feat: update Python processors and add utility scripts
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
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Warren
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#!/opt/homebrew/bin/python3.11
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"""
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Self-implemented ASRX - 自實作說話人分離系統
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基於聲紋嵌入 + 譜聚類
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技術架構:
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1. VAD (Silero VAD) - 語音活動檢測
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2. Speaker Encoder (ECAPA-TDNN) - 聲紋特徵提取
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3. Spectral Clustering - 譜聚類
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4. Post-processing - 後處理
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流程:
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音頻 → VAD → 語音片段 → 聲紋嵌入 → 相似度矩陣 → 譜聚類 → 說話人 ID
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"""
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import sys
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import json
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import time
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import numpy as np
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from pathlib import Path
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# 導入自定義模組
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from vad import load_vad_model, extract_speech_segments
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from speaker_encoder import (
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load_speaker_encoder,
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extract_speaker_embeddings_batch,
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compute_similarity_matrix,
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normalize_embeddings,
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)
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from speaker_cluster import spectral_clustering_speaker, smooth_speaker_labels
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class SelfASRX:
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"""
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自實作說話人分離系統
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"""
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def __init__(self):
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"""初始化模型"""
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print("[SelfASRX] Initializing models...")
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# 載入 VAD 模型
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print("[SelfASRX] Loading VAD model (Silero)...")
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self.vad_model, self.vad_utils = load_vad_model()
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# 載入聲紋模型
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print("[SelfASRX] Loading speaker encoder (ECAPA-TDNN)...")
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self.speaker_encoder = load_speaker_encoder()
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print("[SelfASRX] Models loaded successfully")
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def process(
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self,
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audio_path,
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output_path=None,
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min_speech_duration_ms=500,
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n_speakers=None,
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smooth_window=5,
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):
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"""
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處理音頻文件進行說話人分離
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Args:
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audio_path: 音頻文件路徑
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output_path: 輸出 JSON 路徑（可選）
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min_speech_duration_ms: 最小語音持續時間
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n_speakers: 說話人數量（None=自動估計）
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smooth_window: 平滑窗口大小
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Returns:
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result: 說話人分離結果
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"""
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start_time = time.time()
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print(f"\n[SelfASRX] Processing: {audio_path}")
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print("=" * 60)
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# 步驟 1: VAD - 語音活動檢測
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print("\n[Step 1] Voice Activity Detection...")
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step1_start = time.time()
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speech_segments, wav, sample_rate = extract_speech_segments(
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audio_path,
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self.vad_model,
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self.vad_utils,
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min_speech_duration_ms=min_speech_duration_ms,
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)
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step1_time = time.time() - step1_start
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print(f" Speech segments: {len(speech_segments)}")
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print(f" Total duration: {len(wav) / sample_rate:.2f}s")
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print(f" VAD time: {step1_time:.2f}s")
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if len(speech_segments) == 0:
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print("[SelfASRX] No speech detected!")
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return {"error": "No speech detected", "segments": []}
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# 步驟 2: 聲紋特徵提取
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print("\n[Step 2] Speaker embedding extraction...")
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step2_start = time.time()
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# 提取語音片段音頻
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audio_segments = []
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for start_sec, end_sec in speech_segments:
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start_sample = int(start_sec * sample_rate)
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end_sample = int(end_sec * sample_rate)
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audio_segments.append(wav[start_sample:end_sample])
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# 批量提取嵌入
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embeddings = extract_speaker_embeddings_batch(
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self.speaker_encoder, audio_segments, sample_rate
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)
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# 正規化
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embeddings = normalize_embeddings(embeddings)
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step2_time = time.time() - step2_start
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print(f" Embedding shape: {embeddings.shape}")
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print(f" Embedding time: {step2_time:.2f}s")
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# 步驟 3: 計算相似度矩陣
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print("\n[Step 3] Computing similarity matrix...")
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step3_start = time.time()
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similarity_matrix = compute_similarity_matrix(embeddings, method="cosine")
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step3_time = time.time() - step3_start
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print(f" Similarity matrix shape: {similarity_matrix.shape}")
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print(f" Similarity time: {step3_time:.2f}s")
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# 步驟 4: 譜聚類
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print("\n[Step 4] Spectral clustering...")
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step4_start = time.time()
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speaker_labels, estimated_n_speakers = spectral_clustering_speaker(
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similarity_matrix, n_speakers=n_speakers, auto_estimate=(n_speakers is None)
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)
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# 平滑標籤
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if smooth_window > 1:
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speaker_labels = smooth_speaker_labels(
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speaker_labels, window_size=smooth_window
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)
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step4_time = time.time() - step4_start
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print(f" Estimated speakers: {estimated_n_speakers}")
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print(f" Clustering time: {step4_time:.2f}s")
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# 步驟 5: 建立輸出結果
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print("\n[Step 5] Building output...")
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result = {
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"audio_path": str(audio_path),
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"total_duration": len(wav) / sample_rate,
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"n_speech_segments": len(speech_segments),
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"n_speakers": int(estimated_n_speakers),
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"segments": [],
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}
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for i, ((start, end), label) in enumerate(zip(speech_segments, speaker_labels)):
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result["segments"].append(
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{
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"index": i,
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"start": round(start, 3),
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"end": round(end, 3),
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"duration": round(end - start, 3),
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"speaker": f"SPEAKER_{int(label)}",
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}
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)
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# 統計每個說話人的總時長
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speaker_stats = {}
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for seg in result["segments"]:
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speaker = seg["speaker"]
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if speaker not in speaker_stats:
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speaker_stats[speaker] = {"count": 0, "duration": 0}
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speaker_stats[speaker]["count"] += 1
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speaker_stats[speaker]["duration"] += seg["duration"]
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result["speaker_stats"] = speaker_stats
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total_time = time.time() - start_time
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result["processing_time"] = round(total_time, 2)
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result["realtime_factor"] = round(result["total_duration"] / total_time, 2)
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print(f"\n[SelfASRX] Processing completed!")
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print(f" Total time: {total_time:.2f}s")
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print(f" Realtime factor: {result['realtime_factor']:.2f}x")
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print(f" Detected speakers: {estimated_n_speakers}")
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# 保存結果
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if output_path:
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output_path = Path(output_path)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with open(output_path, "w", encoding="utf-8") as f:
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json.dump(result, f, indent=2, ensure_ascii=False)
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print(f" Results saved to: {output_path}")
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print("=" * 60)
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return result
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def main():
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"""主函數"""
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import argparse
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parser = argparse.ArgumentParser(
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description="Self-implemented ASRX - Speaker Diarization"
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)
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parser.add_argument("audio_path", help="Path to audio file")
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parser.add_argument("-o", "--output", help="Output JSON path")
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parser.add_argument(
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"--min-speech-duration",
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type=int,
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default=500,
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help="Minimum speech duration in ms (default: 500)",
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)
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parser.add_argument(
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"--n-speakers",
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type=int,
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default=None,
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help="Number of speakers (default: auto-estimate)",
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)
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parser.add_argument(
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"--smooth-window",
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type=int,
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default=5,
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help="Smoothing window size (default: 5)",
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)
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args = parser.parse_args()
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# 檢查文件是否存在
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if not Path(args.audio_path).exists():
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print(f"Error: Audio file not found: {args.audio_path}")
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sys.exit(1)
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# 創建 ASRX 實例並處理
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asrx = SelfASRX()
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result = asrx.process(
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args.audio_path,
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args.output,
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min_speech_duration_ms=args.min_speech_duration,
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n_speakers=args.n_speakers,
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smooth_window=args.smooth_window,
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)
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# 顯示結果摘要
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if "error" not in result:
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print(f"\n[Summary]")
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print(f" Audio duration: {result['total_duration']:.2f}s")
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print(f" Speech segments: {result['n_speech_segments']}")
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print(f" Detected speakers: {result['n_speakers']}")
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print(f" Processing time: {result['processing_time']:.2f}s")
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print(f" Realtime factor: {result['realtime_factor']:.2f}x")
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print(f"\n[Speaker Statistics]")
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for speaker, stats in result["speaker_stats"].items():
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pct = stats["duration"] / result["total_duration"] * 100
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print(
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f" {speaker}: {stats['count']} segments, "
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+ f"{stats['duration']:.2f}s ({pct:.1f}%)"
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)
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if __name__ == "__main__":
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main()
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