +1,46 @@
-audio_file = "./tavern_talk/short_transcript.wav"
-
-
-import torchaudio
+# instantiate the pipeline
+from pyannote.audio import Pipeline
 import torch
-from speechbrain.inference.classifiers import EncoderClassifier
-from scipy.cluster.vq import kmeans2
-import numpy as np
-import matplotlib.pyplot as plt
 
-# Load the speaker encoder model
-classifier = EncoderClassifier.from_hparams(
-    source="speechbrain/spkrec-xvect-voxceleb", savedir="tmp_spkrec"
+audio_path = "short_transcript.wav"
+
+pipeline = Pipeline.from_pretrained(
+    "pyannote/speaker-diarization-3.1",
+    use_auth_token="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",
 )
 
-# Load the ASR model from torchaudio
-asr_model = torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H.get_model()
 
-# Define the audio file path
-signal, fs = torchaudio.load(audio_file)
+# run the pipeline on an audio file
+diarization = pipeline(audio_path, min_speakers=6, max_speakers=7)
 
-# Segment the audio into 1-second chunks with a 50% overlap for speaker embeddings
-window_size = int(fs * 1.0)
-overlap = int(fs * 0.5)
-segments = []
-embeddings = []
-
-for start in range(0, signal.shape[1] - window_size, overlap):
-    segment = signal[:, start : start + window_size]
-    segments.append((start / fs, (start + window_size) / fs))
-    embedding = classifier.encode_batch(segment)
-    embeddings.append(embedding.squeeze(0).detach().cpu().numpy())
-
-# Convert embeddings to a 2D numpy array (num_segments x embedding_size)
-embeddings = np.vstack(embeddings)
-
-# Perform KMeans clustering on 2D embeddings
-centroids, labels = kmeans2(embeddings, k=6)  # Adjust 'k' based on number of speakers
-
-# Output diarization results with speaker labels and timestamps
-print("Diarization Results:")
-for i, (start, end) in enumerate(segments):
-    print(f"{start:.2f}s - {end:.2f}s: Speaker {labels[i]}")
-
-# Perform ASR on the entire audio file and display the result
-with torch.inference_mode():
-    asr_transcription = asr_model(signal)[0]  # Extract only the transcription result
-    asr_text = asr_transcription.tolist()
-
-print("\nTranscription Results:")
-print(asr_text)
+# dump the diarization output to disk using RTTM format
+with open("short_transcript.rttm", "w") as rttm:
+    diarization.write_rttm(rttm)
 
 
-# Optional: plot audio waveform with speaker probabilities
-def plot_diarization_with_audio(signal, fs, segments, labels):
-    # Plot audio waveform
-    plt.figure(figsize=(12, 6))
-    time = torch.arange(0, signal.shape[1]) / fs
-    plt.subplot(2, 1, 1)
-    plt.plot(time, signal.t().numpy())
-    plt.title("Audio Waveform")
+import matplotlib.pyplot as plt
+import librosa
+import librosa.display
+
+# Load the audio file and compute its waveform
+audio, sr = librosa.load(audio_path, sr=None)
+
+# Plot the audio waveform
+plt.figure(figsize=(10, 6))
+librosa.display.waveshow(audio, sr=sr, alpha=0.5, color="gray")
 plt.xlabel("Time (s)")
 plt.ylabel("Amplitude")
+plt.title("Speaker Diarization Results")
 
-    # Plot speaker diarization
-    plt.subplot(2, 1, 2)
-    for i, (start, end) in enumerate(segments):
-        speaker_label = labels[i]
-        plt.plot(
-            [start, end],
-            [speaker_label, speaker_label],
-            label=f"Speaker {speaker_label}",
-            linewidth=4,
-        )
+# Plot speaker segments
+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}")
+
+# Avoid duplicate labels in legend
+handles, labels = plt.gca().get_legend_handles_labels()
+by_label = dict(zip(labels, handles))
+plt.legend(by_label.values(), by_label.keys(), loc="upper right")
 
-    plt.xlabel("Time (s)")
-    plt.ylabel("Speaker")
-    plt.title("Speaker Diarization with Probability")
 plt.show()
-
-
-plot_diarization_with_audio(signal, fs, segments, labels)