OpenAI Whisper 教程：语音转文字转录完整指南

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什么是 OpenAI Whisper？

主要特性

• 多语言支持 — 99+ 种语言
• 语音转文字 — 将音频转为文本
• 语音翻译 — 将语音直接译为英文
• 语言检测 — 自动识别说话语言
• 时间戳 — 词级与片段级时间戳
• 开源免费 — MIT 许可，无 API 费用
• 可离线 — 在本地机器上运行
• 多格式 — 支持多种音视频格式

---

Whisper 模型尺寸说明

模型	参数量	速度	准确率	内存	适用场景
tiny	39M	⭐⭐⭐⭐⭐	⭐⭐	~1 GB	快速测试、演示
base	74M	⭐⭐⭐⭐	⭐⭐⭐	~1 GB	简单音频、轻量任务
small	244M	⭐⭐⭐	⭐⭐⭐⭐	~2 GB	通用、均衡
medium	769M	⭐⭐	⭐⭐⭐⭐⭐	~5 GB	嘈杂音频、高准确率
large	1550M	⭐	⭐⭐⭐⭐⭐⭐	~10 GB	最高准确率、生产环境

• 追求速度： 使用 tiny 或 base
• 追求均衡： 使用 small 或 medium
• 追求准确率： 使用 large 或 large-v3
• 生产环境： 多数场景使用 medium 或 large-v2

---

环境要求

• Python 3.8 及以上（推荐 Python 3.9+）
• pip 包管理器
• 已安装 FFmpeg（用于音视频处理）
• （可选） 配备 CUDA 的 NVIDIA GPU 以加速
• （可选） base 模型建议 4GB+ 内存，large 建议 10GB+

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第一步：安装

安装 Whisper

pip install openai-whisper

pip install openai-whisper==20231117

安装 FFmpeg

brew install ffmpeg

sudo apt update
sudo apt install ffmpeg

1. 从 ffmpeg.org 下载 FFmpeg
2. 解压并将其加入系统 PATH
3. 或使用：choco install ffmpeg（Chocolatey）

ffmpeg -version
whisper --version

---

第二步：基础用法 — Python

简单转录

import whisper

# Load model (downloads automatically on first use)
model = whisper.load_model("base")

# Transcribe audio file
result = model.transcribe("audio.mp3")

# Print transcription
print(result["text"])

Hello everyone, welcome to today's meeting. We will discuss the project timeline and upcoming milestones.

带错误处理的完整示例

import whisper
import os

def transcribe_audio(audio_path, model_size="base"):
    """
    Transcribe an audio file using Whisper.
    
    Args:
        audio_path (str): Path to the audio file
        model_size (str): Whisper model size (tiny, base, small, medium, large)
    
    Returns:
        dict: Transcription result with text and segments
    """
    try:
        # Check if audio file exists
        if not os.path.exists(audio_path):
            raise FileNotFoundError(f"Audio file not found: {audio_path}")
        
        # Load the Whisper model
        print(f"Loading Whisper model: {model_size}")
        model = whisper.load_model(model_size)
        
        # Transcribe the audio
        print(f"Transcribing: {audio_path}")
        result = model.transcribe(audio_path)
        
        print(f"✓ Transcription complete!")
        print(f"  Language: {result['language']}")
        print(f"  Duration: {result['segments'][-1]['end']:.2f}s")
        
        return result
    
    except Exception as e:
        print(f"Error during transcription: {str(e)}")
        return None

# Example usage
if __name__ == "__main__":
    audio_file = "meeting.mp3"
    result = transcribe_audio(audio_file, model_size="base")
    
    if result:
        print("\n" + "="*50)
        print("TRANSCRIPTION:")
        print("="*50)
        print(result["text"])

---

第三步：语言检测与指定

自动检测语言

import whisper

model = whisper.load_model("base")
result = model.transcribe("audio.mp3")

print(f"Detected language: {result['language']}")
print(f"Language probability: {result.get('language_probability', 0):.2%}")
print(f"\nTranscription:\n{result['text']}")

指定语言（更快、更准）

import whisper

model = whisper.load_model("base")

# Specify language
result_en = model.transcribe("audio.mp3", language="en")  # English
result_zh = model.transcribe("audio.mp3", language="zh")   # Chinese
result_es = model.transcribe("audio.mp3", language="es")  # Spanish
result_fr = model.transcribe("audio.mp3", language="fr")  # French
result_de = model.transcribe("audio.mp3", language="de")  # German
result_ja = model.transcribe("audio.mp3", language="ja")   # Japanese

print(result_en["text"])

• en - English
• zh - Chinese
• es - Spanish
• fr - French
• de - German
• ja - Japanese
• ko - Korean
• pt - Portuguese
• ru - Russian
• it - Italian

---

第四步：时间戳与分段

访问带时间戳的分段

import whisper

model = whisper.load_model("base")
result = model.transcribe("audio.mp3")

# Print full transcription
print("Full Text:")
print(result["text"])

# Print segments with timestamps
print("\n" + "="*50)
print("Segments with Timestamps:")
print("="*50)

for segment in result["segments"]:
    start = segment["start"]
    end = segment["end"]
    text = segment["text"].strip()
    print(f"[{start:6.2f}s - {end:6.2f}s] {text}")

Full Text:
Hello everyone, welcome to today's meeting. We will discuss the project timeline.

==================================================
Segments with Timestamps:
==================================================
[  0.00s -   5.20s] Hello everyone, welcome to today's meeting.
[  5.20s -  12.50s] We will discuss the project timeline.

将时间戳格式化为时码

def format_timestamp(seconds):
    """Format seconds to HH:MM:SS."""
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    secs = int(seconds % 60)
    return f"{hours:02d}:{minutes:02d}:{secs:02d}"

for segment in result["segments"]:
    start_time = format_timestamp(segment["start"])
    end_time = format_timestamp(segment["end"])
    print(f"[{start_time} - {end_time}] {segment['text']}")

词级时间戳

import whisper

model = whisper.load_model("base")

result = model.transcribe(
    "audio.mp3",
    word_timestamps=True  # Enable word-level timestamps
)

for segment in result["segments"]:
    print(f"\n[{segment['start']:.2f}s - {segment['end']:.2f}s]")
    print(f"Text: {segment['text']}")
    
    # Word-level timestamps
    if "words" in segment:
        print("Words:")
        for word in segment["words"]:
            print(f"  {word['word']} [{word['start']:.2f}s - {word['end']:.2f}s]")

---

第五步：语音翻译

import whisper

model = whisper.load_model("base")

# Translate to English (regardless of source language)
result = model.transcribe("spanish_audio.mp3", task="translate")

print("Translated to English:")
print(result["text"])

# Original transcription (in original language)
result_original = model.transcribe("spanish_audio.mp3", task="transcribe")
print("\nOriginal language transcription:")
print(result_original["text"])

• 国际会议
• 多语言内容处理
• 内容本地化
• 语言学习材料

---

第六步：进阶参数

温度与束宽（beam size）

import whisper

model = whisper.load_model("base")

result = model.transcribe(
    "audio.mp3",
    temperature=0.0,        # Lower = more deterministic (0.0 recommended)
    beam_size=5,            # Higher = more accurate but slower (default: 5)
    best_of=5,              # Number of candidates to consider
    patience=1.0,           # Beam search patience
    condition_on_previous_text=True,  # Use context from previous segments
    initial_prompt="This is a technical meeting about AI and machine learning."  # Context prompt
)

温度取值说明

• temperature=0.0 — 最确定，推荐
• temperature=0.2-0.4 — 略有随机性
• temperature=1.0 — 更“发散”，准确率通常下降

用 initial_prompt 提供上下文

result = model.transcribe(
    "technical_meeting.mp3",
    initial_prompt="This meeting discusses API endpoints, microservices, Kubernetes, and CI/CD pipelines."
)

result = model.transcribe(
    "medical_audio.mp3",
    initial_prompt="This is a medical consultation discussing patient symptoms and treatment options."
)

---

第七步：命令行（CLI）

基础 CLI

whisper audio.mp3

指定模型

whisper audio.mp3 --model small
whisper audio.mp3 --model medium
whisper audio.mp3 --model large-v2

指定语言

whisper audio.mp3 --language en
whisper audio.mp3 --language zh

输出格式

# SRT subtitles
whisper audio.mp3 --output_format srt

# VTT subtitles
whisper audio.mp3 --output_format vtt

# Text file
whisper audio.mp3 --output_format txt

# JSON (with all metadata)
whisper audio.mp3 --output_format json

# TSV (tab-separated values)
whisper audio.mp3 --output_format tsv

进阶 CLI 选项

# Full example with all options
whisper audio.mp3 \
  --model medium \
  --language en \
  --task transcribe \
  --output_format srt \
  --output_dir ./transcripts \
  --verbose True \
  --temperature 0.0 \
  --beam_size 5 \
  --best_of 5 \
  --fp16 True

CLI 参数速查

选项	说明	默认值
--model	模型尺寸（tiny、base、small、medium、large）	base
--language	语言代码（en、zh、es 等）	自动检测
--task	transcribe 或 translate	transcribe
--output_format	输出格式（txt、srt、vtt、json、tsv）	txt
--output_dir	输出目录	当前目录
--temperature	采样温度	0.0
--beam_size	束搜索宽度	5
--best_of	候选数量	5
--fp16	使用 FP16 精度（GPU）	True
--verbose	详细日志	False

---

第八步：支持的音视频格式

支持的格式

• 音频： MP3、WAV、M4A、FLAC、OGG、AAC、WMA
• 视频： MP4、AVI、MKV、MOV、WebM、FLV
• 流式： 可处理音频流

格式示例

import whisper

model = whisper.load_model("base")

# Audio formats
model.transcribe("audio.mp3")
model.transcribe("audio.wav")
model.transcribe("audio.m4a")
model.transcribe("audio.flac")

# Video formats (extracts audio automatically)
model.transcribe("video.mp4")
model.transcribe("video.mkv")
model.transcribe("video.webm")

---

第九步：完整生产示例

import whisper
import json
from pathlib import Path
from datetime import datetime

class WhisperTranscriber:
    """Production-ready Whisper transcription service."""
    
    def __init__(self, model_size="base"):
        """Initialize transcriber with specified model."""
        print(f"Loading Whisper model: {model_size}")
        self.model = whisper.load_model(model_size)
        print("✓ Model loaded successfully")
    
    def transcribe_file(self, audio_path, output_dir="transcripts", **kwargs):
        """
        Transcribe audio file and save results.
        
        Args:
            audio_path: Path to audio file
            output_dir: Directory to save outputs
            **kwargs: Additional transcribe parameters
        """
        audio_path = Path(audio_path)
        if not audio_path.exists():
            raise FileNotFoundError(f"Audio file not found: {audio_path}")
        
        output_path = Path(output_dir)
        output_path.mkdir(exist_ok=True)
        
        print(f"\nTranscribing: {audio_path.name}")
        
        # Transcribe
        result = self.model.transcribe(
            str(audio_path),
            word_timestamps=True,
            **kwargs
        )
        
        # Prepare output data
        output_data = {
            "file": str(audio_path),
            "transcribed_at": datetime.now().isoformat(),
            "language": result["language"],
            "language_probability": result.get("language_probability", 0),
            "duration": result["segments"][-1]["end"] if result["segments"] else 0,
            "text": result["text"],
            "segments": result["segments"]
        }
        
        # Save outputs
        base_name = audio_path.stem
        
        # Save as text
        text_file = output_path / f"{base_name}.txt"
        with open(text_file, "w", encoding="utf-8") as f:
            f.write(result["text"])
        
        # Save as JSON
        json_file = output_path / f"{base_name}.json"
        with open(json_file, "w", encoding="utf-8") as f:
            json.dump(output_data, f, indent=2, ensure_ascii=False)
        
        # Save as SRT
        srt_file = output_path / f"{base_name}.srt"
        self._save_srt(result["segments"], srt_file)
        
        print(f"✓ Transcription saved:")
        print(f"  - Text: {text_file}")
        print(f"  - JSON: {json_file}")
        print(f"  - SRT: {srt_file}")
        
        return output_data
    
    def _save_srt(self, segments, output_path):
        """Save segments as SRT subtitle file."""
        with open(output_path, "w", encoding="utf-8") as f:
            for i, segment in enumerate(segments, start=1):
                start = self._format_srt_time(segment["start"])
                end = self._format_srt_time(segment["end"])
                text = segment["text"].strip()
                f.write(f"{i}\n{start} --> {end}\n{text}\n\n")
    
    def _format_srt_time(self, seconds):
        """Format seconds to SRT timestamp."""
        hours = int(seconds // 3600)
        minutes = int((seconds % 3600) // 60)
        secs = int(seconds % 60)
        millis = int((seconds % 1) * 1000)
        return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}"

# Usage
if __name__ == "__main__":
    transcriber = WhisperTranscriber(model_size="base")
    
    result = transcriber.transcribe_file(
        "meeting.mp3",
        output_dir="transcripts",
        language="en",
        temperature=0.0
    )
    
    print(f"\nLanguage: {result['language']}")
    print(f"Duration: {result['duration']:.2f}s")
    print(f"\nTranscription preview:")
    print(result['text'][:200] + "...")

---

第十步：最佳实践

1. 选择合适的模型

# For speed (testing, demos)
model = whisper.load_model("tiny")

# For balance (general use)
model = whisper.load_model("base")  # or "small"

# For accuracy (production)
model = whisper.load_model("medium")  # or "large-v2"

2. 已知语言时尽量指定

# Faster and more accurate
result = model.transcribe("audio.mp3", language="en")

# Instead of auto-detection
result = model.transcribe("audio.mp3")  # Slower

3. 使用合适的温度

# Recommended for most cases
result = model.transcribe("audio.mp3", temperature=0.0)

# For creative content (not recommended for transcription)
result = model.transcribe("audio.mp3", temperature=0.2)

4. 用 initial_prompt 提供上下文

# Technical content
result = model.transcribe(
    "meeting.mp3",
    initial_prompt="This meeting discusses software architecture, APIs, and deployment strategies."
)

# Medical content
result = model.transcribe(
    "consultation.mp3",
    initial_prompt="This is a medical consultation about patient symptoms and treatment."
)

5. 复用模型实例

# Load once, reuse multiple times
model = whisper.load_model("base")

# Process multiple files
for audio_file in ["file1.mp3", "file2.mp3", "file3.mp3"]:
    result = model.transcribe(audio_file)
    # Process result...

6. 处理超长音频

import whisper
from pydub import AudioSegment

def transcribe_long_audio(audio_path, chunk_length_ms=600000):  # 10 minutes
    """Transcribe long audio by splitting into chunks."""
    model = whisper.load_model("base")
    
    # Load audio
    audio = AudioSegment.from_file(audio_path)
    duration_ms = len(audio)
    
    all_text = []
    all_segments = []
    
    # Process in chunks
    for i in range(0, duration_ms, chunk_length_ms):
        chunk = audio[i:i + chunk_length_ms]
        chunk_path = f"chunk_{i}.wav"
        chunk.export(chunk_path, format="wav")
        
        result = model.transcribe(chunk_path)
        all_text.append(result["text"])
        all_segments.extend(result["segments"])
        
        # Clean up chunk file
        os.remove(chunk_path)
    
    return {
        "text": " ".join(all_text),
        "segments": all_segments
    }

---

常见问题与解决

问题 1：找不到 FFmpeg

# Install FFmpeg
# macOS
brew install ffmpeg

# Ubuntu/Debian
sudo apt install ffmpeg

# Verify
ffmpeg -version

问题 2：内存不足

# Use smaller model
model = whisper.load_model("base")  # Instead of "large"

# Or use CPU
import torch
model = whisper.load_model("base", device="cpu")

# Or process in chunks (see above)

问题 3：转录很慢

# Use GPU if available
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model = whisper.load_model("base", device=device)

# Use smaller model
model = whisper.load_model("tiny")  # or "base"

# Reduce beam size (faster but slightly less accurate)
result = model.transcribe("audio.mp3", beam_size=1)

问题 4：准确率低

# Use larger model
model = whisper.load_model("medium")  # or "large"

# Specify language
result = model.transcribe("audio.mp3", language="en")

# Provide context
result = model.transcribe(
    "audio.mp3",
    initial_prompt="Context about the audio content..."
)

# Use optimal settings
result = model.transcribe(
    "audio.mp3",
    temperature=0.0,
    beam_size=5,
    best_of=5
)

---

应用场景

1. 播客转录

model = whisper.load_model("medium")
result = model.transcribe("podcast.mp3", language="en")

# Save transcript
with open("podcast_transcript.txt", "w") as f:
    f.write(result["text"])

2. 生成 YouTube 字幕

model = whisper.load_model("base")
result = model.transcribe("video.mp4", language="en")

# Generate SRT
# (Use CLI: whisper video.mp4 --output_format srt)

3. 会议记录

model = whisper.load_model("base")
result = model.transcribe(
    "meeting.mp3",
    language="en",
    initial_prompt="This is a business meeting discussing project updates and deadlines."
)

# Save with timestamps
for segment in result["segments"]:
    print(f"[{segment['start']:.0f}s] {segment['text']}")

4. 访谈转录

model = whisper.load_model("medium")
result = model.transcribe("interview.mp3", language="en")

# Export for editing
with open("interview.txt", "w") as f:
    for segment in result["segments"]:
        f.write(f"[{segment['start']:.2f}s] {segment['text']}\n")

5. 多语言内容翻译

model = whisper.load_model("base")

# Translate to English
result = model.transcribe("spanish_audio.mp3", task="translate")
print(result["text"])  # English translation

---

Whisper 与其他方案对比

特性	Whisper	云端 API	Faster-Whisper
成本	免费	按分钟计费	免费
离线	✅	❌	✅
速度	中等	快	快（约 2–4 倍）
准确率	高	高	高（相当）
部署	简单	非常简单	简单
实时	❌	✅	❌
隐私	✅ 本地	❌ 云端	✅ 本地

• 需要免费、离线转录
• 隐私要求高
• 需要自主掌控基础设施
• 处理批量文件或归档内容

• 需要实时转录
• 希望托管式基础设施
• 有预算支付 API 费用
• 需要企业级支持

---

延伸阅读

• Whisper Python 示例 — 更详细的 Python 示例
• Faster-Whisper 指南 — 约 2–4 倍速度的转录
• Whisper 准确率技巧 — 提升转录质量
• Whisper 转录稿格式化 — 格式化输出（SRT、VTT、JSON）
• 会议场景下的 Whisper — 面向会议的转录

---

总结

• Whisper 支持 99+ 种语言，准确率表现良好
• 按需求选择模型尺寸
• 已知语言时显式指定可提升表现
• 使用词级时间戳获得精细时间对齐
• 多文件处理时复用模型实例
• 生产部署可考虑 faster-whisper

---