GigaAM-Multilingual-ONNX

GigaAM Multilingual ONNX

FP32 ONNX exports of all four revisions of ai-sage/GigaAM-Multilingual: ssl, ctc, large_ssl, and large_ctc.

The original model and its code are licensed under the MIT License. These files were exported with the source model's built-in to_onnx() implementation.

Files

Every directory contains the ONNX graph, the source model YAML configuration, and validation.json with versions and PyTorch-versus-ONNX numerical results.

ONNX interface

The ONNX graphs contain the neural encoder and, for CTC variants, the CTC head. Audio loading and log-mel feature extraction remain outside the graph, matching the upstream ONNX export design.

Inputs:

• features: float32 [batch, 64, frames] log-mel features;
• feature_lengths: int64 [batch] feature lengths.

Outputs:

• CTC: log_probs float32 [batch, encoded_frames, 71] and encoded_lengths;
• SSL: encoded float32 [batch, hidden_size, encoded_frames] and encoded_len.

Feature extraction settings are included in each YAML file. All variants use 16 kHz mono audio, 64 mel bins, n_fft=320, win_length=320, hop_length=160, and center=false.

Validation

The graphs were checked with onnx.checker and executed with ONNX Runtime on a real 16 kHz Russian speech sample. Outputs were compared against the matching PyTorch source revision. CTC validation additionally requires identical greedy transcriptions.

Validation environment: PyTorch 2.10.0, ONNX 1.22.0, and ONNX Runtime 1.27.0.

Source revisions

• ctc: 2f8a57144e6ec3adfd32fe0484d9ea9913305bc8
• ssl: ac7c6db08133f83478451a659f8470ee8ab47a2d
• large_ctc: 3905cd51c3ed4e88c8edf33f3302969ba480a327
• large_ssl: c459e9d21c1c61a0d4b83fc37c1b5cadd1657506

See the upstream model card for training data, benchmarks, intended languages, limitations, and citation.