{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-kresnik--wav2vec2-large-xlsr-korean","slug":"kresnik--wav2vec2-large-xlsr-korean","name":"wav2vec2-large-xlsr-korean","type":"model","url":"https://huggingface.co/kresnik/wav2vec2-large-xlsr-korean","page_url":"https://unfragile.ai/kresnik--wav2vec2-large-xlsr-korean","categories":["voice-audio"],"tags":["transformers","pytorch","safetensors","wav2vec2","automatic-speech-recognition","speech","audio","ko","dataset:kresnik/zeroth_korean","license:apache-2.0","model-index","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-kresnik--wav2vec2-large-xlsr-korean__cap_0","uri":"capability://data.processing.analysis.korean.speech.to.text.transcription.with.multilingual.pretraining","name":"korean speech-to-text transcription with multilingual pretraining","description":"Converts Korean audio waveforms to text using a wav2vec2 architecture pretrained on 53 languages via XLSR (Cross-Lingual Speech Representations) and fine-tuned on the Zeroth Korean dataset. The model uses self-supervised learning on raw audio to learn acoustic representations, then applies a language-specific linear projection layer trained on Korean speech data to map acoustic features to Korean phonemes and words. Processes raw PCM audio at 16kHz sample rate through a convolutional feature extractor followed by transformer encoder blocks.","intents":["Build Korean voice assistants or chatbots that understand spoken Korean commands","Transcribe Korean audio recordings or live speech streams for documentation or accessibility","Create Korean speech-to-text APIs for mobile or web applications","Fine-tune a Korean ASR model on domain-specific vocabulary (medical, legal, technical Korean)"],"best_for":["Korean-language application developers building voice interfaces","Teams deploying on-device speech recognition without cloud dependencies","Researchers working on multilingual speech processing with Korean language support","Organizations needing open-source Korean ASR without licensing restrictions"],"limitations":["Trained on Zeroth Korean dataset which may have limited domain coverage — performance degrades on accented, noisy, or heavily technical Korean speech","No built-in language model rescoring — relies on acoustic model alone, producing phonetically plausible but sometimes grammatically incorrect transcriptions","Requires 16kHz mono audio input — automatic resampling not included, must preprocess audio externally","Model size ~314MB in fp32 — inference latency ~2-5x real-time on CPU, requires GPU for near-realtime performance","No confidence scores or per-token probabilities exposed — cannot identify uncertain regions in transcription"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","transformers library 4.5.0+","librosa or similar audio processing library for waveform loading and resampling","16kHz mono PCM audio input"],"input_types":["raw audio waveform (numpy array or tensor)","audio file path (WAV, MP3, FLAC with preprocessing)","streaming audio buffer (requires batching logic)"],"output_types":["text string (Korean transcription)","token-level logits (for downstream processing)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-kresnik--wav2vec2-large-xlsr-korean__cap_1","uri":"capability://data.processing.analysis.acoustic.feature.extraction.via.self.supervised.wav2vec2.encoder","name":"acoustic feature extraction via self-supervised wav2vec2 encoder","description":"Extracts learned acoustic representations from raw audio using the wav2vec2 encoder backbone without the final classification head. The model applies a convolutional feature extractor (7 layers, 512 channels) to downsample raw waveforms, then passes through 12 transformer encoder layers with attention mechanisms to produce contextualized acoustic embeddings. These embeddings capture phonetic and speaker information in a 768-dimensional space, useful for downstream tasks beyond transcription.","intents":["Extract speaker embeddings for speaker identification or verification tasks","Generate acoustic features for custom downstream classifiers (emotion detection, language identification)","Analyze acoustic similarity between Korean speech samples for clustering or retrieval","Use as a frozen feature extractor for low-resource Korean speech tasks"],"best_for":["Researchers building custom Korean speech processing pipelines","Teams implementing speaker diarization or speaker verification on Korean audio","Developers creating Korean speech emotion or intent classification systems","Engineers optimizing inference by reusing extracted features across multiple downstream models"],"limitations":["Embeddings are context-dependent (position in sequence matters) — cannot directly compare isolated phoneme representations","No explicit speaker normalization — speaker identity leaks into embeddings, affecting speaker-agnostic tasks","Dimensionality (768) requires dimensionality reduction for efficient similarity search or clustering","Temporal resolution ~50ms per frame — may lose fine-grained acoustic details for tonal or prosodic analysis"],"requires":["Python 3.7+","PyTorch 1.9+","transformers library 4.5.0+","16kHz mono audio input"],"input_types":["raw audio waveform (numpy array or PyTorch tensor)","audio file path (with external preprocessing)"],"output_types":["acoustic embeddings (768-dimensional vectors, shape [sequence_length, 768])","pooled embeddings (mean/max pooling over time for fixed-size representation)"],"categories":["data-processing-analysis","feature-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-kresnik--wav2vec2-large-xlsr-korean__cap_2","uri":"capability://code.generation.editing.fine.tuning.on.custom.korean.speech.datasets","name":"fine-tuning on custom korean speech datasets","description":"Enables adaptation of the pretrained wav2vec2 model to domain-specific Korean speech by unfreezing the classification head and optionally the encoder layers, then training on custom labeled audio data. The model uses CTC (Connectionist Temporal Classification) loss to align variable-length audio sequences with Korean text transcriptions without requiring forced alignment. Supports mixed-precision training and gradient accumulation for efficient training on consumer GPUs.","intents":["Adapt the model to specialized Korean vocabulary (medical terminology, legal jargon, technical domains)","Improve accuracy on accented or regional Korean dialects not well-represented in Zeroth dataset","Reduce word error rate on noisy audio (car, street, office environments) via domain-specific training","Create custom Korean ASR models for proprietary or confidential speech data"],"best_for":["Organizations with domain-specific Korean speech corpora (medical, legal, customer service)","Teams needing to improve accuracy on regional Korean accents or dialects","Researchers experimenting with Korean speech processing architectures","Companies requiring on-premises models without cloud API dependencies"],"limitations":["Requires 100+ hours of labeled Korean audio for meaningful improvement — small datasets (<10 hours) risk overfitting","CTC loss assumes monotonic alignment — struggles with speech containing long pauses, stuttering, or overlapping speakers","No built-in data augmentation (SpecAugment, time-stretching) — must implement externally for robustness","Fine-tuning the full model (encoder + head) requires significant GPU memory (~16GB for batch size 8) and training time (hours to days)","No curriculum learning or hard example mining — training on imbalanced datasets (common words vs rare words) produces suboptimal results"],"requires":["Python 3.7+","PyTorch 1.9+ with CUDA support (for GPU training)","transformers library 4.5.0+","Labeled Korean speech dataset in WAV format with corresponding text transcriptions","GPU with 16GB+ VRAM for efficient training (CPU training possible but very slow)"],"input_types":["audio files (WAV, FLAC, MP3 with preprocessing)","text transcriptions (UTF-8 encoded Korean text)","training configuration (learning rate, batch size, epochs)"],"output_types":["fine-tuned model checkpoint (PyTorch .pt or safetensors format)","training metrics (loss curves, WER on validation set)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-kresnik--wav2vec2-large-xlsr-korean__cap_3","uri":"capability://automation.workflow.batch.inference.with.dynamic.padding.for.variable.length.audio","name":"batch inference with dynamic padding for variable-length audio","description":"Processes multiple Korean audio samples of different lengths in a single batch using dynamic padding and attention masks. The model pads shorter sequences to match the longest sequence in the batch, applies attention masks to ignore padding tokens, and processes all samples through the encoder in parallel. This approach maximizes GPU utilization and reduces per-sample inference latency compared to processing audio sequentially.","intents":["Transcribe multiple Korean audio files efficiently in a single batch operation","Build real-time Korean speech-to-text APIs that handle concurrent requests","Process large Korean audio archives (podcasts, call recordings) with minimal latency","Optimize inference throughput on edge devices or servers with limited GPU memory"],"best_for":["Backend services handling multiple concurrent Korean speech transcription requests","Batch processing pipelines for archival Korean audio (news, podcasts, meetings)","Edge deployment scenarios where throughput matters more than latency","Cost-conscious teams optimizing GPU utilization for Korean ASR inference"],"limitations":["Padding overhead increases memory usage — batch of 10 audio samples with lengths [5s, 3s, 2s, ...] uses memory for 10x5s = 50s of audio even though total is ~30s","Optimal batch size depends on GPU memory and audio duration — no automatic batch size tuning","Attention masks prevent the model from attending across padding, but do not reduce computation — padded tokens still consume FLOPs","Dynamic padding requires CPU-side preprocessing — adds ~10-50ms overhead per batch for padding and mask creation","No built-in batching across multiple GPUs — requires external distributed inference framework (Ray, Triton)"],"requires":["Python 3.7+","PyTorch 1.9+ with CUDA support","transformers library 4.5.0+","GPU with sufficient VRAM for batch size (typically 8-32 samples depending on audio length)"],"input_types":["list of audio waveforms (variable length, 16kHz mono)","batch configuration (batch size, padding strategy)"],"output_types":["list of transcriptions (Korean text strings)","batch processing metrics (throughput, latency)"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-kresnik--wav2vec2-large-xlsr-korean__cap_4","uri":"capability://automation.workflow.streaming.online.inference.with.sliding.window.buffering","name":"streaming/online inference with sliding window buffering","description":"Enables real-time Korean speech-to-text transcription by processing audio in fixed-size chunks (e.g., 1-2 second windows) with overlap to maintain context. The model maintains a sliding buffer of recent audio frames, processes new incoming chunks through the encoder, and outputs partial transcriptions incrementally. Requires careful management of attention context across chunk boundaries to avoid artifacts at segment boundaries.","intents":["Build real-time Korean voice assistants that respond to spoken commands with minimal latency","Create live Korean speech-to-text captions for meetings, lectures, or broadcasts","Implement Korean voice-controlled applications (smart home, automotive) with sub-second latency","Develop interactive Korean speech recognition for accessibility (live transcription for deaf/hard-of-hearing users)"],"best_for":["Real-time Korean voice assistant developers (Alexa, Google Assistant competitors)","Live captioning services for Korean media or events","Accessibility teams building Korean speech-to-text for assistive technology","Mobile/embedded systems requiring low-latency Korean speech recognition"],"limitations":["Chunk-based processing introduces latency — minimum ~500ms to 2 seconds before first transcription output (depends on chunk size and model inference time)","Context loss at chunk boundaries — model may mispronounce words spanning chunk boundaries due to limited attention context","Requires careful tuning of chunk size and overlap — too small chunks reduce context quality, too large chunks increase latency","No built-in confidence scoring or uncertainty quantification — difficult to detect when transcription is unreliable","Streaming requires stateful inference — cannot easily parallelize across multiple GPUs without complex state synchronization","Audio buffering and chunk management must be implemented externally — no built-in streaming API in transformers library"],"requires":["Python 3.7+","PyTorch 1.9+ with CUDA support","transformers library 4.5.0+","Audio streaming framework (PyAudio, WebRTC, or custom)","Latency budget of 500ms-2s (depends on application requirements)"],"input_types":["streaming audio chunks (fixed-size buffers, 16kHz mono)","chunk configuration (size, overlap, stride)"],"output_types":["partial/incremental transcriptions (Korean text)","confidence scores (if post-processing applied)"],"categories":["automation-workflow","real-time-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-kresnik--wav2vec2-large-xlsr-korean__cap_5","uri":"capability://data.processing.analysis.multilingual.transfer.learning.from.xlsr.pretraining","name":"multilingual transfer learning from xlsr pretraining","description":"Leverages cross-lingual speech representations learned from 53 languages during XLSR pretraining to improve Korean ASR performance with limited labeled data. The model's encoder has learned language-agnostic acoustic patterns (phoneme-like units, prosody, speaker characteristics) that transfer effectively to Korean. Fine-tuning only the task-specific CTC head requires minimal Korean data compared to training from scratch.","intents":["Build Korean ASR systems with limited labeled Korean speech data (< 100 hours)","Improve robustness to Korean speech variations (accents, noise, speaking styles) via multilingual pretraining","Adapt the model to low-resource Korean dialects or specialized Korean speech (children, elderly, non-native speakers)","Understand what acoustic patterns the model learned from multilingual pretraining"],"best_for":["Teams with limited Korean speech corpora but access to other language data","Researchers studying cross-lingual transfer in speech processing","Low-resource Korean language communities building speech technology","Organizations needing to quickly prototype Korean ASR without extensive data collection"],"limitations":["Multilingual pretraining may introduce language interference — model may confuse Korean with phonetically similar languages (Japanese, Mandarin)","Transfer learning effectiveness depends on similarity between pretraining languages and Korean — benefits diminish for very different language families","No explicit mechanism to control which languages' representations transfer — cannot selectively use only high-resource language pretraining","Encoder may have learned language-specific patterns that don't generalize to Korean — requires validation on Korean test set","Fine-tuning on small Korean datasets may overfit to pretraining distribution — requires careful regularization (dropout, early stopping)"],"requires":["Python 3.7+","PyTorch 1.9+","transformers library 4.5.0+","Minimum 10-50 hours of labeled Korean speech for meaningful fine-tuning (less than training from scratch)"],"input_types":["Korean speech audio (16kHz mono)","Korean text transcriptions","optional: other language speech data for continued pretraining"],"output_types":["fine-tuned Korean ASR model","transfer learning metrics (improvement over baseline)"],"categories":["data-processing-analysis","transfer-learning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","transformers library 4.5.0+","librosa or similar audio processing library for waveform loading and resampling","16kHz mono PCM audio input","PyTorch 1.9+","16kHz mono audio input","PyTorch 1.9+ with CUDA support (for GPU training)","Labeled Korean speech dataset in WAV format with corresponding text transcriptions","GPU with 16GB+ VRAM for efficient training (CPU training possible but very slow)"],"failure_modes":["Trained on Zeroth Korean dataset which may have limited domain coverage — performance degrades on accented, noisy, or heavily technical Korean speech","No built-in language model rescoring — relies on acoustic model alone, producing phonetically plausible but sometimes grammatically incorrect transcriptions","Requires 16kHz mono audio input — automatic resampling not included, must preprocess audio externally","Model size ~314MB in fp32 — inference latency ~2-5x real-time on CPU, requires GPU for near-realtime performance","No confidence scores or per-token probabilities exposed — cannot identify uncertain regions in transcription","Embeddings are context-dependent (position in sequence matters) — cannot directly compare isolated phoneme representations","No explicit speaker normalization — speaker identity leaks into embeddings, affecting speaker-agnostic tasks","Dimensionality (768) requires dimensionality reduction for efficient similarity search or clustering","Temporal resolution ~50ms per frame — may lose fine-grained acoustic details for tonal or prosodic analysis","Requires 100+ hours of labeled Korean audio for meaningful improvement — small datasets (<10 hours) risk overfitting","builder identity is not verified yet","no observed match outcomes 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