Whisper Large V3
Whisper V3 delivers high-accuracy speech recognition across 99 languages. Ideal for transcription, subtitles, and accessibility.
api_example.sh
Technical Specifications
Model Architecture & Performance
Pricing
Pay-per-use, no commitments
API Reference
Complete parameter documentation
| Parameter | Type | Default | Description |
|---|---|---|---|
| task | select | transcribe | Choose whether to transcribe to the original language or translate to English. |
| language | select | en | Select the spoken language (auto-detect if unsure). |
| temperature | number | 0 | Controls randomness of output — 0.0 means deterministic. |
| initial_prompt | string | Business meeting conversation | Guides the model to better understand the audio context. |
| word_timestamps | boolean | true | Return per-word timestamps for the transcription. |
| vad_filter | boolean | true | Turn it on when you have long pauses or background noise. Leave off for tightly trimmed clips to save compute. |
| return_segments | boolean | true | Return transcription with time-segment metadata. |
| output_format | select | json | Choose the transcription output format. |
Explore the full request and response schema in our external API documentation
Performance
Strengths & considerations
| Strengths | Considerations |
|---|---|
Multilingual Timestamp support Handles noisy input Robust accuracy | Large model size Slow for long files Occasional misalignment |
Enterprise
Platform Integration
Docker Support
Official Docker images for containerized deployments
Kubernetes Ready
Production-grade KBS manifests and Helm charts
SDK Libraries
Official SDKs for Python, Javascript, Go, and Java
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