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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 | |