Audio

Apache 2.0Open source weights and codeUpdated August 2026

Whisper Base

Compact Whisper checkpoint for lightweight multilingual transcription and translation where modest hardware matters more than peak accuracy.

OpenAI · Whisper

Editorial review

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

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

Developers who want a practical starting point for local captioning, voice notes, and CPU-leaning ASR experiments.

Who should use it

  • Developers who want a practical starting point for local captioning, voice notes, and CPU-leaning ASR experiments.
  • Builders who want local or self-hosted testing options.

Common workflows

  • CPU-friendly transcription experiments
  • 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.
  • Developers who want a practical starting point for local captioning, voice notes, and CPU-leaning ASR experiments.
  • 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

Related resources

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

Hardware~0.15 GB in fp16 (73M 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-base)

Model ecosystem connections

Use these next-step links to move from this profile into related tools, comparisons, guides, stacks, and curated shortlists.