{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-online-demo","slug":"online-demo","name":"Online Demo","type":"webapp","url":"https://seamless.metademolab.com/expressive?utm_source=metaai&utm_medium=web&utm_campaign=fair10&utm_content=blog","page_url":"https://unfragile.ai/online-demo","categories":["automation"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-online-demo__cap_0","uri":"capability://text.generation.language.expressive.speech.to.speech.translation.with.emotion.preservation","name":"expressive speech-to-speech translation with emotion preservation","description":"Translates spoken input across 100+ language pairs while preserving speaker emotion, prosody, and vocal characteristics through a unified encoder-decoder architecture trained on multilingual speech data. The system uses a single model that handles both speech recognition and synthesis end-to-end, maintaining emotional nuance by learning disentangled representations of content and speaker identity during training.","intents":["I need to translate a customer support call while keeping the agent's tone and emotional delivery intact","I want to dub video content in multiple languages without losing the original speaker's personality","I need to preserve sarcasm and emotional context when translating international team meetings"],"best_for":["content creators and video producers working with multilingual audiences","customer service teams handling international calls with emotional sensitivity requirements","media companies needing expressive dubbing without re-recording talent"],"limitations":["Emotion preservation quality degrades with heavy background noise or poor audio quality","Supported languages are limited to the 100+ languages in the training corpus; rare languages may have degraded performance","Real-time processing latency varies by language pair and audio length; longer clips may require batch processing","Emotional nuance transfer works best for languages with similar phonetic and prosodic structures"],"requires":["Audio input in common formats (WAV, MP3, M4A) with sample rate 16kHz or higher","Internet connection for cloud-based inference via the online demo","Source and target language codes from the supported language list"],"input_types":["audio/wav","audio/mp3","audio/m4a","raw speech samples"],"output_types":["audio/wav","translated speech with preserved prosody","speaker identity embeddings"],"categories":["text-generation-language","audio-processing","multilingual-translation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-online-demo__cap_1","uri":"capability://text.generation.language.multilingual.automatic.speech.recognition.with.cross.lingual.transfer","name":"multilingual automatic speech recognition with cross-lingual transfer","description":"Recognizes speech in 100+ languages using a single unified model trained with multilingual data, leveraging cross-lingual acoustic and linguistic patterns to improve accuracy even for low-resource languages. The architecture uses shared encoder layers that learn language-agnostic phonetic representations, with language-specific decoder heads that adapt to phoneme inventories and prosodic patterns of each language.","intents":["I need to transcribe international team meetings with participants speaking different languages","I want to build a speech interface that works across multiple languages without deploying separate models","I need accurate transcription for low-resource languages that don't have dedicated ASR models"],"best_for":["multinational organizations with multilingual communication needs","developers building global voice interfaces and accessibility features","researchers working with low-resource language documentation"],"limitations":["Accuracy varies significantly across languages; high-resource languages (English, Mandarin) achieve 95%+ WER while low-resource languages may be 15-20% WER","Code-switching (mixing multiple languages in single utterance) has degraded performance compared to single-language speech","Accented speech and non-native speakers may have higher error rates than native speakers","Real-time transcription requires streaming inference; batch processing has lower latency but higher memory overhead"],"requires":["Audio input at 16kHz sample rate minimum","Language code specification or automatic language detection","Internet connection for cloud inference via demo interface"],"input_types":["audio/wav","audio/mp3","streaming audio","raw PCM samples"],"output_types":["text transcription","confidence scores per token","language identification"],"categories":["text-generation-language","data-processing-analysis","multilingual-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-online-demo__cap_2","uri":"capability://text.generation.language.text.to.speech.synthesis.with.speaker.identity.control","name":"text-to-speech synthesis with speaker identity control","description":"Converts text input into natural-sounding speech across 100+ languages with fine-grained control over speaker characteristics including voice timbre, pitch, speaking rate, and emotional tone. The system uses a neural vocoder architecture that conditions on speaker embeddings and linguistic features, allowing synthesis of diverse voices without requiring speaker-specific training data through speaker embedding interpolation.","intents":["I need to generate voiceovers in multiple languages with consistent speaker identity across languages","I want to create accessible audio versions of documents while controlling voice characteristics","I need to synthesize speech with specific emotional tone or speaking style for interactive applications"],"best_for":["accessibility teams creating audio content from text documents","game and interactive media developers needing diverse character voices","content creators producing multilingual voiceovers without hiring talent"],"limitations":["Speaker identity transfer across languages works best when target language has similar phonetic inventory to source language","Emotional tone synthesis is limited to emotions present in training data; novel emotional combinations may sound unnatural","Synthesis latency increases with text length; very long documents require chunking and concatenation","Naturalness degrades for highly technical or domain-specific terminology not well-represented in training data"],"requires":["Text input in supported language","Optional speaker embedding or speaker ID from reference audio","Internet connection for cloud-based synthesis"],"input_types":["text","language code","speaker embedding (optional)","prosody control parameters (optional)"],"output_types":["audio/wav","audio/mp3","speaker embeddings"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-online-demo__cap_3","uri":"capability://text.generation.language.real.time.streaming.speech.translation.with.low.latency","name":"real-time streaming speech translation with low latency","description":"Processes audio input in streaming chunks to produce translated speech output with minimal latency (typically 1-3 seconds behind live speech), using a streaming-aware encoder-decoder architecture that processes partial audio frames and generates incremental translations. The system buffers audio strategically to balance latency against translation quality, using attention mechanisms that can operate on incomplete input sequences.","intents":["I need live interpretation for international video conferences with minimal delay","I want to build a real-time translation feature for live streaming or broadcasting","I need to translate phone calls or voice conversations with acceptable latency for natural dialogue"],"best_for":["video conferencing platform developers adding real-time translation features","live event broadcasters needing simultaneous interpretation","telecommunications companies offering multilingual call services"],"limitations":["Streaming latency introduces 1-3 second delay which may feel unnatural for fast-paced conversations","Translation quality may be lower than batch processing due to incomplete context at decision points","Requires continuous network connection; network interruptions cause audio gaps and translation failures","Memory footprint is higher than batch processing due to buffering and state maintenance across chunks"],"requires":["Streaming audio input at 16kHz sample rate","Persistent network connection with low jitter","Chunk size configuration (typically 20-40ms audio frames)","Source and target language specification"],"input_types":["streaming audio","audio chunks","raw PCM samples"],"output_types":["streaming audio output","incremental translations","confidence scores"],"categories":["text-generation-language","automation-workflow","real-time-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-online-demo__cap_4","uri":"capability://data.processing.analysis.language.identification.and.automatic.source.language.detection","name":"language identification and automatic source language detection","description":"Automatically detects the source language of input speech without explicit language specification, using a language identification classifier trained on acoustic patterns across 100+ languages. The system operates as a preprocessing step that feeds detected language codes into downstream ASR and translation models, enabling fully automatic speech translation without user intervention.","intents":["I need to handle multilingual input without knowing the source language in advance","I want to build a voice interface that automatically adapts to the user's language","I need to process mixed-language audio and identify language boundaries"],"best_for":["voice interface developers building language-agnostic applications","customer service systems handling calls from international customers","accessibility applications serving multilingual user bases"],"limitations":["Language identification accuracy decreases for short audio clips (< 3 seconds); requires minimum 5-10 seconds for reliable detection","Code-switching and multilingual utterances may be misidentified as a single language or produce inconsistent results","Accuracy varies by language; closely related languages (Spanish/Portuguese, Hindi/Urdu) have higher confusion rates","Accented speech and non-native speakers may be misidentified, especially for languages with limited training data"],"requires":["Audio input at 16kHz sample rate minimum","Minimum 5-10 seconds of audio for reliable detection","Internet connection for cloud inference"],"input_types":["audio/wav","audio/mp3","streaming audio"],"output_types":["language code","confidence scores","language probabilities for top-N candidates"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-online-demo__cap_5","uri":"capability://automation.workflow.batch.processing.of.audio.files.with.translation.pipeline","name":"batch processing of audio files with translation pipeline","description":"Processes multiple audio files or long-form audio content through the complete speech-to-speech translation pipeline (ASR → translation → TTS) with optimized throughput and resource utilization. The system queues audio files, processes them through shared model instances, and outputs translated audio with metadata tracking, enabling efficient processing of large volumes without per-file model loading overhead.","intents":["I need to translate a library of recorded lectures or training videos into multiple languages","I want to process overnight batches of customer support recordings for multilingual analysis","I need to generate dubbed versions of video content in bulk for multiple language markets"],"best_for":["content production teams with large volumes of video/audio to translate","enterprises processing historical recordings for multilingual accessibility","media companies creating localized content for multiple markets"],"limitations":["Batch processing introduces latency (minutes to hours depending on volume); not suitable for real-time applications","Output quality depends on input audio quality; poor quality audio cannot be improved by batch processing","Large batch jobs may timeout or fail; requires robust error handling and retry logic","Storage requirements scale with batch size; large video files require significant temporary storage"],"requires":["Audio/video files in supported formats (WAV, MP3, M4A, MP4)","Sufficient storage for input and output files","Source and target language specifications","Internet connection for cloud processing"],"input_types":["audio/wav","audio/mp3","audio/m4a","video/mp4","batch manifests (JSON/CSV)"],"output_types":["audio/wav","audio/mp3","translated audio files","processing metadata and logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Audio input in common formats (WAV, MP3, M4A) with sample rate 16kHz or higher","Internet connection for cloud-based inference via the online demo","Source and target language codes from the supported language list","Audio input at 16kHz sample rate minimum","Language code specification or automatic language detection","Internet connection for cloud inference via demo interface","Text input in supported language","Optional speaker embedding or speaker ID from reference audio","Internet connection for cloud-based synthesis","Streaming audio input at 16kHz sample rate"],"failure_modes":["Emotion preservation quality degrades with heavy background noise or poor audio quality","Supported languages are limited to the 100+ languages in the training corpus; rare languages may have degraded performance","Real-time processing latency varies by language pair and audio length; longer clips may require batch processing","Emotional nuance transfer works best for languages with similar phonetic and prosodic structures","Accuracy varies significantly across languages; high-resource languages (English, Mandarin) achieve 95%+ WER while low-resource languages may be 15-20% WER","Code-switching (mixing multiple languages in single utterance) has degraded performance compared to single-language speech","Accented speech and non-native speakers may have higher error rates than native speakers","Real-time transcription requires streaming inference; batch processing has lower latency but higher memory overhead","Speaker identity transfer across languages works best when target language has similar phonetic inventory to source language","Emotional tone synthesis is limited to emotions present in training data; novel emotional combinations may sound unnatural","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.579Z","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=online-demo","compare_url":"https://unfragile.ai/compare?artifact=online-demo"}},"signature":"9707dllwmh+lN6Za8GmBPWjR2u/agadw806fPDO/1zqzdU0FF41ci7DkCIz9dYGsxP8vqKt7XsbsBoLwjUFdBw==","signedAt":"2026-06-23T06:39:24.206Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/online-demo","artifact":"https://unfragile.ai/online-demo","verify":"https://unfragile.ai/api/v1/verify?slug=online-demo","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"}}