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

# At the top of your file:
SILENCE_PATH = Path(__file__).parent.absolute() / "Silence.mp3"

# 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):
    voice3 ="en-US-BrianMultilingualNeural - en-US (Male)"  #good for reading
    voice1F ="en-US-EmmaNeural - en-US (Female)"
    voice2 = "it-IT-GiuseppeMultilingualNeural - it-IT (Male)"
    voice2F = "en-US-JennyNeural - en-US (Female)"
    voice1 = "en-AU-WilliamNeural - en-AU (Male)"
    voice3F = "en-HK-YanNeural - en-HK (Female)"
    voice4 = "en-GB-MaisieNeural - en-GB (Female)"  #Child
    voice5 = "en-GB-RyanNeural - en-GB (Male)" #Old Man

    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)
    for part in parts:
        if re.match(r'SS\d+\.?\d*', part):
            if SILENCE_PATH.exists():
                audio_segments.append(str(SILENCE_PATH))
                print(f"Silence added at {SILENCE_PATH}")
            else:
                # Create silent segment programmatically
                silent_audio = AudioSegment.silent(duration=1000)  # 1 second
                with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
                    silent_audio.export(tmp_file.name, format="mp3")
                    audio_segments.append(tmp_file.name)
                    print(f"Created silent segment at {tmp_file.name}")
        elif part.strip():
            processed_text = part
            current_voice = voice
            current_rate = rate
            current_pitch = pitch
            if part.startswith("1F"):
                processed_text = part[2:]
                current_voice = voice1F.split(" - ")[0]
            elif part.startswith("2F"):
                processed_text = part[2:]
                current_voice = voice2F.split(" - ")[0]
            elif part.startswith("3F"):
                processed_text = part[2:]
                current_voice = voice3F.split(" - ")[0]
            elif part.startswith("1M"):
                processed_text = part[2:]
                current_voice = voice1.split(" - ")[0]
            elif part.startswith("2M"):
                processed_text = part[2:]
                current_voice = voice2.split(" - ")[0]
            elif part.startswith("3M"):
                processed_text = part[2:]
                current_voice = voice3.split(" - ")[0]
            elif part.startswith("1C"):
                processed_text = part[2:]
                current_voice = voice4.split(" - ")[0]
            elif part.startswith("1O"):
                processed_text = part[2:]
                current_voice = voice5.split(" - ")[0]
                current_pitch = -30
                current_rate = -20
            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[:]
            rate_str = f"{current_rate:+d}%"
            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()]
    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 = male, other voices 1F:US_Emma, 2F:US_Jenny, 3F:HK_Yan, 1M:AU_Will, 2M:IT_Guiseppe,3M:US_Brian,  1C: Childvoice, 1O = OldMan
    You can insert silence using the marker 'SS' (This will insert a Silence period from the Silence.mp3 file).
    Enter your text, select a voice, and adjust the speech rate and pitch.
    The application will process your text paragraph by paragraph (separated by two blank lines).
    """

    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="Voicecloning.be Text-to-Speech with Silence Insertion (Paragraph by Paragraph)",
        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()