# Wizper with Timestamp

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

## API Information

- **Model Slug:** wizper-with-timestamp
- **Branded URL:** https://www.eachlabs.ai/openai/whisper/wizper-with-timestamp
- **Provider:** Openai
- **Category:** Voice to Text
- **Output Type:** object
- **Status:** active
- **Version:** 0.0.1
- **Base Cost:** $0.00108 per processing second
- **Estimated Processing Time:** 1 seconds
- **Last Updated:** 2026-08-16
- **Interactive Demo:** https://www.eachlabs.ai/ai-models/wizper-with-timestamp

## Pricing

- **Charge Type:** dynamic
- **Estimate:** $0.00108 per processing second
- **Pricing Details:** default: $0.00108

## Input Schema

| Parameter | Type | Required | Default | Constraints | Description |
|-----------|------|----------|---------|-------------|-------------|
| audio_url | string | Yes | - | - | URL of the audio file to transcribe. Supported formats: mp3, mp4, mpeg, mpga, m4a, wav or webm. |
| task | string | No | transcribe | transcribe, translate | Task to perform on the audio file. Either transcribe or translate. |
| language | string | No | - | af,am,ar,as,az,ba,be,bg,bn,bo,br,bs,ca,cs,cy,da,de,el,en,es,et,eu,fa,fi,fo,fr,gl,gu,ha,haw,he,hi,hr,ht,hu,hy,id,is,it,ja,jw,ka,kk,km,kn,ko,la,lb,ln,lo,lt,lv,mg,mi,mk,ml,mn,mr,ms,mt,my,ne,nl,nn,no,oc,pa,pl,ps,pt,ro,ru,sa,sd,si,sk,sl,sn,so,sq,sr,su,sv,sw,ta,te,tg,th,tk,tl,tr,tt,uk,ur,uz,vi,yi,yo,zh | Language of the audio file. If translate is selected as the task, the audio will be translated to English, regardless of the language selected. If `None` is passed, the language will be automatically detected. This will also increase the inference time. |
| chunk_level | string | No | segment | - | Level of the chunks to return. |
| max_segment_len | integer | No | 29 | 10–29 | Maximum speech segment duration in seconds before splitting. |
| merge_chunks | boolean | No | true | - | Whether to merge consecutive chunks. When enabled, chunks are merged if their combined duration does not exceed max_segment_len. |
| version | string | No | 3 | - | Version of the model to use. All of the models are the Whisper large variant. |

## Example Request

```bash
curl -X POST https://api.eachlabs.ai/v1/prediction/ \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "wizper-with-timestamp",
  "input": {
    "audio_url": "https://cdn-us.eachlabs.ai/defaults/a8c3c8b8f7f893180f019703d657b7e5dec788d068ebe320b1eacc8db006d7ee.wav"
  }
}'
```

## Output Schema

Response returned by `GET /v1/prediction/{id}` when the job completes:

```json
{
  "status": "success",
  "predictionID": "string",
  "output": "object",
  "metrics": {
    "predict_time": "number (seconds)"
  }
}
```

## Polling

```bash
curl https://api.eachlabs.ai/v1/prediction/{PREDICTION_ID} \
  -H "Authorization: Bearer YOUR_API_KEY"
```

| Status | Meaning |
|--------|---------|
| `processing` | Still running — poll again |
| `success` | Done — read `output` |
| `error` | Failed — read `message` / `details` |

## Webhook (alternative to polling)

Pass `"webhook_url": "https://your.host/path"` in the create request. Eachlabs POSTs this payload when the job ends:

```json
{
  "exec_id": "prediction-uuid",
  "status": "succeeded",
  "output": "https://...",
  "error": ""
}
```

`status` is `"succeeded"` or `"failed"`. `exec_id` equals the `predictionID` from create. Return 2xx within 30 seconds.

## Errors

Error body: `{ "status": "error", "message": "...", "details": "..." }`

| Code | Meaning |
|------|---------|
| `400` | Invalid input |
| `401` | Missing / invalid `Authorization` bearer token |
| `404` | Unknown model or prediction id |
| `429` | Rate limit — 100 creates / min, 10 concurrent per key |
| `5xx` | Retry with backoff |

## 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.

## Usage Notes

- API Base URL: `https://api.eachlabs.ai/v1`
- Authentication: send `Authorization: Bearer YOUR_API_KEY`. Generate a key from the Eachlabs dashboard at https://www.eachlabs.ai/dashboard/api-keys.
- File-typed parameters (`*_url`, `image_url`, `video_url`, `audio_url`, etc.) accept publicly-reachable HTTPS URLs only. Upload your asset first (GCS / S3 / your CDN) and pass the resulting URL. Data-URIs and localhost URLs are rejected.
- For structured parameters (arrays / objects) send real JSON values, not stringified payloads.
- Monetary values are reported in USD; per-token / per-megapixel rates may be billed in micro-cents internally.
- Prefer `webhook_url` over polling for long-running predictions — see the Webhook Callback section.