Agent Skills
› synthetic-sciences/openscience
› whisper
whisper
GitHubOpenAI通用语音识别模型，支持99种语言的转录、翻译及语言识别。适用于播客转录、会议记录、多语言音频处理及嘈杂环境下的语音转文字任务，提供多种模型尺寸以平衡速度与精度。
Trigger Scenarios
需要将音频转换为文本
进行多语言语音识别或翻译
播客或视频内容转录
会议自动化笔记生成
Install
npx skills add synthetic-sciences/openscience --skill whisper -g -y
SKILL.md
Frontmatter
{
"name": "whisper",
"tags": [
"Whisper",
"Speech Recognition",
"ASR",
"Multimodal",
"Multilingual",
"OpenAI",
"Speech-To-Text",
"Transcription",
"Translation",
"Audio Processing"
],
"author": "Synthetic Sciences",
"license": "MIT",
"version": "1.0.0",
"category": "llm-tools",
"description": "OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.",
"dependencies": [
"openai-whisper",
"transformers",
"torch"
]
}
Whisper - Robust Speech Recognition
OpenAI's multilingual speech recognition model.
When to use Whisper
Use when:
- Speech-to-text transcription (99 languages)
- Podcast/video transcription
- Meeting notes automation
- Translation to English
- Noisy audio transcription
- Multilingual audio processing
Metrics:
- 72,900+ GitHub stars
- 99 languages supported
- Trained on 680,000 hours of audio
- MIT License
Use alternatives instead:
- AssemblyAI: Managed API, speaker diarization
- Deepgram: Real-time streaming ASR
- Google Speech-to-Text: Cloud-based
Quick start
Installation
# Requires Python 3.8-3.11
pip install -U openai-whisper
# Requires ffmpeg
# macOS: brew install ffmpeg
# Ubuntu: sudo apt install ffmpeg
# Windows: choco install ffmpeg
Basic transcription
import whisper
# Load model
model = whisper.load_model("base")
# Transcribe
result = model.transcribe("audio.mp3")
# Print text
print(result["text"])
# Access segments
for segment in result["segments"]:
print(f"[{segment['start']:.2f}s - {segment['end']:.2f}s] {segment['text']}")
Model sizes
# Available models
models = ["tiny", "base", "small", "medium", "large", "turbo"]
# Load specific model
model = whisper.load_model("turbo") # Fastest, good quality
| Model | Parameters | English-only | Multilingual | Speed | VRAM |
|---|---|---|---|---|---|
| tiny | 39M | ✓ | ✓ | ~32x | ~1 GB |
| base | 74M | ✓ | ✓ | ~16x | ~1 GB |
| small | 244M | ✓ | ✓ | ~6x | ~2 GB |
| medium | 769M | ✓ | ✓ | ~2x | ~5 GB |
| large | 1550M | ✗ | ✓ | 1x | ~10 GB |
| turbo | 809M | ✗ | ✓ | ~8x | ~6 GB |
Recommendation: Use turbo for best speed/quality, base for prototyping
Transcription options
Language specification
# Auto-detect language
result = model.transcribe("audio.mp3")
# Specify language (faster)
result = model.transcribe("audio.mp3", language="en")
# Supported: en, es, fr, de, it, pt, ru, ja, ko, zh, and 89 more
Task selection
# Transcription (default)
result = model.transcribe("audio.mp3", task="transcribe")
# Translation to English
result = model.transcribe("spanish.mp3", task="translate")
# Input: Spanish audio → Output: English text
Initial prompt
# Improve accuracy with context
result = model.transcribe(
"audio.mp3",
initial_prompt="This is a technical podcast about machine learning and AI."
)
# Helps with:
# - Technical terms
# - Proper nouns
# - Domain-specific vocabulary
Timestamps
# Word-level timestamps
result = model.transcribe("audio.mp3", word_timestamps=True)
for segment in result["segments"]:
for word in segment["words"]:
print(f"{word['word']} ({word['start']:.2f}s - {word['end']:.2f}s)")
Temperature fallback
# Retry with different temperatures if confidence low
result = model.transcribe(
"audio.mp3",
temperature=(0.0, 0.2, 0.4, 0.6, 0.8, 1.0)
)
Command line usage
# Basic transcription
whisper audio.mp3
# Specify model
whisper audio.mp3 --model turbo
# Output formats
whisper audio.mp3 --output_format txt # Plain text
whisper audio.mp3 --output_format srt # Subtitles
whisper audio.mp3 --output_format vtt # WebVTT
whisper audio.mp3 --output_format json # JSON with timestamps
# Language
whisper audio.mp3 --language Spanish
# Translation
whisper spanish.mp3 --task translate
Batch processing
import os
audio_files = ["file1.mp3", "file2.mp3", "file3.mp3"]
for audio_file in audio_files:
print(f"Transcribing {audio_file}...")
result = model.transcribe(audio_file)
# Save to file
output_file = audio_file.replace(".mp3", ".txt")
with open(output_file, "w") as f:
f.write(result["text"])
Real-time transcription
# For streaming audio, use faster-whisper
# pip install faster-whisper
from faster_whisper import WhisperModel
model = WhisperModel("base", device="cuda", compute_type="float16")
# Transcribe with streaming
segments, info = model.transcribe("audio.mp3", beam_size=5)
for segment in segments:
print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")
GPU acceleration
import whisper
# Automatically uses GPU if available
model = whisper.load_model("turbo")
# Force CPU
model = whisper.load_model("turbo", device="cpu")
# Force GPU
model = whisper.load_model("turbo", device="cuda")
# 10-20× faster on GPU
Integration with other tools
Subtitle generation
# Generate SRT subtitles
whisper video.mp4 --output_format srt --language English
# Output: video.srt
With LangChain
from langchain.document_loaders import WhisperTranscriptionLoader
loader = WhisperTranscriptionLoader(file_path="audio.mp3")
docs = loader.load()
# Use transcription in RAG
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())
Extract audio from video
# Use ffmpeg to extract audio
ffmpeg -i video.mp4 -vn -acodec pcm_s16le audio.wav
# Then transcribe
whisper audio.wav
Best practices
- Use turbo model - Best speed/quality for English
- Specify language - Faster than auto-detect
- Add initial prompt - Improves technical terms
- Use GPU - 10-20× faster
- Batch process - More efficient
- Convert to WAV - Better compatibility
- Split long audio - <30 min chunks
- Check language support - Quality varies by language
- Use faster-whisper - 4× faster than openai-whisper
- Monitor VRAM - Scale model size to hardware
Performance
| Model | Real-time factor (CPU) | Real-time factor (GPU) |
|---|---|---|
| tiny | ~0.32 | ~0.01 |
| base | ~0.16 | ~0.01 |
| turbo | ~0.08 | ~0.01 |
| large | ~1.0 | ~0.05 |
Real-time factor: 0.1 = 10× faster than real-time
Language support
Top-supported languages:
- English (en)
- Spanish (es)
- French (fr)
- German (de)
- Italian (it)
- Portuguese (pt)
- Russian (ru)
- Japanese (ja)
- Korean (ko)
- Chinese (zh)
Full list: 99 languages total
Limitations
- Hallucinations - May repeat or invent text
- Long-form accuracy - Degrades on >30 min audio
- Speaker identification - No diarization
- Accents - Quality varies
- Background noise - Can affect accuracy
- Real-time latency - Not suitable for live captioning
Resources
- GitHub: https://github.com/openai/whisper ⭐ 72,900+
- Paper: https://arxiv.org/abs/2212.04356
- Model Card: https://github.com/openai/whisper/blob/main/model-card.md
- Colab: Available in repo
- License: MIT
Version History
- e9844a4 Current 2026-07-11 17:27
Dependencies
- required openai-whisper
- required transformers
-
required
torch
