app.py

##fix overlap, remove silence, leave a tiny bit of silence
## Simplified
## Add 0 after prefix make it permanent voice

import spaces
import gradio as gr
import edge_tts
import asyncio
import tempfile
import os
import re
from pathlib import Path
from pydub.silence import detect_nonsilent
from pydub import AudioSegment

default_voice_short= ""
check1 = False  # set global variable to check to see if process_text is begin of transcript line or not.

def strip_silence(audio: AudioSegment, silence_thresh=-40, min_silence_len=100, silence_padding_ms=100):
    from pydub.silence import detect_nonsilent
    # Detect non-silent regions
    nonsilent = detect_nonsilent(audio, min_silence_len=min_silence_len, silence_thresh=silence_thresh)
    # If no speech is detected, return a small silent audio (not totally empty)
    if not nonsilent:
        return AudioSegment.silent(duration=silence_padding_ms)
    # Start and end of the first and last non-silent segments
    start_trim = nonsilent[0][0]
    end_trim = nonsilent[-1][1]
    # Add padding before and after the trimmed audio
    # Ensure the padding doesn't exceed the trimmed boundaries

    if not check1:
        silence_padding_ms=00
    start_trim = max(0, start_trim - silence_padding_ms)  # Ensure no negative start
    end_trim = min(len(audio), end_trim + silence_padding_ms)  # Ensure end doesn't go past audio length
    # Return the trimmed and padded audio
    # Debugging: print input arguments
    print(f"Audio length: {len(audio)} ms")
    print(f"Silence threshold: {silence_thresh} dB")
    print(f"Minimum silence length: {min_silence_len} ms")
    print(f"Silence padding: {silence_padding_ms} ms")
    print(f"Check1: {check1}**")
    return audio[start_trim:end_trim]


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}

async def generate_audio_with_voice_prefix(text_segment, default_voice, rate, pitch):
    global default_voice_short  # Use the global variable
    global check1   # Use the global variable
    """Generates audio for a text segment, handling voice prefixes, retries, and fallback."""
    print(f"Text: {text_segment}")  #Debug
    voice_map = {
        "1F": ("en-GB-SoniaNeural", 25, 0),
        "2F": ("en-US-JennyNeural", 0, 0),
        "3F": ("en-HK-YanNeural", 0, 0),
        "4F": ("en-US-EmmaNeural", 0, 0),
        "1M": ("en-AU-WilliamNeural", 0, 0),
        "2M": ("en-GB-RyanNeural", 0, 0),
        "3M": ("en-US-BrianMultilingualNeural", 0, 0),
        "4M": ("en-GB-ThomasNeural", 0, 0),
        "1O": ("en-GB-RyanNeural", -20, -10),
        "1C": ("en-GB-MaisieNeural", 0, 0),
        "1V": ("vi-VN-HoaiMyNeural", 0, 0),
        "2V": ("vi-VN-NamMinhNeural", 0, 0),
        "3V": ("en-US-EmmaMultilingualNeural", 0, 0),
        "4V": ("en-US-BrianMultilingualNeural", 0, 0),
        "5V": ("en-US-AvaMultilingualNeural", 0, 0),
        "6V": ("en-US-AndrewMultilingualNeural", 0, 0),
        "7V": ("de-DE-SeraphinaMultilingualNeural", 0, 0),
        "8V": ("ko-KR-HyunsuMultilingualNeural", 0, 0),
    }
    if default_voice_short == "":
        current_voice_full = default_voice
        current_voice_short = current_voice_full.split(" - ")[0] if current_voice_full else ""
    else:
        current_voice_short = default_voice_short
    current_rate = rate
    current_pitch = pitch
    processed_text = text_segment.strip()
    
    detect = False

    prefix = processed_text[:2]
    if prefix in voice_map:
        current_voice_short, pitch_adj, rate_adj = voice_map[prefix]
        current_pitch += pitch_adj
        current_rate += rate_adj
        detect = True

    match = re.search(r'[A-Za-z]+\-?\d+', processed_text)
    if match:
        group = match.group()
        prefix_only = ''.join(filter(str.isalpha, group))
        number = int(''.join(ch for ch in group if ch.isdigit() or ch == '-'))
        if number == 0:
            default_voice_short= current_voice_short
        current_pitch += number
        processed_text = re.sub(r'[A-Za-z]+\-?\d+', '', processed_text, count=1).strip()
        processed_text = processed_text[len(prefix_only):].strip()
    elif detect:
        processed_text = processed_text[2:].strip()

    if processed_text:
        rate_str = f"{current_rate:+d}%"
        pitch_str = f"{current_pitch:+d}Hz"

        # Retry logic
        for attempt in range(3):
            try:
                communicate = edge_tts.Communicate(processed_text, current_voice_short, rate=rate_str, pitch=pitch_str)
                with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
                    audio_path = tmp_file.name
                    await communicate.save(audio_path)

                audio = AudioSegment.from_mp3(audio_path)
                if not check1:
                    print(f"not last part of sentence - SHORT silence")
                    audio = strip_silence(audio, silence_thresh=-40, min_silence_len=50, silence_padding_ms=50)  ##silence between sentences
                else:
                    audio = strip_silence(audio, silence_thresh=-40, min_silence_len=50, silence_padding_ms=100)  ##less silence for mid-sentence segments
                    print(f"Last part of sentence - long silence")
                stripped_path = tempfile.mktemp(suffix=".mp3")
                audio.export(stripped_path, format="mp3")
                return stripped_path
            except Exception as e:
                print(f"Edge TTS Failed# {attempt}:: {e}")  #Debug
                if attempt == 2:
                    # Final failure: return 500ms of silence
                    silent_audio = AudioSegment.silent(duration=500)
                    fallback_path = tempfile.mktemp(suffix=".mp3")
                    silent_audio.export(fallback_path, format="mp3")
                    return fallback_path
                await asyncio.sleep(0.5)  # brief wait before retry

    return None

async def process_transcript_line(line, default_voice, rate, pitch):
    """Processes a single transcript line with HH:MM:SS.milliseconds timestamp and quoted text segments."""
    match = re.match(r'(\d{2}):(\d{2}):(\d{2}),(\d{3})\s+(.*)', line) # Modified timestamp regex
    if match:
        count = 0
        hours, minutes, seconds, milliseconds, text_parts = match.groups()
        start_time_ms = (
            int(hours) * 3600000 +
            int(minutes) * 60000 +
            int(seconds) * 1000 +
            int(milliseconds)
        )
        audio_segments = []
        split_parts = re.split(r'(")', text_parts)  # Split by quote marks, keeping the quotes
        # Initialize a variable to track if it's the first iteration
        global check1   # Use the global variable
        check1 = False
        process_next = False
        for part in split_parts:
            if part == '"':  #process text that are inside quote
                process_next = not process_next
                check1 = False             # set it to False 
                continue
            if process_next and part.strip():
                #if part == split_parts[-1]: # check if this is laster iteration, 
                #    check1 = False             # set it to False                
                audio_path = await generate_audio_with_voice_prefix(part, default_voice, rate, pitch)
                if audio_path:
                    audio_segments.append(audio_path)
            elif not process_next and part.strip():
                audio_path = await generate_audio_with_voice_prefix(part, default_voice, rate, pitch) # Process unquoted text with default voice
                if audio_path:
                    audio_segments.append(audio_path)
        
        return start_time_ms, audio_segments
    return None, None

async def transcript_to_speech(transcript_text, voice, rate, pitch):
    if not transcript_text.strip():
        return None, gr.Warning("Please enter transcript text.")
    if not voice:
        return None, gr.Warning("Please select a voice.")

    lines = transcript_text.strip().split('\n')
    timed_audio_segments = []
    max_end_time_ms = 0
    previous_end_time_ms = 0
    i = 0

    while i < len(lines):
        start_time, audio_paths = await process_transcript_line(lines[i], voice, rate, pitch)
        if start_time is not None and audio_paths:
            combined_line_audio = AudioSegment.empty()
            for path in audio_paths:
                try:
                    audio = AudioSegment.from_mp3(path)
                    #audio = strip_silence(audio, silence_thresh=-40, min_silence_len=100)
                    combined_line_audio += audio
                    #combined_line_audio = strip_silence(combined_line_audio, silence_thresh=-40, min_silence_len=100)
                    os.remove(path)
                except FileNotFoundError:
                    print(f"Warning: Audio file not found: {path}")

            current_audio_duration = len(combined_line_audio)
            intended_start_time = start_time
            
            # Check duration until the next timestamp
            if i + 1 < len(lines):
                next_start_time_line = lines[i+1]
                next_start_time_match = re.match(r'(\d{2}):(\d{2}):(\d{2}),(\d{3})\s+.*', next_start_time_line)
                if next_start_time_match:
                    next_h, next_m, next_s, next_ms = next_start_time_match.groups()
                    next_start_time_ms = (int(next_h) * 3600000 + int(next_m) * 60000 + int(next_s) * 1000 + int(next_ms))
                    duration_to_next = next_start_time_ms - start_time
                else:
                    duration_to_next = float('inf')  # Or some other large value

                if current_audio_duration > duration_to_next:
                    # Hold and append audio from subsequent lines
                    j = i + 1
                    while j < len(lines):
                        next_start_time, next_audio_paths = await process_transcript_line(lines[j], voice, rate, pitch)
                        if next_start_time is not None and next_audio_paths:
                            for next_path in next_audio_paths:
                                try:
                                    next_audio = AudioSegment.from_mp3(next_path)
                                    combined_line_audio += next_audio
                                    os.remove(next_path)
                                except FileNotFoundError:
                                    print(f"Warning: Audio file not found: {next_path}")
                            current_audio_duration = len(combined_line_audio)
                            
                            #check duration to the next timestamp.
                            if j + 1 < len(lines):
                                next_start_time_line_2 = lines[j+1]
                                next_start_time_match_2 = re.match(r'(\d{2}):(\d{2}):(\d{2}),(\d{3})\s+.*', next_start_time_line_2)
                                if next_start_time_match_2:
                                    next_h_2, next_m_2, next_s_2, next_ms_2 = next_start_time_match_2.groups()
                                    next_start_time_ms_2 = (int(next_h_2) * 3600000 + int(next_m_2) * 60000 + int(next_s_2) * 1000 + int(next_ms_2))
                                    duration_to_next_2 = next_start_time_ms_2 - start_time
                                    if current_audio_duration <= duration_to_next_2:
                                        break
                                else:
                                    break
                            j += 1
                        else:
                            break
                    i = j #update i to j
                
                timed_audio_segments.append({'start': intended_start_time, 'audio': combined_line_audio})
                previous_end_time_ms = max(previous_end_time_ms, intended_start_time + current_audio_duration)
                max_end_time_ms = max(max_end_time_ms, previous_end_time_ms)
        elif audio_paths:
            for path in audio_paths:
                try:
                    os.remove(path)
                except FileNotFoundError:
                    pass # Clean up even if no timestamp
        i += 1

    if not timed_audio_segments:
        return None, "No processable audio segments found."

    print(f"Combining Audio - final stage.")    
    final_audio = AudioSegment.silent(duration=max_end_time_ms, frame_rate=24000)
    for segment in timed_audio_segments:
        final_audio = final_audio.overlay(segment['audio'], position=segment['start'])

    combined_audio_path = tempfile.mktemp(suffix=".mp3")
    final_audio.export(combined_audio_path, format="mp3")
    global default_voice_short  # Use the global variable
    default_voice_short=""
    print(f"Job done! reset voice back to default.")
    return combined_audio_path, None

@spaces.GPU
def tts_interface(transcript, voice, rate, pitch):
    audio, warning = asyncio.run(transcript_to_speech(transcript, voice, rate, pitch))
    return audio, warning

async def create_demo():
    voices = await get_voices()
    default_voice = "en-US-AndrewMultilingualNeural - en-US (Male)"
    description = """
    Process timestamped text (HH:MM:SS,milliseconds) with voice changes within quotes.
    Format: `HH:MM:SS,milliseconds "VoicePrefix Text" more text "1F Different Voice"
    Example:
    ```
    00:00:00,000 "This is the default voice." more default. "1F Now a female voice." and back to default.
    00:00:05,000 "1C Yes," said the child, "it is fun!"
    ```
    ***************************************************************************************************
     <b>   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
        1O : en-GB-RyanNeural"
        1C : en-GB-MaisieNeural
        1V : vi-VN-HoaiMyNeural
        2V : vi-VN-NamMinhNeural
        3V : en-US-EmmaMultilingualNeural
        4V : en-US-BrianMultilingualNeural
        5V : en-US-AvaMultilingualNeural
        6V : en-US-AndrewMultilingualNeural
        7V : de-DE-SeraphinaMultilingualNeural
        8V : ko-KR-HyunsuMultilingualNeural  </b>
    ****************************************************************************************************
    """
    demo = gr.Interface(
        fn=tts_interface,
        inputs=[
            gr.Textbox(label="Timestamped Text with Voice Changes", lines=10, placeholder='00:00:00,000 "Text" more text "1F Different Voice"'),
            gr.Dropdown(choices=[""] + list(voices.keys()), label="Select Default 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) # Removed the duplicate value argument
        ],
        outputs=[
            gr.Audio(label="Generated Audio", type="filepath"),
            gr.Markdown(label="Warning", visible=False)
        ],
        title="TTS with HH:MM:SS,milliseconds and In-Quote Voice Switching",
        description=description,
        analytics_enabled=False,
        allow_flagging=False
    )
    return demo

if __name__ == "__main__":
    demo = asyncio.run(create_demo())
    demo.launch()