Omni Asr Ctc 300m v2 Mizonal3 E2 Lus v2026.06

--- language: - lus license: apache-2.0 pipeline_tag: automatic-speech-recognition base_model: facebook/omniASR-CTC-300M tags: - generated_from_trainer datasets: - andrewbawitlung/MiZonal-v3.0 metrics: - wer - cer model-index: - name: omni-asr-ctc-300m-v2-mizonal3-E2-lus-v2026.06 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: MiZonal v3.0 type: andrewbawitlung/MiZonal-v3.0 config: default split: test metrics: - name: Wer type: wer value: 16.4327 - name: Cer type: cer value: 3.0023 - name: Real Time Factor type: rtf value: 0.0061 ---  > **Disclaimer / Notice:** Details for these are in Peer Review and publications of the paper will be made available soon for more details. # omni-asr-ctc-300m-v2-mizonal3-E2-lus-v2026.06 This model is a fine-tuned version of [facebook/omniASR-CTC-300M](https://huggingface.co/facebook/omniASR-CTC-300M) on the **MiZonal v3.0** dataset. It achieves the following results on the evaluation set: - Wer: 16.4327 - Cer: 3.0023 - Real Time Factor: 0.0061 ## Quick Inference ```python # Google Colab Ready Quick Inference # 1. Install dependencies: !pip install huggingface_hub omnilingual-asr torchaudio # 2. Download the model checkpoint directly from Hugging Face: import torch from huggingface_hub import hf_hub_download model_path = hf_hub_download( repo_id="andrewbawitlung/omni-asr-ctc-300m-v2-mizonal3-E2-lus-v2026.06", filename="sdp_00.pt" ) # 3. Load the base model pipeline and inject fine-tuned weights: from omnilingual_asr.models.inference.pipeline import ASRInferencePipeline print("Loading base model pipeline...") pipeline = ASRInferencePipeline(model_card="omniASR_CTC_300M_v2") print("Loading fine-tuned checkpoint weights...") checkpoint = torch.load(model_path, map_location="cpu") state_dict = checkpoint['model'] if 'model' in checkpoint else checkpoint pipeline.model.load_state_dict(state_dict, strict=False) # 4. Run transcription (make sure audio files are present in the specified paths): audio_files = ["/content/segment_003.wav", "/content/segment_108.wav"] lang = ["lus_Latn", "lus_Latn"] transcriptions = pipeline.transcribe(audio_files, lang=lang, batch_size=2) print("Transcription 0:", transcriptions[0]) print("Transcription 1:", transcriptions[1]) ``` ## Model descripti...

Source context: 0 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo andrewbawitlung/omni-asr-ctc-300m-v2-mizonal3-E2-lus-v2026.06