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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Captures audio and video from Zoom, Google Meet, and Teams calls directly through browser extension or native app integration without requiring a meeting bot to be invited. The capture mechanism operates client-side at the browser/app level, intercepting the media stream before it reaches the meeting platform's servers, then streams or buffers the raw audio/video for post-processing. This approach eliminates the need for explicit bot invitations and reduces meeting participant friction.
Unique: Eliminates bot-based recording by capturing at the browser/app level rather than injecting a participant into the meeting, reducing UX friction and meeting participant visibility compared to Otter.ai, Fireflies.io, or Fathom which use bot-based approaches
vs alternatives: Superior UX friction vs bot-based competitors because no bot appears in participant list and no explicit invite is required, though technical implementation details are opaque
Converts captured meeting audio into timestamped text transcripts with speaker identification, enabling users to search and reference specific moments in calls. The transcription pipeline processes audio post-meeting (latency unknown) and generates word-level timestamps, allowing clips and summaries to reference exact moments. Speaker attribution mechanism is undisclosed but implied by action item extraction and CRM logging features that track who said what.
Unique: Integrates speaker attribution with transcription to enable action-item tracking and CRM logging by speaker, whereas generic transcription tools (Otter.ai, Fireflies) treat transcripts as undifferentiated text without deep speaker-action mapping
vs alternatives: Tighter integration with downstream CRM and action-item systems because speaker attribution is built into the transcription pipeline rather than post-processed, reducing latency and improving accuracy of speaker-action mapping
Offers a free tier that includes unlimited meeting recording, transcription, and basic summarization without time limits or meeting count restrictions. The free tier is designed to reduce friction for individual users and small teams to adopt tl;dv before upgrading to paid features. Specific limitations of the free tier (e.g., storage limits, feature restrictions, user seat limits) are not disclosed in documentation.
Unique: Offers unlimited meeting recording on free tier without meeting count or storage limits (claimed), whereas competitors like Otter.ai and Fireflies impose strict free tier limits (e.g., 600 minutes/month for Otter.ai) to drive paid upgrades
vs alternatives: Lower barrier to entry for individual users and small teams because unlimited recording on free tier means no surprise paywalls when hitting quota limits, whereas competitors force upgrade after hitting free tier limits
Generates post-meeting summaries using AI models with user-selectable frameworks (MEDDIC, Smart AI Topics, custom) that structure the summary output to match sales, product, or marketing workflows. The summarization engine processes the full transcript and produces abstractive summaries (not just extractive highlights) in 1-5 minutes (claimed 'instantly' but latency unknown). Users can define custom summary templates via prompts, enabling role-specific summaries (e.g., 'extract only objections and how they were handled' for sales, 'extract feature requests and prioritize by frequency' for product).
Unique: Offers framework-based summarization (MEDDIC, Smart AI Topics) with custom prompt templates, whereas competitors like Otter.ai and Fireflies provide generic summaries without role-specific structuring or template customization
vs alternatives: Better for sales and product teams because summaries are pre-structured for domain-specific workflows (MEDDIC for sales, feature extraction for product) rather than generic bullet-point recaps, reducing post-processing work
Identifies commitments, tasks, and next steps mentioned during meetings and extracts them as structured action items with speaker attribution, due date inference, and optional CRM task creation. The extraction uses NLP/LLM-based pattern matching to identify phrases like 'I'll send you', 'we need to', 'by next week', etc., and maps them to speakers and inferred deadlines. Extracted action items can be automatically logged to CRM systems or exported as task lists.
Unique: Combines speaker attribution with action item extraction to automatically assign tasks to the right person, whereas generic action item tools (Otter.ai, Fireflies) extract items without reliable speaker mapping, requiring manual assignment
vs alternatives: More actionable than competitor action item extraction because items are pre-assigned to speakers, reducing manual work and improving accountability tracking in CRM workflows
Automatically logs meeting summaries, transcripts, action items, and call outcomes to CRM systems (specific platforms unknown) without manual data entry. The integration maps tl;dv outputs (summary, action items, speaker attribution) to CRM fields (call notes, next steps, deal stage, etc.) and creates or updates CRM records based on meeting participants and detected deal context. Supports auto-drafting of follow-up emails and task creation within the CRM.
Unique: Integrates meeting intelligence (summaries, action items, speaker attribution) directly into CRM workflows with auto-field population and follow-up drafting, whereas competitors like Otter.ai and Fireflies provide transcripts/summaries but require manual CRM entry or generic Zapier integration
vs alternatives: Reduces manual CRM data entry by 80%+ for sales teams because meeting outputs are automatically mapped to CRM fields and tasks, whereas competitors require copy-paste or generic workflow automation that doesn't understand meeting context
Enables full-text and semantic search across all recorded meetings to find specific topics, speakers, or moments, then generates shareable video/audio clips of matching segments. The search mechanism is undisclosed but likely combines transcript keyword matching with semantic embeddings to find conceptually similar moments across meetings. Clip generation extracts the relevant audio/video segment with context (speaker name, timestamp, summary) and produces a shareable link or downloadable file.
Unique: Combines semantic search with automatic clip generation to enable quick sharing of meeting moments, whereas competitors like Otter.ai and Fireflies provide search but require manual clip creation or don't support video clip generation
vs alternatives: Better for marketing and training use cases because clips are automatically generated from search results with context (speaker, timestamp, summary), enabling quick creation of highlight reels without manual video editing
Analyzes patterns across multiple meetings to identify trends, recurring themes, and aggregate insights, then generates custom reports via email or dashboard. The analysis engine processes summaries and transcripts from multiple meetings, applies user-defined custom prompts (e.g., 'extract all customer pain points and rank by frequency'), and produces structured reports with visualizations (charts, tables — format unknown). Reports can be scheduled (daily, weekly, monthly) or generated on-demand.
Unique: Enables custom prompt-based trend analysis across meeting archives, allowing teams to define their own analysis criteria rather than pre-built reports, whereas competitors like Otter.ai and Fireflies focus on individual meeting summaries without cross-meeting aggregation
vs alternatives: More flexible for diverse use cases (sales objection tracking, product feature extraction, marketing pain point identification) because custom prompts allow teams to define their own analysis logic rather than using pre-built report templates
+3 more capabilities
Transcribes audio in 98 languages to text in the original language using a Transformer sequence-to-sequence architecture trained on 680,000 hours of diverse internet audio. The system uses mel spectrogram feature extraction via FFmpeg integration, processes audio through an AudioEncoder that generates embeddings, then applies an autoregressive TextDecoder with task-specific tokens to produce language-native transcriptions. Language-specific models (e.g., tiny.en, base.en) optimize for English-only workloads with reduced parameter count.
Unique: Unified multitasking Transformer model replaces traditional multi-stage speech pipelines (VAD → language detection → ASR → post-processing) with single forward pass; trained on 680K hours of internet audio providing robustness to background noise, accents, and technical speech unlike studio-trained competitors
vs alternatives: Outperforms Google Cloud Speech-to-Text and Azure Speech Services on non-English languages and noisy audio due to diverse training data; open-source allows local deployment without API latency or privacy concerns
Translates non-English speech directly to English text in a single forward pass using the same Transformer architecture as transcription, but with a translation task token prepended to the decoder input. The model learns to skip intermediate transcription and generate English output directly from audio embeddings, avoiding cascading errors from intermediate transcription steps. Supports 98 source languages translating to English only.
Unique: Direct audio-to-English translation without intermediate transcription step — the decoder learns to skip source language text generation and output English directly, reducing error propagation and latency compared to cascade approaches (transcribe → translate)
vs alternatives: Faster and more accurate than Google Translate + Google Speech-to-Text pipeline because it avoids intermediate transcription errors; open-source allows offline deployment unlike cloud translation APIs
Whisper Large v3 scores higher at 59/100 vs tl;dv at 56/100.
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Normalizes variable-length audio to exactly 30 seconds via `whisper.pad_or_trim()`: audio shorter than 30 seconds is padded with silence (zeros) to reach 30 seconds, audio longer than 30 seconds is trimmed to first 30 seconds. This ensures consistent input shape (80×3000 mel spectrogram) for the model, avoiding shape mismatches and enabling batch processing. Padding strategy is simple zero-padding rather than sophisticated techniques like repetition or interpolation.
Unique: Simple zero-padding strategy is computationally efficient and deterministic, but acoustically naive — alternative approaches (silence detection, repetition) not implemented in base library
vs alternatives: Simpler than librosa-based preprocessing with sophisticated padding; deterministic behavior aids reproducibility; zero-padding is fast but may introduce artifacts vs more sophisticated techniques
Returns transcription results as structured JSON objects containing: transcribed text, language code, duration, segments (with timing and text), and optional confidence metrics. The `model.transcribe()` API returns a dictionary with keys like 'text' (full transcript), 'language' (detected language), 'segments' (list of segment objects with start/end times and text). This structured format enables downstream processing (subtitle generation, database storage, API responses) without string parsing.
Unique: Structured output format is built into high-level API rather than requiring manual parsing — segments include timing and text, enabling direct use for subtitle generation or timeline-based applications
vs alternatives: More structured than raw text output; less detailed than forced alignment tools that provide phoneme-level information; JSON format is language-agnostic and integrates easily with web APIs
Detects the spoken language in audio by processing mel spectrograms through the AudioEncoder and using a language classification head that outputs probability distributions over 98 supported languages. The model leverages 680K hours of multilingual training data to recognize language characteristics from acoustic features alone, without requiring transcription. Language detection occurs as a preliminary step in the transcription pipeline and can be called independently via the language detection task token.
Unique: Language detection is integrated into the same Transformer model as transcription/translation via task tokens, allowing shared AudioEncoder computation and single model load — not a separate classifier, reducing memory footprint and inference overhead
vs alternatives: More accurate than acoustic-only language identification (e.g., librosa-based approaches) because it leverages semantic understanding from 680K hours of training; faster than transcription-based detection (identify language from first few words) because it uses acoustic features directly
Provides six model variants (tiny 39M, base 74M, small 244M, medium 769M, large 1550M, turbo 809M) with different parameter counts, VRAM requirements (1-10GB), and inference speeds (10x-1x relative to large). Each size trades accuracy for speed — tiny runs ~10x faster but with ~5-10% lower WER (word error rate), while large provides best accuracy at 10GB VRAM cost. Turbo variant (809M params) optimizes large-v3 for 8x speedup with minimal accuracy loss but lacks translation support.
Unique: Discrete model size family with published speed/accuracy/VRAM tradeoff matrix allows developers to make informed selection based on deployment constraints; turbo variant represents architectural optimization (knowledge distillation or pruning) achieving 8x speedup with <5% accuracy loss, distinct from simply using smaller base model
vs alternatives: More transparent tradeoff options than Whisper API (single model) or competitors like Deepgram (proprietary size selection); open-source allows local benchmarking on own hardware rather than relying on vendor performance claims
Automatically segments audio longer than 30 seconds into overlapping windows, processes each window independently through the transcription pipeline, and merges results with overlap handling to produce seamless full-length transcripts. The system uses `whisper.pad_or_trim()` to normalize each segment to exactly 30 seconds (padding with silence if needed), then applies the decoder to each segment and concatenates outputs while managing word-level boundaries and timestamp continuity across segment edges.
Unique: Sliding window approach with automatic overlap and boundary handling is built into high-level `model.transcribe()` API — developers don't manually implement segmentation, unlike lower-level APIs that require explicit window management
vs alternatives: Simpler than building custom segmentation logic; more robust than naive concatenation because it handles word-level boundary issues; faster than streaming approaches because it processes segments in parallel on GPU
Generates precise word-level timestamps (start and end times in milliseconds) for each word in the transcript by leveraging the decoder's attention weights and token alignment information. The system maps output tokens back to audio frames using the attention mechanism, then converts frame indices to millisecond timestamps based on the mel spectrogram hop length (20ms per frame). Timestamps are returned as part of the structured output alongside transcribed text.
Unique: Word-level timestamps are derived from attention weight alignment rather than separate timestamp prediction head — leverages existing decoder computation without additional model parameters, but introduces ±100-200ms uncertainty from frame quantization
vs alternatives: More granular than segment-level timestamps (which only mark 30-second boundaries); less accurate than forced alignment tools (e.g., Montreal Forced Aligner) but requires no phonetic lexicon or manual annotation
+4 more capabilities