Issue faced in Nemo Model Inference using Farward collection
Your reproducible steps are unclear as you are using some variables which are out of scope from what described. 

However for forward, signal should be BxT and signal_length should be B size **tensors**. 

 Waveform.shape[idx] would return int but not torch tensor. 

Can you change it to `torch.tensor([waveform.shape[1]])` and retry? @Pri4tam Assuming tha tthe above uggestions solved your bug.. Please reopen if it is unresolved.
**Describe the bug**

The farward nemo collection of Nemo ASR model requires following parameters.

-  input_signal – Tensor that represents a batch of raw audio signals, of shape [B, T]. T here represents timesteps, with 1 second of audio represented as self.sample_rate number of floating point values.

- input_signal_length – Vector of length B, that contains the individual lengths of the audio sequences.

**Steps/Code to reproduce bug**
Load the model
hi_nemo_asr=nemo_asr.models.EncDecCTCModelBPE.restore_from(en_model_path).cpu()
waveform, sample_rate = torchaudio.load(speech_file[0])
input_signal_length=input_signal.shape[1]
log_prob,_,_=hi_nemo_asr.forward(input_signal=waveform,input_signal_length=waveform.shape[1])

This gives the following error
IndexError: tuple index out of range

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
-Os =Linux
- PyTorch version =2.1.0
- Python version=3.8.10

