How to properly pass audio signal to asr QuartzNet without saving audio? 
**Describe the bug**

Used instructions from notebooks:
https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Streaming_ASR.ipynb
https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Online_ASR_Microphone_Demo.ipynb

For some reason, the recognized text differs from what the model produces by the boxed method through reading the saved files. But we don't want to save the files after we've received the sound.
1. Can you please tell me how nemo models read sound from files?

2. Can anyone come across a solution on how to correctly read the bytes of an audio file on the stt service?
If you pass it like this:

```python
with open(filename, "rb") as fileb:
    abon = base64.b64encode(fileb.read())
    data = {
        "audio_content":  audio_content,
    }
    response = requests.post(
        "http://localhost:8557/stt", json=data)
```

**Steps/Code to reproduce bug**
 ```python
   from scipy.io import wavfile
   import scipy.signal

class AudioEncoder(json.JSONEncoder):
    """Класс сериализации семплированного аудио"""

    def default(self, obj):
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        elif isinstance(obj, bytes):
            return str(obj, encoding='utf-8')
        return json.JSONEncoder.default(self, obj)

    # Example without passing bytes
    target_sr = 16000
    sample_rate_hertz, samples = wavfile.read(filename)
    number_of_samples = round(
        len(samples) * float(target_sr) / sample_rate_hertz)
    samples = scipy.signal.resample(samples, number_of_samples)
    audio_content = base64.b64encode(samples).decode("utf-8")
    data = {
        "audio_content":  json.dumps(audio_content, cls=AudioEncoder),
    }
    response = requests.post(
        "http://localhost:8557/stt", json=data)
```
On service stt:
```python
audio_content = base64.b64decode(request.audio.audio_content)
audio_signal = np.frombuffer(audio_content)  # , dtype=np.float32

asr = ASR(16000)
asr.transcribe(audio_signal)  # does not produce the same output as the box method


from audio_dataset import speech_collate_fn, AudioDataLayer

class ASR:
    asr_model = _ASR_MODEL
    beam_search_lm = _BEAM_SEARCH_LM

    def __init__(self, sample_rate_hertz):
        assert self.asr_model is not None, "Модель не инициализирована"

        self.vocab = list(self.asr_model.decoder.vocabulary)
        self.vocab.append("_")

        self.sr = sample_rate_hertz

        self.data_layer = AudioDataLayer(self.sr)
        self.data_loader = DataLoader(
            self.data_layer, batch_size=1, collate_fn=speech_collate_fn
        )
        self.prev_char = ""

    def _get_batch_pred(self, signal):
        self.data_layer.set_signal(signal)
        batch = next(iter(self.data_loader))
        audio_signal, audio_signal_len = batch
        audio_signal, audio_signal_len = \
            audio_signal.to(self.asr_model.device), \
            audio_signal_len.to(self.asr_model.device)
        log_probs, encoded_len, predictions = self.asr_model.forward(
            input_signal=audio_signal, input_signal_length=audio_signal_len
        )
        return log_probs  # .cpu().numpy()[0]

    @staticmethod
    def _decode_sequence_ctc_with_max_prob(logits, vocab):
        s = ""
        for i in range(logits.shape[0]):
            s += vocab[np.argmax(logits[i])]
        return s

    def merge_decoded_sequence_from_ctc(self, sequence):
        s_merged = ""
        for i in range(len(sequence)):
            if sequence[i] != self.prev_char:
                self.prev_char = sequence[i]
                if self.prev_char != "_":
                    s_merged += self.prev_char
        return s_merged

    @staticmethod
    def _to_logits_tensor(logits):
        logits = [torch.tensor(logit) for logit in logits]
        log_probs_length = torch.tensor([logit.shape[0] for logit in logits])
        logits_tensor = torch.nn.utils.rnn.pad_sequence(
            logits, batch_first=True)

        return logits_tensor, log_probs_length

    def _infer_beam_search(self, logits_tensor, log_probs_length):
        assert self.beam_search_lm is not None, "Модель не инициализирована"
        hyps = list()

        for j in range(logits_tensor.shape[0]):
            hyps.append(
                self.beam_search_lm.forward(
                    log_probs=logits_tensor[j].unsqueeze(0),
                    log_probs_length=log_probs_length[j].unsqueeze(0),
                )[0][0][1]
            )

        return hyps

    @torch.no_grad()
    def transcribe(self, audio_signal, use_lm=False):
        logits = self._get_batch_pred(audio_signal)
        if use_lm:
            logits_tensor, log_probs_length = self._to_logits_tensor(logits)
            transcription = self._infer_beam_search(
                logits_tensor, log_probs_length)[0]
        else:
            transcription_ctc = self._decode_sequence_ctc_with_max_prob(
                logits.cpu().numpy()[0], self.vocab
            )
            transcription = self.merge_decoded_sequence_from_ctc(
                transcription_ctc)
        return transcription

```
audio_dataset.py
```python
# audio_dataset.py

from nemo.core.classes import IterableDataset
import torch
# import numpy as np
from nemo.core.neural_types import NeuralType, AudioSignal, LengthsType
from common_utils.log import get_logger
from typing import Tuple

logger = get_logger(__name__)


def speech_collate_fn(batch: Tuple[torch.FloatTensor, torch.LongTensor]):
    """collate batch of audio sig, audio len
    Args:
        batch (FloatTensor, LongTensor):  A tuple of tensors of signal, signal lengths.
        This collate func assumes the signals are 1d torch tensors (i.e. mono audio).
    """

    _, audio_lengths = zip(*batch)

    max_audio_len = 0
    has_audio = audio_lengths[0] is not None
    if has_audio:
        max_audio_len = max(audio_lengths).item()

    audio_signal = []
    for sig, sig_len in batch:
        assert sig.dim() == 1, "Ожидается монозвук"
        if has_audio:
            sig_len = sig_len.item()
            if sig_len < max_audio_len:
                pad = (0, max_audio_len - sig_len)
                sig = torch.nn.functional.pad(sig, pad)
            audio_signal.append(sig)

    if has_audio:
        audio_signal = torch.stack(audio_signal)
        audio_lengths = torch.stack(audio_lengths)
    else:
        audio_signal, audio_lengths = None, None

    return audio_signal, audio_lengths


# simple data layer to pass audio signal
class AudioDataLayer(IterableDataset):
    @property
    def output_types(self):
        return {
            "audio_signal": NeuralType(("B", "T"), AudioSignal(freq=self._sample_rate)),
            "a_sig_length": NeuralType(tuple("B"), LengthsType()),
        }

    def __init__(self, sample_rate):
        super().__init__()
        self._sample_rate = sample_rate
        self.output = True

    def __iter__(self):
        return self

    def __next__(self):
        if not self.output:
            raise StopIteration
        self.output = False
        return torch.as_tensor(self.signal, dtype=torch.float32), torch.as_tensor(
            self.signal_shape, dtype=torch.int64
        )

    def set_signal(self, signal):
        # нормализуем сигнал в диапозон значений: [-1; 1]
        self.signal = signal.astype('float32') / 32768.
        self.signal_shape = self.signal.size
        self.output = True

    def __len__(self):
        return 1

```

**Expected behavior**

I expect recognition via` nemo_asr.models.EncDecCTCModel.restore_from(...).transcribe([file], batch_size=1) `to match what we did.


**Environment details**


Run on CPU (docker image: python:3.7-slim)

- OS Linux
- PyTorch 1.10
- Python 3.7

**Additional context**

Add any other context about the problem here.
Example: CPU model
