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| 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 | |
| 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 process_transcript_line(line, voice, rate, pitch): | |
| """Processes a single transcript line to extract time, voice commands, and generate audio.""" | |
| match = re.match(r'(\d+):(\d+)(?:\.(\d+))?\s+(.*)', line) | |
| if match: | |
| minutes, seconds, milliseconds_str, text_with_commands = match.groups() | |
| start_time_ms = int(minutes) * 60000 + int(seconds) * 1000 + (int(milliseconds_str) * 10 if milliseconds_str else 0) | |
| if not text_with_commands.strip(): | |
| return start_time_ms, None | |
| current_voice = voice | |
| current_rate = rate | |
| current_pitch = pitch | |
| processed_text = text_with_commands | |
| voice1 = "en-AU-WilliamNeural - en-AU (Male)" | |
| voice1F ="en-GB-SoniaNeural - en-GB (Female)" | |
| voice2 = "en-GB-RyanNeural - en-GB (Male)" | |
| voice2F = "en-US-JennyNeural - en-US (Female)" | |
| voice3 ="en-US-BrianMultilingualNeural - en-US (Male)" #good for reading | |
| voice3F = "en-HK-YanNeural - en-HK (Female)" | |
| voice4 = "en-GB-ThomasNeural - en-GB (Male)" | |
| voice4F ="en-US-EmmaNeural - en-US (Female)" | |
| voice5 = "en-GB-RyanNeural - en-GB (Male)" #Old Man | |
| voice6 = "en-GB-MaisieNeural - en-GB (Female)" #Child | |
| if text_with_commands.startswith("1F"): | |
| current_voice = voice1F.split(" - ")[0] | |
| current_pitch = 25 | |
| processed_text = text_with_commands[2:].strip() | |
| elif text_with_commands.startswith("2F"): | |
| current_voice = voice2F.split(" - ")[0] | |
| processed_text = text_with_commands[2:].strip() | |
| elif text_with_commands.startswith("3F"): | |
| current_voice = voice3F.split(" - ")[0] | |
| processed_text = text_with_commands[2:].strip() | |
| elif text_with_commands.startswith("4F"): | |
| current_voice = voice4F.split(" - ")[0] | |
| processed_text = text_with_commands[2:].strip() | |
| elif text_with_commands.startswith("1M"): | |
| current_voice = voice1.split(" - ")[0] | |
| processed_text = text_with_commands[2:].strip() | |
| elif text_with_commands.startswith("2M"): | |
| current_voice = voice2.split(" - ")[0] | |
| processed_text = text_with_commands[2:].strip() | |
| elif text_with_commands.startswith("3M"): | |
| current_voice = voice3.split(" - ")[0] | |
| processed_text = text_with_commands[2:].strip() | |
| elif text_with_commands.startswith("4M"): | |
| current_voice = voice4.split(" - ")[0] | |
| processed_text = text_with_commands[2:].strip() | |
| elif text_with_commands.startswith("1O"): # Old man voice | |
| current_voice = voice5.split(" - ")[0] | |
| current_pitch = -20 | |
| current_rate = -10 | |
| processed_text = text_with_commands[2:].strip() | |
| elif text_with_commands.startswith("1C"): #Child voice | |
| current_voice = voice6.split(" - ")[0] | |
| processed_text = text_with_commands[2:].strip() | |
| rate_str = f"{current_rate:+d}%" | |
| pitch_str = f"{current_pitch:+d}Hz" | |
| communicate = edge_tts.Communicate(processed_text, current_voice, 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) | |
| return start_time_ms, audio_path | |
| 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') | |
| audio_segments_with_time = [] | |
| max_end_time_ms = 0 | |
| for line in lines: | |
| start_time, audio_path = await process_transcript_line(line, voice, rate, pitch) | |
| if start_time is not None and audio_path: | |
| audio = AudioSegment.from_mp3(audio_path) | |
| audio_segments_with_time.append({'start': start_time, 'audio': audio, 'path': audio_path}) | |
| max_end_time_ms = max(max_end_time_ms, start_time + len(audio)) | |
| elif audio_path: | |
| os.remove(audio_path) # Clean up even if no timestamp | |
| if not audio_segments_with_time: | |
| return None, "No valid transcript lines found." | |
| # Create initial silence audio | |
| final_audio = AudioSegment.silent(duration=max_end_time_ms, frame_rate=24000) | |
| for segment in audio_segments_with_time: | |
| final_audio = final_audio.overlay(segment['audio'], position=segment['start']) | |
| os.remove(segment['path']) # Clean up individual audio files | |
| combined_audio_path = tempfile.mktemp(suffix=".mp3") | |
| final_audio.export(combined_audio_path, format="mp3") | |
| return combined_audio_path, None | |
| 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 YouTube transcript text with timestamps to generate synchronized audio. | |
| Each line should be in the format: `minutes:seconds[.milliseconds] text`. | |
| Voice prefixes (e.g., 1F, 1C) can be used at the beginning of a line to switch voices. | |
| Example: | |
| ``` | |
| 0:00 This | |
| 0:14 is the story of little Red Riding Hood | |
| 0:38 1F Grandma isn’t feeling very well. | |
| 0:48 1C Yes, said Little Red Riding Hood. | |
| ``` | |
| """ | |
| demo = gr.Interface( | |
| fn=tts_interface, | |
| inputs=[ | |
| gr.Textbox(label="YouTube Transcript", lines=10, placeholder="0:00 This\n0:14 is the story...\n0:38 1F Grandma..."), | |
| gr.Dropdown(choices=[""] + list(voices.keys()), label="Select 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) | |
| ], | |
| outputs=[ | |
| gr.Audio(label="Generated Audio", type="filepath"), | |
| gr.Markdown(label="Warning", visible=False) | |
| ], | |
| title="TTS for YouTube Transcripts with Voice Switching", | |
| description=description, | |
| analytics_enabled=False, | |
| allow_flagging=False | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| demo = asyncio.run(create_demo()) | |
| demo.launch() |