adding ggml_to_pt script

models/ggml_to_pt.py

@@ -0,0 +1,109 @@
+import struct
+import torch
+import numpy as np
+from collections import OrderedDict
+from pathlib import Path
+import sys
+
+if len(sys.argv) < 3:
+ print(
+ "Usage: convert-ggml-to-pt.py model.bin dir-output\n")
+ sys.exit(1)
+
+fname_inp = Path(sys.argv[1])
+dir_out = Path(sys.argv[2])
+fname_out = dir_out / "torch-model.pt"
+
+
+
+# Open the ggml file
+with open(fname_inp, "rb") as f:
+ # Read magic number and hyperparameters
+ magic_number, n_vocab, n_audio_ctx, n_audio_state, n_audio_head, n_audio_layer, n_text_ctx, n_text_state, n_text_head, n_text_layer, n_mels, use_f16 = struct.unpack("12i", f.read(48))
+ print(f"Magic number: {magic_number}")
+ print(f"Vocab size: {n_vocab}")
+ print(f"Audio context size: {n_audio_ctx}")
+ print(f"Audio state size: {n_audio_state}")
+ print(f"Audio head size: {n_audio_head}")
+ print(f"Audio layer size: {n_audio_layer}")
+ print(f"Text context size: {n_text_ctx}")
+ print(f"Text head size: {n_text_head}")
+ print(f"Mel size: {n_mels}")
+ # Read mel filters
+ # mel_filters = np.fromfile(f, dtype=np.float32, count=n_mels * 2).reshape(n_mels, 2)
+ # print(f"Mel filters: {mel_filters}")
+ filters_shape_0 = struct.unpack("i", f.read(4))[0]
+ print(f"Filters shape 0: {filters_shape_0}")
+ filters_shape_1 = struct.unpack("i", f.read(4))[0]
+ print(f"Filters shape 1: {filters_shape_1}")
+
+ # Read tokenizer tokens
+ # bytes = f.read(4)
+ # print(bytes)
+
+
+ # for i in range(filters.shape[0]):
+ # for j in range(filters.shape[1]):
+ # fout.write(struct.pack("f", filters[i][j]))
+ mel_filters = np.zeros((filters_shape_0, filters_shape_1))
+
+ for i in range(filters_shape_0):
+ for j in range(filters_shape_1):
+ mel_filters[i][j] = struct.unpack("f", f.read(4))[0]
+
+ bytes_data = f.read(4) 
+ num_tokens = struct.unpack("i", bytes_data)[0]
+ tokens = {}
+
+
+ for _ in range(num_tokens):
+ token_len = struct.unpack("i", f.read(4))[0]
+ token = f.read(token_len)
+ tokens[token] = {}
+
+ # Read model variables
+ model_state_dict = OrderedDict()
+ while True:
+ try:
+ n_dims, name_length, ftype = struct.unpack("iii", f.read(12))
+ except struct.error:
+ break # End of file
+ dims = [struct.unpack("i", f.read(4))[0] for _ in range(n_dims)]
+ dims = dims[::-1]
+ name = f.read(name_length).decode("utf-8")
+ if ftype == 1: # f16
+ data = np.fromfile(f, dtype=np.float16, count=np.prod(dims)).reshape(dims)
+ else: # f32
+ data = np.fromfile(f, dtype=np.float32, count=np.prod(dims)).reshape(dims)
+
+
+ if name in ["encoder.conv1.bias", "encoder.conv2.bias"]:
+
+ data = data[:, 0]
+
+
+ model_state_dict[name] = torch.from_numpy(data)
+
+# Now you have the model's state_dict stored in model_state_dict
+# You can load this state_dict into a model with the same architecture
+
+# dims = ModelDimensions(**checkpoint["dims"])
+# model = Whisper(dims)
+from whisper import Whisper, ModelDimensions
+dims = ModelDimensions(
+ n_mels=n_mels,
+ n_audio_ctx=n_audio_ctx,
+ n_audio_state=n_audio_state,
+ n_audio_head=n_audio_head,
+ n_audio_layer=n_audio_layer,
+ n_text_ctx=n_text_ctx,
+ n_text_state=n_text_state,
+ n_text_head=n_text_head,
+ n_text_layer=n_text_layer,
+ n_vocab=n_vocab,
+)
+model = Whisper(dims) # Replace with your model's class
+model.load_state_dict(model_state_dict)
+
+# Save the model in PyTorch format
+torch.save(model.state_dict(), fname_out)