| You can also manually replicate the results of the pipeline if you'd like: | |
| Load a processor to preprocess the audio file and transcription and return the input as PyTorch tensors: | |
| from transformers import AutoProcessor | |
| processor = AutoProcessor.from_pretrained("stevhliu/my_awesome_asr_mind_model") | |
| inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") | |
| Pass your inputs to the model and return the logits: | |
| from transformers import AutoModelForCTC | |
| model = AutoModelForCTC.from_pretrained("stevhliu/my_awesome_asr_mind_model") | |
| with torch.no_grad(): | |
| logits = model(**inputs).logits | |
| Get the predicted input_ids with the highest probability, and use the processor to decode the predicted input_ids back into text: | |
| import torch | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.batch_decode(predicted_ids) | |
| transcription | |
| ['I WOUL LIKE O SET UP JOINT ACOUNT WTH Y PARTNER'] |