diff options
Diffstat (limited to 'llama.cpp/tools/tts/convert_pt_to_hf.py')
| -rw-r--r-- | llama.cpp/tools/tts/convert_pt_to_hf.py | 180 |
1 files changed, 180 insertions, 0 deletions
diff --git a/llama.cpp/tools/tts/convert_pt_to_hf.py b/llama.cpp/tools/tts/convert_pt_to_hf.py new file mode 100644 index 0000000..ebd55d9 --- /dev/null +++ b/llama.cpp/tools/tts/convert_pt_to_hf.py @@ -0,0 +1,180 @@ +# convert the https://huggingface.co/novateur/WavTokenizer-large-speech-75token to HF format +# the goal is to be able to reuse the convert_hf_to_gguf.py after that to create a GGUF file with the WavTokenizer decoder +# +# TODO: this script is LLM-generated and probably very inefficient and should be rewritten + +import torch +import json +import os +import sys +import re + +from safetensors.torch import save_file + +# default +model_path = './model.pt' + +# read from CLI +if len(sys.argv) > 1: + model_path = sys.argv[1] + +# get the directory of the input model +path_dst = os.path.dirname(model_path) + +print(f"Loading model from {model_path}") + +model = torch.load(model_path, map_location='cpu') + +#print(model) + +# print all keys +for key in model.keys(): + print(key) + if key == 'hyper_parameters': + #print(model[key]) + # dump as json pretty + print(json.dumps(model[key], indent=4)) + #if key != 'state_dict' and key != 'optimizer_states': + # print(model[key]) + +# Check if the loaded model is a state_dict or a model instance +if isinstance(model, torch.nn.Module): + state_dict = model.state_dict() +else: + state_dict = model + +# Print the structure of the state_dict to understand its format +print("State dictionary keys:") +for key in state_dict.keys(): + print(key) + +# Ensure the state_dict is flat and contains only torch.Tensor objects +def flatten_state_dict(state_dict, parent_key='', sep='.'): + items = [] + items_new = [] + + for k, v in state_dict.items(): + new_key = f"{parent_key}{sep}{k}" if parent_key else k + if isinstance(v, torch.Tensor): + items.append((new_key, v)) + elif isinstance(v, dict): + items.extend(flatten_state_dict(v, new_key, sep=sep).items()) + return dict(items) + + size_total_mb = 0 + + for key, value in list(items): + # keep only what we need for inference + if not key.startswith('state_dict.feature_extractor.encodec.quantizer.') and \ + not key.startswith('state_dict.backbone.') and \ + not key.startswith('state_dict.head.out'): + print('Skipping key: ', key) + continue + + new_key = key + + new_key = new_key.replace('state_dict.', '') + new_key = new_key.replace('pos_net', 'posnet') + + # check if matches "backbone.posnet.%d.bias" or "backbone.posnet.%d.weight" + if new_key.startswith("backbone.posnet."): + match = re.match(r"backbone\.posnet\.(\d+)\.(bias|weight)", new_key) + if match: + new_key = f"backbone.posnet.{match.group(1)}.norm.{match.group(2)}" + + # "feature_extractor.encodec.quantizer.vq.layers.0._codebook.embed" -> "backbone.embedding.weight" + if new_key == "feature_extractor.encodec.quantizer.vq.layers.0._codebook.embed": + new_key = "backbone.embedding.weight" + + # these are the only rows used + # ref: https://github.com/edwko/OuteTTS/blob/a613e79c489d8256dd657ea9168d78de75895d82/outetts/wav_tokenizer/audio_codec.py#L100 + if new_key.endswith("norm.scale.weight"): + new_key = new_key.replace("norm.scale.weight", "norm.weight") + value = value[0] + + if new_key.endswith("norm.shift.weight"): + new_key = new_key.replace("norm.shift.weight", "norm.bias") + value = value[0] + + if new_key.endswith("gamma"): + new_key = new_key.replace("gamma", "gamma.weight") + + # convert from 1D [768] to 2D [768, 1] so that ggml_add can broadcast the bias + if (new_key.endswith("norm.weight") or new_key.endswith("norm1.weight") or new_key.endswith("norm2.weight") or new_key.endswith(".bias")) and (new_key.startswith("backbone.posnet") or new_key.startswith("backbone.embed.bias")): + value = value.unsqueeze(1) + + if new_key.endswith("dwconv.bias"): + value = value.unsqueeze(1) + + size_mb = value.element_size() * value.nelement() / (1024 * 1024) + print(f"{size_mb:8.2f} MB - {new_key}: {value.shape}") + + size_total_mb += size_mb + + #print(key, '->', new_key, ': ', value) + #print(key, '->', new_key) + + items_new.append((new_key, value)) + + print(f"Total size: {size_total_mb:8.2f} MB") + + return dict(items_new) + +flattened_state_dict = flatten_state_dict(state_dict) + + +# Convert the model to the safetensors format +output_path = path_dst + '/model.safetensors' +save_file(flattened_state_dict, output_path) + +print(f"Model has been successfully converted and saved to {output_path}") + +# Calculate the total size of the .safetensors file +total_size = os.path.getsize(output_path) + +# Create the weight map +weight_map = { + "model.safetensors": ["*"] # Assuming all weights are in one file +} + +# Create metadata for the index.json file +metadata = { + "total_size": total_size, + "weight_map": weight_map +} + +# Save the metadata to index.json +index_path = path_dst + '/index.json' +with open(index_path, 'w') as f: + json.dump(metadata, f, indent=4) + +print(f"Metadata has been saved to {index_path}") + +config = { + "architectures": [ + "WavTokenizerDec" + ], + "hidden_size": 1282, + "n_embd_features": 512, + "n_ff": 2304, + "vocab_size": 4096, + "n_head": 1, + "layer_norm_epsilon": 1e-6, + "group_norm_epsilon": 1e-6, + "group_norm_groups": 32, + "max_position_embeddings": 8192, # ? + "n_layer": 12, + "posnet": { + "n_embd": 768, + "n_layer": 6 + }, + "convnext": { + "n_embd": 768, + "n_layer": 12 + }, +} + +with open(path_dst + '/config.json', 'w') as f: + json.dump(config, f, indent=4) + +print(f"Config has been saved to {path_dst + 'config.json'}") |