whisper

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:

• 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

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

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

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

• 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

Agent operating procedure

1. Check the environment. Confirm GPU memory, model checkpoint and licence, framework versions and input formats.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Run inference on a single sample at low resolution or short length first.
4. Execute the full task using the instructions and references above.
5. Validate the result. Inspect outputs qualitatively and with task metrics; check licence restrictions for generated content.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
Out-of-memory on large inputs	Reduce resolution or sequence length, use half precision, or tile the input.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Check model and dataset licences before commercial or published use.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• transformers: Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audi…