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whisper

✓ Official★ 11

by firecrawl · part of firecrawl/ai-research-skills

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes…

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🧩 One of 7 skills in the firecrawl/ai-research-skills package — works on its own, and pairs well with its siblings.

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes…

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name: whisper description: 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. version: 1.0.0 author: Orchestra Research license: MIT tags: [Whisper, Speech Recognition, ASR, Multimodal, Multilingual, OpenAI, Speech-To-Text, Transcription, Translation, Audio Processing] dependencies: [openai-whisper, transformers, torch]

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

Model sizes

Copy & paste — that's it
# 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

Copy & paste — that's it
# 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

Copy & paste — that's it
# 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

Copy & paste — that's it
# 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

Copy & paste — that's it
# 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

Copy & paste — that's it
# 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)
)

Batch processing

Copy & paste — that's it
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

Copy & paste — that's it
# 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

Copy & paste — that's it
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

Copy & paste — that's it
# Generate SRT subtitles
whisper video.mp4 --output_format srt --language English

# Output: video.srt

With LangChain

Copy & paste — that's it
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

Copy & paste — that's it
# 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

Resources