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