Turn audio and video into text across 99 languages, translate speech into English, and create timestamped subtitles. The skill covers model selection, transcription settings, and batch processing for recordings such as meetings and podcasts.
中文简介正在准备中。当前显示英语简介。
Transcribe and translate speech in 99 languages.
安装前可以查看交给 AI 代理的指令内容。
OpenAI's multilingual speech recognition model.
Use when:
Metrics:
Use alternatives instead:
# 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
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']}")
# 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
# 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
# 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
# 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
# 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)")
# 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)
)
# 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
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"])
# 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}")
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
# Generate SRT subtitles
whisper video.mp4 --output_format srt --language English
# Output: video.srt
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())
# Use ffmpeg to extract audio
ffmpeg -i video.mp4 -vn -acodec pcm_s16le audio.wav
# Then transcribe
whisper audio.wav
| 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
Top-supported languages:
Full list: 99 languages total
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Set up the 1Password CLI and use stored passwords or API keys in commands and configuration templates. Choose an authentication method, retrieve secret references, and pass credentials to processes through environment variables.
中文简介正在准备中。当前显示英语简介。
Build an Excel workbook linking the income statement, balance sheet, and cash flow statement. Populate historical figures, create formula-based forecasts, compare scenarios, and check that balances and cash totals agree across all three statements.
中文简介正在准备中。当前显示英语简介。
Adapt existing PyTorch training scripts to use multiple GPUs or machines with limited code changes. The skill guides configuration, launch commands, mixed precision, which uses lower-precision numbers, and DeepSpeed or FSDP settings for distributing training work and memory.
中文简介正在准备中。当前显示英语简介。
Configure Hermes to run AI models through Actual Computer on your own hardware. Set up either the hosted relay or a local connection, select models, verify responses, and troubleshoot request errors or empty output.
中文简介正在准备中。当前显示英语简介。
Test an app from the perspective of a frustrated, less technical user. Find confusing steps and barriers, separate actionable usability problems from personal complaints, and produce screenshots, suggested fixes, and prioritized tickets.
中文简介正在准备中。当前显示英语简介。
Resolve Git merge conflicts between two agents by comparing both changes and their stated goals. Combine compatible work, explain choices where designs differ, and verify the merged result with project builds or tests.
中文简介正在准备中。当前显示英语简介。