import gradio as gr import tempfile import openai import requests import os from functools import partial def tts( input_text: str, model: str, voice: str, api_key: str, response_format: str = "mp3", speed: float = 1.0, ) -> str: """ Convert input text to speech using OpenAI's Text-to-Speech API. Parameters: input_text (str): The text to be converted to speech. model (str): The model to use for synthesis (e.g., 'tts-1', 'tts-1-hd'). voice (str): The voice profile to use (e.g., 'alloy', 'echo', 'fable', etc.). api_key (str): OpenAI API key. response_format (str): The audio format of the output file (default is 'mp3'). speed (float): The speed of the synthesized speech (0.25 to 4.0). Returns: str: File path to the generated audio file. Raises: gr.Error: If input parameters are invalid or API call fails. """ if not api_key.strip(): raise gr.Error( "API key is required. Get an API key at: https://platform.openai.com/account/api-keys" ) if not input_text.strip(): raise gr.Error("Input text cannot be empty.") openai.api_key = api_key try: response = openai.audio.speech.create( model=model, voice=voice, input=input_text, response_format=response_format, speed=speed, ) except openai.error.OpenAIError as e: # Catch OpenAI exceptions raise gr.Error(f"An OpenAI error occurred: {e}") except Exception as e: # Catch any other exceptions raise gr.Error(f"An unexpected error occurred: {e}") # Save the audio content to a temporary file file_extension = f".{response_format}" with tempfile.NamedTemporaryFile(suffix=file_extension, delete=False) as temp_file: response.stream_to_file(temp_file.name) temp_file_path = temp_file.name return temp_file_path def main(): """ Main function to create and launch the Gradio interface. """ MODEL_OPTIONS = ["tts-1", "tts-1-hd"] VOICE_OPTIONS = ["alloy", "echo", "fable", "onyx", "nova", "shimmer"] RESPONSE_FORMAT_OPTIONS = ["mp3", "opus", "aac", "flac", "wav", "pcm"] # Predefine voice previews URLs VOICE_PREVIEW_URLS = { voice: f"https://cdn.openai.com/API/docs/audio/{voice}.wav" for voice in VOICE_OPTIONS } # Download audio previews to disk before initiating the interface PREVIEW_DIR = "voice_previews" os.makedirs(PREVIEW_DIR, exist_ok=True) VOICE_PREVIEW_FILES = {} for voice, url in VOICE_PREVIEW_URLS.items(): local_file_path = os.path.join(PREVIEW_DIR, f"{voice}.wav") if not os.path.exists(local_file_path): try: response = requests.get(url) response.raise_for_status() with open(local_file_path, "wb") as f: f.write(response.content) except requests.exceptions.RequestException as e: print(f"Failed to download {voice} preview: {e}") VOICE_PREVIEW_FILES[voice] = local_file_path # Set static paths for Gradio to serve gr.set_static_paths(paths=[PREVIEW_DIR]) # Create the 'preview_audio' component with gr.Blocks(title="OpenAI - Text to Speech") as demo: with gr.Row(): with gr.Column(scale=1): # Function to play the selected voice sample def play_voice_sample(voice): return gr.update( value=VOICE_PREVIEW_FILES[voice], label=f"Preview Voice: {voice.capitalize()}", ) preview_audio = gr.Audio( interactive=False, label="Echo", value=VOICE_PREVIEW_FILES['echo'], visible=True, show_download_button=False, show_share_button=False, autoplay=False, ) with gr.Group(): # Create buttons for each voice for voice in VOICE_OPTIONS: voice_button = gr.Button( value=f"{voice.capitalize()}", variant="secondary", size="sm", ) voice_button.click( fn=partial(play_voice_sample, voice=voice), outputs=preview_audio, ) with gr.Column(scale=1): api_key_input = gr.Textbox( label="OpenAI API Key", info="https://platform.openai.com/account/api-keys", type="password", placeholder="Enter your OpenAI API Key", ) model_dropdown = gr.Dropdown( choices=MODEL_OPTIONS, label="Model", value="tts-1", info="Select tts-1 for speed or tts-1-hd for quality.", ) voice_dropdown = gr.Dropdown( choices=VOICE_OPTIONS, label="Voice Options", value="echo", ) response_format_dropdown = gr.Dropdown( choices=RESPONSE_FORMAT_OPTIONS, label="Response Format", value="mp3", ) speed_slider = gr.Slider( minimum=0.25, maximum=4.0, step=0.05, label="Voice Speed", value=1.0, ) with gr.Column(scale=2): # Initialize the input textbox with the desired label input_textbox = gr.Textbox( label="Input Text (0000 / 4096 chars)", lines=10, placeholder="Type your text here...", ) # Function to update the label with the character count def update_label(input_text): char_count = len(input_text) new_label = f"Input Text ({char_count:04d} / 4096 chars)" return gr.update(label=new_label) # Update the label when the text changes, with progress hidden input_textbox.change( fn=update_label, inputs=input_textbox, outputs=input_textbox, show_progress='hidden', # Hide the progress indicator ) submit_button = gr.Button( "Convert Text to Speech", variant="primary", ) with gr.Column(scale=1): output_audio = gr.Audio(label="Output Audio") # Define the event handler for the submit button with error handling def on_submit( input_text, model, voice, api_key, response_format, speed ): audio_file = tts( input_text, model, voice, api_key, response_format, speed ) return audio_file # Trigger the conversion when the submit button is clicked submit_button.click( fn=on_submit, inputs=[ input_textbox, model_dropdown, voice_dropdown, api_key_input, response_format_dropdown, speed_slider, ], outputs=output_audio, ) # Launch the Gradio app with error display enabled demo.launch(show_error=True) if __name__ == "__main__": main()