| from dataset import Dataset | |
| from model import Models | |
| def data(dataset): | |
| for i, item in enumerate(dataset): | |
| yield {**item["audio"], "reference": item["norm_text"]} | |
| def streamed_infernce(dataset, pipeline): | |
| # placeholders for predictions and references | |
| predictions = [] | |
| references = [] | |
| # run streamed inference | |
| for out in pipeline(data(dataset), batch_size=16): | |
| predictions.append(pipeline(out["text"])) | |
| references.append(out["reference"][0]) | |
| return predictions, references |