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catalog / Audio & Speech / WhisperX
⬡PipelineAudio & SpeechFree

WhisperX

Whisper with fast forced alignment, accurate word-level timestamps, and multi-speaker diarization.

@ai-supply
Installs224k
⟳ upstream v3.8.6 · updated 2mo ago
↗ Source repository
← More Audio & SpeechAudio & Speech leaderboard →How we grade security →Source ↗
! Grade B · 88/100 · ReviewSecurity assessment
✓No compromise signals7capabilities surfaced1known CVE9of 20 OWASP controls clear
External endpoints declaredExternal endpoints declaredSuspicious network referencesSuspicious network references
scanned 17d ago·osv · gitleaks · opengrep · picklescan + heuristics·full breakdown in the Security tab ↓

WhisperX

WhisperX extends OpenAI Whisper with phoneme-based forced alignment for word-level timestamps accurate to ±20ms, and integrates pyannote.audio for speaker diarization — letting you output speaker-labelled transcripts in one command.

Key Features

  • Word-level timestamps: phoneme alignment via wav2vec2 gives far more accurate boundaries than Whisper's built-in timestamps
  • Speaker diarization: plug in a HuggingFace pyannote token to automatically label each segment by speaker
  • Batched inference: chunked audio with faster-whisper backend for 70× real-time throughput on GPU
  • Language detection: automatic per-segment language ID for multilingual recordings
  • SRT/VTT output: emit subtitle files directly from the CLI
  • Minimal code change: drop-in replacement for whisper.load_model in existing pipelines

Quick Start

pip install whisperx

# Transcribe with word timestamps and speaker labels
whisperx audio.mp3 \
  --model large-v3 \
  --diarize \
  --hf_token hf_xxx \
  --output_format srt
import whisperx

model = whisperx.load_model("large-v3", device="cuda", compute_type="float16")
result = model.transcribe("audio.mp3", batch_size=16)
aligned = whisperx.align(result["segments"], ...)
npx ai-supply add whisperx-forced-alignment-diarization

Curated mirror of the open-source WhisperX (BSD-2-Clause). Get it from the source.

Rating rank
#1
of 6 in Audio & Speech
Install rank
#1
of 6 in Audio & Speech
Security score
88/100 · B
review
Security rank
#2
of 6 in Audio & Speech
Installs
224k
cat avg 109k
This listing vs category average
Installs
this
cat avg
Security (of 100)
this
cat avg
Adoption trend
See the Audio & Speech leaderboard →
! Security: Review · 8888/100 · grade Bscanned 17d ago
✓ no compromise signals8 risk-surface · 5/20 OWASP controls flagged

Compromise signals — malicious or tampered code (leaked secrets, backdoors, a dropped executable) — reduce the score, and known dependency CVEs carry a bounded penalty (they warrant review but never QUARANTINE — update the dependency to clear). Other dangerous-by-capability traits are risk surface, expected for some capabilities. Every finding is mapped to its OWASP control below.

What this capability can do · med confidence (static)
⚑ filesystem⚑ shell⚑ secrets
egress → www.buymeacoffee.com, huggingface.co, user-images.githubusercontent.com, www.recall.ai, img.shields.io, arxiv.org, twitter.com, eval.ai +10
17 steps⚑ uses secretswww.buymeacoffee.comactions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83ddastral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86zizmorcore/zizmor-action@5f14fd08f7cf1cb1609c1e344975f152c7ee938d

Findings mapped to the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any flagged control for the exact findings — compromise reduces the score; expected/risk-surface do not, except a known CVE, which carries a small bounded penalty (high/critical → Review).

OWASP Top 10 for LLM Applications
⚠LLM03Supply Chainhigh
Vulnerable/compromised dependencies, models or archives in the artifact.
•Vulnerable dependencies — 98 known vulnerabilities in: aiohttp@3.12.15, filelock@3.19.1, fonttools@4.60.1, idna@3.10, mako@1.3.10, nltk@3.9.4, pillow@11.3.0, protobuf@6.32.1 (CWE-1395)known CVE · -12 pts
⚠LLM05Improper Output Handlingmedium
Code that pipes model/user output into shell, eval, SQL or paths unsafely.
•Suspicious code patterns — OS command execution · m-bain-whisperX-8dcdec1/whisperx/audio.py (CWE-78)risk surface
⚠LLM06Excessive Agencymedium
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 1 distinct host(s) · m-bain-whisperX-8dcdec1/.github/FUNDING.ymlrisk surface
•External endpoints declared — 3 distinct host(s) · m-bain-whisperX-8dcdec1/.gitignorerisk surface
•External endpoints declared — 19 distinct host(s) · m-bain-whisperX-8dcdec1/README.mdrisk surface
•External endpoints declared — 2 distinct host(s) · m-bain-whisperX-8dcdec1/pyproject.tomlrisk surface
§LLM09MisinformationGovernance
Artifacts designed to produce false/deceptive output.
Detectable only by runtime behavioral evaluation; addressed via responsible-use attestation.
◷LLM10Unbounded ConsumptionRuntime-enforced
Unbounded loops/recursion causing DoS or runaway cost.
Enforced at runtime by the gateway (rate limits + spend caps + size caps); static check flags unbounded loops.
✓LLM01Prompt InjectionPassed
✓LLM02Sensitive Information DisclosurePassed
✓LLM04Data and Model PoisoningPassed
Backdoors/poisoning in training data or serialized models.
Behavioral poisoning needs model execution; static check covers unsafe serialization + dataset skew only.
✓LLM07System Prompt LeakagePassed
✓LLM08Vector and Embedding WeaknessesPassed
PII or plaintext source leakage in embedding/vector exports.
Embedding inversion/poisoning is largely runtime; static check covers PII in vector exports.
OWASP Machine Learning Security Top 10
⚠ML06AI Supply Chainhigh
Compromised PyPI/npm packages, typosquats, unsafe serialized models.
•Vulnerable dependencies — 98 known vulnerabilities in: aiohttp@3.12.15, filelock@3.19.1, fonttools@4.60.1, idna@3.10, mako@1.3.10, nltk@3.9.4, pillow@11.3.0, protobuf@6.32.1 (CWE-1395)known CVE · -12 pts
⚠ML09Output Integritymedium
Middleware tampering with model outputs in transit.
Gateway enforces TLS + response integrity; static check flags output-rewriting code.
•Suspicious code patterns — OS command execution · m-bain-whisperX-8dcdec1/whisperx/audio.py (CWE-78)risk surface
§ML01Input Manipulation (Adversarial)Governance
Models vulnerable to adversarial perturbations.
Requires runtime robustness evaluation; addressed via publisher robustness attestation.
§ML03Model InversionGovernance
Training data reconstructable from a model's outputs.
Runtime/evaluation property; addressed via model-card data-provenance + DP attestation.
§ML04Membership InferenceGovernance
Determining whether a record was in the training set.
Runtime/evaluation property; addressed via overfitting disclosure + DP attestation.
§ML08Model SkewingGovernance
Models trained on skewed data producing biased output.
Requires fairness evaluation; addressed via model-card bias/limitations disclosure.
✓ML02Data PoisoningPassed
Poisoned training datasets with triggers or anomalous distributions.
Static check covers trigger phrasing, PII and label skew; full poisoning detection is runtime.
✓ML05Model TheftPassed
Unlicensed re-distribution / license-incompatible derivatives.
Static check verifies license declaration; extraction throttling is runtime.
✓ML07Transfer Learning AttackPassed
Backdoored base models / LoRA adapters propagating to derivatives.
Backdoor detection needs behavioral probing; static check covers unsafe serialization + provenance.
✓ML10Model Poisoning (Weights)Passed
Tampered model weight files; integrity must be verifiable.
Static check enforces safe formats + records a content hash for downstream verification.
Other findings (6) · hygiene / uncategorized
•Unrecognized file type — '.gitignore' is not on the allowlist · m-bain-whisperX-8dcdec1/.gitignorerisk surface
•Unrecognized file type — '.python-version' is not on the allowlist · m-bain-whisperX-8dcdec1/.python-versionrisk surface
•Suspicious network references — suspicious TLD (6 URLs) · m-bain-whisperX-8dcdec1/EXAMPLES.mdrisk surface
•Unrecognized file type — '.?' is not on the allowlist · m-bain-whisperX-8dcdec1/LICENSErisk surface
•Unrecognized file type — '.in' is not on the allowlist · m-bain-whisperX-8dcdec1/MANIFEST.inrisk surface
•Suspicious network references — suspicious TLD (55 URLs) · m-bain-whisperX-8dcdec1/README.mdrisk surface
✔ verified source · pinned m-bain-whisperX-8dcdec1
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/whisperx-forced-alignment-diarization/check). Click a policy:

Consume WhisperX programmatically. Authenticate with an API key or session — see Authorize an agent.

# Agents: CHECK BEFORE YOU INSTALL (no auth) — score, grade, level, capability manifest
curl https://ai-supply.store/api/v1/trust/whisperx-forced-alignment-diarization

# Gate against your org policy (returns { pass, violations })
curl -X POST https://ai-supply.store/api/v1/trust/whisperx-forced-alignment-diarization/check \
  -H "Content-Type: application/json" \
  -d '{"minGrade":"B","denyPermissions":["shell"],"denyUnknownEgress":true}'

# CLI
npx ai-supply add whisperx-forced-alignment-diarization

# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/whisperx-forced-alignment-diarization/install \
  -H "Authorization: Bearer $AIM_KEY"

# MCP tool
install_listing({ "slug": "whisperx-forced-alignment-diarization" })
OpenAPI spec →
vlatest
! Security: Review · 881mo ago

Curated mirror — latest upstream source. See the repository for tagged releases.

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