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| import spaces | |
| import gradio as gr | |
| import edge_tts | |
| import asyncio | |
| import tempfile | |
| import os | |
| import re # Import the regular expression module | |
| from pathlib import Path | |
| # At the top of your file: | |
| SILENCE_PATH = Path(__file__).parent.absolute() / "Silence.mp3" | |
| # 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} | |
| # Text-to-speech function for a single paragraph with SS handling | |
| async def paragraph_to_speech(text, voice, rate, pitch): | |
| voice3 ="en-US-BrianMultilingualNeural - en-US (Male)" #good for reading | |
| voice1F ="en-US-EmmaNeural - en-US (Female)" | |
| voice2 = "it-IT-GiuseppeMultilingualNeural - it-IT (Male)" | |
| voice2F = "en-US-JennyNeural - en-US (Female)" | |
| voice1 = "en-AU-WilliamNeural - en-AU (Male)" | |
| voice3F = "en-HK-YanNeural - en-HK (Female)" | |
| voice4 = "en-GB-MaisieNeural - en-GB (Female)" #Child | |
| voice5 = "en-GB-RyanNeural - en-GB (Male)" #Old Man | |
| if not text.strip(): | |
| return None, [] # Return None for audio path and empty list for silence | |
| audio_segments = [] | |
| silence_durations = [] | |
| parts = re.split(r'(SS\d+\.?\d*)', text) | |
| for part in parts: | |
| if re.match(r'SS\d+\.?\d*', part): | |
| if SILENCE_PATH.exists(): | |
| audio_segments.append(str(SILENCE_PATH)) | |
| print(f"Silence added at {SILENCE_PATH}") | |
| else: | |
| print(f"Silence.mp3 file NOT FOUND") | |
| elif part.strip(): | |
| processed_text = part | |
| current_voice = voice | |
| current_rate = rate | |
| current_pitch = pitch | |
| if part.startswith("1F"): | |
| processed_text = part[2:] | |
| current_voice = voice1F.split(" - ")[0] | |
| elif part.startswith("2F"): | |
| processed_text = part[2:] | |
| current_voice = voice2F.split(" - ")[0] | |
| elif part.startswith("3F"): | |
| processed_text = part[2:] | |
| current_voice = voice3F.split(" - ")[0] | |
| elif part.startswith("1M"): | |
| processed_text = part[2:] | |
| current_voice = voice1.split(" - ")[0] | |
| elif part.startswith("2M"): | |
| processed_text = part[2:] | |
| current_voice = voice2.split(" - ")[0] | |
| elif part.startswith("3M"): | |
| processed_text = part[2:] | |
| current_voice = voice3.split(" - ")[0] | |
| elif part.startswith("1C"): | |
| processed_text = part[2:] | |
| current_voice = voice4.split(" - ")[0] | |
| elif part.startswith("1O"): | |
| processed_text = part[2:] | |
| current_voice = voice5.split(" - ")[0] | |
| current_pitch = -30 | |
| current_rate = -20 | |
| else: | |
| # Use selected voice, or fallback to default | |
| #voice_short_name = (voice or default_voice).split(" - ")[0] | |
| current_voice = (voice or default_voice).split(" - ")[0] | |
| processed_text=part[:] | |
| 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: | |
| tmp_path = tmp_file.name | |
| await communicate.save(tmp_path) | |
| audio_segments.append(tmp_path) | |
| else: | |
| audio_segments.append(None) # Empty string | |
| return audio_segments, silence_durations | |
| # Main text-to-speech function that processes paragraphs and silence | |
| async def text_to_speech(text, voice, rate, pitch): | |
| if not text.strip(): | |
| return None, gr.Warning("Please enter text to convert.") | |
| if not voice: | |
| return None, gr.Warning("Please select a voice.") | |
| paragraphs = [p.strip() for p in re.split(r'"', text) if p.strip()] | |
| final_audio_segments = [] | |
| for paragraph in paragraphs: | |
| audio_paths, silence_times = await paragraph_to_speech(paragraph, voice, rate, pitch) | |
| if audio_paths: | |
| for i, path in enumerate(audio_paths): | |
| final_audio_segments.append(path) | |
| if i < len(silence_times): | |
| final_audio_segments.append(silence_times[i]) | |
| if not any(isinstance(item, str) for item in final_audio_segments): | |
| return None, None # No actual audio generated | |
| if all(not isinstance(item, str) for item in final_audio_segments): | |
| return None, "Only silence markers found." | |
| combined_audio_path = tempfile.mktemp(suffix=".mp3") | |
| with open(combined_audio_path, 'wb') as outfile: | |
| for segment in final_audio_segments: | |
| if isinstance(segment, str): | |
| try: | |
| with open(segment, 'rb') as infile: | |
| outfile.write(infile.read()) | |
| os.remove(segment) # Clean up individual files | |
| except FileNotFoundError: | |
| print(f"Warning: Audio file not found: {segment}") | |
| return combined_audio_path, None | |
| # Gradio interface function | |
| def tts_interface(text, voice, rate, pitch): | |
| audio, warning = asyncio.run(text_to_speech(text, voice, rate, pitch)) | |
| return audio, warning | |
| # Create Gradio application | |
| import gradio as gr | |
| async def create_demo(): | |
| voices = await get_voices() | |
| default_voice = "en-US-AndrewMultilingualNeural - en-US (Male)" # 👈 Pick one of the available voices | |
| description = """ | |
| Default = male, other voices 1F:US_Emma, 2F:US_Jenny, 3F:HK_Yan, 1M:AU_Will, 2M:IT_Guiseppe,3M:US_Brian, 1C: Childvoice, 1O = OldMan | |
| You can insert silence using the marker 'SS' (This will insert a Silence period from the Silence.mp3 file). | |
| Enter your text, select a voice, and adjust the speech rate and pitch. | |
| The application will process your text paragraph by paragraph (separated by two blank lines). | |
| """ | |
| demo = gr.Interface( | |
| fn=tts_interface, | |
| inputs=[ | |
| gr.Textbox(label="Input Text", lines=5, placeholder="Separate paragraphs with two blank lines. Use 'SS[duration]' for silence."), | |
| 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="Voicecloning.be Text-to-Speech with Silence Insertion (Paragraph by Paragraph)", | |
| description=description, | |
| article="Process text paragraph by paragraph for smoother output and insert silence markers.", | |
| analytics_enabled=False, | |
| allow_flagging=False | |
| ) | |
| return demo | |
| # Run the application | |
| if __name__ == "__main__": | |
| demo = asyncio.run(create_demo()) | |
| demo.launch() |