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app.py

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+import gradio as gr
+import torch
+import numpy as np
+import datetime
+from pyannote.audio import Audio
+from pyannote.core import Segment
+from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
+from sklearn.cluster import AgglomerativeClustering
+
+# Load the model (runs once when the Space starts)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+embedding_model = PretrainedSpeakerEmbedding(
+    "speechbrain/spkrec-ecapa-voxceleb",
+    device=device
+)
+audio_helper = Audio()
+
+def time_str(secs):
+    return str(datetime.timedelta(seconds=round(secs)))
+
+def process_audio(audio_file, num_speakers):
+    # 'audio_file' is the path to the uploaded file provided by Gradio
+    duration = audio_helper.get_duration(audio_file)
+    
+    # 1. Extract Voiceprints (Embeddings)
+    step = 2.0
+    embeddings = []
+    timestamps = []
+    
+    for start in np.arange(0, duration, step):
+        end = min(duration, start + step)
+        clip = Segment(start, end)
+        waveform, sample_rate = audio_helper.crop(audio_file, clip)
+        
+        # Ensure mono for the model
+        if waveform.shape[0] > 1:
+            waveform = waveform.mean(dim=0, keepdim=True)
+            
+        embeddings.append(embedding_model(waveform[None]))
+        timestamps.append((start, end))
+    
+    embeddings = np.nan_to_num(np.array(embeddings))
+    
+    # 2. Perform Clustering based on user input (num_speakers)
+    clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
+    labels = clustering.labels_
+    
+    # 3. Create the Output String
+    result = "--- SPEAKER DIARIZATION TIMELINE ---\n\n"
+    current_speaker = None
+    
+    for i, label in enumerate(labels):
+        speaker_name = f"Speaker {label + 1}"
+        start, end = timestamps[i]
+        
+        if speaker_name != current_speaker:
+            result += f"[{time_str(start)}] {speaker_name} starts speaking\n"
+            current_speaker = speaker_name
+            
+    return result
+
+# 4. Define the Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎙️ Speaker Diarization Tool")
+    
+    with gr.Row():
+        input_audio = gr.Audio(type="filepath", label="1. Upload or Record Audio")
+        num_spks = gr.Slider(minimum=1, maximum=10, value=2, step=1, label="2. Number of Speakers")
+    
+    btn = gr.Button("Analyze Speakers")
+    output_text = gr.Textbox(label="3. Diarization Results", lines=10)
+
+    btn.click(fn=process_audio, inputs=[input_audio, num_spks], outputs=output_text)
+
+demo.launch()

requirements.txt

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+pyannote.audio
+speechbrain
+gradio
+torch
+numpy
+scikit-learn