#!/usr/bin/env python3 """ Build default assets based on configuration This script reads configuration from sdkconfig and builds the appropriate assets.bin for the current board configuration. Usage: ./build_default_assets.py --sdkconfig --builtin_text_font \ --default_emoji_collection --output """ import argparse import io import os import shutil import sys import json import struct from datetime import datetime # ============================================================================= # Pack model functions (from pack_model.py) # ============================================================================= def struct_pack_string(string, max_len=None): """ pack string to binary data. if max_len is None, max_len = len(string) + 1 else len(string) < max_len, the left will be padded by struct.pack('x') """ if max_len == None : max_len = len(string) else: assert len(string) <= max_len left_num = max_len - len(string) out_bytes = None for char in string: if out_bytes == None: out_bytes = struct.pack('b', ord(char)) else: out_bytes += struct.pack('b', ord(char)) for i in range(left_num): out_bytes += struct.pack('x') return out_bytes def read_data(filename): """Read binary data, like index and mndata""" data = None with open(filename, "rb") as f: data = f.read() return data def pack_models(model_path, out_file="srmodels.bin"): """ Pack all models into one binary file by the following format: { model_num: int model1_info: model_info_t model2_info: model_info_t ... model1_index,model1_data,model1_MODEL_INFO model1_index,model1_data,model1_MODEL_INFO ... }model_pack_t { model_name: char[32] file_number: int file1_name: char[32] file1_start: int file1_len: int file2_name: char[32] file2_start: int // data_len = info_start - data_start file2_len: int ... }model_info_t """ models = {} file_num = 0 model_num = 0 for root, dirs, _ in os.walk(model_path): for model_name in dirs: models[model_name] = {} model_dir = os.path.join(root, model_name) model_num += 1 for _, _, files in os.walk(model_dir): for file_name in files: file_num += 1 file_path = os.path.join(model_dir, file_name) models[model_name][file_name] = read_data(file_path) model_num = len(models) header_len = 4 + model_num*(32+4) + file_num*(32+4+4) out_bin = struct.pack('I', model_num) # model number data_bin = None for key in models: model_bin = struct_pack_string(key, 32) # + model name model_bin += struct.pack('I', len(models[key])) # + file number in this model for file_name in models[key]: model_bin += struct_pack_string(file_name, 32) # + file name if data_bin == None: model_bin += struct.pack('I', header_len) data_bin = models[key][file_name] model_bin += struct.pack('I', len(models[key][file_name])) else: model_bin += struct.pack('I', header_len+len(data_bin)) data_bin += models[key][file_name] model_bin += struct.pack('I', len(models[key][file_name])) out_bin += model_bin assert len(out_bin) == header_len if data_bin != None: out_bin += data_bin out_file = os.path.join(model_path, out_file) with open(out_file, "wb") as f: f.write(out_bin) # ============================================================================= # Build assets functions (from build.py) # ============================================================================= def ensure_dir(directory): """Ensure directory exists, create if not""" os.makedirs(directory, exist_ok=True) def copy_file(src, dst): """Copy file""" if os.path.exists(src): shutil.copy2(src, dst) print(f"Copied: {src} -> {dst}") return True else: print(f"Warning: Source file does not exist: {src}") return False def copy_directory(src, dst): """Copy directory""" if os.path.exists(src): shutil.copytree(src, dst, dirs_exist_ok=True) print(f"Copied directory: {src} -> {dst}") return True else: print(f"Warning: Source directory does not exist: {src}") return False def process_sr_models(wakenet_model_dirs, multinet_model_dirs, build_dir, assets_dir): """Process SR models (wakenet and multinet) and generate srmodels.bin""" if not wakenet_model_dirs and not multinet_model_dirs: return None # Create SR models build directory sr_models_build_dir = os.path.join(build_dir, "srmodels") if os.path.exists(sr_models_build_dir): shutil.rmtree(sr_models_build_dir) os.makedirs(sr_models_build_dir) models_processed = 0 # Copy wakenet models if available if wakenet_model_dirs: for wakenet_model_dir in wakenet_model_dirs: wakenet_name = os.path.basename(wakenet_model_dir) wakenet_dst = os.path.join(sr_models_build_dir, wakenet_name) if copy_directory(wakenet_model_dir, wakenet_dst): models_processed += 1 print(f"Added wakenet model: {wakenet_name}") # Copy multinet models if available if multinet_model_dirs: for multinet_model_dir in multinet_model_dirs: multinet_name = os.path.basename(multinet_model_dir) multinet_dst = os.path.join(sr_models_build_dir, multinet_name) if copy_directory(multinet_model_dir, multinet_dst): models_processed += 1 print(f"Added multinet model: {multinet_name}") if models_processed == 0: print("Warning: No SR models were successfully processed") return None # Use pack_models function to generate srmodels.bin srmodels_output = os.path.join(sr_models_build_dir, "srmodels.bin") try: pack_models(sr_models_build_dir, "srmodels.bin") print(f"Generated: {srmodels_output}") # Copy srmodels.bin to assets directory copy_file(srmodels_output, os.path.join(assets_dir, "srmodels.bin")) return "srmodels.bin" except Exception as e: print(f"Error: Failed to generate srmodels.bin: {e}") return None def process_text_font(text_font_file, assets_dir): """Process text_font parameter""" if not text_font_file: return None # Copy input file to build/assets directory font_filename = os.path.basename(text_font_file) font_dst = os.path.join(assets_dir, font_filename) if copy_file(text_font_file, font_dst): return font_filename return None def process_emoji_collection(emoji_collection_dir, assets_dir): """Process emoji_collection parameter""" if not emoji_collection_dir: return [] emoji_list = [] # Copy each image from input directory to build/assets directory for root, dirs, files in os.walk(emoji_collection_dir): for file in files: if file.lower().endswith(('.png', '.gif')): # Copy file src_file = os.path.join(root, file) dst_file = os.path.join(assets_dir, file) if copy_file(src_file, dst_file): # Get filename without extension filename_without_ext = os.path.splitext(file)[0] # Add to emoji list emoji_list.append({ "name": filename_without_ext, "file": file }) return emoji_list def process_extra_files(extra_files_dir, assets_dir): """Process default_assets_extra_files parameter""" if not extra_files_dir: return [] if not os.path.exists(extra_files_dir): print(f"Warning: Extra files directory not found: {extra_files_dir}") return [] extra_files_list = [] # Copy each file from input directory to build/assets directory for root, dirs, files in os.walk(extra_files_dir): for file in files: # Skip hidden files and directories if file.startswith('.'): continue # Copy file src_file = os.path.join(root, file) dst_file = os.path.join(assets_dir, file) if copy_file(src_file, dst_file): extra_files_list.append(file) if extra_files_list: print(f"Processed {len(extra_files_list)} extra files from: {extra_files_dir}") return extra_files_list def generate_index_json(assets_dir, srmodels, text_font, emoji_collection, extra_files=None, multinet_model_info=None): """Generate index.json file""" index_data = { "version": 1 } if srmodels: index_data["srmodels"] = srmodels if text_font: index_data["text_font"] = text_font if emoji_collection: index_data["emoji_collection"] = emoji_collection if extra_files: index_data["extra_files"] = extra_files if multinet_model_info: index_data["multinet_model"] = multinet_model_info # Write index.json index_path = os.path.join(assets_dir, "index.json") with open(index_path, 'w', encoding='utf-8') as f: json.dump(index_data, f, indent=4, ensure_ascii=False) print(f"Generated: {index_path}") def generate_config_json(build_dir, assets_dir): """Generate config.json file""" config_data = { "include_path": os.path.join(build_dir, "include"), "assets_path": assets_dir, "image_file": os.path.join(build_dir, "output", "assets.bin"), "lvgl_ver": "9.3.0", "assets_size": "0x400000", "support_format": ".png, .gif, .jpg, .bin, .json", "name_length": "32", "split_height": "0", "support_qoi": False, "support_spng": False, "support_sjpg": False, "support_sqoi": False, "support_raw": False, "support_raw_dither": False, "support_raw_bgr": False } # Write config.json config_path = os.path.join(build_dir, "config.json") with open(config_path, 'w', encoding='utf-8') as f: json.dump(config_data, f, indent=4, ensure_ascii=False) print(f"Generated: {config_path}") return config_path # ============================================================================= # Simplified SPIFFS assets generation (from spiffs_assets_gen.py) # ============================================================================= def compute_checksum(data): checksum = sum(data) & 0xFFFF return checksum def sort_key(filename): basename, extension = os.path.splitext(filename) return extension, basename def pack_assets_simple(target_path, include_path, out_file, assets_path, max_name_len=32): """ Simplified version of pack_assets that handles basic file packing """ merged_data = bytearray() file_info_list = [] skip_files = ['config.json'] # Ensure output directory exists os.makedirs(os.path.dirname(out_file), exist_ok=True) os.makedirs(include_path, exist_ok=True) file_list = sorted(os.listdir(target_path), key=sort_key) for filename in file_list: if filename in skip_files: continue file_path = os.path.join(target_path, filename) if not os.path.isfile(file_path): continue file_name = os.path.basename(file_path) file_size = os.path.getsize(file_path) file_info_list.append((file_name, len(merged_data), file_size, 0, 0)) # Add 0x5A5A prefix to merged_data merged_data.extend(b'\x5A' * 2) with open(file_path, 'rb') as bin_file: bin_data = bin_file.read() merged_data.extend(bin_data) total_files = len(file_info_list) mmap_table = bytearray() for file_name, offset, file_size, width, height in file_info_list: if len(file_name) > max_name_len: print(f'Warning: "{file_name}" exceeds {max_name_len} bytes and will be truncated.') fixed_name = file_name.ljust(max_name_len, '\0')[:max_name_len] mmap_table.extend(fixed_name.encode('utf-8')) mmap_table.extend(file_size.to_bytes(4, byteorder='little')) mmap_table.extend(offset.to_bytes(4, byteorder='little')) mmap_table.extend(width.to_bytes(2, byteorder='little')) mmap_table.extend(height.to_bytes(2, byteorder='little')) combined_data = mmap_table + merged_data combined_checksum = compute_checksum(combined_data) combined_data_length = len(combined_data).to_bytes(4, byteorder='little') header_data = total_files.to_bytes(4, byteorder='little') + combined_checksum.to_bytes(4, byteorder='little') final_data = header_data + combined_data_length + combined_data with open(out_file, 'wb') as output_bin: output_bin.write(final_data) # Generate header file current_year = datetime.now().year asset_name = os.path.basename(assets_path) header_file_path = os.path.join(include_path, f'mmap_generate_{asset_name}.h') with open(header_file_path, 'w') as output_header: output_header.write('/*\n') output_header.write(' * SPDX-FileCopyrightText: 2022-{} Espressif Systems (Shanghai) CO LTD\n'.format(current_year)) output_header.write(' *\n') output_header.write(' * SPDX-License-Identifier: Apache-2.0\n') output_header.write(' */\n\n') output_header.write('/**\n') output_header.write(' * @file\n') output_header.write(" * @brief This file was generated by esp_mmap_assets, don't modify it\n") output_header.write(' */\n\n') output_header.write('#pragma once\n\n') output_header.write("#include \"esp_mmap_assets.h\"\n\n") output_header.write(f'#define MMAP_{asset_name.upper()}_FILES {total_files}\n') output_header.write(f'#define MMAP_{asset_name.upper()}_CHECKSUM 0x{combined_checksum:04X}\n\n') output_header.write(f'enum MMAP_{asset_name.upper()}_LISTS {{\n') for i, (file_name, _, _, _, _) in enumerate(file_info_list): enum_name = file_name.replace('.', '_') output_header.write(f' MMAP_{asset_name.upper()}_{enum_name.upper()} = {i}, /*!< {file_name} */\n') output_header.write('};\n') print(f'All files have been merged into {os.path.basename(out_file)}') # ============================================================================= # Configuration and main functions # ============================================================================= def read_wakenet_from_sdkconfig(sdkconfig_path): """ Read wakenet models from sdkconfig (based on movemodel.py logic) Returns a list of wakenet model names """ if not os.path.exists(sdkconfig_path): print(f"Warning: sdkconfig file not found: {sdkconfig_path}") return [] models = [] with io.open(sdkconfig_path, "r") as f: for label in f: label = label.strip("\n") if 'CONFIG_SR_WN' in label and '#' not in label[0]: if '_NONE' in label: continue if '=' in label: label = label.split("=")[0] if '_MULTI' in label: label = label[:-6] model_name = label.split("_SR_WN_")[-1].lower() models.append(model_name) return models def read_multinet_from_sdkconfig(sdkconfig_path): """ Read multinet models from sdkconfig (based on movemodel.py logic) Returns a list of multinet model names """ if not os.path.exists(sdkconfig_path): print(f"Warning: sdkconfig file not found: {sdkconfig_path}") return [] with io.open(sdkconfig_path, "r") as f: models_string = '' for label in f: label = label.strip("\n") if 'CONFIG_SR_MN' in label and label[0] != '#': models_string += label models = [] if "CONFIG_SR_MN_CN_MULTINET3_SINGLE_RECOGNITION" in models_string: models.append('mn3_cn') elif "CONFIG_SR_MN_CN_MULTINET4_5_SINGLE_RECOGNITION_QUANT8" in models_string: models.append('mn4q8_cn') elif "CONFIG_SR_MN_CN_MULTINET4_5_SINGLE_RECOGNITION" in models_string: models.append('mn4_cn') elif "CONFIG_SR_MN_CN_MULTINET5_RECOGNITION_QUANT8" in models_string: models.append('mn5q8_cn') elif "CONFIG_SR_MN_CN_MULTINET6_QUANT" in models_string: models.append('mn6_cn') elif "CONFIG_SR_MN_CN_MULTINET6_AC_QUANT" in models_string: models.append('mn6_cn_ac') elif "CONFIG_SR_MN_CN_MULTINET7_QUANT" in models_string: models.append('mn7_cn') elif "CONFIG_SR_MN_CN_MULTINET7_AC_QUANT" in models_string: models.append('mn7_cn_ac') if "CONFIG_SR_MN_EN_MULTINET5_SINGLE_RECOGNITION_QUANT8" in models_string: models.append('mn5q8_en') elif "CONFIG_SR_MN_EN_MULTINET5_SINGLE_RECOGNITION" in models_string: models.append('mn5_en') elif "CONFIG_SR_MN_EN_MULTINET6_QUANT" in models_string: models.append('mn6_en') elif "CONFIG_SR_MN_EN_MULTINET7_QUANT" in models_string: models.append('mn7_en') if "MULTINET6" in models_string or "MULTINET7" in models_string: models.append('fst') return models def read_wake_word_type_from_sdkconfig(sdkconfig_path): """ Read wake word type configuration from sdkconfig Returns a dict with wake word type info """ if not os.path.exists(sdkconfig_path): print(f"Warning: sdkconfig file not found: {sdkconfig_path}") return { 'use_esp_wake_word': False, 'use_afe_wake_word': False, 'use_custom_wake_word': False, 'wake_word_disabled': True } config_values = { 'use_esp_wake_word': False, 'use_afe_wake_word': False, 'use_custom_wake_word': False, 'wake_word_disabled': False } with io.open(sdkconfig_path, "r") as f: for line in f: line = line.strip("\n") if line.startswith('#'): continue # Check for wake word type configuration if 'CONFIG_USE_ESP_WAKE_WORD=y' in line: config_values['use_esp_wake_word'] = True elif 'CONFIG_USE_AFE_WAKE_WORD=y' in line: config_values['use_afe_wake_word'] = True elif 'CONFIG_USE_CUSTOM_WAKE_WORD=y' in line: config_values['use_custom_wake_word'] = True elif 'CONFIG_WAKE_WORD_DISABLED=y' in line: config_values['wake_word_disabled'] = True return config_values def read_custom_wake_word_from_sdkconfig(sdkconfig_path): """ Read custom wake word configuration from sdkconfig Returns a dict with custom wake word info or None if not configured """ if not os.path.exists(sdkconfig_path): print(f"Warning: sdkconfig file not found: {sdkconfig_path}") return None config_values = {} with io.open(sdkconfig_path, "r") as f: for line in f: line = line.strip("\n") if line.startswith('#') or '=' not in line: continue # Check for custom wake word configuration if 'CONFIG_USE_CUSTOM_WAKE_WORD=y' in line: config_values['use_custom_wake_word'] = True elif 'CONFIG_CUSTOM_WAKE_WORD=' in line and not line.startswith('#'): # Extract string value (remove quotes) value = line.split('=', 1)[1].strip('"') config_values['wake_word'] = value elif 'CONFIG_CUSTOM_WAKE_WORD_DISPLAY=' in line and not line.startswith('#'): # Extract string value (remove quotes) value = line.split('=', 1)[1].strip('"') config_values['display'] = value elif 'CONFIG_CUSTOM_WAKE_WORD_THRESHOLD=' in line and not line.startswith('#'): # Extract numeric value value = line.split('=', 1)[1] try: config_values['threshold'] = int(value) except ValueError: try: config_values['threshold'] = float(value) except ValueError: print(f"Warning: Invalid threshold value: {value}") config_values['threshold'] = 20 # default (will be converted to 0.2) # Return config only if custom wake word is enabled and required fields are present if (config_values.get('use_custom_wake_word', False) and 'wake_word' in config_values and 'display' in config_values and 'threshold' in config_values): return { 'wake_word': config_values['wake_word'], 'display': config_values['display'], 'threshold': config_values['threshold'] / 100.0 # Convert to decimal (20 -> 0.2) } return None def get_language_from_multinet_models(multinet_models): """ Determine language from multinet model names Returns 'cn', 'en', or None """ if not multinet_models: return None # Check for Chinese models cn_indicators = ['_cn', 'cn_'] en_indicators = ['_en', 'en_'] has_cn = any(any(indicator in model for indicator in cn_indicators) for model in multinet_models) has_en = any(any(indicator in model for indicator in en_indicators) for model in multinet_models) # If both or neither, default to cn if has_cn and not has_en: return 'cn' elif has_en and not has_cn: return 'en' else: return 'cn' # Default to Chinese def get_wakenet_model_paths(model_names, esp_sr_model_path): """ Get the full paths to the wakenet model directories Returns a list of valid model paths """ if not model_names: return [] valid_paths = [] for model_name in model_names: wakenet_model_path = os.path.join(esp_sr_model_path, 'wakenet_model', model_name) if os.path.exists(wakenet_model_path): valid_paths.append(wakenet_model_path) else: print(f"Warning: Wakenet model directory not found: {wakenet_model_path}") return valid_paths def get_multinet_model_paths(model_names, esp_sr_model_path): """ Get the full paths to the multinet model directories Returns a list of valid model paths """ if not model_names: return [] valid_paths = [] for model_name in model_names: multinet_model_path = os.path.join(esp_sr_model_path, 'multinet_model', model_name) if os.path.exists(multinet_model_path): valid_paths.append(multinet_model_path) else: print(f"Warning: Multinet model directory not found: {multinet_model_path}") return valid_paths def get_text_font_path(builtin_text_font, xiaozhi_fonts_path): """ Get the text font path if needed Returns the font file path or None if no font is needed """ if not builtin_text_font or 'basic' not in builtin_text_font: return None # Convert from basic to common font name # e.g., font_puhui_basic_16_4 -> font_puhui_common_16_4.bin font_name = builtin_text_font.replace('basic', 'common') + '.bin' font_path = os.path.join(xiaozhi_fonts_path, 'cbin', font_name) if os.path.exists(font_path): return font_path else: print(f"Warning: Font file not found: {font_path}") return None def get_emoji_collection_path(default_emoji_collection, xiaozhi_fonts_path): """ Get the emoji collection path if needed Returns the emoji directory path or None if no emoji collection is needed """ if not default_emoji_collection: return None emoji_path = os.path.join(xiaozhi_fonts_path, 'png', default_emoji_collection) if os.path.exists(emoji_path): return emoji_path else: print(f"Warning: Emoji collection directory not found: {emoji_path}") return None def build_assets_integrated(wakenet_model_paths, multinet_model_paths, text_font_path, emoji_collection_path, extra_files_path, output_path, multinet_model_info=None): """ Build assets using integrated functions (no external dependencies) """ # Create temporary build directory temp_build_dir = os.path.join(os.path.dirname(output_path), "temp_build") assets_dir = os.path.join(temp_build_dir, "assets") try: # Clean and create directories if os.path.exists(temp_build_dir): shutil.rmtree(temp_build_dir) ensure_dir(temp_build_dir) ensure_dir(assets_dir) print("Starting to build assets...") # Process each component srmodels = process_sr_models(wakenet_model_paths, multinet_model_paths, temp_build_dir, assets_dir) if (wakenet_model_paths or multinet_model_paths) else None text_font = process_text_font(text_font_path, assets_dir) if text_font_path else None emoji_collection = process_emoji_collection(emoji_collection_path, assets_dir) if emoji_collection_path else None extra_files = process_extra_files(extra_files_path, assets_dir) if extra_files_path else None # Generate index.json generate_index_json(assets_dir, srmodels, text_font, emoji_collection, extra_files, multinet_model_info) # Generate config.json for packing config_path = generate_config_json(temp_build_dir, assets_dir) # Load config and pack assets with open(config_path, 'r') as f: config_data = json.load(f) # Use simplified packing function include_path = config_data['include_path'] image_file = config_data['image_file'] pack_assets_simple(assets_dir, include_path, image_file, "assets", int(config_data['name_length'])) # Copy final assets.bin to output location if os.path.exists(image_file): shutil.copy2(image_file, output_path) print(f"Successfully generated assets.bin: {output_path}") # Show size information total_size = os.path.getsize(output_path) print(f"Assets file size: {total_size / 1024:.2f}K ({total_size} bytes)") return True else: print(f"Error: Generated assets.bin not found: {image_file}") return False except Exception as e: print(f"Error: Failed to build assets: {e}") return False finally: # Clean up temporary directory if os.path.exists(temp_build_dir): shutil.rmtree(temp_build_dir) def main(): parser = argparse.ArgumentParser(description='Build default assets based on configuration') parser.add_argument('--sdkconfig', required=True, help='Path to sdkconfig file') parser.add_argument('--builtin_text_font', help='Builtin text font name (e.g., font_puhui_basic_16_4)') parser.add_argument('--emoji_collection', help='Default emoji collection name (e.g., emojis_32)') parser.add_argument('--output', required=True, help='Output path for assets.bin') parser.add_argument('--esp_sr_model_path', help='Path to ESP-SR model directory') parser.add_argument('--xiaozhi_fonts_path', help='Path to xiaozhi-fonts component directory') parser.add_argument('--extra_files', help='Path to extra files directory to be included in assets') args = parser.parse_args() # Set default paths if not provided if not args.esp_sr_model_path or not args.xiaozhi_fonts_path: # Calculate project root from script location script_dir = os.path.dirname(os.path.abspath(__file__)) project_root = os.path.dirname(script_dir) if not args.esp_sr_model_path: args.esp_sr_model_path = os.path.join(project_root, "managed_components", "espressif__esp-sr", "model") if not args.xiaozhi_fonts_path: args.xiaozhi_fonts_path = os.path.join(project_root, "components", "xiaozhi-fonts") print("Building default assets...") print(f" sdkconfig: {args.sdkconfig}") print(f" builtin_text_font: {args.builtin_text_font}") print(f" emoji_collection: {args.emoji_collection}") print(f" output: {args.output}") # Read wake word type configuration from sdkconfig wake_word_config = read_wake_word_type_from_sdkconfig(args.sdkconfig) # Read SR models from sdkconfig wakenet_model_names = read_wakenet_from_sdkconfig(args.sdkconfig) multinet_model_names = read_multinet_from_sdkconfig(args.sdkconfig) # Apply wake word logic to decide which models to package wakenet_model_paths = [] multinet_model_paths = [] # 1. Only package wakenet models if USE_ESP_WAKE_WORD=y or USE_AFE_WAKE_WORD=y if wake_word_config['use_esp_wake_word'] or wake_word_config['use_afe_wake_word']: wakenet_model_paths = get_wakenet_model_paths(wakenet_model_names, args.esp_sr_model_path) elif wakenet_model_names: print(f" Note: Found wakenet models {wakenet_model_names} but wake word type is not ESP/AFE, skipping") # 2. Error check: if USE_CUSTOM_WAKE_WORD=y but no multinet models selected, report error if wake_word_config['use_custom_wake_word'] and not multinet_model_names: print("Error: USE_CUSTOM_WAKE_WORD is enabled but no multinet models are selected in sdkconfig") print("Please select appropriate CONFIG_SR_MN_* options in menuconfig, or disable USE_CUSTOM_WAKE_WORD") sys.exit(1) # 3. Only package multinet models if USE_CUSTOM_WAKE_WORD=y if wake_word_config['use_custom_wake_word']: multinet_model_paths = get_multinet_model_paths(multinet_model_names, args.esp_sr_model_path) elif multinet_model_names: print(f" Note: Found multinet models {multinet_model_names} but USE_CUSTOM_WAKE_WORD is disabled, skipping") # Print model information (only for models that will actually be packaged) if wakenet_model_paths: print(f" wakenet models: {', '.join(wakenet_model_names)} (will be packaged)") if multinet_model_paths: print(f" multinet models: {', '.join(multinet_model_names)} (will be packaged)") # Get text font path if needed text_font_path = get_text_font_path(args.builtin_text_font, args.xiaozhi_fonts_path) # Get emoji collection path if needed emoji_collection_path = get_emoji_collection_path(args.emoji_collection, args.xiaozhi_fonts_path) # Get extra files path if provided extra_files_path = args.extra_files # Read custom wake word configuration custom_wake_word_config = read_custom_wake_word_from_sdkconfig(args.sdkconfig) multinet_model_info = None if custom_wake_word_config and multinet_model_paths: # Determine language from multinet models language = get_language_from_multinet_models(multinet_model_names) # Build multinet_model info structure multinet_model_info = { "language": language, "duration": 3000, # Default duration in ms "threshold": custom_wake_word_config['threshold'], "commands": [ { "command": custom_wake_word_config['wake_word'], "text": custom_wake_word_config['display'], "action": "wake" } ] } print(f" custom wake word: {custom_wake_word_config['wake_word']} ({custom_wake_word_config['display']})") print(f" wake word language: {language}") print(f" wake word threshold: {custom_wake_word_config['threshold']}") # Check if we have anything to build if not wakenet_model_paths and not multinet_model_paths and not text_font_path and not emoji_collection_path and not extra_files_path and not multinet_model_info: print("Warning: No assets to build (no SR models, text font, emoji collection, extra files, or custom wake word)") # Create an empty assets.bin file os.makedirs(os.path.dirname(args.output), exist_ok=True) with open(args.output, 'wb') as f: pass # Create empty file print(f"Created empty assets.bin: {args.output}") return # Build the assets success = build_assets_integrated(wakenet_model_paths, multinet_model_paths, text_font_path, emoji_collection_path, extra_files_path, args.output, multinet_model_info) if not success: sys.exit(1) print("Build completed successfully!") if __name__ == "__main__": main()