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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "3bedf0dc-8d8e-4ede-a9e6-b8f35136aa00",
"metadata": {},
"outputs": [],
"source": [
"#|default_exp app"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "667802a7-0f36-4136-a381-e66210b20462",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"#tts_openai_secrets.py content:\n",
"#import os\n",
"#os.environ['OPENAI_API_KEY'] = 'sk-XXXXXXXXXXXXXXXXXXXXXX'\n",
"import os\n",
"secret_import_failed = False\n",
"try:\n",
" _ = os.environ['OPENAI_API_KEY']\n",
" print('OPENAI_API_KEY environment variable was found.')\n",
"except:\n",
" print('OPENAI_API_KEY environment variable was not found.')\n",
" secret_import_failed = True\n",
"try:\n",
" GRADIO_PASSWORD = os.environ['GRADIO_PASSWORD']\n",
" print('GRADIO_PASSWORD environment variable was found.')\n",
"except:\n",
" print('GRADIO_PASSWORD environment variable was not found.')\n",
" secret_import_failed = True\n",
"\n",
"if secret_import_failed == True:\n",
" import tts_openai_secrets\n",
" GRADIO_PASSWORD = os.environ['GRADIO_PASSWORD']\n",
" print('import tts_openai_secrets succeeded')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d9863fc-969e-409b-8e20-b9c3cd2cc3e7",
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"try:\n",
" import nbdev\n",
"except:\n",
" print('to convert this notebook to app.py you need to pip install nbdev')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f486d3a",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"import gradio as gr\n",
"import openai\n",
"from pydub import AudioSegment\n",
"import io"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ffd33b4-cb9b-4c01-bff6-4c3102854ab6",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"try:\n",
" tts_models = [o.id for o in openai.models.list().data if 'tts' in o.id]\n",
" print('successfully got tts model list:', tts_models)\n",
"except:\n",
" tts_models = ['tts-1']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ddbca5d-4b04-43ab-afaf-430802980e78",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"tts_voices = ['alloy', 'echo', 'fable', 'onyx', 'nova', 'shimmer']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24674094-4d47-4e48-b591-55faabcff8df",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"def split_text(input_text, max_length=4000, lookback=1000):\n",
" # If the text is shorter than the max_length, return it as is\n",
" if len(input_text) <= max_length:\n",
" return [input_text]\n",
"\n",
" chunks = []\n",
" while input_text:\n",
" # Check if the remaining text is shorter than the max_length\n",
" if len(input_text) <= max_length:\n",
" chunks.append(input_text)\n",
" break\n",
"\n",
" # Define the split point, initially set to max_length\n",
" split_point = max_length\n",
"\n",
" # Look for a newline in the last 'lookback' characters\n",
" newline_index = input_text.rfind('\\n', max_length-lookback, max_length)\n",
" if newline_index != -1:\n",
" split_point = newline_index + 1 # Include the newline in the current chunk\n",
"\n",
" # If no newline, look for a period followed by space\n",
" elif '. ' in input_text[max_length-lookback:max_length]:\n",
" # Find the last '. ' in the lookback range\n",
" period_index = input_text.rfind('. ', max_length-lookback, max_length)\n",
" split_point = period_index + 2 # Split after the space\n",
"\n",
" # Split the text and update the input_text\n",
" chunks.append(input_text[:split_point])\n",
" input_text = input_text[split_point:]\n",
"\n",
" return chunks"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e6224ae5-3792-42b2-8392-3abd42998a50",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"def concatenate_mp3(mp3_files):\n",
" if len(mp3_files) == 1:\n",
" return mp3_files[0]\n",
" else:\n",
" # Initialize an empty AudioSegment object for concatenation\n",
" combined = AudioSegment.empty()\n",
" \n",
" # Write out audio file responses as individual files for debugging\n",
" # for idx, mp3_data in enumerate(mp3_files):\n",
" # with open(f'./{idx}.mp3', 'wb') as f:\n",
" # f.write(mp3_data)\n",
"\n",
" # Loop through the list of mp3 binary data\n",
" for mp3_data in mp3_files:\n",
" # Convert binary data to an audio segment\n",
" audio_segment = AudioSegment.from_file(io.BytesIO(mp3_data), format=\"mp3\")\n",
" # Concatenate this segment to the combined segment\n",
" combined += audio_segment\n",
"\n",
" # Export the combined segment to a new mp3 file\n",
" # Use a BytesIO object to handle this in memory\n",
" combined_mp3 = io.BytesIO()\n",
" combined.export(combined_mp3, format=\"mp3\")\n",
"\n",
" # Seek to the start so it's ready for reading\n",
" combined_mp3.seek(0)\n",
"\n",
" return combined_mp3.getvalue()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5388e860",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"def create_speech(input_text, model='tts-1', voice='alloy', progress=gr.Progress()):\n",
" # Split the input text into chunks\n",
" chunks = split_text(input_text)\n",
"\n",
" # Initialize the progress bar\n",
" progress(0, desc=\"Starting TTS processing...\")\n",
"\n",
" # Initialize a list to hold the audio data of each chunk\n",
" audio_data = []\n",
"\n",
" # Create a client instance for OpenAI\n",
" client = openai.OpenAI()\n",
"\n",
" # Calculate the progress increment for each chunk\n",
" progress_increment = 1.0 / len(chunks)\n",
"\n",
" # Process each chunk\n",
" for i, chunk in enumerate(chunks):\n",
" response = client.audio.speech.create(\n",
" model=model,\n",
" voice=voice,\n",
" input=chunk,\n",
" speed=1.0\n",
" )\n",
" # Append the audio content of the response to the list\n",
" audio_data.append(response.content)\n",
"\n",
" # Update the progress bar\n",
" progress((i + 1) * progress_increment, desc=f\"Processing chunk {i + 1} of {len(chunks)}\")\n",
"\n",
" # Close the client connection\n",
" client.close()\n",
"\n",
" # Concatenate the audio data from all chunks\n",
" combined_audio = concatenate_mp3(audio_data)\n",
"\n",
" # Final update to the progress bar\n",
" progress(1, desc=\"Processing completed\")\n",
"\n",
" return combined_audio\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "236dd8d3-4364-4731-af93-7dcdec6f18a1",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"def get_input_text_len(input_text):\n",
" return len(input_text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e4fb3159-579b-4271-bc96-4cd1e2816eca",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"with gr.Blocks(title='OpenAI TTS', head='OpenAI TTS') as app:\n",
" gr.Markdown(\"# OpenAI TTS\")\n",
" gr.Markdown(\"Start typing below and then click **Go** to create the speech from your text. The current limit is 4,000 characters.\")\n",
" with gr.Row():\n",
" input_text = gr.Textbox(max_lines=100, label=\"Enter text here\")\n",
" with gr.Row():\n",
" tts_model_dropdown = gr.Dropdown(value='tts-1',choices=tts_models, label='Model')\n",
" tts_voice_dropdown = gr.Dropdown(value='alloy',choices=tts_voices,label='Voice')\n",
" input_text_length = gr.Label(label=\"Number of characters\")\n",
" output_audio = gr.Audio()\n",
" input_text.input(fn=get_input_text_len, inputs=input_text, outputs=input_text_length)\n",
" go_btn = gr.Button(\"Go\")\n",
" go_btn.click(fn=create_speech, inputs=[input_text, tts_model_dropdown, tts_voice_dropdown], outputs=[output_audio])\n",
" clear_btn = gr.Button('Clear')\n",
" clear_btn.click(fn=lambda: '', outputs=input_text)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a00648a1-891b-470b-9959-f5d502055713",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"launch_kwargs = {'auth':('username',GRADIO_PASSWORD),\n",
" 'auth_message':'Please log in to Mat\\'s TTS App with username: username and password.'}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4b534fe7-4337-423e-846a-1bdb7cccc4ea",
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"#Notebook launch\n",
"app.launch(**launch_kwargs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb886d45",
"metadata": {},
"outputs": [],
"source": [
"#| export\n",
"#.py launch\n",
"if __name__ == \"__main__\":\n",
" app.launch(**launch_kwargs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "28e8d888-e790-46fa-bbac-4511b9ab796c",
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"app.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "afbc9699-4d16-4060-88f4-cd1251754cbd",
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"gr.close_all()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0420310d-930b-4904-8bd4-3458ad8bdbd3",
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"nbdev.export.nb_export('app.ipynb',lib_path='.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9869749d-bc7c-4e24-9dbc-403f665d6200",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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