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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'] |