| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Whisper implements a fixed-size round-robin database that pre-allocates disk space for time-series metrics and overwrites oldest data when capacity is reached, eliminating dynamic allocation overhead. Data is written sequentially by the Carbon ingestion service into pre-defined storage slots indexed by timestamp, enabling predictable disk usage and O(1) write performance regardless of dataset age. The circular buffer design trades retention flexibility for guaranteed storage footprint and consistent write latency.
Unique: Uses fixed-size circular buffer design with pre-allocated disk space and sequential writes, eliminating dynamic allocation and enabling deterministic write performance — unlike traditional databases that grow dynamically and require compaction/vacuuming
vs alternatives: Provides guaranteed O(1) write latency and predictable disk footprint for long-term metric storage, whereas InfluxDB and Prometheus require active compaction and dynamic scaling to maintain performance
Whisper supports creating multiple aggregation levels (e.g., 1-minute raw data, 10-minute averages, 1-hour summaries) within a single database file, enabling efficient long-term storage by downsampling older data while preserving high-resolution recent data. Each retention tier is a separate round-robin buffer with different time granularity and aggregation function (average, sum, last, etc.), allowing queries to select appropriate resolution based on time range. This hierarchical approach reduces storage for historical data while maintaining query flexibility without requiring separate database files.
Unique: Implements multi-resolution storage within single database files using stacked round-robin buffers with configurable aggregation functions per tier, enabling space-efficient long-term retention without separate database files or external aggregation pipelines
vs alternatives: More storage-efficient than Prometheus for long-term retention because aggregation happens at write-time into fixed-size buffers, whereas Prometheus requires external tools like Thanos for downsampling and long-term storage
Whisper integrates with the Carbon service through a write API that accepts timestamped metric data and routes writes to appropriate storage slots based on metric name and timestamp. Carbon handles network protocol parsing (plaintext, pickle, AMQP) and metric name validation, then delegates to Whisper for storage, which maps timestamps to fixed storage offsets using modulo arithmetic on the circular buffer. This separation enables Carbon to handle protocol complexity while Whisper focuses on efficient storage, with no direct user-facing API — all writes flow through Carbon.
Unique: Delegates protocol handling to Carbon while Whisper focuses purely on storage, using timestamp-based modulo arithmetic for O(1) write routing without index lookups — enabling high-throughput ingestion without database overhead
vs alternatives: Simpler write path than InfluxDB (no query parsing, no schema validation at storage layer) but less flexible than Prometheus (no built-in scraping, requires external metric collection)
Whisper provides a read interface consumed by Graphite-web to retrieve time-series data for graph rendering and API responses. Queries specify metric name and time range, and Whisper maps the time range to storage offsets in the circular buffer, reading only the relevant data without scanning the entire file. Graphite-web handles query parsing, metric pattern matching, and result formatting (PNG, CSV, JSON, XML), while Whisper provides the low-level storage retrieval. This separation enables Graphite-web to support complex queries (wildcards, functions, aggregations) without Whisper needing to understand query semantics.
Unique: Provides time-range-based retrieval using circular buffer offset calculation, enabling efficient reads without index structures — Graphite-web handles all query complexity, keeping Whisper focused on fast storage access
vs alternatives: Simpler query interface than InfluxDB (no query language, no server-side aggregation) but requires external tool (Graphite-web) for complex queries, whereas Prometheus includes query language in storage layer
Whisper requires upfront schema definition specifying retention policies (how long to keep data at each resolution) and aggregation functions (how to downsample data between tiers). Schema is typically defined in a configuration file (storage-schemas.conf) that maps metric name patterns to retention/aggregation rules, which Carbon uses to create Whisper database files with matching structure. Once created, schema is immutable — changing retention or aggregation requires recreating the database file and losing historical data. This design trades flexibility for predictability: storage footprint and query performance are determined at schema definition time.
Unique: Requires upfront schema definition with immutable retention/aggregation configuration, enabling deterministic storage allocation and performance but requiring careful planning — unlike dynamic schema systems (InfluxDB, Prometheus) that adapt to incoming data
vs alternatives: Provides predictable storage costs and performance through schema-driven design, whereas Prometheus and InfluxDB require active management of retention policies and cardinality limits to prevent unbounded growth
Whisper stores each metric as a single binary file on disk containing a header (metadata about retention tiers and aggregation) followed by pre-allocated round-robin buffers. Files are created at metric creation time with full size allocated upfront, eliminating fragmentation and enabling direct offset calculation for reads/writes. The binary format is compact and uncompressed, optimized for sequential access patterns typical of time-series workloads. No indexing structures are maintained — metric discovery relies on filesystem directory scanning, making Whisper suitable for systems with moderate metric cardinality (thousands to tens of thousands) but not billions of unique metrics.
Unique: Uses single-file-per-metric design with pre-allocated binary storage and no indexing, enabling O(1) offset calculation for reads/writes but requiring filesystem-level metric discovery — simpler than database systems but less scalable than indexed storage
vs alternatives: Simpler operational model than InfluxDB or Prometheus (no compaction, no WAL management) but less efficient for high-cardinality metrics (billions of unique series) where filesystem overhead becomes prohibitive
Reimplements OpenAI's Whisper ASR model using CTranslate2, a specialized inference engine for Transformer models that applies operator-level optimizations (graph compilation, memory pooling, quantization-aware kernels) to achieve 4x faster transcription than the original implementation while maintaining identical accuracy. The WhisperModel class wraps CTranslate2's compiled model format, enabling CPU and GPU inference with automatic device selection and fallback mechanisms.
Unique: Uses CTranslate2's compiled model format with operator-level kernel optimizations and memory pooling rather than PyTorch's dynamic graph execution, enabling 4x speedup through reduced memory allocations and fused operations. Includes automatic model conversion pipeline from Hugging Face Hub with 13+ pre-optimized variants.
vs alternatives: 4x faster than openai/whisper on CPU, maintains identical accuracy, requires no FFmpeg installation, and provides pre-converted models eliminating conversion overhead for end users.
BatchedInferencePipeline class implements a queue-based parallel processing architecture that groups multiple audio files into batches and processes them through the CTranslate2 inference engine simultaneously, achieving 3-5x additional speedup over sequential WhisperModel transcription. Uses dynamic batch sizing based on available GPU/CPU memory and implements work-stealing scheduling to balance load across processing threads.
Unique: Implements work-stealing queue scheduler with dynamic batch sizing that adapts to available GPU memory at runtime, rather than fixed batch sizes. Integrates directly with CTranslate2's batch inference API, avoiding Python-level serialization overhead.
vs alternatives: 3-5x faster than sequential WhisperModel for batch jobs, requires no external orchestration framework (vs Ray/Dask), and automatically manages GPU memory allocation without manual tuning.
faster-whisper scores higher at 25/100 vs whisper at 20/100.
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Implements audio decoding using PyAV (Python bindings for FFmpeg libraries) bundled as a dependency, eliminating the need for separate FFmpeg installation. The decode_audio() utility supports 100+ audio formats (MP3, WAV, FLAC, M4A, OGG, OPUS, AIFF, etc.) and automatically resamples to 16kHz mono, handling format detection, channel mixing, and sample rate conversion in a single pass.
Unique: Bundles PyAV as a dependency, eliminating separate FFmpeg installation while supporting 100+ audio formats. Implements single-pass decoding with automatic resampling to 16kHz mono, avoiding multi-step preprocessing pipelines.
vs alternatives: No FFmpeg installation required (vs. librosa/soundfile which require FFmpeg), supports 100+ formats natively, and single-pass preprocessing reduces I/O overhead vs. separate decode-then-resample steps.
Provides model conversion utilities that transform OpenAI's PyTorch Whisper checkpoints into optimized CTranslate2 format, applying graph compilation, operator fusion, and quantization during conversion. The conversion process is one-time offline operation that generates hardware-optimized model files, enabling fast inference without requiring PyTorch at runtime.
Unique: Implements offline conversion pipeline that applies graph compilation, operator fusion, and quantization at conversion time, generating hardware-optimized models. Pre-converted models available for download, eliminating conversion step for end users.
vs alternatives: Offline conversion enables aggressive optimization (operator fusion, graph compilation) not possible at runtime, pre-converted models eliminate user-side conversion complexity, and quantization during conversion is irreversible (prevents accidental precision loss).
Provides format_timestamp() utility and output formatting options that convert transcription results into standard subtitle formats (SRT, VTT) and JSON, with configurable timestamp precision and segment boundaries. The formatter handles edge cases like overlapping segments, missing timestamps, and language-specific formatting rules.
Unique: Provides unified formatting interface supporting multiple output formats (SRT, VTT, JSON) with configurable timestamp precision and segment boundaries. Handles edge cases like overlapping segments and missing timestamps automatically.
vs alternatives: Single utility handles multiple output formats (vs. separate tools for each format), configurable timestamp precision enables use cases from video editing to accessibility, and automatic edge case handling reduces post-processing.
Integrates Silero VAD v6 model to detect speech segments and remove silence from audio before transcription, reducing processing time by ~50% by skipping non-speech regions. The VAD pipeline operates as a preprocessing stage that segments audio into speech/non-speech chunks, filters out silence, and passes only active speech regions to the Whisper encoder, reducing token count and inference cost.
Unique: Uses Silero VAD v6 as a preprocessing stage integrated into the audio pipeline, not as post-processing filtering. Segments audio into speech chunks before encoding, reducing token count and Whisper encoder load proportionally to silence duration.
vs alternatives: ~50% faster transcription on audio with >30% silence, requires no external VAD library installation (Silero bundled), and operates at inference time rather than requiring separate preprocessing steps.
Extracts word-level timestamps by analyzing cross-attention weights between the Whisper decoder and encoder outputs, mapping each decoded token to its corresponding audio time region. The mechanism leverages the Transformer's attention patterns to align subword tokens to audio frames, then aggregates token-level alignments into word-level boundaries without requiring external alignment models or post-processing.
Unique: Extracts alignment directly from Whisper's cross-attention weights without external alignment models (vs. forced alignment tools like Montreal Forced Aligner). Operates during inference, not as post-processing, enabling real-time timestamp generation.
vs alternatives: No external alignment model required, timestamps generated during transcription with zero additional latency, and accuracy matches Whisper's own token predictions.
Automatically detects the language of input audio by processing the first 30 seconds through Whisper's language identification head, which outputs probability scores across 99 supported languages. The detection runs as a lightweight preprocessing step before full transcription, enabling single-pass multilingual pipelines without requiring language hints or separate language detection models.
Unique: Leverages Whisper's built-in language identification head (trained on 99 languages) rather than external language detection models. Runs as lightweight preprocessing step using only the first 30 seconds of audio, enabling fast language routing.
vs alternatives: Supports 99 languages natively (vs. 50-60 for most external language ID tools), requires no additional model downloads, and integrates seamlessly into transcription pipeline.
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