snac_utils

utils/snac_utils.py

@@ -0,0 +1,146 @@
+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
+