MCPInstall

whisper

by ovachieverMedia & Creative

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.

Installs
65
Last 24h
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Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/ovachiever/droid-tings --skill whisper
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads whisper automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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
ModelParametersEnglish-onlyMultilingualSpeedVRAM
tiny39M✓✓~32x~1 GB
base74M✓✓~16x~1 GB
small244M✓✓~6x~2 GB
medium769M✓✓~2x~5 GB
large1550M✗✓1x~10 GB
turbo809M✗✓~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

  1. Use turbo model - Best speed/quality for English
  2. Specify language - Faster than auto-detect
  3. Add initial prompt - Improves technical terms
  4. Use GPU - 10-20× faster
  5. Batch process - More efficient
  6. Convert to WAV - Better compatibility
  7. Split long audio - <30 min chunks
  8. Check language support - Quality varies by language
  9. Use faster-whisper - 4× faster than openai-whisper
  10. Monitor VRAM - Scale model size to hardware

Performance

ModelReal-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

  1. Hallucinations - May repeat or invent text
  2. Long-form accuracy - Degrades on >30 min audio
  3. Speaker identification - No diarization
  4. Accents - Quality varies
  5. Background noise - Can affect accuracy
  6. Real-time latency - Not suitable for live captioning

Resources

FAQ

What does the whisper skill do?

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.

How do I install whisper?

Run npx skills add https://github.com/ovachiever/droid-tings --skill whisper in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/whisper/ (personal) or .claude/skills/whisper/ (project).

Which AI agents can use whisper?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is whisper safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains whisper?

It is published by ovachiever in ovachiever/droid-tings. It has 65 recorded installs.

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