{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-whisperx","slug":"whisperx","name":"whisperX","type":"repo","url":"https://github.com/m-bain/whisperX","page_url":"https://unfragile.ai/whisperx","categories":["voice-audio"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-whisperx__cap_0","uri":"capability://data.processing.analysis.word.level.timestamp.alignment.via.forced.phoneme.recognition","name":"word-level timestamp alignment via forced phoneme recognition","description":"WhisperX achieves sub-second word-level timestamp precision by performing forced alignment using wav2vec2 acoustic models after ASR transcription. The system extracts phoneme sequences from the transcribed text, aligns them against the audio's acoustic features using dynamic time warping or similar alignment algorithms, and produces precise start/end timestamps for each word. This two-stage approach (ASR → alignment) decouples transcription quality from timestamp accuracy, enabling accurate timing even when Whisper's native utterance-level timestamps drift by seconds.","intents":["I need precise word-level timing for video subtitle generation or audio synchronization","I want to extract exact timestamps for specific words in a transcript for downstream processing","I need to build a searchable transcript where users can click to jump to exact word positions"],"best_for":["video production teams requiring frame-accurate subtitle timing","accessibility engineers building synchronized caption systems","researchers analyzing speech patterns with millisecond-level precision"],"limitations":["Alignment quality degrades on heavily accented speech or non-native speakers due to wav2vec2 training data bias","Requires additional inference pass post-ASR, adding ~15-30% latency overhead per audio file","Language support limited to wav2vec2 model availability (primarily English, some European languages)"],"requires":["PyTorch 1.9+","wav2vec2 model checkpoint (auto-downloaded, ~360MB for English)","Audio sample rate 16kHz (resampled automatically if needed)"],"input_types":["audio waveform (numpy array or torch tensor)","ASR transcript with word boundaries from Whisper"],"output_types":["structured JSON with word-level timing: [{word, start_time, end_time}, ...]"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisperx__cap_1","uri":"capability://automation.workflow.batched.asr.inference.with.70x.realtime.speedup","name":"batched asr inference with 70x realtime speedup","description":"WhisperX implements batched transcription using faster-whisper (CTranslate2 backend) instead of OpenAI's sequential Whisper API, enabling parallel processing of multiple audio segments. The system performs VAD-based segmentation to identify speech regions, groups segments into batches, and processes them in a single forward pass through the model. This architecture reduces GPU memory footprint to <8GB for large-v2 model (vs 10-11GB for sequential Whisper) while achieving 70x realtime transcription speed by eliminating per-segment model loading overhead and leveraging CTranslate2's quantization and kernel optimizations.","intents":["I need to transcribe large audio files or batches of files efficiently without GPU memory constraints","I want to process hours of audio in minutes rather than hours for production pipelines","I need to reduce cloud compute costs by maximizing GPU utilization per inference call"],"best_for":["media companies processing large video libraries for transcription","speech analytics platforms requiring high-throughput ASR","teams deploying on resource-constrained GPUs (8GB VRAM or less)"],"limitations":["Batching requires VAD preprocessing, adding ~5-10% latency for VAD inference per file","Batch size is dynamic based on segment length and GPU memory; no manual batch size control exposed","CTranslate2 quantization may reduce WER by 0.5-1% compared to full-precision Whisper on some accents"],"requires":["CUDA 11.0+ or CPU fallback (significantly slower)","faster-whisper library (auto-installed as dependency)","CTranslate2 backend (auto-installed, ~500MB for model weights)"],"input_types":["audio file path (MP3, WAV, M4A, FLAC, OGG)","audio waveform as numpy array or torch tensor"],"output_types":["JSON with transcription segments: [{text, start, end, confidence}, ...]"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisperx__cap_10","uri":"capability://data.processing.analysis.confidence.scoring.and.quality.metrics.per.segment","name":"confidence scoring and quality metrics per segment","description":"WhisperX provides confidence scores for each transcribed segment, indicating the model's certainty in the transcription. These scores are derived from Whisper's logit outputs during decoding and reflect the probability of the predicted token sequence. Confidence scores are attached to each segment in the output, enabling downstream applications to filter low-confidence segments or flag them for manual review. Additionally, WhisperX can compute Word Error Rate (WER) if reference transcriptions are available, providing quantitative quality metrics for evaluation and benchmarking.","intents":["I want to identify low-confidence transcriptions that may need manual review","I need to filter out unreliable segments from automated processing pipelines","I want to measure transcription quality using WER metrics for benchmarking"],"best_for":["quality assurance teams reviewing transcriptions before publication","systems requiring confidence-based filtering for downstream processing","researchers benchmarking ASR model performance"],"limitations":["Confidence scores are relative to the model's training distribution; low scores don't guarantee errors in all domains","WER computation requires reference transcriptions; not available for production transcriptions without manual annotation","Confidence scores are segment-level, not word-level; fine-grained confidence per word is not available","Confidence calibration varies across languages and domains; thresholds must be tuned per use case"],"requires":["Completed transcription with confidence scores","Optional: reference transcriptions for WER computation"],"input_types":["transcription segments with logit outputs"],"output_types":["confidence scores (0-1 per segment)","WER metric (if reference available)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisperx__cap_11","uri":"capability://tool.use.integration.configurable.model.selection.and.custom.model.loading","name":"configurable model selection and custom model loading","description":"WhisperX supports multiple Whisper model sizes (tiny, base, small, medium, large) and enables users to specify custom model paths or Hugging Face model IDs. The system loads models on-demand and caches them locally to avoid repeated downloads. For alignment and diarization stages, users can specify alternative wav2vec2 or pyannote models, enabling experimentation with different model variants. Model selection is configurable via CLI flags or Python API parameters, and the system validates model compatibility before loading. This flexibility enables users to trade off accuracy vs speed/memory based on their constraints.","intents":["I want to use a smaller, faster model (tiny/base) for real-time transcription instead of large-v2","I need to use a custom or fine-tuned Whisper model for domain-specific transcription","I want to experiment with different alignment or diarization models to improve accuracy"],"best_for":["teams with varying accuracy/speed tradeoff requirements","researchers fine-tuning models on domain-specific data","applications requiring real-time transcription with lower latency"],"limitations":["Smaller models (tiny, base) have significantly lower WER; accuracy degrades ~5-15% vs large-v2","Custom model loading requires compatible architecture; incompatible models will fail at runtime","Model caching is local-only; no distributed caching for multi-machine deployments","Model selection is static per transcription; cannot switch models mid-pipeline"],"requires":["Model weights on disk or Hugging Face model ID","Compatible model architecture (Whisper-compatible for ASR, wav2vec2-compatible for alignment)"],"input_types":["model size string (tiny, base, small, medium, large) or model path"],"output_types":["loaded model object ready for inference"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisperx__cap_2","uri":"capability://data.processing.analysis.speaker.diarization.with.speaker.id.attribution","name":"speaker diarization with speaker id attribution","description":"WhisperX integrates pyannote-audio's speaker diarization models to identify and label distinct speakers in multi-speaker audio. The system performs speaker embedding extraction on speech segments, clusters embeddings using agglomerative clustering, and assigns speaker IDs (speaker_0, speaker_1, etc.) to each transcribed segment. The diarization stage runs after ASR and alignment, enriching each word-level timestamp with speaker attribution. This enables downstream applications to track who said what and when, with speaker labels propagated through the entire transcript hierarchy.","intents":["I need to identify which speaker said each sentence in a multi-speaker conversation or meeting","I want to generate speaker-labeled transcripts for meeting minutes or interview analysis","I need to separate and analyze speech patterns by individual speaker in a group conversation"],"best_for":["meeting transcription and analysis platforms","podcast and interview post-production workflows","accessibility teams generating speaker-labeled captions for video"],"limitations":["Requires Hugging Face API token for pyannote model download (free tier available)","Speaker clustering is unsupervised; cannot guarantee consistent speaker IDs across multiple files without manual mapping","Accuracy degrades with >4 speakers or heavy background noise; WER increases ~2-5% on noisy audio","Diarization adds ~20-40% latency overhead per audio file depending on duration and speaker count"],"requires":["Hugging Face account and API token (free tier sufficient)","pyannote-audio library (auto-installed as dependency)","Audio with at least 2 distinct speakers for meaningful diarization"],"input_types":["audio waveform with multiple speakers","ASR transcript with segment boundaries from prior stages"],"output_types":["JSON with speaker labels: [{text, start, end, speaker}, ...]"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisperx__cap_3","uri":"capability://data.processing.analysis.voice.activity.detection.based.segmentation.with.hallucination.reduction","name":"voice activity detection-based segmentation with hallucination reduction","description":"WhisperX uses voice activity detection (VAD) to identify speech regions in audio before ASR, segmenting the audio into speech-only chunks. The VAD stage runs before transcription and filters out silence, background noise, and non-speech regions, reducing the input to the ASR model. This preprocessing step enables two benefits: (1) reduces hallucination artifacts where Whisper generates spurious text during silence, and (2) enables efficient batching by providing natural segment boundaries. The VAD model (typically Silero VAD or similar) produces confidence scores and segment timestamps that guide the ASR batching strategy.","intents":["I want to reduce transcription hallucinations where the model generates text during silence or noise","I need to segment long audio files into manageable chunks for efficient processing","I want to skip silence regions to reduce transcription time and improve output quality"],"best_for":["noisy audio environments (meetings, podcasts with background noise)","long-form audio transcription where silence is common","applications requiring high-quality transcripts with minimal hallucination"],"limitations":["VAD models are language-agnostic but may misclassify music or non-speech sounds as speech","VAD threshold tuning is required for optimal performance; default threshold may be too aggressive or lenient depending on audio characteristics","Adds ~5-10% latency overhead for VAD inference per file"],"requires":["Silero VAD model (auto-downloaded, ~40MB) or alternative VAD backend","Audio sample rate 16kHz (resampled automatically)"],"input_types":["raw audio waveform (mono or stereo, auto-converted to mono)"],"output_types":["list of speech segment boundaries: [{start_time, end_time}, ...]"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisperx__cap_4","uri":"capability://data.processing.analysis.multi.language.asr.with.language.detection","name":"multi-language asr with language detection","description":"WhisperX supports transcription in 99+ languages using Whisper's multilingual model, with automatic language detection via Whisper's encoder. The system detects the language from the first 30 seconds of audio by analyzing the acoustic features and comparing against language-specific phoneme distributions. Once detected, the appropriate language-specific tokenizer and decoder are loaded, and transcription proceeds with language-aware beam search. The language detection is automatic but can be overridden via configuration, enabling forced transcription in a specific language if detection fails.","intents":["I need to transcribe audio in languages other than English without manual language specification","I want to automatically detect the language of audio and transcribe accordingly","I need to handle multilingual content where speakers switch languages mid-conversation"],"best_for":["global media companies processing content in multiple languages","international platforms requiring automatic language detection","researchers analyzing speech across language families"],"limitations":["Language detection accuracy is ~95% for clear speech but degrades to ~70-80% for accented or code-switched speech","Code-switching (mixing multiple languages) is not explicitly handled; detection picks the dominant language","Some low-resource languages (e.g., minority indigenous languages) have lower WER due to limited training data in Whisper's training set","Language-specific tokenizers add ~5-10ms latency per transcription"],"requires":["Whisper multilingual model (large-v2 or base model, auto-downloaded)","Audio sample rate 16kHz"],"input_types":["audio waveform in any of 99+ supported languages"],"output_types":["JSON with detected language code and transcription: {language: 'en', text: '...', segments: [...]}"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisperx__cap_5","uri":"capability://automation.workflow.command.line.interface.for.batch.transcription.workflows","name":"command-line interface for batch transcription workflows","description":"WhisperX provides a comprehensive CLI that orchestrates the entire transcription pipeline (VAD → ASR → alignment → diarization) with a single command. The CLI accepts audio file paths or directories, applies configuration flags for model selection, language, speaker count, and output format, and produces structured output files (JSON, VTT, SRT, TSV). The CLI manages model lifecycle (loading, caching, unloading) and memory optimization automatically, enabling non-technical users to run complex multi-stage pipelines without writing code. Output can be written to multiple formats simultaneously, supporting downstream integrations with video editors, subtitle tools, and analytics platforms.","intents":["I want to transcribe a folder of audio files without writing Python code","I need to generate subtitle files (SRT, VTT) directly from audio for video editing","I want to batch-process media with consistent settings across multiple files"],"best_for":["video producers and editors using command-line workflows","DevOps engineers building transcription pipelines in CI/CD systems","non-technical users who prefer CLI over Python API"],"limitations":["CLI does not support streaming input; requires complete audio file on disk","Output format selection is limited to JSON, VTT, SRT, TSV; custom output formats require Python API","No built-in progress reporting for long transcriptions; users must monitor logs manually","GPU selection is automatic; no explicit device specification for multi-GPU systems"],"requires":["Python 3.8+","whisperx package installed via pip","CUDA 11.0+ for GPU acceleration (CPU fallback available but slow)"],"input_types":["audio file path (MP3, WAV, M4A, FLAC, OGG)","directory path for batch processing"],"output_types":["JSON, VTT, SRT, TSV files written to disk"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisperx__cap_6","uri":"capability://tool.use.integration.python.api.for.programmatic.pipeline.orchestration","name":"python api for programmatic pipeline orchestration","description":"WhisperX exposes a Python API that enables fine-grained control over each pipeline stage (VAD, ASR, alignment, diarization) with conditional execution and custom model loading. The API provides a `load_model()` function to load ASR, alignment, and diarization models with configurable device placement and precision (FP32, FP16, INT8), and a `transcribe()` function that orchestrates the pipeline with optional stage skipping. Users can access intermediate outputs (VAD segments, raw ASR results, aligned timestamps, speaker labels) at each stage, enabling custom post-processing or integration with external systems. The API manages model caching and memory cleanup automatically, preventing GPU memory leaks in long-running applications.","intents":["I want to integrate WhisperX into my Python application with custom preprocessing or postprocessing","I need to run only specific pipeline stages (e.g., alignment without diarization) for performance optimization","I want to access intermediate outputs from each stage for debugging or custom analysis"],"best_for":["Python developers building speech processing applications","researchers experimenting with different pipeline configurations","teams integrating WhisperX into larger ML systems"],"limitations":["API does not support streaming input; requires complete audio file or pre-loaded waveform","Model loading is synchronous; no async/await support for concurrent transcriptions","Memory management is automatic but not fully configurable; users cannot manually control model unloading","No built-in error recovery; pipeline fails on any stage error without partial result preservation"],"requires":["Python 3.8+","whisperx package installed via pip","PyTorch 1.9+","CUDA 11.0+ for GPU (CPU fallback available)"],"input_types":["audio file path (string)","audio waveform (numpy array or torch tensor)","sample rate (int, typically 16000)"],"output_types":["dictionary with keys: text, segments (list of dicts with word-level timing and speaker labels)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisperx__cap_7","uri":"capability://data.processing.analysis.audio.preprocessing.and.format.normalization","name":"audio preprocessing and format normalization","description":"WhisperX automatically handles audio preprocessing including format detection, resampling, and channel conversion. The system accepts audio in multiple formats (MP3, WAV, M4A, FLAC, OGG) and automatically resamples to 16kHz mono (Whisper's native sample rate) using librosa or ffmpeg. The preprocessing stage detects audio duration, validates sample rate, and handles edge cases like stereo-to-mono conversion with channel mixing. This preprocessing is transparent to users and runs before VAD, ensuring consistent input to downstream stages regardless of source audio characteristics.","intents":["I want to transcribe audio files in various formats without manual preprocessing","I need to handle audio with different sample rates and channel configurations automatically","I want to validate audio before processing to catch format issues early"],"best_for":["applications accepting user-uploaded audio in unknown formats","batch processing pipelines with heterogeneous audio sources","teams without audio engineering expertise"],"limitations":["Resampling may introduce artifacts for audio with sample rates <8kHz (below Nyquist for 16kHz target)","Stereo-to-mono conversion uses simple channel averaging; no advanced downmixing algorithms","ffmpeg dependency required for some formats (MP3, M4A); adds ~500MB to installation size","Resampling adds ~2-5% latency per file depending on original sample rate and duration"],"requires":["librosa library (auto-installed)","ffmpeg (auto-installed or system dependency)","Audio file on disk or waveform in memory"],"input_types":["audio file path (MP3, WAV, M4A, FLAC, OGG)","audio waveform (numpy array) with sample rate"],"output_types":["normalized audio waveform (16kHz mono, numpy array)"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisperx__cap_8","uri":"capability://data.processing.analysis.output.formatting.with.multiple.subtitle.and.transcript.formats","name":"output formatting with multiple subtitle and transcript formats","description":"WhisperX generates transcription output in multiple formats (JSON, VTT, SRT, TSV) from a single transcription run, enabling direct integration with video editors, subtitle tools, and analytics platforms. Each format preserves word-level timestamps and speaker labels where applicable. JSON output includes full metadata (confidence scores, segment boundaries, speaker IDs), while VTT and SRT formats are optimized for video players and subtitle editors. TSV format enables import into spreadsheet applications for manual review and editing. The output generation is decoupled from transcription, allowing users to regenerate outputs in different formats without re-running the pipeline.","intents":["I want to generate subtitle files (SRT, VTT) directly from transcription for video editing","I need to export transcripts in multiple formats for different downstream tools","I want to preserve word-level timing and speaker labels in exported files"],"best_for":["video production teams using subtitle editors (Premiere, Final Cut, DaVinci)","accessibility teams generating captions for video platforms","researchers exporting transcripts for analysis in spreadsheet tools"],"limitations":["SRT format has 1-second granularity limitation; word-level timing is rounded to nearest second","VTT format supports millisecond precision but some video players may not render speaker labels correctly","TSV export loses hierarchical structure; segments are flattened to rows","Custom output formats require Python API; CLI is limited to built-in formats"],"requires":["Completed transcription with word-level timestamps","Output directory with write permissions"],"input_types":["transcription dictionary with segments and word-level timing"],"output_types":["JSON (full metadata)","VTT (video subtitle format with millisecond precision)","SRT (video subtitle format with 1-second granularity)","TSV (spreadsheet-compatible format)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisperx__cap_9","uri":"capability://automation.workflow.gpu.memory.optimization.with.model.quantization.and.selective.loading","name":"gpu memory optimization with model quantization and selective loading","description":"WhisperX optimizes GPU memory usage through INT8 quantization (via CTranslate2) and selective model loading, reducing the large-v2 model footprint from 10-11GB to <8GB. The system loads only the required models for the enabled pipeline stages (ASR, alignment, diarization can be independently enabled/disabled), and unloads models after use to prevent memory accumulation in long-running applications. CTranslate2's quantization reduces model weights to 8-bit integers while maintaining accuracy, and the batching strategy ensures efficient GPU utilization by processing multiple segments per forward pass. Memory profiling is built-in, enabling users to monitor GPU usage and identify bottlenecks.","intents":["I want to run WhisperX on a GPU with <8GB VRAM without running out of memory","I need to process multiple audio files sequentially without GPU memory leaks","I want to understand GPU memory usage and optimize for my hardware constraints"],"best_for":["teams with resource-constrained GPUs (8GB VRAM or less)","cloud environments with per-instance memory limits","long-running transcription services requiring memory stability"],"limitations":["INT8 quantization may reduce WER by 0.5-1% on some accents due to precision loss","Selective model loading requires manual configuration; no automatic optimization based on available VRAM","Memory profiling adds ~2-3% overhead per transcription","Model unloading is not instantaneous; GPU memory may not be immediately freed after unload"],"requires":["CUDA 11.0+ for GPU acceleration","CTranslate2 backend (auto-installed)","At least 8GB VRAM for large-v2 model"],"input_types":["configuration flags for model selection and quantization"],"output_types":["memory usage statistics (peak VRAM, average utilization)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+","wav2vec2 model checkpoint (auto-downloaded, ~360MB for English)","Audio sample rate 16kHz (resampled automatically if needed)","CUDA 11.0+ or CPU fallback (significantly slower)","faster-whisper library (auto-installed as dependency)","CTranslate2 backend (auto-installed, ~500MB for model weights)","Completed transcription with confidence scores","Optional: reference transcriptions for WER computation","Model weights on disk or Hugging Face model ID","Compatible model architecture (Whisper-compatible for ASR, wav2vec2-compatible for alignment)"],"failure_modes":["Alignment quality degrades on heavily accented speech or non-native speakers due to wav2vec2 training data bias","Requires additional inference pass post-ASR, adding ~15-30% latency overhead per audio file","Language support limited to wav2vec2 model availability (primarily English, some European languages)","Batching requires VAD preprocessing, adding ~5-10% latency for VAD inference per file","Batch size is dynamic based on segment length and GPU memory; no manual batch size control exposed","CTranslate2 quantization may reduce WER by 0.5-1% compared to full-precision Whisper on some accents","Confidence scores are relative to the model's training distribution; low scores don't guarantee errors in all domains","WER computation requires reference transcriptions; not available for production transcriptions without manual annotation","Confidence scores are segment-level, not word-level; fine-grained confidence per word is not available","Confidence calibration varies across languages and domains; thresholds must be tuned per use case","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.689Z","last_scraped_at":"2026-05-03T14:00:25.471Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=whisperx","compare_url":"https://unfragile.ai/compare?artifact=whisperx"}},"signature":"0cASTZ+yKFm8jHxAJtAsXRz60980b7PsUyDvdbmJPGNHb1xZ+fEs+tzEGEadRreUYEU31YUTlPcWCHhfJOn1BQ==","signedAt":"2026-06-20T17:30:21.224Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/whisperx","artifact":"https://unfragile.ai/whisperx","verify":"https://unfragile.ai/api/v1/verify?slug=whisperx","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}