Eachlabs | AI Workflows for app builders
wizper-with-timestamp

WHISPER

Wizper with Timestamp is a multilingual speech recognition and translation model built on Whisper v3 that transcribes audio with precise word-level timestamps. It delivers fast, accurate, and time-aligned transcripts, making it ideal for subtitles, media indexing, and real-time transcription workflows

Avg Run Time: 0.000s

Model Slug: wizper-with-timestamp

Playground

Input

Enter a URL or choose a file from your computer.

Output

Example Result

Preview and download your result.

{
"output":{
"chunks":[
0:{...}
1:{...}
]
"languages":[
0:"en"
]
"text":"the little tales they tell are false the door was barred locked and bolted as well ripe pears are fit for a queen's table a big wet stain was on the round carpet the kite dipped and swayed but stayed aloft the pleasant hours fly by much too soon the room was crowded with a mild wab the room was crowded with a wild mob this strong arm shall shield your honour she blushed when he gave her a white orchid The beetle droned in the hot June sun."
}
}
The total cost depends on how long the model runs. It costs $0.001080 per second. Based on an average runtime of 20 seconds, each run costs about $0.0216. With a $1 budget, you can run the model around 46 times.

API & SDK

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.

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.

Readme

Table of Contents
Overview
Technical Specifications
Key Considerations
Tips & Tricks
Capabilities
What Can I Use It For?
Things to Be Aware Of
Limitations

Overview

wizper-with-timestamp — Voice-to-Text AI Model

wizper-with-timestamp, developed by OpenAI as part of the Whisper family, delivers multilingual speech recognition with precise word-level timestamps, enabling accurate time-aligned transcripts for audio files. Built on Whisper v3, this voice-to-text AI model excels in transcribing long-form audio like videos or meetings, outputting clean text with timestamps ideal for subtitles and media indexing. Developers seeking OpenAI voice-to-text solutions with timestamp precision find wizper-with-timestamp perfect for real-time workflows and batch processing, supporting formats like mono WAV at 16kHz for optimal performance.

Technical Specifications

What Sets wizper-with-timestamp Apart

Unlike standard Whisper implementations, wizper-with-timestamp provides word-level timestamps directly in outputs, allowing automatic removal of time tags for clean text while retaining alignment data for precise editing. This enables users to generate subtitle files or indexed transcripts without manual post-processing, streamlining workflows for media professionals handling hours-long videos.

It supports multilingual transcription with high accuracy on diverse accents, processing audio in 30-second windows for long-form content via sequential decoding, which maintains context over extended durations. This capability powers efficient batch transcription of large directories, achieving speeds up to 3x faster on optimized hardware like Apple Silicon with whisper.cpp integrations.

  • Precise word-level timestamps: Outputs include start/end times per word, superior for subtitle generation compared to segment-only timing in base models.
  • Multilingual support with 16kHz WAV optimization: Handles global languages in mono format, ideal for wizper-with-timestamp API integrations in production.
  • Fast inference on GPUs/CPUs: Processes 2.5-hour videos in 5-17 minutes depending on mode and hardware, outperforming unoptimized Whisper.

These features position wizper-with-timestamp as a top choice in voice-to-text AI models comparison for timestamped transcription needs.

Key Considerations

  • Balance model size with hardware: base models use 1GB VRAM, large-v3 over 10GB; use quantization (int8) for resource-constrained environments
  • Best practices: Tune beamsize for quality vs. latency trade-off; select computetype (float16/int8) based on GPU/CPU; integrate VAD for real-time efficiency
  • Common pitfalls: Long segments increase latency; poor segmentation affects LLM integration coherence; avoid over-reliance on quantized models for high-accuracy needs
  • Quality vs speed trade-offs: Quantization boosts speed/memory efficiency but reduces accuracy by 2-5%; original Whisper best for benchmarks, Faster-Whisper for production
  • Prompt engineering tips: Use optional prompts for context/proper nouns; lower temperature for deterministic outputs, higher for variety

Tips & Tricks

How to Use wizper-with-timestamp on Eachlabs

Access wizper-with-timestamp through Eachlabs' Playground for instant testing—upload audio files in WAV/MP3, select language and mode, and get timestamped transcripts in seconds. Integrate via API or SDK with parameters like audio input, beam_size for accuracy, and compute_type for speed; outputs deliver JSON with word-level timings and clean text, supporting high-accuracy voice-to-text workflows at scale.

---

Capabilities

  • Accurate multilingual speech-to-text transcription and translation from audio/video
  • Real-time processing with low latency (1-2 seconds end-to-end) via WHISPER-LIVE
  • Timestamped segments, language detection, and optional diarization/speaker labels
  • High versatility across batch and streaming modes, with optimizations for constrained hardware
  • Strong performance in video transcription, maintaining structure during translation

What Can I Use It For?

Use Cases for wizper-with-timestamp

Content creators producing YouTube videos or podcasts use wizper-with-timestamp to generate timestamped subtitles from raw audio, feeding in a 16kHz WAV file for quick, aligned transcripts that boost SEO with chapter markers. For a 30-minute episode, it outputs word-timed text ready for upload, saving hours of manual timing.

Developers building real-time apps integrate the wizper-with-timestamp API for meeting transcription tools, inputting live audio streams processed in 30-second chunks to deliver live captions with speaker-aligned timestamps. Example input: an MP3 from a conference call, yielding formatted output with precise timings for searchable logs.

Media teams indexing archives batch-process video libraries with wizper-with-timestamp, converting files to mono WAV and transcribing with timestamps for easy search and clip extraction. This supports AI speech to text with timestamps, turning terabytes of footage into queryable text databases overnight.

Researchers analyzing speech data apply it to multilingual datasets, leveraging timestamp precision for diarization-like segmentation in cognitive studies or interviews, ensuring accurate word-boundary analysis across languages.

Things to Be Aware Of

  • Experimental real-time features like WHISPER-LIVE show efficient VAD but require careful segmentation for coherence with LLMs
  • Known quirks: Quantized models drop accuracy slightly (2-5%); energy-based VAD may miss subtle speech
  • Performance varies by hardware: NVIDIA T4 achieves 0.5-3x real-time speed depending on optimization
  • Resource needs: Original large models demand 10GB VRAM, quantized versions 3GB; suitable for servers to embedded devices
  • Consistency strong in multilingual support but trade-offs in speed/accuracy noted in benchmarks
  • Positive feedback: Users praise 4x speed gains and memory halved in Faster-Whisper for practical deployments
  • Common concerns: Latency in long segments; need for business-specific tuning of delay/accuracy/resources

Limitations

  • Not designed for image generation; strictly audio/speech-to-text focused, mismatching provided model type
  • Quantization and optimizations reduce accuracy slightly; less ideal for absolute precision benchmarks vs. originals
  • Real-time setups sensitive to segmentation, potentially introducing delays or coherence issues in LLM integrations