##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"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=10) ##silence between sentences else: audio = strip_silence(audio, silence_thresh=-40, min_silence_len=100, silence_padding_ms=200) ##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(): 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(): if part == split_parts[-1]: # check if this is laster iteration, check1 = True # set it to True 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!" ``` *************************************************************************************************** 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 **************************************************************************************************** """ 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()