{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-facebook--wav2vec2-base-960h","slug":"facebook--wav2vec2-base-960h","name":"wav2vec2-base-960h","type":"model","url":"https://huggingface.co/facebook/wav2vec2-base-960h","page_url":"https://unfragile.ai/facebook--wav2vec2-base-960h","categories":["voice-audio"],"tags":["transformers","pytorch","tf","safetensors","wav2vec2","automatic-speech-recognition","audio","hf-asr-leaderboard","en","dataset:librispeech_asr","arxiv:2006.11477","license:apache-2.0","model-index","eval-results","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-facebook--wav2vec2-base-960h__cap_0","uri":"capability://data.processing.analysis.speech.to.text.transcription.with.self.supervised.pretraining","name":"speech-to-text-transcription-with-self-supervised-pretraining","description":"Converts raw audio waveforms to text using a self-supervised wav2vec2 architecture that first learns universal speech representations from 960 hours of unlabeled LibriSpeech audio, then fine-tunes a linear classification head on labeled data to map acoustic frames to phonemes/characters. The model uses a multi-layer convolutional feature extractor followed by a transformer encoder with quantized codebook learning, enabling it to capture both low-level acoustic patterns and high-level linguistic structure without requiring phonetic annotations during pretraining.","intents":["I need to transcribe English speech audio files to text with reasonable accuracy for downstream NLP tasks","I want to build a speech recognition system without collecting large labeled datasets","I need to understand what words were spoken in an audio recording for accessibility or documentation purposes","I'm building a voice-controlled application and need to convert user speech input to text commands"],"best_for":["developers building English-language speech recognition systems with moderate accuracy requirements","teams prototyping voice interfaces or accessibility features without large labeled audio datasets","researchers experimenting with self-supervised speech models and transfer learning approaches","organizations deploying ASR on edge devices or cost-constrained cloud infrastructure"],"limitations":["English-only model — no support for multilingual or code-switched speech","Trained on read speech from LibriSpeech dataset — performance degrades significantly on noisy, accented, or spontaneous conversational audio","Base model size (95M parameters) requires ~380MB GPU memory; inference latency ~100-200ms per second of audio on consumer GPUs","No built-in language model decoding — outputs character-level predictions without grammatical constraints, leading to spelling errors on homophones","Requires audio preprocessing (16kHz mono resampling) — incompatible with raw multi-channel or variable-rate audio without external conversion"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+ (model supports both via HuggingFace transformers)","librosa or soundfile library for audio loading and resampling to 16kHz","transformers library version 4.5.0+","GPU with 4GB+ VRAM recommended (CPU inference possible but ~10x slower)","HuggingFace account or local model weights (~380MB disk space)"],"input_types":["audio waveform (numpy array, shape [sample_rate * duration])","audio file path (WAV, MP3, FLAC — requires librosa for format conversion)","raw PCM bytes at 16kHz mono sample rate"],"output_types":["text string (character-level transcription)","logits tensor (raw model outputs before argmax, shape [time_steps, vocab_size])","attention weights (optional, for interpretability)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--wav2vec2-base-960h__cap_1","uri":"capability://data.processing.analysis.batch.audio.processing.with.dynamic.padding","name":"batch-audio-processing-with-dynamic-padding","description":"Processes multiple variable-length audio samples in a single forward pass by dynamically padding shorter sequences to match the longest sample in the batch, then applying attention masks to prevent the model from attending to padded regions. The implementation uses HuggingFace's feature extractor to normalize audio amplitude and convert to mel-spectrogram-like representations, with optional mixed-precision (FP16) computation to reduce memory footprint by 50% while maintaining numerical stability through gradient scaling.","intents":["I need to transcribe a folder of audio files with different durations efficiently without processing them one-by-one","I want to maximize GPU utilization by batching variable-length audio samples together","I'm building a real-time transcription service and need to handle multiple concurrent audio streams","I need to reduce inference latency and memory usage when processing large audio datasets"],"best_for":["backend engineers building batch transcription pipelines for large audio corpora","ML engineers optimizing inference throughput on shared GPU clusters","teams deploying ASR microservices that receive concurrent transcription requests"],"limitations":["Dynamic padding adds ~5-10% computational overhead compared to fixed-length batching","Batch size is constrained by longest audio sample in batch — a single 30-second clip forces all samples to pad to 30 seconds, wasting compute","No built-in support for streaming/chunked audio — requires buffering entire audio file before inference","Attention masks prevent cross-sample attention but don't reduce actual computation — all padded tokens still consume FLOPs"],"requires":["transformers library 4.5.0+","PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration","librosa 0.9+ for audio resampling","sufficient GPU memory: ~2GB for batch_size=8 with 30-second audio samples"],"input_types":["list of audio file paths (WAV, MP3, FLAC)","list of numpy arrays with variable sample counts","list of raw PCM byte strings"],"output_types":["batch of transcription strings","batch of logits tensors with shape [batch_size, time_steps, vocab_size]","batch processing metadata (processing time per sample, token counts)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--wav2vec2-base-960h__cap_2","uri":"capability://data.processing.analysis.acoustic.feature.extraction.with.learned.representations","name":"acoustic-feature-extraction-with-learned-representations","description":"Extracts learned acoustic representations from raw audio by passing waveforms through a 7-layer convolutional feature extractor (stride=5, kernel=10) that downsamples audio by 320x, then applies layer normalization and passes through a 12-layer transformer encoder with 768 hidden dimensions. The model learns to extract phonetically-relevant features during self-supervised pretraining on unlabeled audio, producing contextualized embeddings that capture both local acoustic properties (formants, pitch) and long-range linguistic dependencies (phoneme context, word boundaries).","intents":["I need to extract meaningful audio embeddings for downstream tasks like speaker verification or emotion detection","I want to analyze what acoustic patterns the model learned during pretraining","I'm building a speech similarity search system and need dense audio representations","I need to understand model predictions by inspecting intermediate layer activations"],"best_for":["researchers studying learned speech representations and self-supervised learning","engineers building multi-task speech systems that reuse acoustic features","teams implementing speaker verification or voice biometrics on top of ASR"],"limitations":["Embeddings are context-dependent — same phoneme produces different vectors depending on surrounding speech, making simple similarity matching unreliable","Temporal resolution is 50ms (16kHz audio / 320x downsampling) — fine-grained acoustic details are lost","Embeddings are 768-dimensional — require dimensionality reduction (PCA, UMAP) for visualization or efficient similarity search","No speaker normalization — embeddings contain speaker identity information, confounding speaker-independent acoustic analysis"],"requires":["transformers library 4.5.0+","PyTorch 1.9+","audio preprocessing pipeline (librosa for resampling to 16kHz)"],"input_types":["raw audio waveform (numpy array at 16kHz)","audio file path (WAV, MP3, FLAC)"],"output_types":["hidden state tensor with shape [time_steps, 768] (contextualized embeddings)","pooled representation (mean-pooled across time, shape [768])","attention weights from transformer layers (for interpretability)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--wav2vec2-base-960h__cap_3","uri":"capability://data.processing.analysis.quantized.codebook.learning.for.discrete.speech.units","name":"quantized-codebook-learning-for-discrete-speech-units","description":"During pretraining, the model learns a discrete codebook of 320 quantized vectors (product quantization with 2 groups of 160 codes each) that represent prototypical acoustic patterns. For each audio frame, the model's quantizer selects the nearest codebook entry using straight-through estimators for gradient flow, forcing the model to compress continuous acoustic signals into discrete units. This quantization acts as a bottleneck that encourages the feature extractor to learn invariant representations, similar to how vector quantization works in VQ-VAE architectures.","intents":["I want to understand what discrete acoustic units the model discovered during pretraining","I need to compress audio into discrete tokens for downstream processing (e.g., feeding to a language model)","I'm analyzing what acoustic patterns the model considers equivalent or interchangeable","I want to build a speech tokenizer that maps continuous audio to discrete units"],"best_for":["researchers studying discrete speech representations and quantization in self-supervised learning","engineers building speech-to-text systems that require discrete intermediate representations","teams implementing audio compression or speech tokenization pipelines"],"limitations":["Codebook is learned during pretraining and frozen during fine-tuning — cannot adapt to new acoustic patterns in target domain","Quantization introduces information loss — continuous acoustic details are discarded, potentially hurting performance on tasks requiring fine-grained acoustic analysis","Straight-through estimators for gradient flow are biased approximations — can cause training instability if learning rates are not carefully tuned","Product quantization with 2 groups limits expressiveness — only 320 discrete units for representing all possible acoustic patterns in speech"],"requires":["transformers library with wav2vec2 model implementation","PyTorch 1.9+ (quantization requires custom CUDA kernels for efficiency)","understanding of vector quantization and straight-through estimators"],"input_types":["raw audio waveform (16kHz mono)"],"output_types":["discrete code indices with shape [time_steps] (integer IDs from 0-319)","quantized embeddings with shape [time_steps, 768] (continuous vectors after codebook lookup)","codebook vectors with shape [320, 768] (the learned discrete units)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--wav2vec2-base-960h__cap_4","uri":"capability://code.generation.editing.fine.tuning.with.ctc.loss.for.character.level.transcription","name":"fine-tuning-with-ctc-loss-for-character-level-transcription","description":"Adapts the pretrained wav2vec2 model to the speech recognition task by adding a linear projection layer that maps 768-dimensional hidden states to a vocabulary of 32 characters (a-z, space, apostrophe, pipe for word boundaries). Training uses Connectionist Temporal Classification (CTC) loss, which aligns variable-length audio sequences to variable-length character sequences without requiring frame-level annotations. CTC marginalizes over all possible alignments, allowing the model to learn where to place character boundaries automatically from only transcript-level supervision.","intents":["I want to adapt the pretrained model to recognize speech in my specific domain or accent","I need to fine-tune the model on labeled audio data to improve accuracy on my use case","I'm building a custom speech recognizer and need to understand the training procedure","I want to reduce the vocabulary size or add domain-specific characters (e.g., punctuation, numbers)"],"best_for":["ML engineers fine-tuning ASR models on domain-specific audio datasets","teams building speech recognition systems for specialized applications (medical, legal, technical domains)","researchers experimenting with different CTC loss variants or alignment strategies"],"limitations":["CTC assumes conditional independence between output characters given the input — cannot model character-level language patterns (e.g., 'q' almost always followed by 'u')","Requires aligned audio-transcript pairs — no support for weakly-supervised learning from transcripts without timestamps","Fine-tuning on small datasets (<10 hours) often leads to overfitting — requires careful regularization (dropout, weight decay, early stopping)","Character-level output lacks grammatical constraints — produces spelling errors and nonsensical words without external language model decoding","CTC loss is sensitive to class imbalance — common characters (vowels, spaces) dominate training, hurting rare character recognition"],"requires":["labeled audio dataset with transcripts (minimum 10 hours recommended)","transformers library 4.5.0+ with CTC loss implementation","PyTorch 1.9+ or TensorFlow 2.4+","GPU with 8GB+ VRAM for batch training (batch_size=16-32)","audio preprocessing pipeline (librosa for resampling, normalization)"],"input_types":["audio file paths (WAV, MP3, FLAC)","corresponding text transcripts (one per audio file)","optional: sample weights for class balancing"],"output_types":["fine-tuned model weights (saved as PyTorch checkpoint or HuggingFace format)","character-level transcriptions (text strings)","training metrics (loss, character error rate, word error rate)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--wav2vec2-base-960h__cap_5","uri":"capability://automation.workflow.inference.with.cpu.and.gpu.acceleration","name":"inference-with-cpu-and-gpu-acceleration","description":"Supports inference on both CPU and GPU hardware with automatic device placement and mixed-precision computation. On GPU, uses FP16 (half-precision) computation to reduce memory footprint by 50% and increase throughput by 2-3x through tensor cores, with automatic gradient scaling to prevent underflow. On CPU, falls back to FP32 computation with optional quantization (INT8) for 4x memory reduction at the cost of ~1-2% accuracy loss. The implementation uses PyTorch's native device abstraction, allowing seamless switching between hardware without code changes.","intents":["I need to run speech recognition on edge devices or servers without GPU access","I want to reduce inference latency for real-time transcription on consumer hardware","I'm deploying the model to cloud infrastructure and need to optimize cost by using CPU instances","I need to support both GPU and CPU inference in the same application"],"best_for":["backend engineers deploying ASR to heterogeneous infrastructure (mix of CPU and GPU servers)","mobile/edge developers targeting devices without dedicated accelerators","teams optimizing inference cost by using cheaper CPU instances for non-latency-critical workloads"],"limitations":["CPU inference is ~10-20x slower than GPU — 1 second of audio takes 10-20 seconds on CPU, unsuitable for real-time applications","FP16 computation requires GPU with compute capability 7.0+ (RTX, A100, etc.) — older GPUs (GTX 1080) fall back to FP32","INT8 quantization reduces accuracy by 1-2% and requires calibration on representative data — not suitable for high-accuracy applications","Mixed-precision training requires careful tuning of loss scaling — can cause training instability if not properly configured","No built-in support for model parallelism — cannot split model across multiple GPUs for inference on very large models"],"requires":["PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration (optional)","transformers library 4.5.0+","for INT8 quantization: PyTorch with quantization support (requires compilation from source on some systems)","sufficient RAM: 2GB for FP32, 1GB for FP16, 512MB for INT8"],"input_types":["audio waveform (numpy array at 16kHz)","audio file path"],"output_types":["text transcription","logits tensor (optional, for downstream processing)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--wav2vec2-base-960h__cap_6","uri":"capability://data.processing.analysis.multilingual.transfer.learning.through.pretrained.representations","name":"multilingual-transfer-learning-through-pretrained-representations","description":"Although trained only on English LibriSpeech data, the model's self-supervised pretraining on raw audio learns universal acoustic patterns that transfer to other languages. The learned feature extractor captures language-agnostic properties (pitch, formants, spectral structure) that generalize across linguistic boundaries. Fine-tuning on small amounts of target-language data (1-10 hours) achieves reasonable accuracy without retraining from scratch, because the transformer encoder has already learned to extract relevant acoustic information. This transfer learning approach reduces labeled data requirements for new languages by 10-100x compared to training from scratch.","intents":["I want to build speech recognition for a language with limited labeled data","I need to quickly prototype ASR for a new language without collecting large datasets","I'm building a multilingual system and want to reuse the English model's learned representations","I want to understand how much target-language data is needed to achieve acceptable accuracy"],"best_for":["teams building ASR for low-resource languages with <100 hours of labeled audio","researchers studying cross-lingual transfer learning in speech","organizations rapidly prototyping multilingual voice interfaces"],"limitations":["English-centric pretraining may bias the model toward English phonetic patterns — performance on phonologically distant languages (tonal languages, click consonants) is significantly worse than on Germanic/Romance languages","Transfer learning effectiveness depends heavily on linguistic similarity — languages with similar phoneme inventories (Dutch, German) transfer better than typologically distant languages (Mandarin, Arabic)","Fine-tuning on small target-language datasets often leads to overfitting — requires careful regularization and may need 50-100 hours of data to match English performance","No built-in support for code-switching or multilingual mixing — model trained on monolingual English and may struggle with code-switched speech","Character-level output assumes Latin alphabet — non-Latin scripts (Cyrillic, Arabic, CJK) require custom vocabulary and character encoding"],"requires":["target-language audio dataset (minimum 1-10 hours for basic fine-tuning, 50+ hours for production quality)","target-language text transcripts aligned with audio","transformers library 4.5.0+","understanding of cross-lingual transfer learning and domain adaptation"],"input_types":["target-language audio files","target-language transcripts"],"output_types":["fine-tuned model for target language","character-level transcriptions in target language","transfer learning metrics (data efficiency, convergence speed)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--wav2vec2-base-960h__cap_7","uri":"capability://automation.workflow.streaming.inference.with.chunked.audio.processing","name":"streaming-inference-with-chunked-audio-processing","description":"Enables real-time transcription of streaming audio by processing fixed-size chunks (e.g., 1-second windows) sequentially without buffering the entire audio file. The transformer encoder uses causal masking (attending only to past and current frames, not future frames) to ensure that predictions for each chunk depend only on previously-seen audio. Overlapping chunks (e.g., 50% overlap) are used to maintain context across chunk boundaries, preventing transcription artifacts at chunk edges. The implementation accumulates predictions across chunks and applies post-processing (removing duplicate characters, merging overlapping predictions) to produce coherent transcriptions.","intents":["I need to transcribe live audio streams (e.g., from a microphone or network stream) with minimal latency","I want to build a real-time voice assistant that responds to user speech as it's being spoken","I'm processing very long audio files and need to avoid loading the entire file into memory","I need to reduce latency for interactive applications like live captioning or simultaneous interpretation"],"best_for":["engineers building real-time transcription services (live captioning, voice assistants)","teams processing very long audio files with memory constraints","applications requiring sub-second latency for user interaction"],"limitations":["Causal masking prevents the model from using future context — accuracy is 1-2% worse than non-causal inference because the model cannot look ahead to resolve ambiguities","Chunk boundaries introduce artifacts — overlapping chunks and post-processing add complexity and can produce stuttering or repeated words","Streaming inference requires careful buffer management — incorrect chunk overlap or timing can cause dropped audio or synchronization issues","No built-in support for variable-length chunks — all chunks must be the same size, limiting flexibility for adaptive bitrate audio","Latency depends on chunk size — smaller chunks (100ms) have lower latency but higher computational overhead; larger chunks (1s) are more efficient but have higher latency"],"requires":["transformers library with streaming support (requires custom implementation or third-party library like Faster Whisper)","audio streaming library (e.g., sounddevice, pyaudio for microphone input)","real-time audio buffering and synchronization logic","GPU recommended for sub-second latency (CPU inference too slow for real-time)"],"input_types":["audio stream (microphone input, network stream, file stream)","fixed-size audio chunks (e.g., 1-second windows at 16kHz = 16,000 samples)"],"output_types":["streaming transcriptions (partial and final predictions)","confidence scores per character (optional)","latency metrics (time from audio capture to transcription output)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+ (model supports both via HuggingFace transformers)","librosa or soundfile library for audio loading and resampling to 16kHz","transformers library version 4.5.0+","GPU with 4GB+ VRAM recommended (CPU inference possible but ~10x slower)","HuggingFace account or local model weights (~380MB disk space)","transformers library 4.5.0+","PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration","librosa 0.9+ for audio resampling","sufficient GPU memory: ~2GB for batch_size=8 with 30-second audio samples"],"failure_modes":["English-only model — no support for multilingual or code-switched speech","Trained on read speech from LibriSpeech dataset — performance degrades significantly on noisy, accented, or spontaneous conversational audio","Base model size (95M parameters) requires ~380MB GPU memory; inference latency ~100-200ms per second of audio on consumer GPUs","No built-in language model decoding — outputs character-level predictions without grammatical constraints, leading to spelling errors on homophones","Requires audio preprocessing (16kHz mono resampling) — incompatible with raw multi-channel or variable-rate audio without external conversion","Dynamic padding adds ~5-10% computational overhead compared to fixed-length batching","Batch size is constrained by longest audio sample in batch — a single 30-second clip forces all samples to pad to 30 seconds, wasting compute","No built-in support for streaming/chunked audio — requires buffering entire audio file before inference","Attention masks prevent cross-sample attention but don't reduce actual computation — all padded tokens still consume FLOPs","Embeddings are context-dependent — same phoneme produces different vectors depending on surrounding speech, making simple similarity matching unreliable","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7482234259186746,"quality":0.41,"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":1210723,"model_likes":396}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=facebook--wav2vec2-base-960h","compare_url":"https://unfragile.ai/compare?artifact=facebook--wav2vec2-base-960h"}},"signature":"mn+UyWV0A4Hzwus3PuqttqoXyu6q/ovhU0aZ1gidQ10qDUtK6peZGpccr2elqwVQL4S2RLqwvzC0ovzqjUEQDA==","signedAt":"2026-06-21T01:42:59.538Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/facebook--wav2vec2-base-960h","artifact":"https://unfragile.ai/facebook--wav2vec2-base-960h","verify":"https://unfragile.ai/api/v1/verify?slug=facebook--wav2vec2-base-960h","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"}}