import gradio as gr from pydub import AudioSegment import edge_tts import os import asyncio import uuid import re from concurrent.futures import ThreadPoolExecutor from typing import List, Tuple import math class TimingManager: def __init__(self): self.current_time = 0 self.segment_gap = 100 # ms gap between segments def get_timing(self, duration): start_time = self.current_time end_time = start_time + duration self.current_time = end_time + self.segment_gap return start_time, end_time def get_audio_length(audio_file): audio = AudioSegment.from_file(audio_file) return len(audio) / 1000 def format_time_ms(milliseconds): seconds, ms = divmod(int(milliseconds), 1000) mins, secs = divmod(seconds, 60) hrs, mins = divmod(mins, 60) return f"{hrs:02}:{mins:02}:{secs:02},{ms:03}" def smart_text_split(text, words_per_line, lines_per_segment): # Define natural break patterns end_sentence = r'[.!?]+' mid_sentence = r'[,;:]+' # First split by major punctuation sentences = [] current = "" # Clean the text and ensure proper spacing after punctuation text = re.sub(r'([.!?,;:])\s*', r'\1 ', text).strip() # Split into initial chunks by strong punctuation chunks = re.split(f'({end_sentence})', text) temp_sentences = [] for i in range(0, len(chunks)-1, 2): if i+1 < len(chunks): temp_sentences.append(chunks[i] + chunks[i+1]) else: temp_sentences.append(chunks[i]) # Further process each sentence for sentence in temp_sentences: # Split by mid-sentence punctuation if sentence is too long if len(sentence.split()) > words_per_line * 2: sub_chunks = re.split(f'({mid_sentence})', sentence) for i in range(0, len(sub_chunks)-1, 2): if i+1 < len(sub_chunks): sentences.append(sub_chunks[i] + sub_chunks[i+1]) else: sentences.append(sub_chunks[i]) else: sentences.append(sentence) # Process sentences into lines and segments segments = [] current_segment = [] current_line = [] for sentence in sentences: words = sentence.strip().split() while words: # Determine natural break point break_point = min(words_per_line, len(words)) # Look for natural breaks for i in range(break_point-1, 0, -1): if any(words[i-1].endswith(p) for p in '.!?,;:') or \ any(words[i].startswith(p) for p in '([{'): break_point = i break current_line = words[:break_point] words = words[break_point:] current_segment.append(' '.join(current_line)) if len(current_segment) >= lines_per_segment: segments.append('\n'.join(current_segment)) current_segment = [] # Handle remaining content if current_segment: segments.append('\n'.join(current_segment)) return segments async def process_segment(segment: str, idx: int, voice: str, rate: str, pitch: str, timing_mgr: TimingManager) -> Tuple[str, AudioSegment]: """Process a single segment with accurate timing""" audio_file = f"temp_segment_{idx}_{uuid.uuid4()}.wav" try: tts = edge_tts.Communicate(segment, voice, rate=rate, pitch=pitch) await tts.save(audio_file) segment_audio = AudioSegment.from_file(audio_file) segment_duration = len(segment_audio) # Get timing from manager start_time, end_time = timing_mgr.get_timing(segment_duration) # Format SRT entry srt_content = ( f"{idx}\n" f"{format_time_ms(start_time)} --> {format_time_ms(end_time)}\n" f"{segment}\n\n" ) return srt_content, segment_audio finally: if os.path.exists(audio_file): os.remove(audio_file) async def process_chunk_parallel(chunks: List[str], start_idx: int, voice: str, rate: str, pitch: str, timing_mgr: TimingManager) -> Tuple[str, AudioSegment]: """Process chunks with sequential timing""" combined_audio = AudioSegment.empty() srt_content = "" # Process segments sequentially to maintain timing for i, segment in enumerate(chunks, start_idx): srt_part, audio_part = await process_segment(segment, i, voice, rate, pitch, timing_mgr) srt_content += srt_part combined_audio += audio_part return srt_content, combined_audio async def generate_accurate_srt(text, voice, rate, pitch, words_per_line, lines_per_segment): segments = smart_text_split(text, words_per_line, lines_per_segment) timing_mgr = TimingManager() # Process in smaller chunks chunk_size = 5 chunks = [segments[i:i + chunk_size] for i in range(0, len(segments), chunk_size)] final_srt = "" final_audio = AudioSegment.empty() current_index = 1 # Process chunks in parallel but maintain sequential timing chunk_tasks = [] for i, chunk in enumerate(chunks): start_idx = current_index + (i * chunk_size) task = process_chunk_parallel(chunk, start_idx, voice, rate, pitch, timing_mgr) chunk_tasks.append(task) # Gather results in order chunk_results = await asyncio.gather(*chunk_tasks) # Combine results for srt_content, audio_content in chunk_results: final_srt += srt_content final_audio += audio_content # Export final files unique_id = uuid.uuid4() audio_path = f"final_audio_{unique_id}.mp3" srt_path = f"final_subtitles_{unique_id}.srt" final_audio.export(audio_path, format="mp3", bitrate="320k") with open(srt_path, "w", encoding='utf-8') as f: f.write(final_srt) return srt_path, audio_path async def process_text(text, pitch, rate, voice, words_per_line, lines_per_segment): # Set default pitch and rate strings that work well pitch_str = "+0Hz" # neutral pitch rate_str = "+0%" # neutral rate # Only modify if user has changed values if pitch != 0: pitch_str = f"{pitch:+d}Hz" if rate != 0: rate_str = f"{rate:+d}%" srt_path, audio_path = await generate_accurate_srt( text, voice_options[voice], rate_str, pitch_str, words_per_line, lines_per_segment ) return srt_path, audio_path, audio_path # Voice options dictionary (same as before) voice_options = { "Andrew Male": "en-US-AndrewNeural", "Jenny Female": "en-US-JennyNeural", "Guy Male": "en-US-GuyNeural", "Ana Female": "en-US-AnaNeural", "Aria Female": "en-US-AriaNeural", "Brian Male": "en-US-BrianNeural", "Christopher Male": "en-US-ChristopherNeural", "Eric Male": "en-US-EricNeural", "Michelle Male": "en-US-MichelleNeural", "Roger Male": "en-US-RogerNeural", "Natasha Female": "en-AU-NatashaNeural", "William Male": "en-AU-WilliamNeural", "Clara Female": "en-CA-ClaraNeural", "Liam Female ": "en-CA-LiamNeural", "Libby Female": "en-GB-LibbyNeural", "Maisie": "en-GB-MaisieNeural", "Ryan": "en-GB-RyanNeural", "Sonia": "en-GB-SoniaNeural", "Thomas": "en-GB-ThomasNeural", "Sam": "en-HK-SamNeural", "Yan": "en-HK-YanNeural", "Connor": "en-IE-ConnorNeural", "Emily": "en-IE-EmilyNeural", "Neerja": "en-IN-NeerjaNeural", "Prabhat": "en-IN-PrabhatNeural", "Asilia": "en-KE-AsiliaNeural", "Chilemba": "en-KE-ChilembaNeural", "Abeo": "en-NG-AbeoNeural", "Ezinne": "en-NG-EzinneNeural", "Mitchell": "en-NZ-MitchellNeural", "James": "en-PH-JamesNeural", "Rosa": "en-PH-RosaNeural", "Luna": "en-SG-LunaNeural", "Wayne": "en-SG-WayneNeural", "Elimu": "en-TZ-ElimuNeural", "Imani": "en-TZ-ImaniNeural", "Leah": "en-ZA-LeahNeural", "Luke": "en-ZA-LukeNeural" # Add other voices here... } # Create Gradio interface app = gr.Interface( fn=process_text, inputs=[ gr.Textbox(label="Enter Text", lines=10), gr.Slider(label="Pitch Adjustment (Hz)", minimum=-10, maximum=10, value=0, step=1), gr.Slider(label="Rate Adjustment (%)", minimum=-25, maximum=25, value=0, step=1), gr.Dropdown(label="Select Voice", choices=list(voice_options.keys()), value="Jenny Female"), gr.Slider(label="Words per Line", minimum=3, maximum=12, value=6, step=1), gr.Slider(label="Lines per Segment", minimum=1, maximum=4, value=2, step=1) ], outputs=[ gr.File(label="Download SRT"), gr.File(label="Download Audio"), gr.Audio(label="Preview Audio") ], title="Advanced TTS with Configurable SRT Generation", description="Generate perfectly synchronized audio and subtitles with natural speech patterns." ) app.launch()