# Initalize a pipeline from kokoro import KPipeline # from IPython.display import display, Audio # import soundfile as sf import os from huggingface_hub import list_repo_files import uuid import re import gradio as gr # Language mapping dictionary language_map = { "American English": "a", "British English": "b", "Hindi": "h", "Spanish": "e", "French": "f", "Italian": "i", "Brazilian Portuguese": "p", "Japanese": "j", "Mandarin Chinese": "z" } # Print installation instructions if necessary install_messages = { "Japanese": "pip install misaki[ja]", "Mandarin Chinese": "pip install misaki[zh]" } def update_pipeline(Language): """ Updates the pipeline only if the language has changed. """ global pipeline, last_used_language # Print installation instructions if necessary if Language in install_messages: # raise gr.Error(f"To Use {Language} Install: {install_messages[Language]}",duration=10) gr.Warning(f"To Use {Language} Install: {install_messages[Language]}",duration=10) # gr.Warning("Reverting to default English pipeline...", duration=5) # print(f"To use {Language}, install: {install_messages[Language]}") # print("Reverting to default English pipeline...") # Revert to default English and return immediately pipeline = KPipeline(lang_code="a") last_used_language = "a" # Get language code, default to 'a' if not found new_lang = language_map.get(Language, "a") # Only update if the language is different if new_lang != last_used_language: try: pipeline = KPipeline(lang_code=new_lang) last_used_language = new_lang # Update last used language print(f"Pipeline updated to {Language} ({new_lang})") except Exception as e: print(f"Error initializing KPipeline: {e}\nRetrying with default language...") pipeline = KPipeline(lang_code="a") # Fallback to English last_used_language = "a" def get_voice_names(repo_id): """Fetches and returns a list of voice names (without extensions) from the given Hugging Face repository.""" return [os.path.splitext(file.replace("voices/", ""))[0] for file in list_repo_files(repo_id) if file.startswith("voices/")] def create_audio_dir(): """Creates the 'kokoro_audio' directory in the root folder if it doesn't exist.""" root_dir = os.getcwd() # Use current working directory instead of __file__ audio_dir = os.path.join(root_dir, "kokoro_audio") if not os.path.exists(audio_dir): os.makedirs(audio_dir) print(f"Created directory: {audio_dir}") else: print(f"Directory already exists: {audio_dir}") return audio_dir import re def clean_text(text): # Define replacement rules replacements = { "–": " ", # Replace en-dash with space "-": " ", # Replace hyphen with space "**": " ", # Replace double asterisks with space "*": " ", # Replace single asterisk with space "#": " ", # Replace hash with space } # Apply replacements for old, new in replacements.items(): text = text.replace(old, new) # Remove emojis using regex (covering wide range of Unicode characters) emoji_pattern = re.compile( r'[\U0001F600-\U0001F64F]|' # Emoticons r'[\U0001F300-\U0001F5FF]|' # Miscellaneous symbols and pictographs r'[\U0001F680-\U0001F6FF]|' # Transport and map symbols r'[\U0001F700-\U0001F77F]|' # Alchemical symbols r'[\U0001F780-\U0001F7FF]|' # Geometric shapes extended r'[\U0001F800-\U0001F8FF]|' # Supplemental arrows-C r'[\U0001F900-\U0001F9FF]|' # Supplemental symbols and pictographs r'[\U0001FA00-\U0001FA6F]|' # Chess symbols r'[\U0001FA70-\U0001FAFF]|' # Symbols and pictographs extended-A r'[\U00002702-\U000027B0]|' # Dingbats r'[\U0001F1E0-\U0001F1FF]' # Flags (iOS) r'', flags=re.UNICODE) text = emoji_pattern.sub(r'', text) # Remove multiple spaces and extra line breaks text = re.sub(r'\s+', ' ', text).strip() return text def tts_file_name(text): global temp_folder # Remove all non-alphabetic characters and convert to lowercase text = re.sub(r'[^a-zA-Z\s]', '', text) # Retain only alphabets and spaces text = text.lower().strip() # Convert to lowercase and strip leading/trailing spaces text = text.replace(" ", "_") # Replace spaces with underscores # Truncate or handle empty text truncated_text = text[:20] if len(text) > 20 else text if len(text) > 0 else "empty" # Generate a random string for uniqueness random_string = uuid.uuid4().hex[:8].upper() # Construct the file name file_name = f"{temp_folder}/{truncated_text}_{random_string}.wav" return file_name # import soundfile as sf import numpy as np import wave from pydub import AudioSegment from pydub.silence import split_on_silence def remove_silence_function(file_path,minimum_silence=50): # Extract file name and format from the provided path output_path = file_path.replace(".wav", "_no_silence.wav") audio_format = "wav" # Reading and splitting the audio file into chunks sound = AudioSegment.from_file(file_path, format=audio_format) audio_chunks = split_on_silence(sound, min_silence_len=100, silence_thresh=-45, keep_silence=minimum_silence) # Putting the file back together combined = AudioSegment.empty() for chunk in audio_chunks: combined += chunk combined.export(output_path, format=audio_format) return output_path def generate_and_save_audio(text, Language="American English",voice="af_bella", speed=1,remove_silence=False,keep_silence_up_to=0.05): text=clean_text(text) update_pipeline(Language) generator = pipeline(text, voice=voice, speed=speed, split_pattern=r'\n+') save_path=tts_file_name(text) # Open the WAV file for writing with wave.open(save_path, 'wb') as wav_file: # Set the WAV file parameters wav_file.setnchannels(1) # Mono audio wav_file.setsampwidth(2) # 2 bytes per sample (16-bit audio) wav_file.setframerate(24000) # Sample rate # Process each audio chunk for i, (gs, ps, audio) in enumerate(generator): # print(f"{i}. {gs}") # print(f"Phonetic Transcription: {ps}") # display(Audio(data=audio, rate=24000, autoplay=i==0)) print("\n") # Convert the Tensor to a NumPy array audio_np = audio.numpy() # Convert Tensor to NumPy array audio_int16 = (audio_np * 32767).astype(np.int16) # Scale to 16-bit range audio_bytes = audio_int16.tobytes() # Convert to bytes # Write the audio chunk to the WAV file wav_file.writeframes(audio_bytes) if remove_silence: keep_silence = int(keep_silence_up_to * 1000) new_wave_file=remove_silence_function(save_path,minimum_silence=keep_silence) return new_wave_file,new_wave_file return save_path,save_path def ui(): def toggle_autoplay(autoplay): return gr.Audio(interactive=False, label='Output Audio', autoplay=autoplay) # Define examples in the format you mentioned dummy_examples = [ ["Hey, y'all, let’s grab some coffee and catch up!", "American English", "af_bella"], ["I'd like a large coffee, please.", "British English", "bf_isabella"], ["नमस्ते, कैसे हो?", "Hindi", "hf_alpha"], ["Hola, ¿cómo estás?", "Spanish", "ef_dora"], ["Bonjour, comment ça va?", "French", "ff_siwis"], ["Ciao, come stai?", "Italian", "if_sara"], ["Olá, como você está?", "Brazilian Portuguese", "pf_dora"], ["こんにちは、お元気ですか？", "Japanese", "jf_nezumi"], ["你好，你怎么样?", "Mandarin Chinese", "zf_xiaoni"] ] with gr.Blocks() as demo: # gr.Markdown("

KOKORO TTS

") # Larger title with CSS gr.Markdown("[Install on Your Local System](https://github.com/NeuralFalconYT/kokoro_v1)") lang_list = ['American English', 'British English', 'Hindi', 'Spanish', 'French', 'Italian', 'Brazilian Portuguese', 'Japanese', 'Mandarin Chinese'] voice_names = get_voice_names("hexgrad/Kokoro-82M") with gr.Row(): with gr.Column(): text = gr.Textbox(label='Enter Text', lines=3) with gr.Row(): language_name = gr.Dropdown(lang_list, label="Select Language", value=lang_list[0]) with gr.Row(): voice_name = gr.Dropdown(voice_names, label="Choose VoicePack", value=voice_names[0]) with gr.Row(): generate_btn = gr.Button('Generate', variant='primary') with gr.Accordion('Audio Settings', open=False): speed = gr.Slider(minimum=0.25, maximum=2, value=1, step=0.1, label='⚡️Speed', info='Adjust the speaking speed') remove_silence = gr.Checkbox(value=False, label='✂️ Remove Silence From TTS') with gr.Column(): audio = gr.Audio(interactive=False, label='Output Audio', autoplay=True) audio_file = gr.File(label='Download Audio') with gr.Accordion('Enable Autoplay', open=False): autoplay = gr.Checkbox(value=True, label='Autoplay') autoplay.change(toggle_autoplay, inputs=[autoplay], outputs=[audio]) text.submit(generate_and_save_audio, inputs=[text, language_name, voice_name, speed, remove_silence], outputs=[audio, audio_file]) generate_btn.click(generate_and_save_audio, inputs=[text, language_name, voice_name, speed, remove_silence], outputs=[audio, audio_file]) # Add examples to the interface gr.Examples(examples=dummy_examples, inputs=[text, language_name, voice_name]) return demo def tutorial(): # Markdown explanation for language code explanation = """ ## Language Code Explanation: Example: `'af_bella'` - **'a'** stands for **American English**. - **'f_'** stands for **Female** (If it were 'm_', it would mean Male). - **'bella'** refers to the specific voice. The first character in the voice code stands for the language: - **"a"**: American English - **"b"**: British English - **"h"**: Hindi - **"e"**: Spanish - **"f"**: French - **"i"**: Italian - **"p"**: Brazilian Portuguese - **"j"**: Japanese - **"z"**: Mandarin Chinese The second character stands for gender: - **"f_"**: Female - **"m_"**: Male """ with gr.Blocks() as demo2: gr.Markdown("[Install on Your Local System](https://github.com/NeuralFalconYT/kokoro_v1)") gr.Markdown(explanation) # Display the explanation return demo2 import click @click.command() @click.option("--debug", is_flag=True, default=False, help="Enable debug mode.") @click.option("--share", is_flag=True, default=False, help="Enable sharing of the interface.") def main(debug, share): demo1 = ui() demo2 = tutorial() demo = gr.TabbedInterface([demo1, demo2],["Multilingual TTS","VoicePack Explanation"],title="Kokoro TTS",theme='JohnSmith9982/small_and_pretty') demo.queue().launch(debug=debug, share=share) #Run on local network # laptop_ip="192.168.0.30" # port=8080 # demo.queue().launch(debug=debug, share=share,server_name=laptop_ip,server_port=port) # Initialize default pipeline last_used_language = "a" pipeline = KPipeline(lang_code=last_used_language) temp_folder = create_audio_dir() if __name__ == "__main__": main()