##fix overlap, remove silence, leave a tiny bit of silence 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 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 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 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): """Generates audio for a text segment, handling voice prefixes.""" 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() voice1_full = "en-AU-WilliamNeural - en-AU (Male)" voice1_short = voice1_full.split(" - ")[0] voice1F_full ="en-GB-SoniaNeural - en-GB (Female)" voice1F_short = voice1F_full.split(" - ")[0] voice2_full = "en-GB-RyanNeural - en-GB (Male)" voice2_short = voice2_full.split(" - ")[0] voice2F_full = "en-US-JennyNeural - en-US (Female)" voice2F_short = voice2F_full.split(" - ")[0] voice3_full ="en-US-BrianMultilingualNeural - en-US (Male)" #good for reading voice3_short = voice3_full.split(" - ")[0] voice3F_full = "en-HK-YanNeural - en-HK (Female)" voice3F_short = voice3F_full.split(" - ")[0] voice4_full = "en-GB-ThomasNeural - en-GB (Male)" voice4_short = voice4_full.split(" - ")[0] voice4F_full ="en-US-EmmaNeural - en-US (Female)" voice4F_short = voice4_full.split(" - ")[0] voice5_full = "en-GB-RyanNeural - en-GB (Male)" #Old Man voice5_short = voice5_full.split(" - ")[0] voice6_full = "en-GB-MaisieNeural - en-GB (Female)" #Child voice6_short = voice6_full.split(" - ")[0] voice7_full = "vi-VN-HoaiMyNeural - vi-VN (Female)" #Vietnamese voice7_short = voice7_full.split(" - ")[0] voice8_full = "vi-VN-NamMinhNeural - vi-VN (Male)" #Vietnamese voice8_short = voice8_full.split(" - ")[0] voice9F_full = "de-DE-SeraphinaMultilingualNeural - de-DE (Female)" #Vietnamese voice9F_short = voice7_full.split(" - ")[0] voice9_full = "ko-KR-HyunsuMultilingualNeural - ko-KR (Male)" #Vietnamese voice9_short = voice8_full.split(" - ")[0] detect=0 if processed_text.startswith("1F"): current_voice_short = voice1F_short current_pitch = 25 detect=1 #processed_text = processed_text[2:].strip() elif processed_text.startswith("2F"): current_voice_short = voice2F_short #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("3F"): current_voice_short = voice3F_short #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("4F"): current_voice_short = voice4F_short #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("1M"): current_voice_short = voice1_short #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("2M"): current_voice_short = voice2_short #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("3M"): current_voice_short = voice3_short #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("4M"): current_voice_short = voice4_short #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("1O"): # Old man voice current_voice_short = voice5_short current_pitch = -20 current_rate = -10 #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("1C"): #Child voice current_voice_short = voice6_short #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("1V"): #Female VN current_voice_short = voice7_short #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("2V"): current_voice_short = voice8_short #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("3V"): #Female VN current_voice_short = voice9F_short current_pitch = 25 #processed_text = processed_text[2:].strip() detect=1 elif processed_text.startswith("4V"): current_voice_short = voice9_short current_pitch = -20 #processed_text = processed_text[2:].strip() detect=1 #Looking for number following prefix, which are pitch values. #match = re.search(r'[A-Za-z]\d+', part) # Look for a letter followed by one or more digits match = re.search(r'[A-Za-z]+\-?\d+', processed_text) # Look for a letter(s) followed by an optional '-' and digits if match: # Extract the prefix (e.g., '2F') and number (e.g., '-20') prefix = ''.join([ch for ch in match.group() if ch.isalpha()]) # Extract letters (prefix) number = int(''.join([ch for ch in match.group() if ch.isdigit() or ch == '-'])) # Extract digits (number) current_pitch += number # Step 2: Remove the found number from the string new_text = re.sub(r'[A-Za-z]+\-?\d+', '', processed_text, count=1).strip() # Remove prefix and number (e.g., '2F-20') #processed_text = new_text[2:] #cut out the prefix like 1F, 3M etc processed_text = new_text[len(prefix):] # Dynamically remove the prefix part else: if detect: processed_text = processed_text[2:] if processed_text: rate_str = f"{current_rate:+d}%" pitch_str = f"{current_pitch:+d}Hz" 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) # Load the audio from file audio = AudioSegment.from_mp3(audio_path) # Strip silence at start and end audio = strip_silence(audio, silence_thresh=-40, min_silence_len=100) # Save the stripped version back to file stripped_path = tempfile.mktemp(suffix=".mp3") audio.export(stripped_path, format="mp3") return stripped_path 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: 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 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) 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." 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): 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!" ``` *************************************************************************************************** 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", 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()