Spaces:
Running
on
T4
Running
on
T4
File size: 4,816 Bytes
54011d4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
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
|