import datetime import os from string import Template import openai import re import glob import pickle import time import json5 from retrying import retry from code_generator import check_json_script, collect_and_check_audio_data import random import string import utils import voice_presets from code_generator import AudioCodeGenerator # Enable this for debugging USE_OPENAI_CACHE = False openai_cache = [] if USE_OPENAI_CACHE: os.makedirs('cache', exist_ok=True) for cache_file in glob.glob('cache/*.pkl'): with open(cache_file, 'rb') as file: openai_cache.append(pickle.load(file)) def chat_with_gpt(prompt, api_key): if USE_OPENAI_CACHE: filtered_object = list(filter(lambda x: x['prompt'] == prompt, openai_cache)) if len(filtered_object) > 0: response = filtered_object[0]['response'] return response try: openai.api_key = api_key chat = openai.ChatCompletion.create( # model="gpt-3.5-turbo", model="gpt-4", messages=[ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": prompt } ] ) finally: openai.api_key = '' if USE_OPENAI_CACHE: cache_obj = { 'prompt': prompt, 'response': chat['choices'][0]['message']['content'] } with open(f'cache/{time.time()}.pkl', 'wb') as _openai_cache: pickle.dump(cache_obj, _openai_cache) openai_cache.append(cache_obj) return chat['choices'][0]['message']['content'] def get_file_content(filename): with open(filename, 'r') as file: return file.read().strip() def write_to_file(filename, content): with open(filename, 'w') as file: file.write(content) def extract_substring_with_quotes(input_string, quotes="'''"): pattern = f"{quotes}(.*?){quotes}" matches = re.findall(pattern, input_string, re.DOTALL) return matches def try_extract_content_from_quotes(content): if "'''" in content: return extract_substring_with_quotes(content)[0] elif "```" in content: return extract_substring_with_quotes(content, quotes="```")[0] else: return content def maybe_get_content_from_file(content_or_filename): if os.path.exists(content_or_filename): with open(content_or_filename, 'r') as file: return file.read().strip() return content_or_filename # Pipeline Interface Guidelines: # # Init calls: # - Init calls must be called before running the actual steps # - init_session() is called every time a gradio webpage is loaded # # Single Step: # - takes input (file or content) and output path as input # - most of time just returns output content # # Compositional Step: # - takes session_id as input (you have session_id, you have all the paths) # - run a series of steps # This is called for every new gradio webpage def init_session(session_id=''): def uid8(): return ''.join(random.choices(string.ascii_lowercase + string.digits, k=8)) if session_id == '': session_id = f'{datetime.datetime.now().strftime("%Y%m%d%H%M%S")}_{uid8()}' # create the paths os.makedirs(utils.get_session_voice_preset_path(session_id)) os.makedirs(utils.get_session_audio_path(session_id)) return session_id @retry(stop_max_attempt_number=3) def input_text_to_json_script_with_retry(complete_prompt_path, api_key): print(" trying ...") complete_prompt = get_file_content(complete_prompt_path) json_response = try_extract_content_from_quotes(chat_with_gpt(complete_prompt, api_key)) json_data = json5.loads(json_response) try: check_json_script(json_data) collect_and_check_audio_data(json_data) except Exception as err: print(f'JSON ERROR: {err}') retry_complete_prompt = f'{complete_prompt}\n```\n{json_response}```\nThe script above has format error(s). Return the fixed script.\n\nScript:\n' write_to_file(complete_prompt_path, retry_complete_prompt) raise err return json_response # Step 1: input_text to json def input_text_to_json_script(input_text, output_path, api_key): print('Step 1: Writing audio script with LLM ...') input_text = maybe_get_content_from_file(input_text) text_to_audio_script_prompt = get_file_content('prompts/text_to_json.prompt') prompt = f'{text_to_audio_script_prompt}\n\nInput text: {input_text}\n\nScript:\n' complete_prompt_path = output_path / 'complete_input_text_to_audio_script.prompt' write_to_file(complete_prompt_path, prompt) audio_script_response = input_text_to_json_script_with_retry(complete_prompt_path, api_key) generated_audio_script_filename = output_path / 'audio_script.json' write_to_file(generated_audio_script_filename, audio_script_response) return audio_script_response # Step 2: json to char-voice map def json_script_to_char_voice_map(json_script, voices, output_path, api_key): print('Step 2: Parsing character voice with LLM...') json_script_content = maybe_get_content_from_file(json_script) prompt = get_file_content('prompts/audio_script_to_character_voice_map.prompt') presets_str = '\n'.join(f"{preset['id']}: {preset['desc']}" for preset in voices.values()) prompt = Template(prompt).substitute(voice_and_desc=presets_str) prompt = f"{prompt}\n\nAudio script:\n'''\n{json_script_content}\n'''\n\noutput:\n" write_to_file(output_path / 'complete_audio_script_to_char_voice_map.prompt', prompt) char_voice_map_response = try_extract_content_from_quotes(chat_with_gpt(prompt, api_key)) char_voice_map = json5.loads(char_voice_map_response) # enrich char_voice_map with voice preset metadata complete_char_voice_map = {c: voices[char_voice_map[c]] for c in char_voice_map} char_voice_map_filename = output_path / 'character_voice_map.json' write_to_file(char_voice_map_filename, json5.dumps(complete_char_voice_map)) return complete_char_voice_map # Step 3: json to py code def json_script_and_char_voice_map_to_audio_gen_code(json_script_filename, char_voice_map_filename, output_path, result_filename): print('Step 3: Compiling audio script to Python program ...') audio_code_generator = AudioCodeGenerator() code = audio_code_generator.parse_and_generate( json_script_filename, char_voice_map_filename, output_path, result_filename ) write_to_file(output_path / 'audio_generation.py', code) # Step 4: py code to final wav def audio_code_gen_to_result(audio_gen_code_path): print('Step 4: Start running Python program ...') audio_gen_code_filename = audio_gen_code_path / 'audio_generation.py' os.system(f'python {audio_gen_code_filename}') # Function call used by Gradio: input_text to json def generate_json_file(session_id, input_text, api_key): output_path = utils.get_session_path(session_id) # Step 1 return input_text_to_json_script(input_text, output_path, api_key) # Function call used by Gradio: json to result wav def generate_audio(session_id, json_script, api_key): output_path = utils.get_session_path(session_id) output_audio_path = utils.get_session_audio_path(session_id) voices = voice_presets.get_merged_voice_presets(session_id) # Step 2 char_voice_map = json_script_to_char_voice_map(json_script, voices, output_path, api_key) # Step 3 json_script_filename = output_path / 'audio_script.json' char_voice_map_filename = output_path / 'character_voice_map.json' result_wav_basename = f'res_{session_id}' json_script_and_char_voice_map_to_audio_gen_code(json_script_filename, char_voice_map_filename, output_path, result_wav_basename) # Step 4 audio_code_gen_to_result(output_path) result_wav_filename = output_audio_path / f'{result_wav_basename}.wav' print(f'Done all processes, result: {result_wav_filename}') return result_wav_filename, char_voice_map # Convenient function call used by wavjourney_cli def full_steps(session_id, input_text, api_key): json_script = generate_json_file(session_id, input_text, api_key) return generate_audio(session_id, json_script, api_key)