omniASR-CTC-1B-v2/README.md

library_name, tags, pipeline_tag

library_name	tags	pipeline_tag
transformers	speech audio wav2vec2 automatic-speech-recognition	automatic-speech-recognition

omniASR-CTC-1B-v2

Wav2Vec2 CTC ASR model (v2) converted from the OmniLingual fairseq2 checkpoint omniASR_CTC_1B_v2.

This model outputs CTC logits over a SentencePiece vocabulary and can transcribe speech in multiple languages.

Code Base

The code base for the conversion can be found here. I was only able to convert the 300M and 1B models due to GPU limitations. Contributions are welcome.

Model details

Property	Value
HF class	Wav2Vec2ForCTC
Encoder layers	48
Hidden size	1280
Attention heads	16
FFN intermediate	5120
Vocabulary size	10288
Source framework	fairseq2
Source card	omniASR_CTC_1B_v2
Parity verification	✅ Verified

Numerical parity against the original fairseq2 checkpoint has been confirmed: outputs match to within atol=1e-4 on a held-out audio sample.

Sample transcriptions on the held-out audio clip:

Model	Transcript
fairseq2 (source)	concord returned to its place amidst the tents
HuggingFace (this repo)	concord returned to its place amidst the tents

Usage

from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch, torchaudio

processor = AutoProcessor.from_pretrained("aadel4/omniASR-CTC-1B-v2")
model     = Wav2Vec2ForCTC.from_pretrained("aadel4/omniASR-CTC-1B-v2")
model.eval()

waveform, sr = torchaudio.load("audio.wav")
if sr != 16_000:
    waveform = torchaudio.functional.resample(waveform, sr, 16_000)

inputs = processor(
    waveform.squeeze().numpy(), sampling_rate=16_000, return_tensors="pt"
)
with torch.no_grad():
    logits = model(**inputs).logits          # (1, T, vocab)

pred_ids   = torch.argmax(logits, dim=-1)
transcript = processor.decode(pred_ids[0])
print(transcript)