{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-mahmoudashraf--mms-300m-1130-forced-aligner","slug":"mahmoudashraf--mms-300m-1130-forced-aligner","name":"mms-300m-1130-forced-aligner","type":"model","url":"https://huggingface.co/MahmoudAshraf/mms-300m-1130-forced-aligner","page_url":"https://unfragile.ai/mahmoudashraf--mms-300m-1130-forced-aligner","categories":["voice-audio"],"tags":["transformers","pytorch","safetensors","wav2vec2","automatic-speech-recognition","mms","audio","voice","speech","forced-alignment","ab","af","ak","am","ar","as","av","ay","az","ba"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-mahmoudashraf--mms-300m-1130-forced-aligner__cap_0","uri":"capability://data.processing.analysis.multilingual.forced.alignment.with.phoneme.timing","name":"multilingual-forced-alignment-with-phoneme-timing","description":"Performs forced alignment of audio to text transcripts across 1,130 languages using wav2vec2 architecture with MMS (Massively Multilingual Speech) pretraining. The model aligns phoneme-level boundaries by processing raw audio waveforms through a transformer encoder, extracting frame-level acoustic embeddings, and computing dynamic time warping (DTW) or Viterbi decoding to map acoustic frames to input tokens with millisecond-precision timing. This enables downstream applications to know exactly when each word or phoneme occurs in the audio.","intents":["I need to align transcripts to audio for subtitle generation with precise timing","I want to extract phoneme-level timing information for speech synthesis training data","I need to create word-level timestamps for audio-text synchronization in video editing","I'm building a speech recognition dataset and need to validate alignment quality"],"best_for":["speech researchers building multilingual ASR datasets","developers creating subtitle/caption systems for 1000+ languages","teams training speech synthesis models requiring phoneme-level annotations","organizations processing low-resource language audio archives"],"limitations":["Alignment accuracy degrades on noisy audio or heavy accents not well-represented in training data","Requires pre-segmented audio (sentence or utterance level) — does not handle full-length recordings without preprocessing","Inference latency ~1-3 seconds per 10 seconds of audio on GPU, CPU inference significantly slower","Output timing is relative to input audio frames; requires manual calibration for absolute timestamps in some workflows","No built-in confidence scoring per alignment — difficult to identify misaligned segments automatically"],"requires":["PyTorch 1.9+","transformers library 4.30+","librosa or torchaudio for audio loading and preprocessing","GPU with 4GB+ VRAM recommended (CPU inference possible but slow)","Audio in WAV/MP3 format, 16kHz sample rate preferred"],"input_types":["audio (WAV, MP3, FLAC)","text (transcript or phoneme sequence)","sample_rate (integer, typically 16000 Hz)"],"output_types":["JSON with token-to-frame mappings","array of (start_time_ms, end_time_ms, token) tuples","CTM (conversation time-marked) format for forced alignment"],"categories":["data-processing-analysis","speech-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mahmoudashraf--mms-300m-1130-forced-aligner__cap_1","uri":"capability://data.processing.analysis.wav2vec2.acoustic.embedding.extraction","name":"wav2vec2-acoustic-embedding-extraction","description":"Extracts learned acoustic representations from raw audio waveforms by passing them through the wav2vec2 encoder stack (12 transformer layers with ~300M parameters in the base variant). The model learns to encode speech without explicit phonetic labels through contrastive learning on unlabeled audio, producing frame-level embeddings (50 frames per second at 16kHz) that capture phonetic and speaker information. These embeddings can be used directly for downstream tasks like speaker verification, emotion detection, or as features for custom alignment algorithms.","intents":["I need acoustic features for a custom speech processing pipeline without training my own encoder","I want to compare acoustic similarity between audio segments across languages","I'm building a speaker verification system and need robust speaker embeddings","I need to extract phonetic information from speech for linguistic analysis"],"best_for":["researchers prototyping speech processing systems without large labeled datasets","developers building multilingual speaker identification systems","teams creating custom speech analysis tools that need pretrained acoustic features","linguists analyzing phonetic variation across languages"],"limitations":["Embeddings are 768-dimensional (for 300M model) — requires dimensionality reduction for some downstream tasks","Frame-level embeddings are context-dependent; isolated frames have less discriminative power than windowed context","No built-in speaker normalization — speaker identity is entangled with phonetic content in embeddings","Embeddings are not directly interpretable; difficult to debug why specific frames produce certain representations"],"requires":["PyTorch 1.9+","transformers 4.30+","Audio preprocessing (resampling to 16kHz, normalization)","GPU with 4GB+ VRAM for batch processing"],"input_types":["audio (WAV, MP3, FLAC at any sample rate)","batch_size (integer, 1-128 depending on GPU memory)"],"output_types":["tensor of shape (batch_size, num_frames, 768)","numpy array of acoustic embeddings","frame-level feature vectors for downstream ML"],"categories":["data-processing-analysis","speech-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mahmoudashraf--mms-300m-1130-forced-aligner__cap_2","uri":"capability://data.processing.analysis.multilingual.speech.recognition.with.language.agnostic.decoding","name":"multilingual-speech-recognition-with-language-agnostic-decoding","description":"Performs automatic speech recognition across 1,130 languages by decoding wav2vec2 acoustic embeddings through a language-specific or language-agnostic output layer. The model processes raw audio through the shared multilingual encoder, then applies either a CTC (Connectionist Temporal Classification) decoder or a language-specific output projection to produce character/phoneme sequences. Language selection is implicit (determined by acoustic characteristics) or explicit (via language code), enabling the same model weights to handle code-switched speech and language mixing without separate model switching.","intents":["I need to transcribe speech in a language where no commercial ASR service has good coverage","I want a single model that can handle code-switched speech without language detection","I'm building an ASR system for low-resource languages without collecting large labeled datasets","I need to transcribe multilingual audio without running multiple language-specific models"],"best_for":["organizations serving users in 1000+ languages with limited per-language data","researchers studying code-switching and multilingual speech phenomena","developers building ASR for endangered or low-resource languages","teams needing a single model for diverse global audio without language routing logic"],"limitations":["Word error rate (WER) varies dramatically by language (5-10% for high-resource languages like English, 20-50%+ for low-resource languages)","No built-in language identification — requires external language detection for explicit routing in some workflows","Decoding is character-level; no native support for subword tokenization (BPE/SentencePiece) — requires post-processing for morphologically rich languages","CTC decoder produces no confidence scores per token — difficult to identify low-confidence regions","Inference latency ~0.5-1.5x real-time on GPU (varies by audio length and batch size)"],"requires":["PyTorch 1.9+","transformers 4.30+","Audio at 16kHz sample rate","GPU with 4GB+ VRAM for real-time inference","Optional: language code or language identifier for explicit language routing"],"input_types":["audio (WAV, MP3, FLAC)","sample_rate (integer, 16000 Hz required)","language_code (optional, ISO 639-3 format)"],"output_types":["text (UTF-8 encoded transcript)","character sequence","JSON with per-token timing (if using forced alignment)"],"categories":["data-processing-analysis","speech-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mahmoudashraf--mms-300m-1130-forced-aligner__cap_3","uri":"capability://data.processing.analysis.frame.level.token.boundary.detection","name":"frame-level-token-boundary-detection","description":"Identifies precise frame-to-token boundaries by computing alignment scores between acoustic frames and input tokens using the wav2vec2 encoder output and a learned alignment head. The model produces a frame-level probability distribution over tokens (or silence), enabling downstream systems to determine when each character, phoneme, or word begins and ends in the audio. This is the core mechanism enabling forced alignment and can be used independently for tasks like detecting speech boundaries or identifying pauses.","intents":["I need to know exactly which audio frames correspond to which characters in the transcript","I want to detect word boundaries in continuous speech without explicit word segmentation","I'm building a speech-to-text system that needs frame-level confidence scores","I need to identify silence and non-speech regions for audio preprocessing"],"best_for":["speech processing researchers studying alignment quality and acoustic-linguistic mapping","developers building real-time speech-to-text systems with frame-level feedback","teams creating audio editing tools that need precise segment boundaries","researchers analyzing phonetic variation and coarticulation effects"],"limitations":["Boundary detection is probabilistic; soft alignments require thresholding to produce hard boundaries, introducing tuning complexity","Accuracy depends on transcript correctness — mismatches between audio and text produce unreliable boundaries","Frame rate is fixed at ~50 Hz (for 16kHz audio) — cannot produce sub-frame precision","No explicit modeling of silence or non-speech regions — requires post-processing to handle pauses","Cross-lingual boundary detection may be less accurate for language pairs with different phonotactic patterns"],"requires":["PyTorch 1.9+","transformers 4.30+","Aligned audio and transcript (or forced alignment preprocessing)","GPU with 2GB+ VRAM"],"input_types":["audio (WAV, MP3, FLAC at 16kHz)","transcript (character or token sequence)","alignment_threshold (float, 0.0-1.0 for boundary detection)"],"output_types":["array of (token, start_frame, end_frame) tuples","frame-level token probability distribution","JSON with boundary timestamps in milliseconds"],"categories":["data-processing-analysis","speech-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mahmoudashraf--mms-300m-1130-forced-aligner__cap_4","uri":"capability://data.processing.analysis.batch.audio.processing.with.variable.length.handling","name":"batch-audio-processing-with-variable-length-handling","description":"Processes multiple audio files of varying lengths in batches by padding/truncating to a maximum length and applying attention masks to ignore padding tokens. The wav2vec2 architecture uses a feature extractor (CNN) followed by transformer layers with masking, enabling efficient batch processing without requiring all audios to have identical length. This capability handles real-world audio workflows where utterance durations vary significantly (e.g., 0.5 seconds to 30 seconds in a single batch).","intents":["I need to process 1000s of audio files efficiently without resizing each one individually","I want to batch-process variable-length audio on GPU without memory waste from padding","I'm building a data pipeline that needs to handle diverse audio lengths from different sources","I need to optimize throughput for inference on heterogeneous audio datasets"],"best_for":["data engineers building audio processing pipelines for large-scale datasets","developers optimizing inference throughput for production ASR systems","teams processing diverse audio sources (podcasts, conversations, isolated utterances) in single batches","researchers benchmarking model performance across audio length distributions"],"limitations":["Padding overhead increases memory usage; very long audio (>30 seconds) may require smaller batch sizes","Attention masks add ~5-10% computational overhead vs. fixed-length processing","Maximum sequence length is fixed at model architecture time (~500k frames for 300M model); longer audio requires chunking","Batch processing introduces latency variance — per-sample latency depends on batch composition","No built-in streaming/online inference — requires buffering entire utterance before processing"],"requires":["PyTorch 1.9+","transformers 4.30+","GPU with 8GB+ VRAM for large batches (batch_size > 32)","Audio preprocessing library (librosa, torchaudio, or soundfile)"],"input_types":["list of audio arrays (numpy or torch tensors)","sample_rate (integer, 16000 Hz)","batch_size (integer, 1-128)","max_length (integer, in samples or seconds)"],"output_types":["batched embeddings (tensor of shape (batch_size, num_frames, 768))","batched transcripts (list of strings)","batched alignment outputs (list of JSON objects)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+","transformers library 4.30+","librosa or torchaudio for audio loading and preprocessing","GPU with 4GB+ VRAM recommended (CPU inference possible but slow)","Audio in WAV/MP3 format, 16kHz sample rate preferred","transformers 4.30+","Audio preprocessing (resampling to 16kHz, normalization)","GPU with 4GB+ VRAM for batch processing","Audio at 16kHz sample rate","GPU with 4GB+ VRAM for real-time inference"],"failure_modes":["Alignment accuracy degrades on noisy audio or heavy accents not well-represented in training data","Requires pre-segmented audio (sentence or utterance level) — does not handle full-length recordings without preprocessing","Inference latency ~1-3 seconds per 10 seconds of audio on GPU, CPU inference significantly slower","Output timing is relative to input audio frames; 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