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| Ecosystem | 0 | 0 |
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| Pricing | Free | Paid |
| Starting Price | $0.00250/min | — |
| Capabilities | 16 decomposed | 4 decomposed |
| Times Matched | 0 | 0 |
Converts pre-recorded audio to text using AssemblyAI's Universal-3 Pro model, trained on 12.5+ million hours of audio data. Supports context-aware prompting via plain-language instructions and keyterms (up to 1000 words/phrases, max 6 words per phrase) to control transcription behavior. Provides word-level timestamps, speaker role identification, code-switching support, and verbatim mode. Processes audio asynchronously via REST API with per-hour-of-audio billing ($0.21/hr for Universal-3 Pro, $0.15/hr for legacy Universal-2 supporting 99 languages).
Unique: Universal-3 Pro achieves market-leading multilingual accuracy through training on 12.5+ million hours of audio and supports context-aware prompting (plain-language instructions + keyterms) to customize transcription behavior without fine-tuning, differentiating from competitors like Google Cloud Speech-to-Text or AWS Transcribe that require separate model selection or lack flexible prompting
vs alternatives: Faster time-to-accuracy than competitors for domain-specific vocabulary because keyterms prompting doesn't require model retraining, and word-level timestamps are native rather than post-processed
Provides real-time transcription of live audio streams using Universal-3 Pro model via WebSocket-based streaming API. Supports speaker role identification (by name or role, not generic diarization labels) and is built on AssemblyAI's proprietary Voice AI stack optimized for production voice agents. Processes audio with sub-second latency for interactive applications like live call transcription, voice agent interactions, and real-time meeting captions. Billed at $4.50/hr of audio processed.
Unique: Built on proprietary Voice AI stack end-to-end optimized for production voice agents with native speaker role identification (by name/role, not generic labels) and WebSocket streaming, whereas competitors like Google Cloud Speech-to-Text or Azure Speech Services use generic speaker diarization and require separate agent orchestration frameworks
vs alternatives: Lower latency and more natural speaker identification for voice agents because it's purpose-built for conversational AI rather than adapted from batch transcription models
Enables customization of transcription output by providing domain-specific terminology, custom spellings, or keyterms that should be recognized and preserved in the transcript. Supports up to 1000 words/phrases with a maximum of 6 words per phrase. Implemented as a prompting feature that influences the transcription model's output without requiring model fine-tuning. Billed at $0.05/hr of audio processed for Universal-3 Pro (included in base price) and $0.05/hr for Universal-2. Enables accurate transcription of specialized vocabulary, proper nouns, product names, and domain-specific terminology.
Unique: Supports flexible prompting with up to 1000 keyterms (max 6 words per phrase) without requiring model fine-tuning, enabling rapid vocabulary customization for different domains. Implemented as a native feature of Universal-3 Pro (included in base price) and available for Universal-2 ($0.05/hr), whereas competitors like Google Cloud Speech-to-Text require separate phrase lists or custom model training
vs alternatives: Faster vocabulary customization than fine-tuning custom models because keyterms prompting works with pre-trained models, and more flexible than static phrase lists because prompting can handle context-dependent variations
Applies large language models (LLMs) directly to audio data via AssemblyAI's LeMUR (Language Model on Embedded Representations) framework, enabling AI-powered tasks like summarization, question-answering, entity extraction, and custom analysis without requiring separate transcript processing. Processes audio through the transcription pipeline and applies LLM reasoning directly on the transcript representation. Specific LLM models supported, pricing, and integration details not documented in available material. Enables end-to-end audio intelligence workflows without chaining multiple services.
Unique: Integrates LLM reasoning directly into the audio processing pipeline via LeMUR framework, enabling audio-native AI tasks without separate transcript extraction or LLM service calls. Processes audio end-to-end with a single API call, whereas competitors require chaining transcription + separate LLM services
vs alternatives: Simpler integration than separate services because LLM reasoning happens within AssemblyAI's pipeline, and potentially more accurate because LLM can leverage transcript confidence scores and audio metadata for better reasoning
Transcription mode that preserves filler words, false starts, and non-standard speech patterns exactly as spoken, without normalization or cleanup. Implemented as a transcription parameter that disables automatic filler word removal and speech normalization, returning a verbatim record of the audio content. Useful for linguistic analysis, legal documentation, or accessibility applications requiring exact speech representation. Included in base transcription cost (no additional billing).
Unique: Native verbatim mode that preserves exact speech without normalization, enabling accurate linguistic analysis and legal documentation. Implemented as a transcription parameter rather than a separate service, whereas competitors typically require post-processing or manual review to achieve verbatim accuracy
vs alternatives: More accurate verbatim transcription than post-processing approaches because it preserves speech at the transcription level, and simpler integration because verbatim mode is a single API parameter
Handles audio containing multiple languages mixed within a single conversation (code-switching), accurately transcribing each language segment and optionally identifying language boundaries. Implemented as a native feature of Universal-3 Pro that detects language switches and transcribes each segment in the appropriate language. Enables accurate transcription of multilingual conversations without requiring separate language-specific models or manual language selection. Specific language pair support and language detection accuracy not documented in available material.
Unique: Native code-switching support in Universal-3 Pro that automatically detects and transcribes multiple languages without manual language selection, enabling accurate multilingual transcription. Implemented as a single model rather than requiring separate language-specific models or manual switching, whereas competitors typically require explicit language selection or separate models per language
vs alternatives: More accurate code-switching transcription than language-specific models because it's trained to handle language mixing, and simpler integration because no manual language switching is required
Provides precise timing information for each word in the transcript (start and end timestamps) along with per-word confidence scores indicating transcription accuracy. Implemented as a native feature of the transcription output that returns word-level metadata for synchronization with audio/video playback, interactive transcript building, or quality analysis. Enables downstream applications like interactive transcripts, video captions, and transcript-based search with playback seeking.
Unique: Native word-level timestamps and confidence scores integrated into the transcription output, enabling precise synchronization without separate alignment processing. Provides per-word confidence for quality analysis, whereas competitors typically provide only sentence-level or segment-level confidence
vs alternatives: More precise transcript synchronization than post-processing alignment because timestamps are generated during transcription, and more granular quality analysis because per-word confidence enables identification of specific problem areas
Returns precise word-level timing information for each word in the transcript, enabling applications to synchronize text with audio playback, highlight words as they're spoken, or extract segments by time range. Timestamps are returned in milliseconds with start and end times per word.
Unique: Word-level timestamps with millisecond precision enable direct audio-text synchronization without external alignment tools, supporting interactive transcript players and caption generation
vs alternatives: More precise than Google Cloud Speech-to-Text word timing (which has documented latency issues); integrated into transcription output without separate alignment API
+8 more capabilities
Whisper employs a transformer-based architecture trained on a diverse dataset of multilingual audio, leveraging weak supervision to enhance its performance across various languages and accents. This model utilizes a combination of self-supervised learning and fine-tuning techniques to achieve high accuracy in transcription, even in noisy environments. Its ability to generalize from a wide range of audio inputs makes it distinct from traditional speech recognition systems that often rely on extensive labeled datasets.
Unique: Utilizes a large-scale weak supervision approach that allows it to learn from vast amounts of unlabeled audio data, enhancing its adaptability to different languages and accents.
vs alternatives: More versatile than traditional ASR systems due to its training on diverse, unannotated datasets, enabling it to handle a wider range of speech patterns.
Whisper's architecture is designed to support multiple languages by training on a multilingual dataset, allowing it to accurately transcribe audio from various languages without needing separate models for each language. This capability is facilitated by its attention mechanism, which helps the model focus on relevant parts of the audio input while considering language-specific phonetic nuances.
Unique: Trained on a diverse multilingual dataset, allowing it to perform well across various languages without needing separate models.
vs alternatives: More effective in handling multilingual audio than competitors that require distinct models for each language.
Whisper's training includes a variety of noisy audio samples, enabling it to perform well even in challenging acoustic environments. The model incorporates techniques to filter out background noise and focus on the primary speech signal, which enhances its transcription accuracy in real-world scenarios where audio quality may be compromised.
AssemblyAI API scores higher at 55/100 vs Whisper at 19/100. AssemblyAI API also has a free tier, making it more accessible.
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Unique: Incorporates training on noisy audio samples, allowing it to effectively filter background noise and enhance speech clarity during transcription.
vs alternatives: Superior to traditional ASR systems that often falter in noisy environments due to lack of robust training data.
Whisper can process audio input in real-time, leveraging its efficient transformer architecture to transcribe speech as it is spoken. This capability is achieved through a combination of streaming audio processing and incremental decoding, allowing the model to output text continuously without waiting for the entire audio clip to finish.
Unique: Utilizes a streaming architecture that allows for continuous audio processing and transcription, making it suitable for live applications.
vs alternatives: Faster and more responsive than many traditional ASR systems that require buffering before processing.