Delete srt_dubbing.py

srt_dubbing.py

@@ -1,557 +0,0 @@
-from KOKORO.models import build_model
-from KOKORO.utils import tts,tts_file_name,podcast
-import sys
-sys.path.append('.')
-import torch
-import gc 
-print("Loading model...")
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-print(f'Using device: {device}')
-MODEL = build_model('./KOKORO/kokoro-v0_19.pth', device)
-print("Model loaded successfully.")
-
-def tts_maker(text,voice_name="af_bella",speed = 0.8,trim=0,pad_between=0,save_path="temp.wav",remove_silence=False,minimum_silence=50):
-    # Sanitize the save_path to remove any newline characters
-    save_path = save_path.replace('\n', '').replace('\r', '')
-    global MODEL
-    audio_path=tts(MODEL,device,text,voice_name,speed=speed,trim=trim,pad_between_segments=pad_between,output_file=save_path,remove_silence=remove_silence,minimum_silence=minimum_silence)
-    return audio_path
-
-
-model_list = ["kokoro-v0_19.pth", "kokoro-v0_19-half.pth"]
-current_model = model_list[0]
-
-def update_model(model_name):
-    """
-    Updates the TTS model only if the specified model is not already loaded.
-    """
-    global MODEL, current_model
-    if current_model == model_name:
-        return f"Model already set to {model_name}"  # No need to reload
-    model_path = f"./KOKORO/{model_name}"  # Default model path
-    if model_name == "kokoro-v0_19-half.pth":
-        model_path = f"./KOKORO/fp16/{model_name}"  # Update path for specific model
-    # print(f"Loading new model: {model_name}")
-    del MODEL  # Cleanup existing model
-    gc.collect()
-    torch.cuda.empty_cache()  # Ensure GPU memory is cleared
-    MODEL = build_model(model_path, device)
-    current_model = model_name
-    return f"Model updated to {model_name}"
-
-
-
-def text_to_speech(text, model_name="kokoro-v0_19.pth", voice_name="af", speed=1.0, trim=1.0, pad_between_segments=0, remove_silence=True, minimum_silence=0.20):
-    """
-    Converts text to speech using the specified parameters and ensures the model is updated only if necessary.
-    """
-    update_status = update_model(model_name)  # Load the model only if required
-    # print(update_status)  # Log model loading status
-    if not minimum_silence:
-        minimum_silence = 0.05
-    keep_silence = int(minimum_silence * 1000)
-    save_at = tts_file_name(text)
-    audio_path = tts_maker(
-        text, 
-        voice_name, 
-        speed, 
-        trim, 
-        pad_between_segments, 
-        save_at, 
-        remove_silence, 
-        keep_silence
-    )
-    return audio_path
-
-
-
-
-import gradio as gr
-
-# voice_list = [
-#     'af',  # Default voice is a 50-50 mix of af_bella & af_sarah
-#     'af_bella', 'af_sarah', 'am_adam', 'am_michael',
-#     'bf_emma', 'bf_isabella', 'bm_george', 'bm_lewis',
-# ]
-
-
-
-import os
-
-# Get the list of voice names without file extensions
-voice_list = [
-    os.path.splitext(filename)[0]
-    for filename in os.listdir("./KOKORO/voices")
-    if filename.endswith('.pt')
-]
-
-# Sort the list based on the length of each name
-voice_list = sorted(voice_list, key=len)
-
-def toggle_autoplay(autoplay):
-    return gr.Audio(interactive=False, label='Output Audio', autoplay=autoplay)
-
-with gr.Blocks() as demo1:
-    gr.Markdown("# Batched TTS")
-    with gr.Row():
-        with gr.Column():
-            text = gr.Textbox(
-                label='Enter Text',
-                lines=3,
-                placeholder="Type your text here..."
-            )
-            with gr.Row():
-                voice = gr.Dropdown(
-                    voice_list, 
-                    value='af', 
-                    allow_custom_value=False, 
-                    label='Voice', 
-                    info='Starred voices are more stable'
-                )
-            with gr.Row():
-                generate_btn = gr.Button('Generate', variant='primary')
-            with gr.Accordion('Audio Settings', open=False):
-                model_name=gr.Dropdown(model_list,label="Model",value=model_list[0])
-                remove_silence = gr.Checkbox(value=False, label='✂️ Remove Silence From TTS')
-                minimum_silence = gr.Number(
-                    label="Keep Silence Upto (In seconds)", 
-                    value=0.05
-                )
-                speed = gr.Slider(
-                    minimum=0.25, maximum=2, value=1, step=0.1, 
-                    label='⚡️Speed', info='Adjust the speaking speed'
-                )
-                trim = gr.Slider(
-                    minimum=0, maximum=1, value=0, step=0.1, 
-                    label='🔪 Trim', info='How much to cut from both ends of each segment'
-                )   
-                pad_between = gr.Slider(
-                    minimum=0, maximum=2, value=0, step=0.1, 
-                    label='🔇 Pad Between', info='Silent Duration between segments [For Large Text]'
-                )
-                
-        with gr.Column():
-            audio = gr.Audio(interactive=False, label='Output Audio', autoplay=True)
-            with gr.Accordion('Enable Autoplay', open=False):
-                autoplay = gr.Checkbox(value=True, label='Autoplay')
-                autoplay.change(toggle_autoplay, inputs=[autoplay], outputs=[audio])
-
-    text.submit(
-        text_to_speech, 
-        inputs=[text, model_name,voice, speed, trim, pad_between, remove_silence, minimum_silence], 
-        outputs=[audio]
-    )
-    generate_btn.click(
-        text_to_speech, 
-        inputs=[text,model_name, voice, speed, trim, pad_between, remove_silence, minimum_silence], 
-        outputs=[audio]
-    )
-
-def podcast_maker(text,remove_silence=False,minimum_silence=50,model_name="kokoro-v0_19.pth"):
-    global MODEL,device
-    update_model(model_name)
-    if not minimum_silence:
-        minimum_silence = 0.05
-    keep_silence = int(minimum_silence * 1000)
-    podcast_save_at=podcast(MODEL, device,text,remove_silence=remove_silence, minimum_silence=keep_silence)
-    return podcast_save_at
-    
-
-
-dummpy_example="""{af} Hello, I'd like to order a sandwich please.                                                         
-{af_sky} What do you mean you're out of bread?                                                                      
-{af_bella} I really wanted a sandwich though...                                                              
-{af_nicole} You know what, darn you and your little shop!                                                                       
-{bm_george} I'll just go back home and cry now.                                                                           
-{am_adam} Why me?"""
-with gr.Blocks() as demo2:
-    gr.Markdown(
-        """
-    # Multiple Speech-Type Generation
-    This section allows you to generate multiple speech types or multiple people's voices. Enter your text in the format shown below, and the system will generate speech using the appropriate type. If unspecified, the model will use "af" voice.
-    Format:
-    {voice_name} your text here
-    """
-    )
-    with gr.Row():
-        gr.Markdown(
-            """
-            **Example Input:**                                                                      
-            {af} Hello, I'd like to order a sandwich please.                                                         
-            {af_sky} What do you mean you're out of bread?                                                                      
-            {af_bella} I really wanted a sandwich though...                                                              
-            {af_nicole} You know what, darn you and your little shop!                                                                       
-            {bm_george} I'll just go back home and cry now.                                                                           
-            {am_adam} Why me?!                                                                         
-            """
-        )
-    with gr.Row():
-        with gr.Column():
-            text = gr.Textbox(
-                label='Enter Text',
-                lines=7,
-                placeholder=dummpy_example
-            )
-            with gr.Row():
-                generate_btn = gr.Button('Generate', variant='primary')
-            with gr.Accordion('Audio Settings', open=False):
-                remove_silence = gr.Checkbox(value=False, label='✂️ Remove Silence From TTS')
-                minimum_silence = gr.Number(
-                    label="Keep Silence Upto (In seconds)", 
-                    value=0.20
-                )
-        with gr.Column():
-            audio = gr.Audio(interactive=False, label='Output Audio', autoplay=True)
-            with gr.Accordion('Enable Autoplay', open=False):
-                autoplay = gr.Checkbox(value=True, label='Autoplay')
-                autoplay.change(toggle_autoplay, inputs=[autoplay], outputs=[audio])
-
-    text.submit(
-        podcast_maker, 
-        inputs=[text, remove_silence, minimum_silence], 
-        outputs=[audio]
-    )
-    generate_btn.click(
-        podcast_maker, 
-        inputs=[text, remove_silence, minimum_silence], 
-        outputs=[audio]
-    )
-
-
-
-
-import shutil
-import os
-
-# Ensure the output directory exists
-output_dir = "./temp_audio"
-os.makedirs(output_dir, exist_ok=True)
-
-
-
-
-
-
-
-
-
-#@title Generate Audio File From Subtitle
-# from tqdm.notebook import tqdm
-from tqdm import tqdm
-import subprocess
-import json
-import pysrt
-import os
-from pydub import AudioSegment
-import shutil
-import uuid
-import re
-import time
-
-# os.chdir(install_path)
-
-def your_tts(text,audio_path,actual_duration,speed=1.0):
-  global srt_voice_name
-  model_name="kokoro-v0_19.pth"
-  tts_path=text_to_speech(text, model_name, voice_name=srt_voice_name,speed=speed)
-  print(tts_path)
-  tts_audio = AudioSegment.from_file(tts_path)
-  tts_duration = len(tts_audio)
-  if tts_duration > actual_duration:
-    speedup_factor = tts_duration / actual_duration
-    tts_path=text_to_speech(text, model_name, voice_name=srt_voice_name,speed=speedup_factor)
-  print(tts_path)
-  shutil.copy(tts_path,audio_path)
-
-
-
-base_path="."
-import datetime
-def get_current_time():
-    # Return current time as a string in the format HH_MM_AM/PM
-    return datetime.datetime.now().strftime("%I_%M_%p")
-
-def get_subtitle_Dub_path(srt_file_path,Language="en"):
-  file_name = os.path.splitext(os.path.basename(srt_file_path))[0]
-  if not os.path.exists(f"{base_path}/TTS_DUB"):
-    os.mkdir(f"{base_path}/TTS_DUB")
-  random_string = str(uuid.uuid4())[:6]
-  new_path=f"{base_path}/TTS_DUB/{file_name}_{Language}_{get_current_time()}_{random_string}.wav"
-  return new_path
-
-
-
-
-
-
-
-
-def clean_srt(input_path):
-    file_name = os.path.basename(input_path)
-    output_folder = f"{base_path}/save_srt"
-    if not os.path.exists(output_folder):
-        os.mkdir(output_folder)
-    output_path = f"{output_folder}/{file_name}"
-
-    def clean_srt_line(text):
-        bad_list = ["[", "]", "♫", "\n"]
-        for i in bad_list:
-            text = text.replace(i, "")
-        return text.strip()
-
-    # Load the subtitle file
-    subs = pysrt.open(input_path)
-
-    # Iterate through each subtitle and print its details
-    with open(output_path, "w", encoding='utf-8') as file:
-        for sub in subs:
-            file.write(f"{sub.index}\n")
-            file.write(f"{sub.start} --> {sub.end}\n")
-            file.write(f"{clean_srt_line(sub.text)}\n")
-            file.write("\n")
-        file.close()
-    # print(f"Clean SRT saved at: {output_path}")
-    return output_path
-# Example usage
-
-
-
-
-
-
-class SRTDubbing:
-    def __init__(self):
-        pass
-
-    @staticmethod
-    def text_to_speech_srt(text, audio_path, language, actual_duration):
-        tts_filename = "./cache/temp.wav"
-        your_tts(text,tts_filename,actual_duration,speed=1.0)
-        # Check the duration of the generated TTS audio
-        tts_audio = AudioSegment.from_file(tts_filename)
-        tts_duration = len(tts_audio)
-
-        if actual_duration == 0:
-            # If actual duration is zero, use the original TTS audio without modifications
-            shutil.move(tts_filename, audio_path)
-            return
-        # If TTS audio duration is longer than actual duration, speed up the audio
-        if tts_duration > actual_duration:
-            speedup_factor = tts_duration / actual_duration
-            speedup_filename = "./cache/speedup_temp.wav"
-            # Use ffmpeg to change audio speed
-            subprocess.run([
-                "ffmpeg",
-                "-i", tts_filename,
-                "-filter:a", f"atempo={speedup_factor}",
-                speedup_filename,
-                "-y"
-            ], check=True)
-
-            # Replace the original TTS audio with the sped-up version
-            shutil.move(speedup_filename, audio_path)
-        elif tts_duration < actual_duration:
-            # If TTS audio duration is less than actual duration, add silence to match the duration
-            silence_gap = actual_duration - tts_duration
-            silence = AudioSegment.silent(duration=int(silence_gap))
-            new_audio = tts_audio + silence
-
-            # Save the new audio with added silence
-            new_audio.export(audio_path, format="wav")
-        else:
-            # If TTS audio duration is equal to actual duration, use the original TTS audio
-            shutil.move(tts_filename, audio_path)
-
-    @staticmethod
-    def make_silence(pause_time, pause_save_path):
-        silence = AudioSegment.silent(duration=pause_time)
-        silence.export(pause_save_path, format="wav")
-        return pause_save_path
-
-    @staticmethod
-    def create_folder_for_srt(srt_file_path):
-        srt_base_name = os.path.splitext(os.path.basename(srt_file_path))[0]
-        random_uuid = str(uuid.uuid4())[:4]
-        dummy_folder_path = f"{base_path}/dummy"
-        if not os.path.exists(dummy_folder_path):
-            os.makedirs(dummy_folder_path)
-        folder_path = os.path.join(dummy_folder_path, f"{srt_base_name}_{random_uuid}")
-        os.makedirs(folder_path, exist_ok=True)
-        return folder_path
-
-    @staticmethod
-    def concatenate_audio_files(audio_paths, output_path):
-        concatenated_audio = AudioSegment.silent(duration=0)
-        for audio_path in audio_paths:
-            audio_segment = AudioSegment.from_file(audio_path)
-            concatenated_audio += audio_segment
-        concatenated_audio.export(output_path, format="wav")
-
-    def srt_to_dub(self, srt_file_path,dub_save_path,language='en'):
-        result = self.read_srt_file(srt_file_path)
-        new_folder_path = self.create_folder_for_srt(srt_file_path)
-        join_path = []
-        for i in tqdm(result):
-        # for i in result:
-            text = i['text']
-            actual_duration = i['end_time'] - i['start_time']
-            pause_time = i['pause_time']
-            slient_path = f"{new_folder_path}/{i['previous_pause']}"
-            self.make_silence(pause_time, slient_path)
-            join_path.append(slient_path)
-            tts_path = f"{new_folder_path}/{i['audio_name']}"
-            self.text_to_speech_srt(text, tts_path, language, actual_duration)
-            join_path.append(tts_path)
-        self.concatenate_audio_files(join_path, dub_save_path)
-
-    @staticmethod
-    def convert_to_millisecond(time_str):
-      if isinstance(time_str, str):
-          hours, minutes, second_millisecond = time_str.split(':')
-          seconds, milliseconds = second_millisecond.split(",")
-
-          total_milliseconds = (
-              int(hours) * 3600000 +
-              int(minutes) * 60000 +
-              int(seconds) * 1000 +
-              int(milliseconds)
-          )
-
-          return total_milliseconds
-    @staticmethod
-    def read_srt_file(file_path):
-        entries = []
-        default_start = 0
-        previous_end_time = default_start
-        entry_number = 1
-        audio_name_template = "{}.wav"
-        previous_pause_template = "{}_before_pause.wav"
-
-        with open(file_path, 'r', encoding='utf-8') as file:
-            lines = file.readlines()
-            # print(lines)
-            for i in range(0, len(lines), 4):
-                time_info = re.findall(r'(\d+:\d+:\d+,\d+) --> (\d+:\d+:\d+,\d+)', lines[i + 1])
-                start_time = SRTDubbing.convert_to_millisecond(time_info[0][0])
-                end_time = SRTDubbing.convert_to_millisecond(time_info[0][1])
-
-                current_entry = {
-                    'entry_number': entry_number,
-                    'start_time': start_time,
-                    'end_time': end_time,
-                    'text': lines[i + 2].strip(),
-                    'pause_time': start_time - previous_end_time if entry_number != 1 else start_time - default_start,
-                    'audio_name': audio_name_template.format(entry_number),
-                    'previous_pause': previous_pause_template.format(entry_number),
-                }
-
-                entries.append(current_entry)
-                previous_end_time = end_time
-                entry_number += 1
-
-        with open("entries.json", "w") as file:
-            json.dump(entries, file, indent=4)
-        return entries
-srt_voice_name="am_adam"   
-def srt_process(srt_file_path,voice_name,dest_language="en"):
-  global srt_voice_name
-  srt_voice_name=voice_name
-  srt_dubbing = SRTDubbing()
-  dub_save_path=get_subtitle_Dub_path(srt_file_path,dest_language)
-  srt_dubbing.srt_to_dub(srt_file_path,dub_save_path,dest_language)
-  return dub_save_path
-
-# 
-# srt_file_path="./long.srt"
-# dub_audio_path=srt_process(srt_file_path)
-# print(f"Audio file saved at: {dub_audio_path}")
-
-
-
-with gr.Blocks() as demo3:
-
-    gr.Markdown(
-        """
-        # Generate Audio File From Subtitle [Single Speaker Only]
-        
-        To generate subtitles, you can use the [Whisper Turbo Subtitle](https://github.com/NeuralFalconYT/Whisper-Turbo-Subtitle) 
-        
-        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NeuralFalconYT/Whisper-Turbo-Subtitle/blob/main/Whisper_Turbo_Subtitle.ipynb)
-        """
-    )
-    with gr.Row():
-        with gr.Column():
-            srt_file = gr.File(label='Upload .srt Subtitle File Only')
-            with gr.Row():
-                voice = gr.Dropdown(
-                    voice_list, 
-                    value='af', 
-                    allow_custom_value=False, 
-                    label='Voice', 
-                )
-            with gr.Row():
-                generate_btn_ = gr.Button('Generate', variant='primary')
-          
-        with gr.Column():
-            audio = gr.Audio(interactive=False, label='Output Audio', autoplay=True)
-            with gr.Accordion('Enable Autoplay', open=False):
-                autoplay = gr.Checkbox(value=True, label='Autoplay')
-                autoplay.change(toggle_autoplay, inputs=[autoplay], outputs=[audio])
-
-    # srt_file.submit(
-    #     srt_process, 
-    #     inputs=[srt_file, voice], 
-    #     outputs=[audio]
-    # )
-    generate_btn_.click(
-        srt_process, 
-        inputs=[srt_file,voice], 
-        outputs=[audio]
-    )
-    
-    
-display_text = "  \n".join(voice_list)
-
-with gr.Blocks() as demo4:
-    gr.Markdown(f"# Voice Names \n{display_text}")
-
-
-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):
-    demo = gr.TabbedInterface([demo1, demo2,demo3,demo4], ["Batched TTS", "Multiple Speech-Type Generation","SRT Dubbing","Available Voice Names"],title="Kokoro TTS")
-
-    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)
-
-if __name__ == "__main__":
-    main()    
-
-
-##For client side
-# from gradio_client import Client
-# import shutil
-# import os
-# os.makedirs("temp_audio", exist_ok=True)
-# from gradio_client import Client
-# client = Client("http://127.0.0.1:7860/")
-# result = client.predict(
-# 		text="Hello!!",
-# 		model_name="kokoro-v0_19.pth",
-# 		voice_name="af_bella",
-# 		speed=1,
-# 		trim=0,
-# 		pad_between_segments=0,
-# 		remove_silence=False,
-# 		minimum_silence=0.05,
-# 		api_name="/text_to_speech"
-# )
-
-# save_at=f"./temp_audio/{os.path.basename(result)}"
-# shutil.move(result, save_at)
-# print(f"Saved at {save_at}")