{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-japanese","slug":"jonatasgrosman--wav2vec2-large-xlsr-53-japanese","name":"wav2vec2-large-xlsr-53-japanese","type":"model","url":"https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese","page_url":"https://unfragile.ai/jonatasgrosman--wav2vec2-large-xlsr-53-japanese","categories":["voice-audio"],"tags":["transformers","pytorch","jax","wav2vec2","automatic-speech-recognition","audio","speech","xlsr-fine-tuning-week","ja","dataset:common_voice","doi:10.57967/hf/3568","license:apache-2.0","model-index","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-japanese__cap_0","uri":"capability://data.processing.analysis.multilingual.speech.to.text.transcription.japanese","name":"multilingual-speech-to-text-transcription-japanese","description":"Converts Japanese audio waveforms to text using a wav2vec2 architecture pretrained on 53 languages via XLSR (cross-lingual speech representations) and fine-tuned on Common Voice Japanese dataset. The model uses a convolutional feature extractor to downsample raw audio into learned acoustic representations, then applies transformer layers with self-attention to capture long-range phonetic dependencies, enabling accurate transcription without explicit phoneme labels.","intents":["I need to transcribe Japanese audio files to text for downstream NLP tasks","I want to build a speech recognition pipeline that handles Japanese language input","I need to convert voice recordings into searchable text for Japanese content","I'm building a voice assistant or dictation tool for Japanese speakers"],"best_for":["developers building Japanese speech recognition systems","teams processing Japanese audio datasets for transcription","researchers working on multilingual ASR evaluation","startups building voice-first applications for Japanese market"],"limitations":["Fine-tuned only on Common Voice Japanese dataset — may have lower accuracy on domain-specific audio (medical, legal terminology) or heavy accents","Requires audio preprocessing (resampling to 16kHz) — raw audio at other sample rates will degrade accuracy","No built-in language model rescoring — relies purely on acoustic model, may produce grammatically incorrect but phonetically plausible outputs","Inference latency ~1-3 seconds per minute of audio on CPU; GPU acceleration recommended for real-time applications","No speaker diarization or multi-speaker separation — treats all speakers as single stream"],"requires":["Python 3.7+","PyTorch 1.9+ or JAX backend","librosa or torchaudio for audio loading and preprocessing","transformers library 4.5.0+","Audio input at 16kHz sample rate (mono or stereo)","GPU with 8GB+ VRAM recommended for batch inference"],"input_types":["audio waveform (numpy array, shape [samples])","audio file paths (WAV, MP3, FLAC formats via librosa)","raw PCM bytes at 16kHz sample rate"],"output_types":["text string (Japanese hiragana/kanji transcription)","token-level logits (for confidence scoring or downstream tasks)","attention weights (for interpretability)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-japanese__cap_1","uri":"capability://data.processing.analysis.audio.feature.extraction.with.learned.representations","name":"audio-feature-extraction-with-learned-representations","description":"Extracts learned acoustic representations from raw audio waveforms using a convolutional feature extractor (7 conv layers with gating) followed by quantization and transformer encoding. The model outputs contextualized embeddings (1024-dimensional vectors) that capture phonetic and prosodic information, enabling downstream tasks like speaker verification, emotion detection, or acoustic similarity matching without requiring task-specific fine-tuning.","intents":["I need to extract speaker-independent acoustic features for clustering or similarity search","I want to use pretrained audio embeddings as input to my own classification model","I need to build a voice-based authentication system using acoustic representations","I'm creating a speech emotion or intent detection system on top of learned features"],"best_for":["ML engineers building custom audio classification pipelines","researchers studying acoustic representations and phonetic structure","developers creating speaker verification or voice biometrics systems","teams fine-tuning the model for downstream Japanese audio tasks"],"limitations":["Embeddings are 1024-dimensional — may require dimensionality reduction for efficient similarity search or storage","Learned representations are language-specific to Japanese phonetics — may not transfer well to non-Japanese audio without adaptation","No built-in normalization or standardization of embeddings — downstream models may require explicit feature scaling","Extraction requires full audio pass through all transformer layers — cannot be interrupted for streaming applications"],"requires":["Python 3.7+","PyTorch 1.9+ or JAX","transformers library 4.5.0+","Audio preprocessed to 16kHz mono","GPU recommended for batch extraction (CPU inference ~10-30x slower)"],"input_types":["audio waveform (numpy array, float32, shape [samples])","audio file paths (WAV, MP3, FLAC)","raw PCM bytes"],"output_types":["dense embeddings (numpy array, shape [time_steps, 1024])","pooled embeddings (shape [1024] for fixed-size representation)","intermediate layer activations (for interpretability)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-japanese__cap_2","uri":"capability://automation.workflow.batch.audio.transcription.with.padding.and.attention.masking","name":"batch-audio-transcription-with-padding-and-attention-masking","description":"Processes multiple audio samples of variable length in a single forward pass by padding shorter sequences and applying attention masks to prevent the transformer from attending to padding tokens. The implementation uses HuggingFace's data collator pattern to automatically handle variable-length batching, enabling efficient GPU utilization and ~4-8x throughput improvement over sequential processing while maintaining per-sample accuracy.","intents":["I need to transcribe hundreds of audio files efficiently in batch mode","I want to maximize GPU utilization when processing variable-length audio","I'm building a batch transcription service with predictable latency","I need to process audio datasets with heterogeneous durations"],"best_for":["data engineers processing large Japanese audio corpora","teams running offline transcription pipelines","researchers evaluating model performance on test sets","backend services handling asynchronous transcription jobs"],"limitations":["Padding overhead increases memory usage proportionally to longest sequence in batch — very heterogeneous audio lengths reduce efficiency gains","Batch size is constrained by GPU memory (typically 8-32 samples for 16GB VRAM depending on audio duration)","Attention masking adds ~5-10% computational overhead compared to fixed-length processing","No built-in batching across multiple GPUs or distributed inference — requires external orchestration (Ray, Kubernetes)"],"requires":["Python 3.7+","PyTorch 1.9+ with CUDA support (for GPU batching)","transformers library 4.5.0+","GPU with 8GB+ VRAM for batch sizes >4","Audio files preprocessed to 16kHz"],"input_types":["list of audio waveforms (variable length, numpy arrays)","list of audio file paths","DataLoader with custom collate function"],"output_types":["batch of transcription strings (list[str])","batch of logits (shape [batch_size, time_steps, vocab_size])","batch of attention weights"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-japanese__cap_3","uri":"capability://code.generation.editing.fine.tuning.on.custom.japanese.audio.datasets","name":"fine-tuning-on-custom-japanese-audio-datasets","description":"Enables transfer learning by unfreezing and retraining the model on custom Japanese audio datasets using the CTC (Connectionist Temporal Classification) loss function. The fine-tuning process leverages the pretrained XLSR-53 acoustic features and adapts the final linear projection layer to custom vocabulary or domain-specific phonetics, typically requiring 10-100 hours of labeled audio to achieve convergence and 2-5x accuracy improvement over zero-shot performance.","intents":["I want to adapt the model to my domain-specific Japanese audio (medical, legal, technical terminology)","I need to improve accuracy on accented or non-standard Japanese speech","I'm building a custom ASR system for a specific use case with limited labeled data","I want to reduce WER (word error rate) on my proprietary audio dataset"],"best_for":["teams with 10-500 hours of labeled Japanese audio","domain experts building specialized ASR systems","companies with proprietary speech data","researchers studying transfer learning in multilingual ASR"],"limitations":["Requires labeled audio with character-level transcriptions — annotation cost is significant (typically $0.50-2.00 per minute of audio)","Fine-tuning on small datasets (<10 hours) risks overfitting — requires careful regularization and validation set monitoring","CTC loss assumes monotonic alignment between audio and text — fails on heavily corrupted or heavily accented audio with non-linear time warping","No built-in curriculum learning or hard example mining — all samples weighted equally during training","Fine-tuned models are not compatible with the original pretrained checkpoint — requires retraining from scratch for different vocabularies"],"requires":["Python 3.7+","PyTorch 1.9+ with CUDA","transformers library 4.5.0+","datasets library for data loading","10+ hours of labeled Japanese audio (minimum; 100+ hours recommended)","GPU with 16GB+ VRAM for training","Audio files at 16kHz sample rate with character-level transcriptions"],"input_types":["audio files (WAV, MP3, FLAC) at 16kHz","transcription files (plain text, one per audio file)","HuggingFace Dataset object with 'audio' and 'text' columns"],"output_types":["fine-tuned model checkpoint (PyTorch state dict)","training logs (loss, WER, validation metrics)","inference-ready model compatible with original architecture"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-japanese__cap_4","uri":"capability://automation.workflow.real.time.streaming.transcription.with.chunking","name":"real-time-streaming-transcription-with-chunking","description":"Processes audio in fixed-size chunks (e.g., 1-2 second windows) with sliding window overlap to enable low-latency streaming transcription. The model processes each chunk independently with context from previous chunks via a sliding buffer, producing partial transcriptions with ~500ms-2s latency depending on chunk size and hardware, suitable for live speech recognition applications.","intents":["I need to transcribe live audio streams with minimal latency for real-time applications","I'm building a voice assistant that responds to partial transcriptions","I want to implement streaming captions for Japanese video or live events","I need to handle continuous audio input without buffering entire recordings"],"best_for":["developers building real-time voice interfaces","teams implementing live captioning systems","startups creating voice-first applications","researchers studying streaming ASR architectures"],"limitations":["Chunk-based processing introduces boundary artifacts — words split across chunk boundaries may be transcribed incorrectly (5-15% WER increase vs. full-audio processing)","Sliding window overlap adds computational overhead — ~20-30% more inference calls than non-overlapping chunks","No built-in context carry-over between chunks — each chunk is transcribed independently, losing long-range dependencies","Latency is fundamentally bounded by chunk duration plus model inference time — cannot achieve <500ms latency on CPU","Requires careful tuning of chunk size and overlap — too small chunks increase latency, too large chunks increase error rates"],"requires":["Python 3.7+","PyTorch 1.9+ or JAX","transformers library 4.5.0+","Real-time audio input device (microphone) or streaming audio source","GPU recommended for <1s latency (CPU inference ~3-5s per chunk)","Audio preprocessed to 16kHz mono"],"input_types":["audio chunks (numpy arrays, shape [samples], typically 16000-32000 samples)","streaming audio buffer (ring buffer or queue)","raw PCM bytes from audio device"],"output_types":["partial transcription strings (updated incrementally)","confidence scores per chunk","intermediate logits for confidence estimation"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-japanese__cap_5","uri":"capability://planning.reasoning.vocabulary.constrained.decoding.with.language.model.integration","name":"vocabulary-constrained-decoding-with-language-model-integration","description":"Integrates an external Japanese language model or vocabulary constraint during decoding to filter the model's raw predictions and improve accuracy on domain-specific terminology. The approach uses beam search with language model rescoring or constrained decoding (e.g., via trie-based vocabulary matching) to bias predictions toward valid Japanese words or domain-specific terms, reducing hallucinations and improving WER by 10-30% on specialized vocabularies.","intents":["I need to ensure transcriptions only contain valid Japanese words from a custom vocabulary","I want to improve accuracy on domain-specific terminology (medical, legal, technical)","I'm building a system that must avoid hallucinating words outside a known vocabulary","I need to integrate a Japanese language model to improve grammatical coherence"],"best_for":["teams with domain-specific vocabulary requirements","developers building medical or legal transcription systems","companies with proprietary terminology databases","researchers studying constrained decoding in multilingual ASR"],"limitations":["Requires external language model or vocabulary list — no built-in LM provided with the base model","Language model rescoring adds 2-5x inference latency — not suitable for real-time applications without optimization","Vocabulary constraints may reject valid out-of-vocabulary words — requires careful vocabulary curation","Beam search with LM rescoring is computationally expensive — requires GPU and careful beam size tuning","No built-in integration with popular Japanese LMs — requires custom implementation or third-party library"],"requires":["Python 3.7+","PyTorch 1.9+","transformers library 4.5.0+","External language model (e.g., KenLM, Fairseq, or custom neural LM)","Vocabulary list or trie data structure (for constrained decoding)","GPU with 8GB+ VRAM for beam search with LM rescoring"],"input_types":["audio waveform (numpy array)","vocabulary list (list of strings or trie structure)","language model checkpoint or KenLM binary"],"output_types":["constrained transcription string (only contains vocabulary words)","beam search hypotheses with scores","language model scores per hypothesis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-japanese__cap_6","uri":"capability://automation.workflow.model.quantization.and.compression.for.edge.deployment","name":"model-quantization-and-compression-for-edge-deployment","description":"Reduces model size and inference latency by quantizing weights to int8 or float16 precision using PyTorch quantization or ONNX export, enabling deployment on edge devices (mobile, embedded systems) with 4-8x smaller model size and 2-4x faster inference. The quantization process uses post-training quantization or quantization-aware training to maintain accuracy within 1-3% of the full-precision model.","intents":["I need to deploy the model on mobile devices or embedded systems with limited memory","I want to reduce inference latency for real-time applications on CPU-only hardware","I'm building an on-device speech recognition system without cloud connectivity","I need to minimize model size for bandwidth-constrained environments"],"best_for":["mobile developers building on-device ASR","embedded systems engineers with memory constraints","teams building privacy-preserving speech recognition","startups deploying models in bandwidth-limited regions"],"limitations":["Quantization introduces 1-5% accuracy degradation depending on quantization scheme — may be unacceptable for high-accuracy applications","int8 quantization requires careful calibration on representative data — poor calibration can cause 10-20% accuracy loss","ONNX export requires manual operator mapping — not all PyTorch operations are supported, may require model architecture changes","Quantized models are less flexible for fine-tuning — retraining requires full-precision weights","Mobile deployment requires additional frameworks (TensorFlow Lite, Core ML, ONNX Runtime) — adds engineering complexity"],"requires":["Python 3.7+","PyTorch 1.9+ with quantization support","transformers library 4.5.0+","ONNX tools (optional, for ONNX export)","Mobile framework (TensorFlow Lite, Core ML, ONNX Runtime)","Representative calibration data for post-training quantization"],"input_types":["full-precision model checkpoint","calibration dataset (representative audio samples)","quantization configuration (bit width, scheme)"],"output_types":["quantized model checkpoint (int8 or float16)","ONNX model file (for cross-platform deployment)","quantization statistics (accuracy metrics)"],"categories":["automation-workflow","data-processing-analysis"],"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 JAX backend","librosa or torchaudio for audio loading and preprocessing","transformers library 4.5.0+","Audio input at 16kHz sample rate (mono or stereo)","GPU with 8GB+ VRAM recommended for batch inference","PyTorch 1.9+ or JAX","Audio preprocessed to 16kHz mono","GPU recommended for batch extraction (CPU inference ~10-30x slower)","PyTorch 1.9+ with CUDA support (for GPU batching)"],"failure_modes":["Fine-tuned only on Common Voice Japanese dataset — may have lower accuracy on domain-specific audio (medical, legal terminology) or heavy accents","Requires audio preprocessing (resampling to 16kHz) — raw audio at other sample rates will degrade accuracy","No built-in language model rescoring — relies purely on acoustic model, may produce grammatically incorrect but phonetically plausible outputs","Inference latency ~1-3 seconds per minute of audio on CPU; 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