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Model: aadel4/omniASR-CTC-1B-v2
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- speech
+- audio
+- wav2vec2
+- automatic-speech-recognition
+pipeline_tag: automatic-speech-recognition
+---
+
+# omniASR-CTC-1B-v2
+
+Wav2Vec2 CTC ASR model (v2) converted from the [OmniLingual](https://github.com/facebookresearch/omnilingual-asr) 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](https://github.com/ahmedadelattia/omnilingual_to_hf). 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
+
+```python
+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)
+```