Whisper Large V2

Large Whisper checkpoint for multilingual speech recognition and speech-to-English translation when transcription accuracy matters more than small-model efficiency.

OpenAI · Whisper

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesHugging Face model card (openai/whisper-large-v2)

Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.

Best for

Teams running accuracy-first transcription, subtitles, meeting notes, or archive workflows on multilingual audio.

Who should use it

Teams running accuracy-first transcription, subtitles, meeting notes, or archive workflows on multilingual audio.
Builders who want local or self-hosted testing options.

Common workflows

Accuracy-focused transcription baseline
audio workflows
transcription workflows
speech recognition workflows
local workflows

Deployment and hardware notes

Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.

License and usage notes

Apache 2.0. Open source weights and code. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

Open source weights and code model option for Whisper workflows.
Teams running accuracy-first transcription, subtitles, meeting notes, or archive workflows on multilingual audio.
Use the exact checkpoint and quantization that matches your hardware and latency target.

Limitations

Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
Context window and limits: Fixed-length audio windows, not a token context.
Verify the exact model card, provider docs, license, and serving support before production use.

Local workflow notes

Use the exact checkpoint and quantization that matches your hardware and latency target.

Local runtimes: Ollama where supported, LM Studio where supported, llama.cpp where supported, Transformers

Platforms: Windows, macOS, Linux

Sources to verify

Hugging Face model card (openai/whisper-large-v2)

Related resources

Continue with model source notes, local tools, and implementation guides related to this model.

Whisper family hub AI tools directory Model source notes Models directory Guides library

Hardware~3.1 GB in fp16 (1.5B parameters)RuntimeOllama or LM Studio where supported, llama.cpp, Transformers, vLLMContextFixed-length audio windows, not a token contextLast updated2026

Hugging Face model card (openai/whisper-large-v2) →