Incredibly Fast Fhisper

incredibly-fast-whisper

Transcribe 150 minutes of audio in 100 seconds with Incredibly Fast Fhisper

L40S 45GB

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~11 sec

StatusActive

Version

0.0.1

Updated8 days ago

API Reference

View Full Documentation

Prerequisites

Create an API Key from the Eachlabs Console
Install the required dependencies for your chosen language (e.g., requests for Python)

API Integration Steps

1. Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

import requests
import time

API_KEY = "YOUR_API_KEY"  # Replace with your API key
HEADERS = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}

def create_prediction():
    response = requests.post(
        "https://api.eachlabs.ai/v1/prediction/",
        headers=HEADERS,
        json={
            "model": "incredibly-fast-whisper",
            "version": "0.0.1",
            "input": {
  "task": "transcribe",
  "audio": "your_file.audio/mp3",
  "hf_token": "your hf token here",
  "language": "None",
  "timestamp": "chunk",
  "batch_size": "24",
  "diarise_audio": false
}
        }
    )
    prediction = response.json()
    
    if prediction["status"] != "success":
        raise Exception(f"Prediction failed: {prediction}")
    
    return prediction["predictionID"]

2. Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

def get_prediction(prediction_id):
    while True:
        result = requests.get(
            f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",
            headers=HEADERS
        ).json()
        
        if result["status"] == "success":
            return result
        elif result["status"] == "error":
            raise Exception(f"Prediction failed: {result}")
        
        time.sleep(1)  # Wait before polling again

3. Complete Example

Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.

try:
    # Create prediction
    prediction_id = create_prediction()
    print(f"Prediction created: {prediction_id}")
    
    # Get result
    result = get_prediction(prediction_id)
    print(f"Output URL: {result['output']}")
    print(f"Processing time: {result['metrics']['predict_time']}s")
except Exception as e:
    print(f"Error: {e}")

Additional Information

The API uses a two-step process: create prediction and poll for results
Response time: ~11 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Insanely Fast Whisper is a state-of-the-art, high-performance speech-to-text model optimized for lightning-fast transcription and multilingual capabilities. It is based on OpenAI's Whisper model but has been significantly optimized for speed and efficiency, making it ideal for real-time transcription and large-scale audio processing tasks.

Technical Specifications

Baic Model : OpenAI Whisper Large v3.

Optimizations:

Inference speed increased through model quantization and architectural improvements.
Reduced latency for real-time use cases.
Language Support: Over 50 languages, including English, Spanish, Mandarin, and Arabic

Key Considerations

Audio Quality Matters:

Low-quality audio with excessive noise or low bitrates can reduce transcription accuracy.

Multilingual Transcription:

Audio in mixed languages may require manual post-editing for perfect accuracy.

Tips & Tricks

Use Timestamps:

Leverage JSON output for detailed analysis, including word-level timestamps.

Custom Fine-Tuning:

Fine-tune the model with domain-specific datasets for industry-specific transcription needs.

Real-Time Applications:

Pair with streaming services to transcribe live events or meetings.

Capabilities

Real-Time Transcription: Ideal for live meetings, conferences, and events.

Multilingual Transcription: Handles different languages and accents seamlessly.

Noise Tolerance: Performs well even with moderate background noise.

Timestamps: Includes word-level timestamps for precise tracking.

Customizability: Supports fine-tuning for specialized use cases..

What can I use for?

Content Creation: Generate subtitles or transcribe interviews and podcasts.

Live Captions: Provide accessibility for live streams or webinars.

Customer Support: Transcribe call center conversations for analysis and insights.

Education: Create transcriptions of lectures, workshops, or training sessions.

Research: Process large volumes of audio data for linguistic or market analysis.

Meeting Transcripts: Quickly document long meetings or lectures with accurate transcripts.

Things to be aware of

Transcribe a Podcast:

Input: A one-hour podcast with clear audio.
Output: A detailed transcript with timestamps.

Live Streaming:

Integrate the model with live video streams for real-time captions.

Multilingual Scenarios:

Transcribe an audio file containing English, Spanish, and German segments.

Interactive Applications:

Use the model to power speech-to-text features in an application.

Noisy Audio Files:

Test its capabilities by transcribing moderately noisy recordings (e.g., outdoor interviews).

Limitations

Accuracy Variations:

Heavily accented or fast speech may slightly reduce transcription quality.

Challenging Audio Scenarios:

Overlapping speakers may require additional tools to track speaker activity.

Language Specificity:

Clear language settings are necessary for accurate multilingual transcription.

Maximum Audio Length:

While the tool can process long audio files, it supports recordings up to 90 minutes. Extremely long recordings may require significant computational resources.

Supported Formats:

Supports WAV, MP3, and M4A audio formats; ensure compatibility before processing.

Known Limitations:

Performance may vary depending on hardware capabilities and the complexity of the audio content.

Output Format: TEXT

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