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Running
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Zero
File size: 4,816 Bytes
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import torch
import time
import numpy as np
class SnacConfig:
audio_vocab_size = 4096
padded_vocab_size = 4160
end_of_audio = 4097
snac_config = SnacConfig()
def get_time_str():
time_str = time.strftime("%Y%m%d_%H%M%S", time.localtime())
return time_str
def layershift(input_id, layer, stride=4160, shift=152000):
return input_id + shift + layer * stride
def generate_audio_data(snac_tokens, snacmodel, device=None):
audio = reconstruct_tensors(snac_tokens, device)
with torch.inference_mode():
audio_hat = snacmodel.decode(audio)
audio_data = audio_hat.cpu().numpy().astype(np.float64) * 32768.0
audio_data = audio_data.astype(np.int16)
audio_data = audio_data.tobytes()
return audio_data
def get_snac(list_output, index, nums_generate):
snac = []
start = index
for i in range(nums_generate):
snac.append("#")
for j in range(7):
snac.append(list_output[j][start - nums_generate - 5 + j + i])
return snac
def reconscruct_snac(output_list):
if len(output_list) == 8:
output_list = output_list[:-1]
output = []
for i in range(7):
output_list[i] = output_list[i][i + 1 :]
for i in range(len(output_list[-1])):
output.append("#")
for j in range(7):
output.append(output_list[j][i])
return output
def reconstruct_tensors(flattened_output, device=None):
"""Reconstructs the list of tensors from the flattened output."""
if device is None:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def count_elements_between_hashes(lst):
try:
# Find the index of the first '#'
first_index = lst.index("#")
# Find the index of the second '#' after the first
second_index = lst.index("#", first_index + 1)
# Count the elements between the two indices
return second_index - first_index - 1
except ValueError:
# Handle the case where there aren't enough '#' symbols
return "List does not contain two '#' symbols"
def remove_elements_before_hash(flattened_list):
try:
# Find the index of the first '#'
first_hash_index = flattened_list.index("#")
# Return the list starting from the first '#'
return flattened_list[first_hash_index:]
except ValueError:
# Handle the case where there is no '#'
return "List does not contain the symbol '#'"
def list_to_torch_tensor(tensor1):
# Convert the list to a torch tensor
tensor = torch.tensor(tensor1)
# Reshape the tensor to have size (1, n)
tensor = tensor.unsqueeze(0)
return tensor
flattened_output = remove_elements_before_hash(flattened_output)
codes = []
tensor1 = []
tensor2 = []
tensor3 = []
tensor4 = []
n_tensors = count_elements_between_hashes(flattened_output)
if n_tensors == 7:
for i in range(0, len(flattened_output), 8):
tensor1.append(flattened_output[i + 1])
tensor2.append(flattened_output[i + 2])
tensor3.append(flattened_output[i + 3])
tensor3.append(flattened_output[i + 4])
tensor2.append(flattened_output[i + 5])
tensor3.append(flattened_output[i + 6])
tensor3.append(flattened_output[i + 7])
codes = [
list_to_torch_tensor(tensor1).to(device),
list_to_torch_tensor(tensor2).to(device),
list_to_torch_tensor(tensor3).to(device),
]
if n_tensors == 15:
for i in range(0, len(flattened_output), 16):
tensor1.append(flattened_output[i + 1])
tensor2.append(flattened_output[i + 2])
tensor3.append(flattened_output[i + 3])
tensor4.append(flattened_output[i + 4])
tensor4.append(flattened_output[i + 5])
tensor3.append(flattened_output[i + 6])
tensor4.append(flattened_output[i + 7])
tensor4.append(flattened_output[i + 8])
tensor2.append(flattened_output[i + 9])
tensor3.append(flattened_output[i + 10])
tensor4.append(flattened_output[i + 11])
tensor4.append(flattened_output[i + 12])
tensor3.append(flattened_output[i + 13])
tensor4.append(flattened_output[i + 14])
tensor4.append(flattened_output[i + 15])
codes = [
list_to_torch_tensor(tensor1).to(device),
list_to_torch_tensor(tensor2).to(device),
list_to_torch_tensor(tensor3).to(device),
list_to_torch_tensor(tensor4).to(device),
]
return codes
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