openai / whisper · Getting started

Platform metadata records the author-supplied description "Robust Speech Recognition via Large-Scale Weak Supervision" — the project's own tagline, not an analyzer finding.

Observed metadata: Python as primary language (160,526 Python language bytes), an MIT license, a latest release v20250625 published 2025-06-26, and 109,056 stargazers at the 2026-09-14 snapshot.

The analyzer's inferred summary describes a Python project of 45 analyzed files, organized around data, notebooks, tests, and whisper, with whisper/transcribe.py appearing as the main starting place.

Treat the tagline as the author's claim and the structural summary as analyzer inference; neither establishes how the code behaves.

All dependency information in this analysis comes from one parsed manifest: requirements.txt, which yielded seven runtime-scope records — numba, numpy, torch, tqdm, more-itertools, tiktoken, and triton.

• triton is the only pinned entry (>=2.0.0); the other six records carry no version constraint.
• All seven records are runtime scope; the parsed set contains no dev- or test-scope entries.
• The names — torch, tiktoken, and triton among them — suggest a machine-learning-leaning runtime, though the packet does not describe each library's role.

pyproject.toml also exists and is flagged as a project-level important file, but its dependency tables were not parsed, so anything declared there is missing from these records.

This section lists manifest contents, not setup steps: no installation command is verified by this analysis.

The packet contains a single entrypoint record: whisper/transcribe.py, marked verified, containing a Python __main__ execution guard.

The same file is separately flagged as important "because it is likely an entrypoint", consistent with the guard record.

The guard body is:

if __name__ == "__main__":
    cli()

The packet lists five bounded static path records for this entrypoint. Each goes module -> cli (a local call), then cli -> argparse.ArgumentParser or parser.add_argument, marked external_or_unresolved, terminating with reason unresolved_boundary; none is truncated.

The static picture therefore reaches argument construction and stops at that boundary. Which CLI arguments exist, what loads, and what runs after parsing is not established by this analysis.

Five files are flagged as project-level important: README.md, pyproject.toml, requirements.txt, LICENSE, and whisper/transcribe.py (the last one flagged as a likely entrypoint).

The analyzer's learning-path claim advises starting with the manifests and important symbols, then tracing the bounded execution path and inspecting unresolved boundaries before changing code.

Its modify-guide claim advises beginning modification analysis at the whisper/transcribe.py entrypoint and verifying downstream effects manually.

Top-level layout: whisper (18 files), tests (7), 13 root-level files, .github (3), data (2), notebooks (2).

Tests are present — the summary counts 7 test-like files — though the packet does not say what they cover.

• No verified setup commands: install/run/build inference is partial, and installation commands are not verified by this analysis.
• Partial dependency coverage: records come only from requirements-style manifests parsed by the analyzer; pyproject.toml dependency tables are not parsed.
• README-derived text is treated as author-claimed at best: extraction is line-based and may capture code lines instead of prose claims.
• Static analysis only: reflection, runtime dependency injection, dynamic imports, monkey-patching, generated code, framework runtime wiring, and dynamic dispatch are not resolved.

One packet-internal inconsistency: a teaching claim in the packet's how_it_runs section states that no bounded static execution path is available, yet the summary counts 12 bounded execution paths and five path records are listed for the entrypoint.

Net effect: what the program actually does at runtime — CLI arguments, model loading, output behavior — is not established by these records.