app.py

import spaces
import gradio as gr
import edge_tts
import asyncio
import tempfile
import os
import re  # Import the regular expression module
from pathlib import Path
from pydub import AudioSegment

def get_silence(duration_ms=1000):
    # Create silent audio segment with specified parameters
    silent_audio = AudioSegment.silent(
        duration=duration_ms,
        frame_rate=24000  # 24kHz sampling rate
    )
    
    # Set audio parameters
    silent_audio = silent_audio.set_channels(1)  # Mono
    silent_audio = silent_audio.set_sample_width(4)  # 32-bit (4 bytes per sample)
    
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
        # Export with specific bitrate and codec parameters
        silent_audio.export(
            tmp_file.name,
            format="mp3",
            bitrate="48k",
            parameters=[
                "-ac", "1",  # Mono
                "-ar", "24000",  # Sample rate
                "-sample_fmt", "s32",  # 32-bit samples
                "-codec:a", "libmp3lame"  # MP3 codec
            ]
        )
        return tmp_file.name

# Get all available voices
async def get_voices():
    voices = await edge_tts.list_voices()
    return {f"{v['ShortName']} - {v['Locale']} ({v['Gender']})": v['ShortName'] for v in voices}

# Text-to-speech function for a single paragraph with SS handling
async def paragraph_to_speech(text, voice, rate, pitch):
    voice1 = "en-AU-WilliamNeural - en-AU (Male)"    
    voice1F ="en-GB-SoniaNeural - en-GB (Female)"
    voice2 = "en-GB-RyanNeural - en-GB (Male)"
    voice2F = "en-US-JennyNeural - en-US (Female)"
    voice3 ="en-US-BrianMultilingualNeural - en-US (Male)"  #good for reading    
    voice3F = "en-HK-YanNeural - en-HK (Female)"
    voice4 = "en-GB-ThomasNeural - en-GB (Male)"
    voice4F ="en-US-EmmaNeural - en-US (Female)"      
    voice5 = "en-GB-RyanNeural - en-GB (Male)" #Old Man
    voice6 = "en-GB-MaisieNeural - en-GB (Female)"  #Child

    if not text.strip():
        return None, []  # Return None for audio path and empty list for silence

    audio_segments = []
    silence_durations = []
    parts = re.split(r'(SS\d+\.?\d*)', text)  #this one separtate the SS## tag if any in the text.
    for part in parts:          
        if re.match(r'SS\d+\.?\d*', part):  #Check if there is Silence tag
            # At the top of your file:
            #SILENCE_PATH = Path(__file__).parent.absolute() / "Silence.mp3"
            # At the top of your file (assuming you uploaded "Silence.mp3" to root)
            #SILENCE_PATH = Path(__file__).parent.absolute() / "Silence.mp3"
            # At the top of your file:
            #SILENCE_PATH = Path(__file__).parent.absolute() / "static" / "intro.mp3"
            #if SILENCE_PATH.exists():
            #    audio_segments.append(str(SILENCE_PATH))
            #    print(f"Silence.mp3 file found at {SILENCE_PATH} and is inserted")
            #else:
            silence_duration = float(part[2:]) * 1000  # Convert to milliseconds
            print(f"Silence.mp3 file NOT FOUND")
            silence_file_path = get_silence(silence_duration)  # Store the returned filename
            audio_segments.append(silence_file_path)  # Use the stored filename
        elif part.strip():
            detect=0
            processed_text = part
            current_voice = voice
            current_rate = rate
            current_pitch = pitch
            if part.startswith("1F"):
                detect=1
                current_voice = voice1F.split(" - ")[0]
                current_pitch = 25
            elif part.startswith("2F"):
                detect=1
                current_voice = voice2F.split(" - ")[0]
            elif part.startswith("3F"):
                detect=1
                current_voice = voice3F.split(" - ")[0]
            elif part.startswith("4F"):
                #detect=1
                current_voice = voice4F.split(" - ")[0]                
            elif part.startswith("1M"):
                detect=1
                current_voice = voice1.split(" - ")[0]
            elif part.startswith("2M"):
                detect=1
                current_voice = voice2.split(" - ")[0]
            elif part.startswith("3M"):
                detect=1
                current_voice = voice3.split(" - ")[0]
            elif part.startswith("4M"):
                detect=1
                current_voice = voice4.split(" - ")[0]              
            elif part.startswith("1O"):  # Old man voice
                detect=1
                current_voice = voice5.split(" - ")[0]
                current_pitch = -20
                current_rate = -10
            elif part.startswith("1C"):  #Child voice
                detect=1
                current_voice = voice6.split(" - ")[0]                
            else:
                # Use selected voice, or fallback to default
                #voice_short_name = (voice or default_voice).split(" - ")[0]
                current_voice = (voice or default_voice).split(" - ")[0]
                processed_text=part[:]
                # Step 1: Use regex to find the first number, possibly negative, after a prefix (e.g., F-)
            #match = re.search(r'[A-Za-z]\d+', part)  # Look for a letter followed by one or more digits
            match = re.search(r'[A-Za-z]+\-?\d+', part)  # Look for a letter(s) followed by an optional '-' and digits
            if match:
                # Extract the prefix (e.g., '2F') and number (e.g., '-20')
                prefix = ''.join([ch for ch in match.group() if ch.isalpha()])  # Extract letters (prefix)
                number = int(''.join([ch for ch in match.group() if ch.isdigit() or ch == '-']))  # Extract digits (number)
                current_pitch = number
                # Step 2: Remove the found number from the string
                new_text = re.sub(r'[A-Za-z]+\-?\d+', '', part, count=1).strip()  # Remove prefix and number (e.g., '2F-20')
                #processed_text = new_text[2:]  #cut out the prefix like 1F, 3M etc
                processed_text = new_text[len(prefix):]  # Dynamically remove the prefix part
            else:
                if detect:
                   processed_text = part[2:]
            rate_str = f"{current_rate:+d}%"
            #if  part[2:4].isdigit():
            #    processed_text = part[4:]
            #    pitch = int(part[2:4])
            pitch_str = f"{current_pitch:+d}Hz"
            communicate = edge_tts.Communicate(processed_text, current_voice, rate=rate_str, pitch=pitch_str)
            with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
                tmp_path = tmp_file.name
                await communicate.save(tmp_path)
            audio_segments.append(tmp_path)
        else:
            audio_segments.append(None) # Empty string

    return audio_segments, silence_durations

# Main text-to-speech function that processes paragraphs and silence
async def text_to_speech(text, voice, rate, pitch):
    if not text.strip():
        return None, gr.Warning("Please enter text to convert.")
    if not voice:
        return None, gr.Warning("Please select a voice.")

    #paragraphs = [p.strip() for p in re.split(r'"', text) if p.strip()]
    # Split the text using straight quotes (") and curly quotes (“ and ”)
    paragraphs = [p.strip() for p in re.split(r'[“”"]', text) if p.strip()]
    final_audio_segments = []

    for paragraph in paragraphs:
        audio_paths, silence_times = await paragraph_to_speech(paragraph, voice, rate, pitch)
        if audio_paths:
            for i, path in enumerate(audio_paths):
                final_audio_segments.append(path)
                if i < len(silence_times):
                    final_audio_segments.append(silence_times[i])

    if not any(isinstance(item, str) for item in final_audio_segments):
        return None, None  # No actual audio generated

    if all(not isinstance(item, str) for item in final_audio_segments):
        return None, "Only silence markers found."

    combined_audio_path = tempfile.mktemp(suffix=".mp3")
    with open(combined_audio_path, 'wb') as outfile:
        for segment in final_audio_segments:
            if isinstance(segment, str):
                try:
                    with open(segment, 'rb') as infile:
                        outfile.write(infile.read())
                    os.remove(segment)  # Clean up individual files
                except FileNotFoundError:
                    print(f"Warning: Audio file not found: {segment}")
    return combined_audio_path, None

# Gradio interface function
@spaces.GPU
def tts_interface(text, voice, rate, pitch):
    audio, warning = asyncio.run(text_to_speech(text, voice, rate, pitch))
    return audio, warning

# Create Gradio application
import gradio as gr

async def create_demo():
    voices = await get_voices()
    default_voice = "en-US-AndrewMultilingualNeural - en-US (Male)"  # 👈 Pick one of the available voices
    description = """
    Default = <b>"en-US-AndrewMultilingualNeural - en-US (Male), 
    other voices 1F:en-GB-SoniaNeural,    2F:en-US-JennyNeural,  3F:en-HK-YanNeural,  4F:en-US-EmmaNeural
                 1M:en-AU-WilliamNeural,  2M:en-GB-RyanNeural,   3M:en-US-BrianMultilingualNeural,  4M:en-GB-ThomasNeural
                 1C: en-GB-MaisieNeural (Childvoice), 1O = en-GB-RyanNeural (OldMan)"</b>
    You can insert silence using the marker 'SS##' example "SS2.0" 
    Enter your text, select a voice, and adjust the speech rate and pitch. Can also set like 1F-20  or 1M24.
    """

    demo = gr.Interface(
        fn=tts_interface,
        inputs=[
            gr.Textbox(label="Input Text", lines=5, placeholder="Separate paragraphs with two blank lines. Use 'SS[duration]' for silence."),
            gr.Dropdown(choices=[""] + list(voices.keys()), label="Select Voice", value=default_voice),
            gr.Slider(minimum=-50, maximum=50, value=0, label="Speech Rate Adjustment (%)", step=1),
            gr.Slider(minimum=-50, maximum=50, value=0, label="Pitch Adjustment (Hz)", step=1)
        ],
        outputs=[
            gr.Audio(label="Generated Audio", type="filepath"),
            gr.Markdown(label="Warning", visible=False)
        ],
        title="TTS using Edge Engine.. ENGLISH!",
        description=description,
        article="Process text paragraph by paragraph for smoother output and insert silence markers.",
        analytics_enabled=False,
        allow_flagging=False
    )
    return demo

# Run the application
if __name__ == "__main__":
    demo = asyncio.run(create_demo())
    demo.launch()