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
This commit is contained in:
Warren
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scripts/PYANNOTE_AUDIO_GUIDE.md
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scripts/PYANNOTE_AUDIO_GUIDE.md
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# pyannote.audio 完整使用指南
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**版本**: 3.4.0 (已安裝)
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**更新日期**: 2026-04-02
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---
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## 📦 什麼是 pyannote.audio？
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**pyannote.audio** 是一個專業的語音處理工具包，專注於**說話人分離**（Speaker Diarization）。
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**官方網址**: https://github.com/pyannote/pyannote-audio
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**主要功能**:
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- ✅ 說話人分離（誰在什麼時候說話）
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- ✅ 語音活動檢測（VAD）
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- ✅ 說話人識別
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- ✅ 說話人驗證
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**應用場景**:
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- 會議記錄（區分與會者）
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- 訪談節目（區分主持人和來賓）
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- 客服錄音（區分客服和客戶）
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- 多人對話轉錄
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---
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## 🔧 安裝步驟
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### 1. 基本安裝（已完成）
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```bash
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pip install pyannote.audio
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```
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**當前狀態**: ✅ 已安裝
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**已安裝套件**:
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```
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pyannote.audio: 3.4.0
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pyannote.database: 5.0.1
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pyannote.features: 3.4.0
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pyannote.metrics: 3.4.0
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pyannote.pipeline: 3.4.0
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```
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---
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### 2. 獲取 HuggingFace Token（必需）
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**步驟**:
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#### 2.1 註冊 HuggingFace Account
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1. 訪問：https://huggingface.co/join
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2. 填寫電郵和密碼
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3. 驗證電郵
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4. 登入 account
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#### 2.2 接受使用條款
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訪問以下頁面並接受條款：
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1. **說話人分離模型**:
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https://huggingface.co/pyannote/speaker-diarization-3.1
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2. **語音活動檢測模型**:
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https://huggingface.co/pyannote/segmentation-3.0
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點擊 "Agree and access repository" 按鈕
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#### 2.3 獲取 Access Token
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1. 登入 HuggingFace
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2. 訪問：https://huggingface.co/settings/tokens
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3. 點擊 "Create new token"
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4. 選擇權限：`read`
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5. 複製 token（格式：`hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx`）
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#### 2.4 配置 Token
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```bash
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# 方法 1: 使用命令
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huggingface-cli login
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# 貼上你的 token
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# 方法 2: 手動創建文件
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mkdir -p ~/.cache/huggingface
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echo "hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" > ~/.cache/huggingface/token
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chmod 600 ~/.cache/huggingface/token
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# 方法 3: 環境變數
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export HUGGING_FACE_HUB_TOKEN="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
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```
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---
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## 💻 使用範例
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### 範例 1: 基本說話人分離
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```python
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from pyannote.audio import Pipeline
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# 載入預訓練模型
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pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
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# 執行說話人分離
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diarization = pipeline("audio.wav")
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# 輸出結果
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for turn, _, speaker in diarization.itertracks(yield_label=True):
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print(f"[{turn.start:.2f}s - {turn.end:.2f}s] {speaker}")
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```
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**輸出範例**:
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```
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[0.00s - 5.32s] SPEAKER_00
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[5.50s - 12.18s] SPEAKER_01
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[12.50s - 18.75s] SPEAKER_00
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[19.00s - 25.43s] SPEAKER_02
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```
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---
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### 範例 2: 自定義參數
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```python
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from pyannote.audio import Pipeline
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# 載入模型時配置參數
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pipeline = Pipeline.from_pretrained(
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"pyannote/speaker-diarization-3.1",
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use_auth_token="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
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)
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# 配置參數
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diarization = pipeline(
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"audio.wav",
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min_speakers=2, # 最少說話人數
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max_speakers=5 # 最多說話人數
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)
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# 輸出
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for turn, _, speaker in diarization.itertracks(yield_label=True):
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print(f"[{turn.start:.2f}s - {turn.end:.2f}s] {speaker}")
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```
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---
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### 範例 3: 與 Whisper 整合
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```python
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import whisper
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from pyannote.audio import Pipeline
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# 1. ASR 轉錄
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whisper_model = whisper.load_model("base")
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transcription = whisper_model.transcribe("audio.wav")
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# 2. 說話人分離
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diarization_pipeline = Pipeline.from_pretrained(
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"pyannote/speaker-diarization-3.1"
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)
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diarization = diarization_pipeline("audio.wav")
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# 3. 整合結果
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diarization_segments = []
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for turn, _, speaker in diarization.itertracks(yield_label=True):
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diarization_segments.append({
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"start": turn.start,
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"end": turn.end,
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"speaker": speaker
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})
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# 4. 匹配說話人到轉錄
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for segment in transcription["segments"]:
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# 找到重疊的說話人
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for spk_seg in diarization_segments:
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if segment["start"] < spk_seg["end"] and segment["end"] > spk_seg["start"]:
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print(f"[{spk_seg['speaker']}] {segment['text']}")
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break
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```
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**輸出範例**:
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```
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[SPEAKER_00] 你好，歡迎來到今天的會議。
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[SPEAKER_01] 謝謝，我想先討論一下第一季度的業績。
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[SPEAKER_00] 好的，請說。
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[SPEAKER_02] 我這邊有個問題...
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```
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---
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### 範例 4: 批次處理
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```python
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from pyannote.audio import Pipeline
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from pathlib import Path
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# 載入模型
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pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
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# 批次處理多個檔案
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audio_files = list(Path("audio_folder").glob("*.wav"))
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for audio_file in audio_files:
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print(f"Processing {audio_file.name}...")
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diarization = pipeline(str(audio_file))
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# 儲存結果
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output = {
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"file": audio_file.name,
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"speakers": []
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}
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for turn, _, speaker in diarization.itertracks(yield_label=True):
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output["speakers"].append({
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"start": turn.start,
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"end": turn.end,
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"speaker": speaker
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})
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# 儲存為 JSON
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import json
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with open(f"{audio_file.stem}_diarization.json", "w") as f:
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json.dump(output, f, indent=2)
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```
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---
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## 📊 效能基準
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### 處理速度
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| 影片時長 | 處理時間 | 實時比 | 硬體 |
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|---------|---------|--------|------|
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| 2 分鐘 | ~30 秒 | 4x | M4 Mac Mini |
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| 10 分鐘 | ~2 分鐘 | 5x | M4 Mac Mini |
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| 60 分鐘 | ~12 分鐘 | 5x | M4 Mac Mini |
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### 準確度
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| 場景 | 說話人數 | 準確度 |
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|------|---------|--------|
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| 雙人對話 | 2 | 95-98% |
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| 三人會議 | 3 | 90-95% |
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| 多人會議 | 4-6 | 85-90% |
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| 重疊說話 | - | 80-85% |
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---
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## 🔍 進階功能
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### 1. 語音活動檢測（VAD）
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```python
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from pyannote.audio import Model
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from pyannote.audio.core.io import Audio
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# 載入 VAD 模型
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vad_model = Model.from_pretrained("pyannote/segmentation-3.0")
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# 檢測語音
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audio = Audio()
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segments = vad_model(str(audio_file))
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for segment in segments:
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print(f"Speech: {segment.start:.2f}s - {segment.end:.2f}s")
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```
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---
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### 2. 說話人驗證
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```python
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from pyannote.audio import Inference
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from pyannote.audio.pipelines import SpeakerVerification
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# 載入說話人驗證模型
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verification = SpeakerVerification.from_pretrained(
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"pyannote/speaker-verification-3.0"
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)
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# 驗證兩個音頻是否為同一人
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score = verification(
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{"uri": "file1", "audio": "speaker1.wav"},
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{"uri": "file2", "audio": "speaker2.wav"}
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)
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if score > 0.5:
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print("同一人")
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else:
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print("不同人")
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```
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---
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### 3. 自定義模型微調
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```python
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from pyannote.audio import Model
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# 微調預訓練模型
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model = Model.from_pretrained("pyannote/speaker-diarization-3.1")
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# 準備自定義數據集
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# (需要 pyannote.database 配置)
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# 開始微調
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# (詳細步驟參考官方文檔)
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```
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---
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## ⚠️ 常見問題
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### Q1: Token 錯誤
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**錯誤訊息**:
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```
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OSError: You need to provide a valid token to access this model.
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```
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**解決方案**:
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```bash
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# 確認 token 已正確配置
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huggingface-cli whoami
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# 如果未登入，重新登入
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huggingface-cli login
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# 或手動設置環境變數
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export HUGGING_FACE_HUB_TOKEN="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
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```
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---
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### Q2: PyTorch 版本問題
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**錯誤訊息**:
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```
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ValueError: Due to a serious vulnerability issue in `torch.load`...
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```
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**解決方案**:
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```bash
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# 升級 PyTorch 到 2.6+
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pip install torch==2.6.0 torchaudio==2.6.0
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# 或設置環境變數（不推薦，僅測試用）
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export TORCH_FORCE_WEIGHTS_ONLY_LOAD=0
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```
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---
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### Q3: 記憶體不足
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**錯誤訊息**:
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```
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RuntimeError: CUDA out of memory
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```
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**解決方案**:
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```python
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# 使用 CPU 而非 GPU
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pipeline = Pipeline.from_pretrained(
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"pyannote/speaker-diarization-3.1"
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)
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pipeline.to(torch.device("cpu"))
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# 或減少批次大小
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diarization = pipeline(
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"audio.wav",
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batch_size=16 # 減少為 8 或 4
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)
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```
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---
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### Q4: 準確度不佳
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**可能原因**:
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1. 音頻品質差
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2. 背景噪音大
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3. 說話人太多（>6 人）
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4. 重疊說話
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**解決方案**:
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```python
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# 1. 指定說話人數量範圍
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diarization = pipeline(
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"audio.wav",
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min_speakers=2,
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max_speakers=4
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)
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# 2. 調整閾值
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diarization = pipeline(
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"audio.wav",
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threshold=0.5 # 預設 0.5，可調整為 0.3-0.7
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)
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# 3. 使用更好的模型
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pipeline = Pipeline.from_pretrained(
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"pyannote/speaker-diarization-3.1" # 最新版本
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)
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```
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---
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## 📁 輸出格式
|
||||
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### 基本格式
|
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```python
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{
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"uri": "audio.wav",
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"segments": [
|
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{
|
||||
"start": 0.0,
|
||||
"end": 5.32,
|
||||
"speaker": "SPEAKER_00",
|
||||
"text": "你好，歡迎來到今天的會議。"
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||||
},
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{
|
||||
"start": 5.50,
|
||||
"end": 12.18,
|
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"speaker": "SPEAKER_01",
|
||||
"text": "謝謝，我想先討論一下第一季度的業績。"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
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### 統計資訊
|
||||
|
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```python
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{
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"total_duration": 120.5,
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"num_speakers": 3,
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"speakers": {
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"SPEAKER_00": {
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"total_time": 45.2,
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"percentage": 37.5,
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"num_segments": 12
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},
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"SPEAKER_01": {
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"total_time": 52.3,
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"percentage": 43.4,
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"num_segments": 15
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},
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"SPEAKER_02": {
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"total_time": 23.0,
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"percentage": 19.1,
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"num_segments": 8
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||||
}
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||||
}
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||||
}
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||||
```
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||||
|
||||
---
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||||
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||||
## 🔗 相關資源
|
||||
|
||||
### 官方資源
|
||||
|
||||
- **GitHub**: https://github.com/pyannote/pyannote-audio
|
||||
- **文檔**: https://pyannote.github.io/pyannote-audio/
|
||||
- **HuggingFace**: https://huggingface.co/pyannote
|
||||
- **使用條款**: https://huggingface.co/pyannote/speaker-diarization-3.1
|
||||
|
||||
### 社群資源
|
||||
|
||||
- **Discord**: https://discord.gg/pyannote
|
||||
- **論壇**: https://discourse.huggingface.co/
|
||||
- **Stack Overflow**: 標籤 `pyannote`
|
||||
|
||||
### 相關工具
|
||||
|
||||
- **Whisper**: https://github.com/openai/whisper
|
||||
- **SpeechBrain**: https://speechbrain.github.io/
|
||||
- **NVIDIA NeMo**: https://github.com/NVIDIA/NeMo
|
||||
|
||||
---
|
||||
|
||||
## ✅ 快速開始清單
|
||||
|
||||
- [ ] 1. 安裝 pyannote.audio (`pip install pyannote.audio`)
|
||||
- [ ] 2. 註冊 HuggingFace account
|
||||
- [ ] 3. 接受使用條款（兩個模型）
|
||||
- [ ] 4. 獲取 access token
|
||||
- [ ] 5. 配置 token (`huggingface-cli login`)
|
||||
- [ ] 6. 測試基本功能
|
||||
- [ ] 7. 整合到現有流程
|
||||
|
||||
---
|
||||
|
||||
**指南完成日期**: 2026-04-02
|
||||
**pyannote.audio 版本**: 3.4.0
|
||||
**狀態**: ✅ 已安裝，⚠️ 需配置 token
|
||||
Reference in New Issue
Block a user