Whisper CLI vs Shell GPT
Side-by-side comparison to help you choose.
| Feature | Whisper CLI | Shell GPT |
|---|---|---|
| Type | CLI Tool | CLI Tool |
| UnfragileRank | 42/100 | 40/100 |
| Adoption | 1 | 1 |
| Quality | 0 | 0 |
| Ecosystem | 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Transcribes audio in 98 languages to text using a unified Transformer sequence-to-sequence architecture with a shared AudioEncoder that processes mel spectrograms and a language-agnostic TextDecoder that generates tokens autoregressively. The system handles variable-length audio by padding or trimming to 30-second segments and uses FFmpeg for format normalization, enabling end-to-end transcription without language-specific model switching.
Unique: Uses a single unified Transformer encoder-decoder trained on 680,000 hours of diverse internet audio rather than language-specific models, enabling 98-language support through task-specific tokens that signal transcription vs. translation vs. language-identification without model reloading
vs alternatives: Outperforms Google Cloud Speech-to-Text and Azure Speech Services on multilingual accuracy due to larger training dataset diversity, and avoids the latency of model switching required by language-specific competitors
Translates non-English audio directly to English text by injecting a translation task token into the decoder, bypassing intermediate transcription steps. The model learns to map audio embeddings from the shared AudioEncoder directly to English token sequences, leveraging the same Transformer decoder used for transcription but with different task conditioning.
Unique: Implements translation as a task-specific decoder behavior (via special tokens) rather than a separate model, allowing the same AudioEncoder to serve both transcription and translation by conditioning the TextDecoder with a translation task token, eliminating cascading errors from intermediate transcription
vs alternatives: Faster and more accurate than cascading transcription→translation pipelines (e.g., Whisper→Google Translate) because it avoids error propagation and performs direct audio-to-English mapping in a single forward pass
Loads audio files in any format (MP3, WAV, FLAC, OGG, OPUS, M4A) using FFmpeg, resamples to 16kHz mono, and converts to log-mel spectrogram features (80 mel bins, 25ms window, 10ms stride) for model consumption. The pipeline is implemented in whisper.load_audio() and whisper.log_mel_spectrogram(), handling format normalization and feature extraction transparently.
Unique: Abstracts FFmpeg integration and mel spectrogram computation into simple functions (load_audio, log_mel_spectrogram) that handle format detection and resampling automatically, eliminating the need for users to manage FFmpeg subprocess calls or librosa configuration. Supports any FFmpeg-compatible audio format without explicit format specification.
vs alternatives: More flexible than competitors with fixed input formats (e.g., WAV-only) because FFmpeg supports 50+ formats; simpler than manual audio preprocessing because format detection is automatic
Detects the spoken language in audio by analyzing the audio embeddings from the AudioEncoder and using the TextDecoder to predict language tokens, returning the identified language code and confidence score. This leverages the same Transformer architecture used for transcription but extracts language predictions from the first decoded token without generating full transcription.
Unique: Extracts language identification as a byproduct of the decoder's first token prediction rather than using a separate classification head, making it zero-cost when combined with transcription (language already decoded) and supporting 98 languages through the same unified model
vs alternatives: More accurate than statistical language detection (e.g., langdetect, TextCat) on noisy audio because it operates on acoustic features rather than text, and faster than cascading speech-to-text→language detection because language is identified during the first decoding step
Generates precise word-level timestamps by tracking the decoder's attention patterns and token positions during autoregressive decoding, enabling frame-accurate alignment of transcribed text to audio. The system maps each decoded token to its corresponding audio frame through the attention mechanism, producing start/end timestamps for each word without requiring separate alignment models.
Unique: Derives word timestamps from the Transformer decoder's attention weights during autoregressive generation rather than using a separate forced-alignment model, eliminating the need for external tools like Montreal Forced Aligner and enabling timestamps to be generated in a single pass alongside transcription
vs alternatives: Faster than two-pass approaches (transcription + forced alignment with tools like Kaldi or MFA) and more accurate than heuristic time-stretching methods because it uses the model's learned attention patterns to map tokens to audio frames
Provides six model variants (tiny, base, small, medium, large, turbo) with explicit parameter counts, VRAM requirements, and relative speed metrics to enable developers to select the optimal model for their latency/accuracy constraints. Each model is pre-trained and available for download; the system includes English-only variants (tiny.en, base.en, small.en, medium.en) for faster inference on English-only workloads, and turbo (809M params) as a speed-optimized variant of large-v3 with minimal accuracy loss.
Unique: Provides explicit, pre-computed speed/accuracy/memory tradeoff metrics for six model sizes trained on the same 680K-hour dataset, allowing developers to make informed selection decisions without empirical benchmarking. Includes language-specific variants (*.en) that reduce parameters by ~10% for English-only use cases.
vs alternatives: More transparent than competitors (Google Cloud, Azure) which hide model size/speed tradeoffs behind opaque API tiers; enables local optimization decisions without vendor lock-in and supports edge deployment via tiny/base models that competitors don't offer
Processes audio longer than 30 seconds by automatically segmenting into overlapping 30-second windows, transcribing each segment independently, and merging results while handling segment boundaries to maintain context. The system uses the high-level transcribe() API which internally manages segmentation, padding, and result concatenation, avoiding manual segment management and enabling end-to-end processing of hour-long audio files.
Unique: Implements sliding-window segmentation transparently within the high-level transcribe() API rather than exposing it to the user, handling 30-second padding/trimming and segment merging internally. This abstracts away the complexity of manual chunking while maintaining the simplicity of a single function call for arbitrarily long audio.
vs alternatives: Simpler API than competitors requiring manual chunking (e.g., raw PyTorch inference) and more efficient than streaming approaches because it processes entire segments in parallel rather than token-by-token, enabling batch GPU utilization
Automatically detects CUDA-capable GPUs and offloads model computation to GPU, with built-in memory management that handles model loading, activation caching, and intermediate tensor allocation. The system uses PyTorch's device placement and automatic mixed precision (AMP) to optimize memory usage, enabling inference on GPUs with limited VRAM by trading compute precision for memory efficiency.
Unique: Leverages PyTorch's native CUDA integration with automatic device placement — developers specify device='cuda' and the system handles memory allocation, kernel dispatch, and synchronization without explicit CUDA code. Supports automatic mixed precision (AMP) to reduce memory footprint by ~50% with minimal accuracy loss.
vs alternatives: Simpler than competitors requiring manual CUDA kernel optimization (e.g., TensorRT) and more flexible than fixed-precision implementations because AMP adapts to available VRAM dynamically
+3 more capabilities
Generates platform-specific shell commands by detecting the user's OS and $SHELL environment variable, then presents an interactive prompt ([E]xecute, [D]escribe, [A]bort) before execution. Uses the SHELL role system to inject OS context into the LLM prompt, ensuring generated commands work on Linux, macOS, or Windows. The DefaultHandler routes --shell flag to this role, and sgpt/integration.py handles shell hotkey binding for zero-context-switch invocation.
Unique: Detects OS and shell environment at runtime to inject platform-specific context into prompts, then chains interactive execution directly in the CLI without requiring separate copy-paste steps. The role.py SHELL role encapsulates this context injection pattern.
vs alternatives: Faster than web-based command lookup tools (no context-switch) and more reliable than generic LLM command generation because it conditions on actual OS/shell environment rather than generic instructions.
Maintains stateful chat sessions using the ChatHandler and ChatSession classes, storing conversation history in a local cache (sgpt/cache.py). Each --chat <id> invocation appends the new prompt to the session file and retrieves prior context, enabling multi-turn conversations without re-specifying context. Sessions are stored as JSON or text files in ~/.config/shell_gpt/, making them portable and inspectable.
Unique: Implements session persistence as a simple file-based append pattern rather than a database, making sessions human-readable and portable. ChatHandler class owns the session lifecycle, and sgpt/cache.py handles serialization, enabling sessions to survive process restarts.
vs alternatives: Simpler than cloud-based chat tools (no account required, data stays local) and faster than re-uploading context each turn because history is already on disk.
Whisper CLI scores higher at 42/100 vs Shell GPT at 40/100.
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Manages configuration via ~/.config/shell_gpt/.sgptrc file and environment variables (OPENAI_API_KEY, API_BASE_URL, USE_LITELLM, etc.). The sgpt/config.py module reads configuration at startup, with environment variables taking precedence over file-based settings. On first run, sgpt prompts the user for an OpenAI API key and writes it to .sgptrc. Configuration includes LLM backend selection, cache TTL, default model, and other runtime parameters.
Unique: Implements configuration as a two-tier system: file-based defaults in ~/.config/shell_gpt/.sgptrc and environment variable overrides. This allows users to set global defaults while also supporting per-invocation overrides via environment variables, without requiring CLI flags.
vs alternatives: More flexible than CLI-only configuration because settings persist across invocations; more secure than hardcoding secrets in shell scripts because environment variables can be managed by secret management tools.
Supports multi-line prompt input via the --editor flag, which opens the user's $EDITOR (e.g., vim, nano, VS Code) to compose the prompt. The sgpt/utils.py module handles editor invocation and captures the edited text as the prompt. This is useful for complex prompts that are difficult to type on a single command line, or for pasting large code blocks that need explanation.
Unique: Implements editor integration by spawning the $EDITOR process and capturing its output, rather than building a built-in editor. This makes sgpt agnostic to editor choice and allows users to use their preferred editor.
vs alternatives: More flexible than CLI-only input because it supports multi-line text and familiar editor features; more user-friendly than shell escaping complex prompts because the editor handles formatting.
Implements a role system (sgpt/role.py) that wraps user prompts with predefined or custom system instructions. Built-in roles include SHELL (for command generation), CODE (for code snippets), DESCRIBE_SHELL (for explaining commands), and DEFAULT (for general Q&A). Users can create custom roles via --create-role, which stores role definitions as files in ~/.config/shell_gpt/roles/. The DefaultHandler.check_get() method maps CLI flags (--shell, --code, --describe-shell) to roles, then injects the role's system prompt before sending to the LLM.
Unique: Decouples role definitions from code by storing them as files in ~/.config/shell_gpt/roles/, allowing non-developers to create and modify roles without touching Python. The role.py module uses a simple enum-based dispatch pattern (DefaultRoles.check_get()) to map CLI flags to role instances.
vs alternatives: More flexible than hardcoded prompt templates because roles are user-editable files; more discoverable than passing raw system prompts via CLI flags because roles have names and can be listed.
Caches LLM responses using sgpt/cache.py, which stores responses keyed by prompt hash and role. Enabled by default via --cache flag; can be disabled with --no-cache. Cache entries include a TTL (time-to-live) that is configurable in ~/.config/shell_gpt/.sgptrc. When a cached response is found, sgpt returns it immediately without calling the LLM, reducing latency and API costs. Cache is stored as JSON files in ~/.cache/shell_gpt/ or equivalent platform cache directory.
Unique: Implements caching at the Handler level (sgpt/handlers/handler.py) as a transparent layer that intercepts LLM calls, making it work across all roles and modes without per-feature implementation. Cache key includes both prompt and role, ensuring role-specific responses are cached separately.
vs alternatives: Simpler than external cache layers (Redis, Memcached) because it uses local filesystem; faster than re-querying the LLM for identical prompts, especially on slow networks.
Provides a read-eval-print loop (REPL) via the --repl <id> flag, implemented by ReplHandler class. Each iteration accepts a new prompt, sends it to the LLM with prior conversation context, and displays the response without exiting. The REPL maintains session state in memory and persists it to disk (via ChatSession), allowing users to iterate rapidly without re-invoking sgpt. Supports multi-line input via editor integration (--editor flag) for complex prompts.
Unique: Implements REPL as a stateful loop in ReplHandler that maintains conversation context across iterations, using the same ChatSession persistence layer as --chat mode. This allows REPL sessions to be resumed later or inspected as conversation transcripts.
vs alternatives: More integrated than opening a separate ChatGPT web tab because it stays in the terminal and maintains shell context; faster than copy-pasting between terminal and browser.
Abstracts LLM backend selection via sgpt/handlers/handler.py and a configuration flag USE_LITELLM in ~/.config/shell_gpt/.sgptrc. Supports OpenAI API (default), Ollama/local models (via LiteLLM), and Azure OpenAI by routing API calls through either the native OpenAI client or the LiteLLM library. Backend selection is determined at runtime based on configuration, allowing users to swap providers without code changes. The Handler base class owns all LLM interaction, making backend-specific logic centralized.
Unique: Uses a configuration-driven backend selection pattern (USE_LITELLM flag) rather than hardcoding provider logic, allowing users to swap between OpenAI and LiteLLM-compatible providers by editing a config file. The Handler base class is provider-agnostic, delegating actual API calls to the selected client library.
vs alternatives: More flexible than tools locked to a single provider (e.g., Copilot → OpenAI only) because it supports local models and multiple cloud providers; more cost-effective than always using OpenAI because users can choose cheaper or free local alternatives.
+4 more capabilities