Model Architectures

Explore the hierarchical architecture design of Omnilingual ASR models, built on the Wav2Vec2 encoder foundation.

Architecture Overview

Omnilingual ASR models follow a hierarchical design with three main families:

W2V (Encoder)
CTC (Encoder + Projection)
LLM (Encoder-Decoder)

Wav2Vec2 Self-Supervised Learning

[Audio 16kHz] → Feature Extractor → Wav2Vec2 Encoder → [Audio Embeddings]
                (CNN ~320x)         (Transformer)      (1024/1280/2048-dim)

Foundation encoder producing rich contextualized audio representations.

Connectionist Temporal Classification

[Audio 16kHz] → Feature Extractor → Wav2Vec2 Encoder → Linear → [Vocab Logits]
                (CNN ~320x)         (Transformer)      Projection

Parallel prediction with CTC alignment for fast inference.

Language Model with Encoder-Decoder Architecture

[Audio 16kHz] → Feature Extractor → Wav2Vec2 Encoder → Linear → LLaMA → [Vocab Logits]
                (CNN ~320x)         (Transformer)      Projection  Decoder

Autoregressive generation with language conditioning capabilities.

Model Families

W2V Models

Self-supervised learning models that serve as the foundation for all other architectures.

# Input
raw_audio: Tensor  # Shape: [batch_size, audio_length], 16kHz waveform

# Output  
audio_embeddings: Tensor  # Shape: [batch_size, time_steps, embed_dim]
                          # embed_dim: 1024 (300M), 1280 (1B), 2048 (3B/7B)

Architecture Components:

Feature Extractor

CNN layers downsample raw audio by ~320x:

Input: 16kHz waveform
Output: Frame-level features (~50 fps)
Reduces sequence length for efficient processing

Transformer Encoder

Contextual encoding via multi-head self-attention:

Layers: 12 (300M), 24 (1B), 36 (3B), 48 (7B)
Attention heads: 16 (300M/1B), 32 (3B/7B)
Creates rich audio representations

Key Features:

Pre-trained with self-supervised learning
Contextual audio embeddings
Foundation for CTC and LLM variants
Ideal for building custom architectures

Use Cases:

Starting point for fine-tuning
Building custom ASR architectures
Audio feature extraction
Transfer learning applications

CTC Models

Non-autoregressive models using Connectionist Temporal Classification for parallel prediction.

# Input
raw_audio: Tensor  # Shape: [batch_size, audio_length], 16kHz

# Output
vocab_logits: Tensor  # Shape: [batch_size, time_steps, vocab_size]
                      # vocab_size: 9812 or 10288 tokens

Architecture:

Wav2Vec2 Encoder Output (1024/1280/2048-dim)
    ↓
Linear Projection Layer
    ↓
Vocabulary Logits (9812/10288 tokens)
    ↓
CTC Alignment & Decoding
    ↓
Final Transcription

CTC Alignment

CTC allows the model to predict multiple frames per character, then collapses repeated predictions:

Raw predictions:  [h, h, e, e, l, l, l, o, o]
After collapse:   [h, e, l, o]
Final output:     "hello"

Blank tokens (<blank>) separate repeated characters.

Vocabulary

Size: 9812 or 10288 tokens depending on version
Coverage: 1600+ languages
Tokenization: Character-level or subword units
Special tokens: <pad>, <unk>, <blank>

Advantages:

Fast: Parallel prediction (no autoregression)
Efficient: Single forward pass
Lightweight: Simple linear projection
On-device friendly: Lower memory footprint

Limitations:

No language conditioning
No context example support
Lower accuracy than LLM models

Implementation: CTC models use fairseq2’s existing implementations with updated configurations:

Code: fairseq2/models/wav2vec2
Configs: Custom training data configurations

LLM Models

Encoder-decoder architecture with LLaMA-based autoregressive decoder.

# Standard Input
raw_audio: Tensor              # [batch, audio_length], 16kHz
lang_codes: List[str] | None   # Optional: ["eng_Latn", "deu_Latn", ...]

# Zero-Shot Input
raw_audio: Tensor
context_audio: List[Tensor]    # 10 context examples
context_text: List[str]        # Corresponding transcriptions

# Output
transcription: str             # Autoregressive beam search output

Architecture Components:

Audio Encoder

Wav2Vec2 encoder produces audio embeddings

Output dimensions: 1024 (300M), 1280 (1B), 2048 (3B/7B)

Projection Layer

Linear projection to LLaMA decoder space

Projects to 4096 dimensions
Aligns audio with text representations

LLaMA Decoder

Autoregressive transformer decoder

Dimensions: 4096
Generates text tokens sequentially
Supports language conditioning and context

Beam Search

Decodes output logits to final transcription

Configurable beam width
Length normalization
Diverse beam search options

Model Variants:

LLM+LID (Language Conditioning)
LLM+LID Unlimited Length
LLM+ZS (Zero-Shot)

Language-aware models with optional language ID conditioning.Training Strategy:

80% samples with language ID tokens
20% samples without language ID
Robust performance in both scenarios

Input Format:

# With language conditioning
[<lang:eng_Latn>] + audio_embeddings → decoder → "hello world"

# Without language conditioning  
audio_embeddings → decoder → "hello world"

Available Models:

omniASR_LLM_300M_v2
omniASR_LLM_1B_v2
omniASR_LLM_3B_v2
omniASR_LLM_7B_v2

Extended models for transcribing unlimited-length audio.Segmentation Strategy:

Audio/text split into N=15 second segments
Decoder conditioned on previous M=1 segments
Iterative decoding with segment context

Architecture:

# Segment 1
audio_seg_1 → decoder → text_seg_1

# Segment 2 (conditioned on segment 1)
[audio_seg_1, text_seg_1, audio_seg_2] → decoder → text_seg_2

# Segment 3 (conditioned on segment 2)
[audio_seg_2, text_seg_2, audio_seg_3] → decoder → text_seg_3

These models were released as an update and are not described in the original research paper. CER performance is on par with standard LLM+LID models.

Available Models:

omniASR_LLM_Unlimited_300M_v2
omniASR_LLM_Unlimited_1B_v2
omniASR_LLM_Unlimited_3B_v2
omniASR_LLM_Unlimited_7B_v2

In-context learning model for unseen languages.Context Architecture:

Requires exactly 10 context examples
Each example: audio-text pair
Context length: up to 30 seconds per example

Input Format:

# Context examples (10 slots)
context = [
    (audio_1, "transcription 1"),
    (audio_2, "transcription 2"),
    # ... 8 more examples
    (audio_10, "transcription 10")
]

# Input composition
[context_pairs] + target_audio → decoder → transcription

The model has an architectural constraint of exactly 10 context slots. If fewer examples are provided, they’re duplicated to fill slots. If more are provided, they’re cropped.

Available Model:

omniASR_LLM_7B_ZS (7B parameters only)

Input Validation: The Wav2Vec2LlamaModel implementation performs input validation at every forward pass:

# Validation via ensure_valid_forward_inputs()

if has_language_tokens and has_context:
    raise ValueError("Cannot use both language ID and context")
    
if has_context and len(context_examples) != 10:
    raise ValueError("Zero-shot requires exactly 10 context examples")

Additional inputs are encoded in the .example field of Seq2SeqBatch for flexibility. Vocabulary:

Sizes: 9812 / 9818 / 10288 tokens (variant-dependent)
Coverage: 1600+ languages
Special tokens: Language ID tokens, padding, unknown, etc.

Model Size Comparison

300M Parameters

Encoder:

Embedding dim: 1024
Transformer layers: 12
Attention heads: 16

Best for: Resource-constrained environments, mobile, edge devices

1B Parameters

Encoder:

Embedding dim: 1280
Transformer layers: 24
Attention heads: 16

Best for: Balanced accuracy/efficiency, general-purpose ASR

3B Parameters

Encoder:

Embedding dim: 2048
Transformer layers: 36
Attention heads: 32

Best for: High-accuracy requirements, multilingual scenarios

7B Parameters

Encoder:

Embedding dim: 2048
Transformer layers: 48
Attention heads: 32

Best for: Maximum accuracy, research, zero-shot learning

Implementation Details

fairseq2 Integration

All models leverage fairseq2’s configuration system:

# Existing fairseq2 implementations
from fairseq2.models.wav2vec2 import Wav2Vec2Model
from fairseq2.models.wav2vec2.asr import Wav2Vec2AsrModel

# With custom configs for Omnilingual training
model = load_model("omniASR_CTC_1B_v2")

Key Files:

Syntax and Grammar

LLM models use special syntax for different input combinations. The grammar is defined in create_syntax() functions:

Syntax Examples

# Language-conditioned syntax
<lang:eng_Latn> <audio_tokens...> <text_tokens...>

# Zero-shot syntax with context
<context_1_audio> <context_1_text> 
<context_2_audio> <context_2_text>
# ... (10 context pairs)
<target_audio> <text_tokens...>

See model.py for full implementation.

Model Selection Guide

Determine Your Use Case

High throughput? → CTC models
Best accuracy? → LLM models (3B/7B)
Unseen languages? → Zero-shot (7B ZS)
Long audio? → LLM Unlimited models

Consider Resource Constraints

Mobile/Edge? → 300M CTC
Server/Cloud? → 1B/3B/7B LLM
GPU memory? → Smaller models for limited memory

Evaluate Language Requirements

Single language? → CTC sufficient
Multiple languages? → LLM with language codes
Rare languages? → Zero-shot model

Test and Optimize

Start with 1B model as baseline
Compare CTC vs LLM accuracy
Scale up/down based on results

Next Steps

Inference Guide

Learn how to use these models for transcription

Training Guide

Fine-tune models on your own data

Research Paper

Read the full technical details

GitHub Repository

Explore the source code

Get Started

Guides

Models

Advanced

Architecture Overview

Model Families

W2V Models

CTC Models

LLM Models

Model Size Comparison

300M Parameters

1B Parameters

3B Parameters

7B Parameters

Implementation Details

fairseq2 Integration

Syntax and Grammar

Model Selection Guide

Next Steps

Inference Guide

Training Guide

Research Paper

GitHub Repository

Build docs developers (and LLMs) love

Get Started

Guides

Models

Advanced

Documentation Index

​Architecture Overview

​Model Families

​W2V Models

​CTC Models

​LLM Models

​Model Size Comparison

300M Parameters

1B Parameters

3B Parameters

7B Parameters

​Implementation Details

​fairseq2 Integration

​Syntax and Grammar

​Model Selection Guide

​Next Steps

Inference Guide

Training Guide

Research Paper

GitHub Repository

Build docs developers (and LLMs) love

Architecture Overview

Model Families

W2V Models

CTC Models

LLM Models

Model Size Comparison

Implementation Details

fairseq2 Integration

Syntax and Grammar

Model Selection Guide

Next Steps