{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn","slug":"jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn","name":"wav2vec2-large-xlsr-53-chinese-zh-cn","type":"model","url":"https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn","page_url":"https://unfragile.ai/jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn","categories":["voice-audio"],"tags":["transformers","pytorch","jax","wav2vec2","automatic-speech-recognition","audio","speech","xlsr-fine-tuning-week","zh","dataset:common_voice","doi:10.57967/hf/3570","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-chinese-zh-cn__cap_0","uri":"capability://data.processing.analysis.mandarin.chinese.speech.to.text.transcription.with.cross.lingual.transfer.learning","name":"mandarin chinese speech-to-text transcription with cross-lingual transfer learning","description":"Converts Mandarin Chinese (zh-CN) audio waveforms to text using wav2vec2 architecture with XLSR-53 cross-lingual pretraining. The model uses self-supervised learning on 53 languages' unlabeled audio data, then fine-tunes on Common Voice Chinese dataset. It processes raw audio through a convolutional feature extractor (13 layers, stride-2 downsampling) followed by 24 transformer encoder layers with attention mechanisms, outputting character-level predictions that are post-processed into text via CTC (Connectionist Temporal Classification) decoding.","intents":["I need to transcribe Mandarin Chinese audio files to text for downstream NLP tasks","I want to build a voice command interface that understands Chinese speech","I need to process large batches of Chinese audio recordings into searchable text","I'm building a Chinese speech recognition system and want to avoid training from scratch"],"best_for":["Teams building Chinese-language voice assistants or IVR systems","Researchers working on Mandarin speech processing and phonetic analysis","Developers creating accessibility tools for Chinese-speaking users","Companies processing customer service call recordings in Chinese"],"limitations":["Trained only on Common Voice dataset (~50 hours of zh-CN audio) — may underperform on domain-specific accents, technical jargon, or noisy real-world audio","Character error rate (CER) typically 10-15% on test sets — not suitable for high-accuracy legal or medical transcription without post-processing","Requires 16kHz mono audio input — resampling overhead for higher sample rates or stereo files","No built-in language model rescoring — relies on CTC beam search without contextual priors for homophone disambiguation","Inference latency ~0.5-1.5x real-time on CPU, requires GPU for sub-real-time performance on long audio"],"requires":["Python 3.7+","PyTorch 1.9+ or JAX/Flax (model supports both frameworks)","librosa or soundfile for audio loading and preprocessing","transformers library 4.5.0+","Audio input must be 16kHz sample rate, mono channel, PCM format"],"input_types":["audio/wav (16kHz, mono)","audio/mp3 (will be resampled)","numpy arrays (shape: [samples] or [batch, samples])","raw audio bytes"],"output_types":["text (Mandarin Chinese characters and punctuation)","token logits (for confidence scoring or downstream processing)","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-chinese-zh-cn__cap_1","uri":"capability://data.processing.analysis.batch.audio.feature.extraction.with.learned.representations","name":"batch audio feature extraction with learned representations","description":"Extracts dense vector representations (768-dimensional embeddings) from Mandarin Chinese audio by passing waveforms through the wav2vec2 feature encoder and transformer stack without the final classification head. These learned representations capture phonetic and prosodic information useful for downstream tasks like speaker verification, emotion detection, or audio clustering. The extraction process uses the same 13-layer CNN feature extractor (reducing audio to 50Hz frame rate) followed by 24 transformer layers with multi-head attention, producing one embedding per 20ms audio frame.","intents":["I need to extract speaker embeddings from Chinese audio for speaker identification or diarization","I want to cluster similar Chinese speech samples without transcribing them","I need audio representations for downstream ML tasks like emotion or intent classification","I'm building a semantic audio search system for Chinese voice data"],"best_for":["Audio ML engineers building speaker verification or diarization systems","Researchers studying phonetic properties of Mandarin Chinese speech","Teams implementing audio similarity or clustering pipelines","Developers creating voice biometric authentication systems"],"limitations":["Embeddings are task-specific to speech recognition — not optimized for speaker verification or emotion detection without fine-tuning","Frame-level embeddings (50Hz) require temporal pooling for utterance-level representations — no built-in aggregation strategy","768-dimensional vectors require dimensionality reduction for efficient similarity search at scale (>1M utterances)","No speaker normalization — embeddings vary with speaker identity, making cross-speaker comparisons difficult without additional processing"],"requires":["Python 3.7+","PyTorch 1.9+ or JAX","transformers 4.5.0+","Audio preprocessing pipeline (resampling to 16kHz)","GPU recommended for batch processing (CPU inference ~10-50x slower)"],"input_types":["audio/wav (16kHz, mono)","numpy arrays (shape: [samples] or [batch, samples])","audio tensors (PyTorch or JAX)"],"output_types":["embeddings (shape: [frames, 768] or [batch, frames, 768])","pooled embeddings (mean/max aggregation across frames)","attention weights from transformer layers"],"categories":["data-processing-analysis","embedding-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn__cap_2","uri":"capability://data.processing.analysis.real.time.streaming.audio.transcription.with.frame.level.processing","name":"real-time streaming audio transcription with frame-level processing","description":"Processes audio in streaming fashion by accepting variable-length audio chunks and maintaining internal state across chunks, enabling low-latency transcription without buffering entire audio files. The model processes audio through the CNN feature extractor (which has receptive field of ~400ms) and transformer layers with causal masking, allowing each new audio frame to be processed incrementally. Streaming requires careful handling of context windows and CTC beam search state to produce consistent character-level predictions across chunk boundaries.","intents":["I need to transcribe live Chinese speech in real-time for voice assistants or live captioning","I want to process long audio files with minimal memory footprint using streaming","I'm building a low-latency Chinese speech recognition service for mobile or edge devices","I need to handle continuous audio streams from microphones or telephony systems"],"best_for":["Voice assistant developers building real-time Chinese speech interfaces","Teams implementing live captioning or accessibility features for Chinese content","Mobile and edge device developers with memory constraints","Telephony and call center platforms processing Chinese customer interactions"],"limitations":["Streaming inference requires custom implementation — transformers library provides batched inference only, streaming requires external libraries (e.g., faster-whisper patterns or custom ONNX optimization)","CNN receptive field (~400ms) introduces minimum latency — cannot achieve sub-400ms end-to-end latency","CTC beam search state management across chunks is complex — incorrect implementation leads to character repetition or skipping at boundaries","Chunk size selection is critical — too small (<400ms) wastes computation, too large (>5s) increases latency; no automatic optimization","Streaming accuracy typically 2-5% worse than batch processing due to lack of future context for disambiguation"],"requires":["Python 3.7+","PyTorch 1.9+ with custom streaming inference code or third-party library (e.g., faster-whisper, streaming-asr-transformers)","Audio buffering and chunk management logic","GPU strongly recommended for real-time performance (CPU cannot achieve <1x real-time on most hardware)","Audio input at 16kHz sample rate with consistent chunk timing"],"input_types":["audio chunks (numpy arrays, shape: [chunk_samples])","streaming audio buffers from microphone or network","variable-length audio frames (100-5000ms duration)"],"output_types":["partial transcriptions (updated as new chunks arrive)","final transcriptions (when audio stream ends or silence detected)","confidence scores per character or token"],"categories":["data-processing-analysis","real-time-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn__cap_3","uri":"capability://tool.use.integration.multi.framework.model.deployment.with.automatic.format.conversion","name":"multi-framework model deployment with automatic format conversion","description":"Supports deployment across PyTorch, JAX/Flax, and ONNX runtime formats, with automatic conversion and optimization for different hardware targets (CPU, GPU, TPU). The model can be loaded from HuggingFace Hub in any framework, automatically downloading pretrained weights and configuration. ONNX export enables inference on edge devices, mobile platforms, and specialized hardware without Python/PyTorch dependencies. The transformers library handles framework abstraction, allowing identical code to run on PyTorch or JAX with different performance characteristics.","intents":["I need to deploy Chinese speech recognition on edge devices or mobile without PyTorch overhead","I want to run inference on TPUs or specialized hardware using JAX","I need to optimize model inference for production with ONNX quantization and pruning","I'm building a multi-platform service that needs to support different ML frameworks"],"best_for":["MLOps engineers optimizing models for production deployment","Mobile and edge device developers targeting iOS, Android, or embedded systems","Teams using JAX for research or TPU-based infrastructure","Organizations standardizing on ONNX for cross-platform inference"],"limitations":["ONNX export requires manual conversion — not automatically provided by HuggingFace; requires onnx and onnxruntime libraries","JAX version may lag PyTorch in terms of bug fixes and feature parity — not all transformers features available in Flax","ONNX quantization (int8) typically causes 5-10% accuracy degradation on speech recognition tasks","Framework conversion adds ~5-15% inference latency overhead compared to native framework","Mobile deployment requires additional optimization (model compression, quantization) beyond framework conversion"],"requires":["Python 3.7+ (for conversion and deployment setup)","PyTorch 1.9+ OR JAX 0.3.0+ OR ONNX Runtime 1.10+","transformers 4.5.0+","onnx and onnxruntime (for ONNX deployment)","Framework-specific dependencies (torch, jax, onnxruntime)"],"input_types":["HuggingFace model identifier (string)","local model checkpoint (directory with config.json and pytorch_model.bin)","ONNX model file (.onnx)"],"output_types":["PyTorch model (torch.nn.Module)","JAX/Flax model (flax.linen.Module)","ONNX model (serialized .onnx file)","quantized/optimized model (int8 or float16)"],"categories":["tool-use-integration","deployment-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn__cap_4","uri":"capability://code.generation.editing.fine.tuning.on.custom.mandarin.chinese.datasets.with.transfer.learning","name":"fine-tuning on custom mandarin chinese datasets with transfer learning","description":"Enables adaptation of the pretrained XLSR-53 model to domain-specific Chinese audio (medical, legal, technical jargon, regional accents) through supervised fine-tuning on custom labeled datasets. The fine-tuning process freezes the CNN feature extractor and lower transformer layers (which capture universal acoustic features) while training the upper transformer layers and classification head on new data. This transfer learning approach requires only 10-50 hours of labeled audio to achieve domain-specific accuracy improvements, compared to training from scratch which needs 1000+ hours.","intents":["I need to adapt the model to medical or legal Chinese terminology for specialized transcription","I want to improve accuracy on regional Chinese accents or dialects not well-represented in Common Voice","I'm building a domain-specific voice assistant that needs to understand technical jargon","I need to reduce character error rate on my company's specific audio distribution"],"best_for":["Domain experts (medical, legal, technical) building specialized speech systems","Teams with 10-100 hours of labeled domain-specific audio data","Researchers studying transfer learning for low-resource speech recognition","Companies optimizing ASR for internal use cases with proprietary audio characteristics"],"limitations":["Requires labeled audio data with character-level transcriptions — annotation is expensive (~$1-5 per minute of audio)","Fine-tuning on small datasets (<10 hours) risks overfitting — requires careful regularization and validation set management","Character error rate improvements plateau after ~50 hours of domain data — diminishing returns beyond that point","Fine-tuning code not provided by HuggingFace — requires custom training loop using transformers Trainer or manual PyTorch training","Catastrophic forgetting possible if learning rate too high — requires careful hyperparameter tuning to maintain general-purpose accuracy"],"requires":["Python 3.7+","PyTorch 1.9+ with training utilities","transformers 4.5.0+ with Trainer API","datasets library for data loading and preprocessing","10-100 hours of labeled Mandarin Chinese audio with transcriptions","GPU with 8GB+ VRAM (16GB+ recommended for batch size >16)","Validation dataset (10-20% of training data) for hyperparameter tuning"],"input_types":["audio files (16kHz mono WAV/MP3)","transcription files (text, character-level)","dataset objects (HuggingFace datasets format or custom DataLoader)"],"output_types":["fine-tuned model checkpoint (directory with config and weights)","training metrics (loss, CER, WER curves)","evaluation results on validation/test sets"],"categories":["code-generation-editing","transfer-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn__cap_5","uri":"capability://data.processing.analysis.confidence.scoring.and.uncertainty.quantification.per.transcription.token","name":"confidence scoring and uncertainty quantification per transcription token","description":"Provides character-level or token-level confidence scores by extracting softmax probabilities from the model's output logits before CTC decoding. These scores indicate the model's certainty for each predicted character, enabling applications to flag low-confidence regions for human review or alternative hypotheses. The scoring is computed from the raw logits (shape: [time_steps, vocab_size]) before CTC beam search, allowing downstream applications to implement custom confidence thresholding, rejection rules, or confidence-weighted averaging across multiple model runs.","intents":["I need to identify uncertain transcription regions for human review or correction","I want to implement confidence-based filtering to reject low-quality transcriptions","I'm building a system that flags homophones or ambiguous words for clarification","I need to measure transcription reliability for quality assurance in production systems"],"best_for":["Quality assurance teams validating transcription accuracy in production","Human-in-the-loop systems that need to route uncertain predictions to human annotators","Researchers studying model calibration and uncertainty in speech recognition","Call center platforms that need to flag problematic recordings for manual review"],"limitations":["Confidence scores are not well-calibrated — high softmax probability does not guarantee correct prediction; requires empirical calibration on validation data","CTC decoding introduces alignment ambiguity — confidence scores at character level may not reflect true uncertainty due to CTC's many-to-one mapping","No built-in method to extract alternative hypotheses (N-best lists) — requires custom beam search implementation","Confidence scores correlate with model's training data distribution — out-of-domain audio may have artificially high confidence","Computing full logits for every frame adds ~20-30% inference latency compared to greedy decoding"],"requires":["Python 3.7+","PyTorch 1.9+ or JAX","transformers 4.5.0+","Custom post-processing code to extract and interpret logits","Validation dataset to calibrate confidence thresholds for your use case"],"input_types":["audio files (16kHz mono)","model output logits (shape: [time_steps, vocab_size])"],"output_types":["confidence scores per character (shape: [num_characters])","confidence scores per frame (shape: [num_frames])","calibrated confidence thresholds (float between 0-1)","flagged regions (indices of low-confidence characters)"],"categories":["data-processing-analysis","uncertainty-quantification"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.9+ or JAX/Flax (model supports both frameworks)","librosa or soundfile for audio loading and preprocessing","transformers library 4.5.0+","Audio input must be 16kHz sample rate, mono channel, PCM format","PyTorch 1.9+ or JAX","transformers 4.5.0+","Audio preprocessing pipeline (resampling to 16kHz)","GPU recommended for batch processing (CPU inference ~10-50x slower)","PyTorch 1.9+ with custom streaming inference code or third-party library (e.g., faster-whisper, streaming-asr-transformers)"],"failure_modes":["Trained only on Common Voice dataset (~50 hours of zh-CN audio) — may underperform on domain-specific accents, technical jargon, or noisy real-world audio","Character error rate (CER) typically 10-15% on test sets — not suitable for high-accuracy legal or medical transcription without post-processing","Requires 16kHz mono audio input — resampling overhead for higher sample rates or stereo files","No built-in language model rescoring — relies on CTC beam search without contextual priors for homophone disambiguation","Inference latency ~0.5-1.5x real-time on CPU, requires GPU for sub-real-time performance on long audio","Embeddings are task-specific to speech recognition — not optimized for speaker verification or emotion detection without fine-tuning","Frame-level embeddings (50Hz) require temporal pooling for utterance-level representations — no built-in aggregation strategy","768-dimensional vectors require dimensionality reduction for efficient similarity search at scale (>1M utterances)","No speaker normalization — embeddings vary with speaker identity, making cross-speaker comparisons difficult without additional processing","Streaming inference requires custom implementation — transformers library provides batched inference only, streaming requires external libraries (e.g., faster-whisper patterns or custom ONNX optimization)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7111401208612793,"quality":0.37,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:52.901Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":998505,"model_likes":133}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn","compare_url":"https://unfragile.ai/compare?artifact=jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn"}},"signature":"35lJa3s7LrqxMEY6tXr+Ae49QCPscQ4TAtATrDS4qhzd5HI4IrBLuJCzB2SNJ8jyi8Q9QNEy+2oVlyVvBRtvDA==","signedAt":"2026-06-20T03:47:16.752Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn","artifact":"https://unfragile.ai/jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn","verify":"https://unfragile.ai/api/v1/verify?slug=jonatasgrosman--wav2vec2-large-xlsr-53-chinese-zh-cn","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"}}