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:

• 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

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

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

Whisper Language Support Guide

Complete guide to Whisper's multilingual capabilities.

Supported languages (99 total)

Top-tier support (WER < 10%)

• English (en)
• Spanish (es)
• French (fr)
• German (de)
• Italian (it)
• Portuguese (pt)
• Dutch (nl)
• Polish (pl)
• Russian (ru)
• Japanese (ja)
• Korean (ko)
• Chinese (zh)

Good support (WER 10-20%)

• Arabic (ar)
• Turkish (tr)
• Vietnamese (vi)
• Swedish (sv)
• Finnish (fi)
• Czech (cs)
• Romanian (ro)
• Hungarian (hu)
• Danish (da)
• Norwegian (no)
• Thai (th)
• Hebrew (he)
• Greek (el)
• Indonesian (id)
• Malay (ms)

Full list (99 languages)

Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Bashkir, Basque, Belarusian, Bengali, Bosnian, Breton, Bulgarian, Burmese, Cantonese, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Faroese, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Lao, Latin, Latvian, Lingala, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Moldavian, Mongolian, Myanmar, Nepali, Norwegian, Nynorsk, Occitan, Pashto, Persian, Polish, Portuguese, Punjabi, Pushto, Romanian, Russian, Sanskrit, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tagalog, Tajik, Tamil, Tatar, Telugu, Thai, Tibetan, Turkish, Turkmen, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, Yiddish, Yoruba

Usage examples

Auto-detect language

import whisper

model = whisper.load_model("turbo")

# Auto-detect language
result = model.transcribe("audio.mp3")

print(f"Detected language: {result['language']}")
print(f"Text: {result['text']}")

Specify language (faster)

# Specify language for faster transcription
result = model.transcribe("audio.mp3", language="es")  # Spanish
result = model.transcribe("audio.mp3", language="fr")  # French
result = model.transcribe("audio.mp3", language="ja")  # Japanese

Translation to English

# Translate any language to English
result = model.transcribe(
    "spanish_audio.mp3",
    task="translate"  # Translates to English
)

print(f"Original language: {result['language']}")
print(f"English translation: {result['text']}")

Language-specific tips

Chinese

# Chinese works well with larger models
model = whisper.load_model("large")

result = model.transcribe(
    "chinese_audio.mp3",
    language="zh",
    initial_prompt="这是一段关于技术的讨论"  # Context helps
)

Japanese

# Japanese benefits from initial prompt
result = model.transcribe(
    "japanese_audio.mp3",
    language="ja",
    initial_prompt="これは技術的な会議の録音です"
)

Arabic

# Arabic: Use large model for best results
model = whisper.load_model("large")

result = model.transcribe(
    "arabic_audio.mp3",
    language="ar"
)

Model size recommendations

Performance by language

English

• tiny: WER ~15%
• base: WER ~8%
• small: WER ~5%
• medium: WER ~4%
• large: WER ~3%
• turbo: WER ~3.5%

Spanish

• tiny: WER ~20%
• base: WER ~12%
• medium: WER ~6%
• large: WER ~4%

Chinese

• small: WER ~15%
• medium: WER ~8%
• large: WER ~5%

Best practices

1. Use English-only models - Better for small models (tiny/base) 2. Specify language - Faster than auto-detect 3. Add initial prompt - Improves accuracy for technical terms 4. Use larger models - For low-resource languages 5. Test on sample - Quality varies by accent/dialect 6. Consider audio quality - Clear audio = better results 7. Check language codes - Use ISO 639-1 codes (2 letters)

Language detection

# Detect language only (no transcription)
import whisper

model = whisper.load_model("base")

# Load audio
audio = whisper.load_audio("audio.mp3")
audio = whisper.pad_or_trim(audio)

# Make log-Mel spectrogram
mel = whisper.log_mel_spectrogram(audio).to(model.device)

# Detect language
_, probs = model.detect_language(mel)
detected_language = max(probs, key=probs.get)

print(f"Detected language: {detected_language}")
print(f"Confidence: {probs[detected_language]:.2%}")

Resources

• Paper: https://arxiv.org/abs/2212.04356
• GitHub: https://github.com/openai/whisper
• Model Card: https://github.com/openai/whisper/blob/main/model-card.md