app.py

import spaces
import gradio as gr
import edge_tts
import asyncio
import tempfile
import os
import re
from pathlib import Path
from pydub import AudioSegment
import librosa
import soundfile as sf
import numpy as np

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():
    try:
        voices = await edge_tts.list_voices()
        return {f"{v['ShortName']} - {v['Locale']} ({v['Gender']})": v['ShortName'] for v in voices}
    except Exception as e:
        print(f"Error listing voices: {e}")
        return {}

async def generate_audio_with_voice_prefix(text_segment, default_voice, rate, pitch, target_duration_ms=None, speed_adjustment_factor=1.0):
    """Generates audio for a text segment, handling voice prefixes and adjusting rate for duration."""
    current_voice_full = default_voice
    current_voice_short = current_voice_full.split(" - ")[0] if current_voice_full else ""
    current_rate = rate
    current_pitch = pitch
    processed_text = text_segment.strip()
    print(f"Processing this  text segment: {processed_text}") # Debug
    voice_map = {
        "1F": "en-GB-SoniaNeural",
        "2M": "en-GB-RyanNeural",
        "3M": "en-US-BrianMultilingualNeural",
        "2F": "en-US-JennyNeural",
        "1M": "en-AU-WilliamNeural",
        "3F": "en-HK-YanNeural",
        "4M": "en-GB-ThomasNeural",
        "4F": "en-US-EmmaNeural",
        "1O": "en-GB-RyanNeural",  # Old Man
        "1C": "en-GB-MaisieNeural",  # Child
        "1V": "vi-VN-HoaiMyNeural",  # Vietnamese (Female)
        "2V": "vi-VN-NamMinhNeural",  # Vietnamese (Male)
        "3V": "vi-VN-HoaiMyNeural",  # Vietnamese (Female)
        "4V": "vi-VN-NamMinhNeural",  # Vietnamese (Male)
    }
    detect = 0
    for prefix, voice_short in voice_map.items():
        if processed_text.startswith(prefix):
            current_voice_short = voice_short
            if prefix in ["1F", "3F", "1V", "3V"]:
                current_pitch = 25
            elif prefix in ["1O", "4V"]:
                current_pitch = -20
                current_rate = -10
            detect = 1
            processed_text = processed_text[len(prefix):].strip()
            break
    match = re.search(r'([A-Za-z]+)-?(\d+)', processed_text)
    if match:
        prefix_pitch = match.group(1)
        number = int(match.group(2))
        if prefix_pitch in voice_map:
            current_pitch += number
            processed_text = re.sub(r'[A-Za-z]+-?\d+', '', processed_text, count=1).strip()
        elif detect:
            processed_text = processed_text.lstrip('-0123456789').strip() # Remove potential leftover numbers
    elif detect:
        processed_text = processed_text[2:].strip()
    if processed_text:
        rate_str = f"{current_rate:+d}%"
        pitch_str = f"{current_pitch:+d}Hz"
        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)
            if target_duration_ms is not None and os.path.exists(audio_path):
                audio = AudioSegment.from_mp3(audio_path)
                audio_duration_ms = len(audio)
             #print(f"Generated audio duration: {audio_duration_ms}ms, Target duration: {target_duration_ms}ms") # Debug
            Offtext = """
                if audio_duration_ms > target_duration_ms and target_duration_ms > 0:
                    speed_factor = (audio_duration_ms / target_duration_ms) * speed_adjustment_factor
                    #print(f"Speed factor (after user adjustment): {speed_factor}") # Debug
                    if speed_factor > 0:
                        if speed_factor < 1.0:
                            speed_factor = 1.0
                        y, sr = librosa.load(audio_path, sr=None)
                        y_stretched = librosa.effects.time_stretch(y, rate=speed_factor)
                        sf.write(audio_path, y_stretched, sr) 
            """

                if audio_duration_ms > target_duration_ms and target_duration_ms > 0:
                    speed_factor = (audio_duration_ms / target_duration_ms) * speed_adjustment_factor
                    if speed_factor > 0:
                        if speed_factor < 1.0:
                            speed_factor = 1.0
                        y, sr = librosa.load(audio_path, sr=None)
                
                        # Use the phase vocoder for time stretching without pitch change
                        hop_length = 512  # You can adjust this parameter
                        phase_vocoder_output = librosa.phase_vocoder(y, rate=speed_factor, hop_length=hop_length)
                
                        # Reconstruct the audio signal from the phase vocoder output
                        y_stretched = librosa.istft(phase_vocoder_output, hop_length=hop_length, length=len(y) if speed_factor < 1 else None)
                
                        sf.write(audio_path, y_stretched, sr)
                        
                else:
                    print("Generated audio is not longer than target duration, no speed adjustment.") # Debug
            return audio_path
        except Exception as e:
            print(f"Edge TTS error processing '{processed_text}': {e}")
            return None
    return None

async def process_transcript_line(line, default_voice, rate, pitch, speed_adjustment_factor):
    """Processes a single transcript line with HH:MM:SS,milliseconds - HH:MM:SS,milliseconds timestamp."""
    match = re.match(r'(\d{2}):(\d{2}):(\d{2}),(\d{3})\s+-\s+(\d{2}):(\d{2}):(\d{2}),(\d{3})\s+(.*)', line)
    if match:
        start_h, start_m, start_s, start_ms, end_h, end_m, end_s, end_ms, text_parts = match.groups()
        start_time_ms = (
            int(start_h) * 3600000 +
            int(start_m) * 60000 +
            int(start_s) * 1000 +
            int(start_ms)
        )
        end_time_ms = (
            int(end_h) * 3600000 +
            int(end_m) * 60000 +
            int(end_s) * 1000 +
            int(end_ms)
        )
        duration_ms = end_time_ms - start_time_ms
        audio_segments = []
        split_parts = re.split(r'[“”"]', text_parts)
        process_next = False
        for part in split_parts:
            if part == '"':
                process_next = not process_next
                continue
            if process_next and part.strip():
                audio_path = await generate_audio_with_voice_prefix(part, default_voice, rate, pitch, duration_ms, speed_adjustment_factor)
                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, duration_ms, speed_adjustment_factor)
                if audio_path:
                    audio_segments.append(audio_path)
        return start_time_ms, audio_segments, duration_ms
    return None, None, None

async def transcript_to_speech(transcript_text, voice, rate, pitch, speed_adjustment_factor):
    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
    for line in lines:
        start_time, audio_paths, duration = await process_transcript_line(line, voice, rate, pitch, speed_adjustment_factor)
        if start_time is not None and audio_paths:
            combined_line_audio = AudioSegment.empty()
            current_time_ms = start_time
            segment_duration = duration / len(audio_paths) if audio_paths else 0
            for path in audio_paths:
                if path:  # Only process if audio_path is not None (meaning TTS was successful)
                    try:
                        audio = AudioSegment.from_mp3(path)
                        combined_line_audio += audio
                        os.remove(path)
                    except FileNotFoundError:
                        print(f"Warning: Audio file not found: {path}")
            if combined_line_audio:
                timed_audio_segments.append({'start': start_time, 'audio': combined_line_audio})
                max_end_time_ms = max(max_end_time_ms, start_time + len(combined_line_audio))
        elif audio_paths:
            for path in audio_paths:
                if path:
                    try:
                        os.remove(path)
                    except FileNotFoundError:
                        pass # Clean up even if no timestamp
    if not timed_audio_segments:
        return None, "No processable audio segments found."
    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")
    return combined_audio_path, None

@spaces.GPU
def tts_interface(transcript, voice, rate, pitch, speed_adjustment_factor):
    audio, warning = asyncio.run(transcript_to_speech(transcript, voice, rate, pitch, speed_adjustment_factor))
    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 - HH:MM:SS,milliseconds) with voice changes within quotes.
    The duration specified in the timestamp will be used to adjust the speech rate so the generated audio fits within that time.
    You can control the intensity of the speed adjustment using the "Speed Adjustment Factor" slider.
    Format: `HH:MM:SS,milliseconds - HH:MM:SS,milliseconds "VoicePrefix Text" more text "AnotherVoicePrefix More Text"`
    Example:
    ```
    00:00:00,000 - 00:00:05,000 "This is the default voice." more default. "1F Now a female voice." and back to default.
    00:00:05,500 - 00:00:10,250 "1C Yes," said the child, "it is fun!"
    ```
    ***************************************************************************************************
    1M = en-AU-WilliamNeural - en-AU (Male)
    1F = en-GB-SoniaNeural - en-GB (Female)
    2M = en-GB-RyanNeural - en-GB (Male)
    2F = en-US-JennyNeural - en-US (Female)
    3M = en-US-BrianMultilingualNeural - en-US (Male)
    3F = en-HK-YanNeural - en-HK (Female)
    4M = en-GB-ThomasNeural - en-GB (Male)
    4F = en-US-EmmaNeural - en-US (Female)
    1O = en-GB-RyanNeural - en-GB (Male) # Old Man
    1C = en-GB-MaisieNeural - en-GB (Female) # Child
    1V = vi-VN-HoaiMyNeural - vi-VN (Female) # Vietnamese (Female)
    2V = vi-VN-NamMinhNeural - vi-VN (Male) # Vietnamese (Male)
    3V = vi-VN-HoaiMyNeural - vi-VN (Female) # Vietnamese (Female)
    4V = vi-VN-NamMinhNeural - vi-VN (Male) # Vietnamese (Male)
    ****************************************************************************************************
    """
    demo = gr.Interface(
        fn=tts_interface,
        inputs=[
            gr.Textbox(label="Timestamped Text with Voice Changes and Duration", lines=10, placeholder='00:00:00,000 - 00:00:05,000 "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),
            gr.Slider(minimum=0.5, maximum=1.5, value=1.0, step=0.05, label="Speed Adjustment Factor")
        ],
        outputs=[
            gr.Audio(label="Generated Audio", type="filepath"),
            gr.Markdown(label="Warning", visible=False)
        ],
        title="TTS with Duration-Aware Speed Adjustment 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()