fix sampling rate of demo wavs

from torch import nn

import torch

import torchaudio

from torch.profiler import profile, record_function, ProfilerActivity

import datetime

# import boto3

# import datetime

# from botocore.errorfactory import ClientError

hubert_quantizer = f'hubert/hubert_base_ls960_L9_km500.bin' # listed in row "HuBERT Base (~95M params)", column Quantizer

# Placeholder data generation

def get_sinewave(freq=440.0, duration_ms=200, volume=1.0, sample_rate=44100.0):

def get_sinewave(freq=440.0, duration_ms=200, volume=1.0, sample_rate=16000.0):

  # code adapted from https://stackoverflow.com/a/33913403

  audio = []

  num_samples = duration_ms * (sample_rate / 1000.0)

    audio.append(volume * math.sin(2 * math.pi * freq * (x / sample_rate)))

  return audio

def save_wav(file_name, audio, sample_rate=44100.0):

def save_wav(file_name, audio, sample_rate=16000.0):

  # Open up a wav file

  wav_file=wave.open(file_name,"w")

  # wav params

  nchannels = 1

  sampwidth = 2

  # 44100 is the industry standard sample rate - CD quality.  If you need to

  # 16000 is the industry standard sample rate - CD quality.  If you need to

  # save on file size you can adjust it downwards. The stanard for low quality

  # is 8000 or 8kHz.

  nframes = len(audio)

    fine_transformer = fine_transformer

)

with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True, profile_memory=True, use_cuda=True) as prof:

    with record_function("model_inference"):

        generated_wav = audiolm(batch_size = 1)

        output_path = f"{prefix}/out.wav"

sample_rate = 44100

        sample_rate = 16000

        torchaudio.save(output_path, generated_wav.cpu(), sample_rate)

filename = f"{prefix}/profile-{datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S")}.txt"

with open(filename, "w") as f:

    f.write("cpu time sorted:\n")

    f.write(f"{prof.key_averages(group_by_input_shape=True).table(sort_by="cpu_time_total", row_limit=10)}")

    f.write("\n cuda time sorted:\n")

    f.write(f"{prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)}")

    f.write("\ncpu memory self\n") # excludes children memory allocated

    f.write(f"{prof.key_averages().table(sort_by="self_cpu_memory_usage", row_limit=10)}")

    f.write("\ncpu memory\n") # includes children memory allocated

    f.write(f"{prof.key_averages().table(sort_by="cpu_memory_usage", row_limit=10)}\n")