{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-polish","slug":"jonatasgrosman--wav2vec2-large-xlsr-53-polish","name":"wav2vec2-large-xlsr-53-polish","type":"model","url":"https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-polish","page_url":"https://unfragile.ai/jonatasgrosman--wav2vec2-large-xlsr-53-polish","categories":["voice-audio"],"tags":["transformers","pytorch","jax","wav2vec2","automatic-speech-recognition","audio","hf-asr-leaderboard","mozilla-foundation/common_voice_6_0","pl","robust-speech-event","speech","xlsr-fine-tuning-week","dataset:common_voice","dataset:mozilla-foundation/common_voice_6_0","doi:10.57967/hf/3574","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-polish__cap_0","uri":"capability://data.processing.analysis.polish.language.speech.to.text.transcription.with.multilingual.pretraining","name":"polish-language speech-to-text transcription with multilingual pretraining","description":"Converts Polish audio waveforms to text using a wav2vec2 architecture pretrained on 53 languages via XLSR (Cross-Lingual Speech Representations) and fine-tuned on Mozilla Common Voice 6.0 Polish dataset. The model uses self-supervised contrastive learning on raw audio to learn language-agnostic phonetic representations, then applies a Polish-specific linear classification head for character-level transcription. Processes 16kHz mono audio and outputs character sequences with implicit word boundaries.","intents":["Build a Polish speech recognition system without training from scratch","Transcribe Polish audio files in batch or real-time applications","Integrate Polish ASR into voice assistants or accessibility tools","Evaluate ASR performance on Polish language benchmarks"],"best_for":["Polish-language application developers building voice features","Teams deploying multilingual ASR systems with Polish support","Researchers evaluating cross-lingual transfer learning effectiveness","Organizations needing open-source alternatives to proprietary Polish ASR"],"limitations":["Trained on Common Voice data which may have lower audio quality and speaker diversity than commercial datasets","No built-in language model decoding — outputs raw character predictions without grammatical correction or vocabulary constraints","Inference latency scales with audio length; real-time processing requires GPU acceleration for sub-100ms latency","Fine-tuned only on Polish; cross-lingual zero-shot performance on related Slavic languages unknown","No speaker diarization, emotion detection, or confidence scoring — single-speaker transcription only"],"requires":["Python 3.7+","transformers library (>=4.0.0)","librosa or torchaudio for audio preprocessing","PyTorch 1.9+ or JAX backend","Audio input at 16kHz sample rate (resampling required for other rates)","GPU recommended for real-time inference (CPU inference ~5-10x slower)"],"input_types":["audio/wav (16kHz mono PCM)","audio/mp3 (requires preprocessing)","numpy arrays (shape: [samples] or [1, samples])","raw audio bytes"],"output_types":["text (Polish character sequences)","logits (raw model outputs for custom decoding)","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-polish__cap_1","uri":"capability://data.processing.analysis.batch.audio.transcription.with.automatic.preprocessing.and.format.handling","name":"batch audio transcription with automatic preprocessing and format handling","description":"Processes multiple audio files sequentially or in batches, automatically resampling to 16kHz, normalizing amplitude, and handling variable-length inputs through padding/truncation. Integrates with HuggingFace Datasets library for streaming large audio corpora without loading entire datasets into memory. Outputs transcriptions with optional alignment metadata (token-to-timestamp mappings) for downstream applications.","intents":["Transcribe large audio corpora (100s-1000s of files) for dataset creation or evaluation","Process audio from diverse sources (podcasts, interviews, voice messages) with automatic format normalization","Generate training data for downstream NLP tasks (named entity recognition, intent classification on speech)","Evaluate model performance on standardized benchmarks like Common Voice test sets"],"best_for":["Data engineers preparing Polish speech datasets for model training","Researchers conducting large-scale ASR evaluation studies","Teams building speech-to-text pipelines for content indexing or archival","Organizations processing user-generated audio content at scale"],"limitations":["Batch processing throughput limited by GPU memory; typical batch size 8-16 for 16GB VRAM","No automatic language detection — assumes all audio is Polish; mixed-language audio produces degraded output","Preprocessing adds 10-20% latency overhead for resampling and normalization","No built-in error handling for corrupted audio files; requires external validation","Output lacks confidence scores per word; only character-level predictions available"],"requires":["librosa (>=0.9.0) or torchaudio (>=0.10.0) for audio I/O and resampling","datasets library (>=2.0.0) for streaming large audio collections","sufficient disk space for temporary resampled audio if not streaming","GPU with >=8GB VRAM for batch inference (CPU feasible for small batches)"],"input_types":["audio files (WAV, MP3, FLAC, OGG)","HuggingFace Dataset objects with audio column","directory paths with glob patterns","audio URLs (requires download preprocessing)"],"output_types":["text transcriptions (Polish character sequences)","JSON with transcription + metadata (duration, processing time)","CSV for batch evaluation (filename, transcription, reference_text, WER)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-polish__cap_2","uri":"capability://data.processing.analysis.fine.tuning.on.custom.polish.audio.datasets.with.transfer.learning","name":"fine-tuning on custom polish audio datasets with transfer learning","description":"Enables adaptation of the pretrained XLSR-53 model to domain-specific Polish audio (medical dictation, legal proceedings, customer service calls) through supervised fine-tuning on labeled audio-transcript pairs. Leverages the frozen multilingual encoder and retrains only the Polish-specific classification head and optional adapter layers, reducing training data requirements from millions to thousands of hours. Implements gradient accumulation, mixed-precision training, and learning rate scheduling for stable convergence on limited data.","intents":["Adapt the model to specialized Polish domains (medical, legal, technical) with domain-specific vocabulary","Improve accuracy on accented or non-standard Polish speech variants","Create custom models for proprietary applications without sharing audio with cloud providers","Reduce WER on noisy audio (call center, street recordings) through domain-specific fine-tuning"],"best_for":["Organizations with proprietary Polish speech data seeking custom ASR models","Teams building domain-specific voice applications (medical transcription, legal discovery)","Researchers studying transfer learning effectiveness in low-resource speech recognition","Companies requiring on-premises ASR without cloud API dependencies"],"limitations":["Requires minimum 10-50 hours of labeled Polish audio for meaningful improvement; less data risks overfitting","Fine-tuning on GPU takes 2-8 hours depending on dataset size and hardware; CPU training impractical","No automatic hyperparameter tuning; requires manual experimentation with learning rate, batch size, warmup steps","Frozen encoder prevents adaptation to very different acoustic conditions (e.g., underwater, extreme noise); full model fine-tuning needed","No built-in data augmentation (SpecAugment, speed/pitch perturbation); requires external preprocessing"],"requires":["Python 3.7+","transformers (>=4.20.0) with trainer API","datasets library for data loading","PyTorch 1.9+ with CUDA 11.0+ for GPU training","GPU with >=16GB VRAM (V100, A100, or RTX 3090+)","labeled audio dataset in WAV format with corresponding transcripts (CSV or JSON)"],"input_types":["audio files (WAV, MP3) + transcript pairs","HuggingFace Dataset with 'audio' and 'text' columns","directory structure: audio_files/ + transcripts.json"],"output_types":["fine-tuned model checkpoint (PyTorch .bin + config.json)","training logs (loss curves, WER on validation set)","evaluation metrics (character error rate, word error rate)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-polish__cap_3","uri":"capability://data.processing.analysis.real.time.streaming.audio.transcription.with.low.latency.inference","name":"real-time streaming audio transcription with low-latency inference","description":"Processes continuous audio streams (microphone input, live broadcast, VoIP calls) with sub-second latency by implementing sliding-window inference on fixed-size audio chunks (typically 1-2 seconds). Maintains hidden state across chunks to preserve context for character-level predictions, and outputs partial transcriptions incrementally as new audio arrives. Optimized for GPU inference with batch size 1 and quantization support (int8, fp16) for edge deployment.","intents":["Build real-time voice assistant or voice command interfaces in Polish","Transcribe live meetings, podcasts, or broadcasts with minimal delay","Implement voice-to-text input for accessibility applications","Create low-latency speech-to-intent systems for conversational AI"],"best_for":["Developers building real-time voice applications (voice assistants, live transcription)","Teams deploying ASR on edge devices (Raspberry Pi, mobile phones, embedded systems)","Organizations requiring sub-500ms latency for interactive voice experiences","Accessibility tool developers building live captioning systems"],"limitations":["Sliding-window approach introduces ~200-500ms latency due to chunk buffering and model inference time","No built-in voice activity detection (VAD); requires external component to avoid transcribing silence","Streaming context limited to current chunk; long-range dependencies (e.g., pronouns) may be lost across chunk boundaries","Quantized models (int8, fp16) reduce accuracy by 1-3% WER compared to full precision","No automatic punctuation or capitalization in streaming mode; requires post-processing language model"],"requires":["Python 3.7+","transformers library with streaming inference support","PyAudio or sounddevice for microphone input","GPU with >=6GB VRAM for real-time inference (RTX 2060 or better)","16kHz audio stream (resampling on-the-fly adds latency)"],"input_types":["microphone stream (PyAudio, sounddevice)","network audio stream (RTP, WebRTC)","file-based streaming (reading WAV in chunks)"],"output_types":["partial transcriptions (incremental text updates)","confidence scores per character (optional)","timing information (chunk boundaries, latency metrics)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-polish__cap_4","uri":"capability://data.processing.analysis.multilingual.cross.lingual.transfer.evaluation.and.zero.shot.performance.assessment","name":"multilingual cross-lingual transfer evaluation and zero-shot performance assessment","description":"Evaluates the model's ability to transcribe related Slavic languages (Czech, Slovak, Ukrainian) and other languages in the XLSR-53 pretraining set without fine-tuning, by running inference on test sets and computing character/word error rates. Provides diagnostic tools to identify which language families transfer well and which require additional fine-tuning. Outputs confusion matrices and per-language performance metrics to guide multilingual deployment decisions.","intents":["Assess whether the Polish model can handle code-switched speech (Polish + English)","Evaluate zero-shot performance on related Slavic languages for multilingual applications","Identify language pairs requiring separate fine-tuning vs. acceptable cross-lingual transfer","Benchmark cross-lingual transfer effectiveness for research publications"],"best_for":["Researchers studying cross-lingual transfer learning in speech recognition","Teams building multilingual voice applications covering Slavic languages","Organizations evaluating whether to fine-tune separate models per language or use shared models","Academic groups publishing comparative studies on multilingual ASR"],"limitations":["Zero-shot performance on non-XLSR-53 languages (e.g., Basque, Icelandic) is unpredictable and likely poor","No automatic language identification; requires external LID (Language Identification) component for mixed-language audio","Evaluation metrics (WER, CER) assume clean reference transcriptions; noisy or inconsistent annotations skew results","Cross-lingual transfer degrades significantly for languages with different phoneme inventories (e.g., tonal languages)","No built-in analysis of which linguistic features transfer well (phonology, prosody, morphology)"],"requires":["Test datasets for target languages (Common Voice, BABEL, or proprietary corpora)","Reference transcriptions for error rate calculation","Python 3.7+ with jiwer library for WER/CER computation","GPU optional but recommended for batch evaluation"],"input_types":["audio files in target language","reference transcriptions (text files or JSON)","Common Voice dataset splits (automatic download)"],"output_types":["character error rate (CER) and word error rate (WER) per language","confusion matrices (predicted vs. reference characters)","per-utterance error analysis (JSON with predictions and references)","language-pair transfer matrix (Polish → Czech, Polish → Slovak, etc.)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonatasgrosman--wav2vec2-large-xlsr-53-polish__cap_5","uri":"capability://data.processing.analysis.model.quantization.and.compression.for.edge.deployment","name":"model quantization and compression for edge deployment","description":"Converts the full-precision (fp32) model to reduced-precision formats (fp16, int8, int4) using PyTorch quantization or ONNX Runtime, reducing model size from ~360MB to ~90-180MB and enabling inference on resource-constrained devices (mobile phones, Raspberry Pi, embedded systems). Implements post-training quantization (PTQ) without retraining, or quantization-aware training (QAT) for minimal accuracy loss. Provides benchmarking tools to measure latency/throughput tradeoffs across quantization levels.","intents":["Deploy Polish ASR on mobile devices (iOS, Android) with <100MB model footprint","Run inference on edge devices (Raspberry Pi, Jetson Nano) with limited RAM and compute","Reduce inference latency for real-time applications through hardware-optimized quantized kernels","Enable on-device processing without cloud connectivity for privacy-sensitive applications"],"best_for":["Mobile app developers building offline Polish voice features","IoT teams deploying ASR on embedded systems with limited resources","Organizations with privacy requirements preventing cloud audio transmission","Teams optimizing inference cost by reducing GPU memory and compute requirements"],"limitations":["Post-training quantization (PTQ) typically increases WER by 2-5% depending on quantization level","int4 quantization may introduce significant accuracy degradation (5-10% WER increase); int8 preferred for minimal loss","Quantized models require ONNX Runtime or specialized inference engines; not compatible with standard transformers library inference","No automatic hyperparameter tuning for quantization; requires manual calibration on representative data","Quantization benefits (speed, memory) vary by hardware; gains smaller on modern GPUs with native fp16 support"],"requires":["PyTorch 1.9+ with quantization support, or ONNX Runtime 1.10+","Calibration dataset (100-1000 audio samples) for post-training quantization","Target hardware specifications (CPU architecture, available RAM) for optimization","Optional: TensorRT (NVIDIA), CoreML (Apple), or TFLite (Google) for hardware-specific optimization"],"input_types":["full-precision model checkpoint (fp32)","calibration audio dataset (WAV files or HuggingFace Dataset)","quantization configuration (bit-width, calibration method)"],"output_types":["quantized model (ONNX, TorchScript, or framework-specific format)","quantization report (accuracy loss, size reduction, latency benchmarks)","deployment bundle (model + tokenizer + inference code)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","transformers library (>=4.0.0)","librosa or torchaudio for audio preprocessing","PyTorch 1.9+ or JAX backend","Audio input at 16kHz sample rate (resampling required for other rates)","GPU recommended for real-time inference (CPU inference ~5-10x slower)","librosa (>=0.9.0) or torchaudio (>=0.10.0) for audio I/O and resampling","datasets library (>=2.0.0) for streaming large audio collections","sufficient disk space for temporary resampled audio if not streaming","GPU with >=8GB VRAM for batch inference (CPU feasible for small batches)"],"failure_modes":["Trained on Common Voice data which may have lower audio quality and speaker diversity than commercial datasets","No built-in language model decoding — outputs raw character predictions without grammatical correction or vocabulary constraints","Inference latency scales with audio length; 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