omniASR-CTC-7B-v2-ONNX

A dynamic-shape ONNX export of Meta's omniASR_CTC_7B_v2 for inference with fast-omniasr.

Usage

Install the ONNX Runtime and Hugging Face Hub extras:

python -m pip install "fast-omniasr[onnx,hub]"

from fast_omniasr import OmniASR

model = OmniASR.from_pretrained("EmreAkgul/omniASR-CTC-7B-v2-ONNX", backend="onnx")
result = model.transcribe("speech.wav")
print(result.text)

The input must be mono audio. fast-omniasr resamples supported audio files to 16 kHz and normalizes them before inference. Larger model variants require substantially more memory.

Model and conversion

• Upstream model: Meta Omnilingual ASR, omniASR_CTC_7B_v2
• Task: multilingual automatic speech recognition with a CTC output head
• Conversion: official fairseq2 checkpoint to ONNX with torch.onnx.export, opset 18, dynamo=False; no retraining or intentional weight modification
• Tokenizer: upstream omniASR_tokenizer_written_v2.model, included unmodified as tokenizer.model
• Input: audio, float32, shape [1, samples]
• Output: logits, float32, shape [1, frames, 10288]

config.json records SHA-256 hashes and sizes for every runtime asset. OmniASR.from_pretrained() downloads and verifies that manifest. Large exports store tensor data in companion files referenced by model.onnx; all such files are required and are downloaded automatically.

Intended use and limitations

This repository is a format conversion intended for inference with fast-omniasr. It does not change the upstream model's capabilities, language coverage, accuracy, or limitations. Consult the upstream repository for model details, training data, evaluation results, and responsible-use considerations.

ONNX FP32 is the verified runtime path. TensorRT support in fast-omniasr is experimental; FP16 can change recognition results. This repository contains a CTC model and does not support the OmniASR LLM/seq2seq inference path.

Repository files

File	Description
model.onnx	Dynamic ONNX graph; may reference companion external tensor-data files
tokenizer.model	Matching upstream SentencePiece tokenizer
config.json	Runtime asset manifest with sizes and SHA-256 hashes

License and attribution

The upstream Omnilingual ASR code and model weights are licensed under Apache-2.0. This repository redistributes a format-converted version under the same license and identifies the conversion above. It is an independent distribution and is not affiliated with or endorsed by Meta.