# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb. # %% auto 0 __all__ = ['secret_import_failed', 'TEMP', 'TEMP_DIR', 'tts_voices', 'clean_text_prompt', 'OPENAI_CLIENT_TTS_THREADS', 'launch_kwargs', 'queue_kwargs', 'split_text', 'concatenate_mp3', 'create_speech_openai', 'create_speech2', 'create_speech', 'get_input_text_len', 'get_generation_cost', 'authorized'] # %% app.ipynb 1 #tts_openai_secrets.py content: #import os #os.environ['OPENAI_API_KEY'] = 'sk-XXXXXXXXXXXXXXXXXXXXXX' import os secret_import_failed = False try: _ = os.environ['OPENAI_API_KEY'] print('OPENAI_API_KEY environment variable was found.') except: print('OPENAI_API_KEY environment variable was not found.') secret_import_failed = True try: GRADIO_PASSWORD = os.environ['GRADIO_PASSWORD'] print('GRADIO_PASSWORD environment variable was found.') except: print('GRADIO_PASSWORD environment variable was not found.') secret_import_failed = True if secret_import_failed == True: import tts_openai_secrets GRADIO_PASSWORD = os.environ['GRADIO_PASSWORD'] print('import tts_openai_secrets succeeded') # %% app.ipynb 3 import gradio as gr import openai from pydub import AudioSegment import io from datetime import datetime from math import ceil from multiprocessing.pool import ThreadPool from functools import partial from pathlib import Path import uuid from tenacity import ( retry, stop_after_attempt, wait_random_exponential, ) # for exponential backoff # %% app.ipynb 4 TEMP = os.environ['TEMP'] TEMP_DIR = Path(TEMP) print('TEMP Dir:', TEMP_DIR) # %% app.ipynb 5 try: tts_models = [o.id for o in openai.models.list().data if 'tts' in o.id] print('successfully got tts model list:', tts_models) except: tts_models = ['tts-1'] # %% app.ipynb 6 tts_voices = ['alloy', 'echo', 'fable', 'onyx', 'nova', 'shimmer'] # %% app.ipynb 7 clean_text_prompt = """Your job is to clean up text that is going to be fed into a text to speech (TTS) model. You must remove parts of the text that would not normally be spoken such as reference marks `[1]`, spurious citations such as `(Reddy et al., 2021; Wu et al., 2022; Chang et al., 2022; Kondratyuk et al., 2023)` and any other part of the text that is not normally spoken. Please also clean up sections and headers so they are on new lines with proper numbering. You must also clean up any math formulas that are salvageable from being copied from a scientific paper. If they are garbled and do not make sense then remove them. You must carefully perform the text cleanup so it is translated into speech that is easy to listen to however you must not modify the text otherwise. It is critical that you repeat all of the text without modifications except for the cleanup activities you've been instructed to do. Also you must clean all of the text you are given, you may not omit any of it or stop the cleanup task early.""" # %% app.ipynb 8 #Number of threads created PER USER REQUEST. This throttels the # of API requests PER USER request. This is in ADDITION to the Gradio threads. OPENAI_CLIENT_TTS_THREADS = 10 # %% app.ipynb 9 def split_text(input_text, max_length=4000, lookback=1000): # If the text is shorter than the max_length, return it as is if len(input_text) <= max_length: return [input_text] chunks = [] while input_text: # Check if the remaining text is shorter than the max_length if len(input_text) <= max_length: chunks.append(input_text) break # Define the split point, initially set to max_length split_point = max_length # Look for a newline in the last 'lookback' characters newline_index = input_text.rfind('\n', max_length-lookback, max_length) if newline_index != -1: split_point = newline_index + 1 # Include the newline in the current chunk # If no newline, look for a period followed by space elif '. ' in input_text[max_length-lookback:max_length]: # Find the last '. ' in the lookback range period_index = input_text.rfind('. ', max_length-lookback, max_length) split_point = period_index + 2 # Split after the space # Split the text and update the input_text chunks.append(input_text[:split_point]) input_text = input_text[split_point:] return chunks # %% app.ipynb 10 def concatenate_mp3(mp3_files): if len(mp3_files) == 1: return mp3_files[0] else: # Initialize an empty AudioSegment object for concatenation combined = AudioSegment.empty() # Write out audio file responses as individual files for debugging # for idx, mp3_data in enumerate(mp3_files): # with open(f'./{idx}.mp3', 'wb') as f: # f.write(mp3_data) # Loop through the list of mp3 binary data for mp3_data in mp3_files: # Convert binary data to an audio segment audio_segment = AudioSegment.from_file(io.BytesIO(mp3_data), format="mp3") # Concatenate this segment to the combined segment combined += audio_segment #### Return Bytes Method # # Export the combined segment to a new mp3 file # # Use a BytesIO object to handle this in memory # combined_mp3 = io.BytesIO() # combined.export(combined_mp3, format="mp3") # # Seek to the start so it's ready for reading # combined_mp3.seek(0) # return combined_mp3.getvalue() #### Return Filepath Method filepath = TEMP_DIR/(str(uuid.uuid4())+'.mp3') combined.export(filepath, format="mp3") return str(filepath) # %% app.ipynb 11 def create_speech_openai(chunk_idx, input, model='tts-1', voice='alloy', speed=1.0, **kwargs): client = openai.OpenAI() @retry(wait=wait_random_exponential(min=1, max=180), stop=stop_after_attempt(6)) def _create_speech_with_backoff(**kwargs): return client.audio.speech.create(**kwargs) response = _create_speech_with_backoff(input=input, model=model, voice=voice, speed=speed, **kwargs) client.close() return chunk_idx, response.content # %% app.ipynb 12 def create_speech2(input_text, model='tts-1', voice='alloy', profile: gr.OAuthProfile|None=None, progress=gr.Progress(), **kwargs): print('cs2-profile:',profile) assert authorized(profile) is not None,'Unauthorized M' start = datetime.now() # Split the input text into chunks chunks = split_text(input_text) # Initialize the progress bar progress(0, desc=f"Started processing {len(chunks)} text chunks using {OPENAI_CLIENT_TTS_THREADS} threads. ETA is ~{ceil(len(chunks)/OPENAI_CLIENT_TTS_THREADS)} min.") # Initialize a list to hold the audio data of each chunk audio_data = [] # Process each chunk with ThreadPool(processes=OPENAI_CLIENT_TTS_THREADS) as pool: results = pool.starmap( partial(create_speech_openai, model=model, voice=voice, **kwargs), zip(range(len(chunks)),chunks) ) audio_data = [o[1] for o in sorted(results)] # Progress progress(.9, desc=f"Merging audio chunks... {(datetime.now()-start).seconds} seconds to process.") # Concatenate the audio data from all chunks combined_audio = concatenate_mp3(audio_data) # Final update to the progress bar progress(1, desc=f"Processing completed... {(datetime.now()-start).seconds} seconds to process.") print(f"Processing time: {(datetime.now()-start).seconds} seconds.") return combined_audio # %% app.ipynb 13 def create_speech(input_text, model='tts-1', voice='alloy', profile: gr.OAuthProfile|None=None, progress=gr.Progress()): assert authorized(profile) is not None,'Unauthorized M' # Split the input text into chunks chunks = split_text(input_text) # Initialize the progress bar progress(0, desc="Starting TTS processing...") # Initialize a list to hold the audio data of each chunk audio_data = [] # Create a client instance for OpenAI client = openai.OpenAI() # Calculate the progress increment for each chunk progress_increment = 1.0 / len(chunks) # Process each chunk for i, chunk in enumerate(chunks): response = client.audio.speech.create( model=model, voice=voice, input=chunk, speed=1.0 ) # Append the audio content of the response to the list audio_data.append(response.content) # Update the progress bar progress((i + 1) * progress_increment, desc=f"Processing chunk {i + 1} of {len(chunks)}") # Close the client connection client.close() # Concatenate the audio data from all chunks combined_audio = concatenate_mp3(audio_data) # Final update to the progress bar progress(1, desc="Processing completed") return combined_audio # %% app.ipynb 14 def get_input_text_len(input_text): return len(input_text) # %% app.ipynb 15 def get_generation_cost(input_text, tts_model_dropdown): text_len = len(input_text) if tts_model_dropdown.endswith('-hd'): cost = text_len/1000 * 0.03 else: cost = text_len/1000 * 0.015 return "${:,.3f}".format(cost) # %% app.ipynb 16 def authorized(profile: gr.OAuthProfile=None) -> str: print('Profile:', profile) if profile is not None and profile.username in ["matdmiller"]: return f"{profile.username}" else: print('Unauthorized',profile) return None # %% app.ipynb 17 with gr.Blocks(title='OpenAI TTS', head='OpenAI TTS') as app: gr.Markdown("# OpenAI TTS") gr.Markdown("""Start typing below and then click **Go** to create the speech from your text. The current limit is 4,000 characters. For requests longer than 4,000 chars they will be broken into chunks of 4,000 or less chars automatically. [Spaces Link](https://matdmiller-tts-openai.hf.space/)""") with gr.Row(): input_text = gr.Textbox(max_lines=100, label="Enter text here") with gr.Row(): tts_model_dropdown = gr.Dropdown(value='tts-1',choices=tts_models, label='Model') tts_voice_dropdown = gr.Dropdown(value='alloy',choices=tts_voices,label='Voice') input_text_length = gr.Label(label="Number of characters") generation_cost = gr.Label(label="Generation cost") output_audio = gr.Audio() input_text.input(fn=get_input_text_len, inputs=input_text, outputs=input_text_length) input_text.input(fn=get_generation_cost, inputs=[input_text,tts_model_dropdown], outputs=generation_cost) tts_model_dropdown.input(fn=get_generation_cost, inputs=[input_text,tts_model_dropdown], outputs=generation_cost) go_btn = gr.Button("Go") go_btn.click(fn=create_speech2, inputs=[input_text, tts_model_dropdown, tts_voice_dropdown], outputs=[output_audio]) clear_btn = gr.Button('Clear') clear_btn.click(fn=lambda: '', outputs=input_text) gr.LoginButton() m = gr.Markdown('') app.load(authorized, None, m) # %% app.ipynb 18 # launch_kwargs = {'auth':('username',GRADIO_PASSWORD), # 'auth_message':'Please log in to Mat\'s TTS App with username: username and password.'} launch_kwargs = {} queue_kwargs = {'default_concurrency_limit':10} # %% app.ipynb 20 #.py launch if __name__ == "__main__": app.queue(**queue_kwargs) app.launch(**launch_kwargs)