554 lines
22 KiB
Python
554 lines
22 KiB
Python
#!/usr/bin/env python3
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"""
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transcribe_audio.py - MP3 音频批量转录文字脚本
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功能:
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- 扫描单个 MP3 文件或遍历指定目录中的所有 MP3 文件
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- 维护 manifest.json 进度文件，支持断点续传（key 含模型维度，多模型互不覆盖）
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- 使用系统已安装的 whisper 命令行工具转录音频
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- 支持 MP3 切分（处理可能生成超大 txt 的音频文件）
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- 支持含空格和中文的文件名
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- 支持预下载模型 (--preload-models)
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- 支持多模型对比 (--all-models)
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- 支持自定义输出后缀 (--output-suffix)
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支持的模型: tiny / base / small / medium
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推荐语言: zh (中文) / en (英文)，也接受其他 whisper 支持的语言代码
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用法:
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python transcribe_audio.py <MP3文件或目录> [选项]
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示例:
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# 预下载所有支持的模型
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python transcribe_audio.py --preload-models
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# 使用 medium 模型转录中文
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python transcribe_audio.py /path/to/audio/ --model medium --language zh
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# 一次跑 4 个模型对比精度（生成 file.tiny.txt / file.base.txt / ...）
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python transcribe_audio.py /path/to/audio/ --all-models --language zh
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# 自定义输出后缀
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python transcribe_audio.py /path/to/audio.mp3 --model base --output-suffix _v1
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# 超1800秒切分
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python transcribe_audio.py /path/to/audio/ --split-size 1800
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# 重新处理失败项
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python transcribe_audio.py /path/to/audio/ --retry-failed
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# 查看进度状态
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python transcribe_audio.py /path/to/audio/ --status
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"""
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import argparse
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import json
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import os
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import subprocess
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import sys
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import time
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from pathlib import Path
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# ─── 常量 ──────────────────────────────────────────────────────────────────────
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MANIFEST_NAME = "manifest.json"
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AUDIO_EXTS = {".mp3", ".m4a", ".wav", ".flac", ".ogg", ".aac"}
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SUPPORTED_MODELS = ["tiny", "base", "small", "medium"]
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DEFAULT_WHISPER_MODEL = "base"
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DEFAULT_SPLIT_SEGMENT_SECS = 1800 # 每片30分钟
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# ─── 日志 ──────────────────────────────────────────────────────────────────────
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def log(msg: str, level: str = "INFO"):
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ts = time.strftime("%H:%M:%S")
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prefix = {"INFO": " ", "OK": "✅", "ERR": "❌", "WARN": "⚠️ ", "STEP": "▶ "}.get(level, " ")
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print(f"[{ts}] {prefix} {msg}", flush=True)
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# ─── Manifest 操作 (key 包含 model，支持同一音频多模型共存) ────────────────────
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def load_manifest(manifest_path: Path) -> dict:
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if manifest_path.exists():
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with open(manifest_path, encoding="utf-8") as f:
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return json.load(f)
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return {"version": 1, "files": {}}
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def save_manifest(manifest_path: Path, manifest: dict):
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manifest_path.parent.mkdir(parents=True, exist_ok=True)
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with open(manifest_path, "w", encoding="utf-8") as f:
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json.dump(manifest, f, ensure_ascii=False, indent=2)
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def transcribe_key(model: str, audio_path: str, output_suffix: str = "") -> str:
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"""
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manifest key 由 model + output_suffix + 音频路径共同决定，
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因此改变 --output-suffix 会形成新的 key（不会误跳过）。
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"""
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if output_suffix:
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return f"transcribe:{model}:{output_suffix}:{audio_path}"
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return f"transcribe:{model}:{audio_path}"
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def get_transcribe_entry(manifest: dict, audio_path: str, model: str,
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output_suffix: str = "") -> dict:
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key = transcribe_key(model, audio_path, output_suffix)
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if key not in manifest["files"]:
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manifest["files"][key] = {
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"transcribed": False,
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"txt_path": None,
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"split_segments": [],
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"error": None,
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"model": model,
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"language": None,
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"output_suffix": output_suffix or None,
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"updated_at": None,
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}
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return manifest["files"][key]
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def mark_transcribed(manifest: dict, manifest_path: Path, audio_path: str,
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txt_path: str, model: str, language: str = None,
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output_suffix: str = "", segments: list = None):
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entry = get_transcribe_entry(manifest, audio_path, model, output_suffix)
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entry["transcribed"] = True
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entry["txt_path"] = txt_path
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entry["split_segments"] = segments or []
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entry["model"] = model
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entry["language"] = language
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entry["output_suffix"] = output_suffix or None
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entry["error"] = None
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entry["updated_at"] = time.strftime("%Y-%m-%dT%H:%M:%S")
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save_manifest(manifest_path, manifest)
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def mark_transcribe_failed(manifest: dict, manifest_path: Path,
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audio_path: str, model: str, error: str,
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output_suffix: str = ""):
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entry = get_transcribe_entry(manifest, audio_path, model, output_suffix)
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entry["transcribed"] = False
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entry["error"] = error
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entry["updated_at"] = time.strftime("%Y-%m-%dT%H:%M:%S")
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save_manifest(manifest_path, manifest)
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# ─── 模型预下载 ────────────────────────────────────────────────────────────────
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def preload_models(models: list) -> bool:
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"""
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调用 whisper Python API 预下载模型到 ~/.cache/whisper/。
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比调用 whisper CLI 更快（无需额外音频输入）。
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"""
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try:
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import whisper as whisper_lib
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except ImportError:
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log("openai-whisper Python 库未安装，无法预下载", "ERR")
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log("请安装：pip install openai-whisper", "ERR")
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return False
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log(f"开始预下载 {len(models)} 个模型：{', '.join(models)}", "STEP")
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for m in models:
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log(f" 下载 {m}...")
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t0 = time.time()
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try:
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whisper_lib.load_model(m)
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log(f" {m} 就绪 ({time.time() - t0:.1f}s)", "OK")
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except Exception as e:
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log(f" {m} 下载失败: {e}", "ERR")
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return False
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log(f"所有模型下载完成 ✅", "OK")
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return True
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# ─── Whisper / FFmpeg 工具函数 ─────────────────────────────────────────────────
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def check_whisper() -> bool:
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try:
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subprocess.run(["whisper", "--help"], capture_output=True, check=False)
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return True
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except FileNotFoundError:
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log("未找到 whisper 命令，请确认已安装：pip install openai-whisper", "ERR")
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return False
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def check_ffmpeg() -> bool:
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try:
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subprocess.run(["ffmpeg", "-version"], capture_output=True, check=True)
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return True
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except (subprocess.CalledProcessError, FileNotFoundError):
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return False
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def get_audio_duration(audio_path: str) -> float:
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try:
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r = subprocess.run(
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["ffprobe", "-v", "error", "-show_entries", "format=duration",
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"-of", "default=noprint_wrappers=1:nokey=1", audio_path],
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capture_output=True, text=True
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)
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return float(r.stdout.strip())
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except Exception:
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return 0.0
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def split_audio(audio_path: str, output_dir: str, segment_secs: int) -> list:
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"""
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将 MP3 切分为多个片段，返回片段路径列表。
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使用 ffmpeg segment 模式，避免重编码（codec copy）。
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"""
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stem = Path(audio_path).stem
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ext = Path(audio_path).suffix
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out_pattern = os.path.join(output_dir, f"{stem}_seg%03d{ext}")
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cmd = [
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"ffmpeg", "-y", "-i", audio_path,
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"-f", "segment",
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"-segment_time", str(segment_secs),
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"-c", "copy",
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"-reset_timestamps", "1",
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out_pattern
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]
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r = subprocess.run(cmd, capture_output=True)
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if r.returncode != 0:
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return []
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return sorted(str(p) for p in Path(output_dir).glob(f"{stem}_seg*{ext}"))
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def run_whisper(audio_path: str, output_dir: str, model: str,
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language: str = None) -> tuple:
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"""
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调用 whisper 命令转录音频，返回 (success: bool, txt_path_or_err: str)。
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whisper 会自动在 output_dir 生成 <stem>.txt 等文件。
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注意: 多次调用同 output_dir 且相同 stem 时会覆盖，需由调用方即时重命名。
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"""
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cmd = [
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"whisper", audio_path,
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"--model", model,
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"--output_dir", output_dir,
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"--output_format", "txt",
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"--verbose", "False",
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]
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if language:
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cmd += ["--language", language]
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r = subprocess.run(cmd, capture_output=True, text=True)
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stem = Path(audio_path).stem
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txt_path = os.path.join(output_dir, f"{stem}.txt")
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if r.returncode == 0 and os.path.exists(txt_path):
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return True, txt_path
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err = r.stderr[-500:] if r.stderr else "unknown error"
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return False, err
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def merge_txt_files(txt_files: list, merged_path: str) -> bool:
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"""将多个转录片段 txt 按顺序合并为一个完整文件"""
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try:
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with open(merged_path, "w", encoding="utf-8") as fout:
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for i, tf in enumerate(txt_files):
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if i > 0:
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fout.write("\n\n")
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with open(tf, encoding="utf-8") as fin:
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fout.write(fin.read().strip())
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return True
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except Exception:
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return False
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# ─── 文件扫描 ──────────────────────────────────────────────────────────────────
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def scan_audios(source: str) -> list:
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p = Path(source)
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if p.is_file():
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if p.suffix.lower() in AUDIO_EXTS:
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return [str(p.resolve())]
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else:
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log(f"不支持的音频格式: {p.suffix}", "ERR")
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return []
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elif p.is_dir():
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return sorted(str(f.resolve()) for f in p.rglob("*")
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if f.suffix.lower() in AUDIO_EXTS)
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else:
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log(f"路径不存在: {source}", "ERR")
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return []
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def txt_exists(txt_path: str) -> bool:
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return bool(txt_path) and os.path.exists(txt_path) and os.path.getsize(txt_path) > 0
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# ─── 输出文件命名 ──────────────────────────────────────────────────────────────
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def compute_target_txt(out_dir: Path, stem: str, model: str,
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output_suffix: str, tag_model: bool) -> str:
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"""
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生成最终 txt 路径。命名规则：
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- tag_model=True (多模型模式): <stem>.<model>.txt
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- output_suffix 非空: <stem><output_suffix>.txt
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- 都无: <stem>.txt (默认)
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"""
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if tag_model:
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return str(out_dir / f"{stem}.{model}.txt")
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if output_suffix:
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return str(out_dir / f"{stem}{output_suffix}.txt")
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return str(out_dir / f"{stem}.txt")
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# ─── 状态展示 ──────────────────────────────────────────────────────────────────
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def show_status(manifest: dict, all_audios: list):
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print("\n" + "=" * 70)
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print("📊 音频转录进度状态（按 模型 x 音频 展示）")
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print("=" * 70)
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entries = {k: v for k, v in manifest["files"].items() if k.startswith("transcribe:")}
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done = sum(1 for v in entries.values() if v.get("transcribed"))
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failed = sum(1 for v in entries.values() if v.get("error"))
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print(f" Manifest 记录: {len(entries)} 已完成: {done} 失败: {failed}")
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print("=" * 70)
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for a in all_audios:
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print(f"\n 📄 {Path(a).name}")
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matching = [(k, v) for k, v in entries.items() if k.endswith(f":{a}")]
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if not matching:
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print(f" ⏳ 无记录（未转录）")
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continue
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for key, entry in matching:
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m = entry.get("model", "?")
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suffix = entry.get("output_suffix") or ""
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suffix_tag = f" suffix={suffix}" if suffix else ""
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if entry.get("transcribed"):
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size = ""
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if entry.get("txt_path") and os.path.exists(entry["txt_path"]):
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size = f" {os.path.getsize(entry['txt_path']) // 1024}KB"
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lang = entry.get("language") or "auto"
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txt_name = Path(entry['txt_path']).name if entry.get('txt_path') else '?'
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print(f" ✅ [{m:6s}] lang={lang}{suffix_tag}{size} → {txt_name}")
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elif entry.get("error"):
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print(f" ❌ [{m:6s}]{suffix_tag} {entry['error'][:60]}")
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print()
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# ─── 核心：单个音频 + 单个模型 转录 ─────────────────────────────────────────────
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def transcribe_one(audio: str, model: str, out_dir: Path, args,
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manifest: dict, manifest_path: Path,
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has_ffmpeg: bool, tag_model: bool) -> str:
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"""
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返回状态字符串：'ok' / 'skip' / 'skip-failed' / 'error:<msg>'
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"""
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stem = Path(audio).stem
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final_txt = compute_target_txt(out_dir, stem, model, args.output_suffix, tag_model)
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# 多模型模式用 .<model>.txt 命名，output_suffix 在这种情况下强制为空
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effective_suffix = "" if tag_model else args.output_suffix
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entry = get_transcribe_entry(manifest, audio, model, effective_suffix)
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already_done = entry.get("transcribed") and txt_exists(entry.get("txt_path"))
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is_failed = bool(entry.get("error"))
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if already_done:
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return "skip"
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if is_failed and not args.retry_failed:
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return "skip-failed"
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try:
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duration = get_audio_duration(audio) if has_ffmpeg else 0
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need_split = args.split_size and duration > 0 and duration > args.split_size
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if need_split:
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log(f" 音频时长 {duration:.0f}s > {args.split_size}s，启动切分转录")
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seg_dir = out_dir / f"{stem}_segments"
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seg_dir.mkdir(exist_ok=True)
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segments = split_audio(audio, str(seg_dir), args.split_segment)
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if not segments:
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raise RuntimeError("音频切分失败")
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# 每个模型独立子目录，避免片段 txt 互相覆盖
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seg_txt_dir = seg_dir / model
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seg_txt_dir.mkdir(exist_ok=True)
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log(f" 切分为 {len(segments)} 个片段，逐段转录中...")
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seg_txts = []
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for j, seg in enumerate(segments, 1):
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ok, result = run_whisper(seg, str(seg_txt_dir), model, args.language)
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if ok:
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seg_txts.append(result)
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log(f" [{j}/{len(segments)}] {Path(seg).name} → done", "OK")
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else:
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raise RuntimeError(f"片段 {Path(seg).name} 转录失败: {result}")
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if not merge_txt_files(seg_txts, final_txt):
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raise RuntimeError("合并转录片段失败")
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size = os.path.getsize(final_txt)
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log(f" 合并 → {Path(final_txt).name} ({size // 1024} KB)", "OK")
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mark_transcribed(manifest, manifest_path, audio, final_txt,
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model, language=args.language,
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output_suffix=effective_suffix, segments=seg_txts)
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else:
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log(f" 🎙 转录中（模型: {model}, 语言: {args.language or 'auto'}）...")
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ok, whisper_out = run_whisper(audio, str(out_dir), model, args.language)
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if not ok:
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raise RuntimeError(f"转录失败: {whisper_out}")
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if whisper_out != final_txt:
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os.replace(whisper_out, final_txt)
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size = os.path.getsize(final_txt)
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log(f" 输出: {Path(final_txt).name} ({size // 1024} KB)", "OK")
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mark_transcribed(manifest, manifest_path, audio, final_txt,
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model, language=args.language,
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output_suffix=effective_suffix)
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return "ok"
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except Exception as e:
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err_msg = str(e)
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mark_transcribe_failed(manifest, manifest_path, audio, model, err_msg,
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output_suffix=effective_suffix)
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# 清理不完整的输出
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if os.path.exists(final_txt) and os.path.getsize(final_txt) == 0:
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os.remove(final_txt)
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return f"error: {err_msg}"
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# ─── 主流程 ────────────────────────────────────────────────────────────────────
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def main():
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parser = argparse.ArgumentParser(
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description="MP3 音频批量转录工具（基于 openai-whisper）",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog=__doc__
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)
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parser.add_argument("source", nargs="?", default=None,
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help="MP3 文件路径或包含音频的目录（--preload-models 时可省略）")
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parser.add_argument("--output", "-o", default=None,
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help="TXT 输出目录（默认与音频文件同目录）")
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parser.add_argument("--manifest", "-m", default=None,
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help="manifest.json 路径（默认在 source 目录下）")
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parser.add_argument("--model", default=DEFAULT_WHISPER_MODEL,
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choices=SUPPORTED_MODELS,
|
||
help=f"Whisper 模型（默认 {DEFAULT_WHISPER_MODEL}，可选：{'/'.join(SUPPORTED_MODELS)}）")
|
||
parser.add_argument("--language", default=None,
|
||
help="音频语言代码，推荐 zh(中文) / en(英文)（默认自动检测）")
|
||
parser.add_argument("--all-models", action="store_true",
|
||
help=f"对每个音频依次使用所有支持的模型转录 ({'/'.join(SUPPORTED_MODELS)})，"
|
||
f"输出为 <stem>.<model>.txt 便于对比精度")
|
||
parser.add_argument("--output-suffix", default="",
|
||
help="输出文件名后缀，如 _v1 → <stem>_v1.txt（与 --all-models 互斥时被忽略）")
|
||
parser.add_argument("--split-size", type=int, default=None,
|
||
help="切分阈值（秒）：超过此时长的音频会被切分后分别转录")
|
||
parser.add_argument("--split-segment", type=int, default=DEFAULT_SPLIT_SEGMENT_SECS,
|
||
help=f"每个切分片段时长（秒，默认 {DEFAULT_SPLIT_SEGMENT_SECS}）")
|
||
parser.add_argument("--retry-failed", action="store_true",
|
||
help="重新处理上次失败的文件")
|
||
parser.add_argument("--status", action="store_true",
|
||
help="仅查看进度状态，不执行转录")
|
||
parser.add_argument("--preload-models", action="store_true",
|
||
help=f"预下载所有支持的模型（{'/'.join(SUPPORTED_MODELS)}）到本地缓存后退出")
|
||
args = parser.parse_args()
|
||
|
||
# ── 模式1: 仅预下载模型 ──
|
||
if args.preload_models:
|
||
if not preload_models(SUPPORTED_MODELS):
|
||
sys.exit(1)
|
||
return
|
||
|
||
# 从这里开始 source 是必需的
|
||
if not args.source:
|
||
parser.error("需要指定 source 参数（音频文件或目录），除非使用 --preload-models")
|
||
|
||
if not check_whisper():
|
||
sys.exit(1)
|
||
|
||
has_ffmpeg = check_ffmpeg()
|
||
if args.split_size and not has_ffmpeg:
|
||
log("--split-size 需要 ffmpeg，但未找到 ffmpeg 命令", "ERR")
|
||
sys.exit(1)
|
||
|
||
source_path = Path(args.source).resolve()
|
||
base_dir = source_path if source_path.is_dir() else source_path.parent
|
||
manifest_path = Path(args.manifest) if args.manifest else base_dir / MANIFEST_NAME
|
||
|
||
manifest = load_manifest(manifest_path)
|
||
|
||
all_audios = scan_audios(args.source)
|
||
if not all_audios:
|
||
log("未找到任何音频文件", "WARN")
|
||
sys.exit(0)
|
||
|
||
# 决定使用哪些模型
|
||
if args.all_models:
|
||
models_to_run = SUPPORTED_MODELS
|
||
if args.output_suffix:
|
||
log(f"--all-models 已启用，--output-suffix 将被忽略（自动使用 .<model>.txt 命名）", "WARN")
|
||
else:
|
||
models_to_run = [args.model]
|
||
|
||
tag_model = args.all_models
|
||
log(f"扫描到 {len(all_audios)} 个音频文件，将使用 {len(models_to_run)} 个模型：{', '.join(models_to_run)}")
|
||
|
||
# ── 模式2: 仅查看状态 ──
|
||
if args.status:
|
||
show_status(manifest, all_audios)
|
||
return
|
||
|
||
# ── 模式3: 执行转录 ──
|
||
total_jobs = len(all_audios) * len(models_to_run)
|
||
stats = {"ok": 0, "skip": 0, "skip-failed": 0, "error": 0}
|
||
failed_details = []
|
||
|
||
log(f"共 {total_jobs} 个 (音频 × 模型) 任务待处理")
|
||
|
||
job_idx = 0
|
||
for audio in all_audios:
|
||
audio_name = Path(audio).name
|
||
audio_dir = Path(audio).parent
|
||
out_dir = Path(args.output).resolve() if args.output else audio_dir
|
||
out_dir.mkdir(parents=True, exist_ok=True)
|
||
|
||
log(f"\n📄 {audio_name}", "STEP")
|
||
|
||
for model in models_to_run:
|
||
job_idx += 1
|
||
log(f" [{job_idx}/{total_jobs}] 模型: {model}")
|
||
|
||
t0 = time.time()
|
||
result = transcribe_one(audio, model, out_dir, args, manifest,
|
||
manifest_path, has_ffmpeg, tag_model)
|
||
elapsed = time.time() - t0
|
||
|
||
if result == "ok":
|
||
stats["ok"] += 1
|
||
log(f" ⏱ 耗时 {elapsed:.1f}s", "OK")
|
||
elif result == "skip":
|
||
stats["skip"] += 1
|
||
log(f" ⏭ 已完成，跳过", "INFO")
|
||
elif result == "skip-failed":
|
||
stats["skip-failed"] += 1
|
||
log(f" ⚠ 上次失败，加 --retry-failed 可重试", "WARN")
|
||
else:
|
||
stats["error"] += 1
|
||
failed_details.append((audio_name, model, result))
|
||
log(f" ❌ {result}", "ERR")
|
||
|
||
# ── 汇总 ──
|
||
print(f"\n{'='*70}")
|
||
log(f"🏁 完成: 成功 {stats['ok']} 已跳过 {stats['skip']} "
|
||
f"失败跳过 {stats['skip-failed']} 失败 {stats['error']}", "INFO")
|
||
|
||
if failed_details:
|
||
log("失败详情:", "ERR")
|
||
for audio_name, model, err in failed_details:
|
||
print(f" - {audio_name} [模型:{model}] : {err[:80]}")
|
||
print(" 运行时加 --retry-failed 可重新处理失败项")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|