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lang_data.py

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+languages = {
+    "Afrikaans": "af",
+    "Amharic": "am",
+    "Arabic": "ar",
+    "Azerbaijani": "az",
+    "Bulgarian": "bg",
+    "Bengali": "bn",
+    "Bosnian": "bs",
+    "Catalan": "ca",
+    "Czech": "cs",
+    "Welsh": "cy",
+    "Danish": "da",
+    "German": "de",
+    "Greek": "el",
+    "English": "en",
+    "Spanish": "es",
+    "French": "fr",
+    "Irish": "ga",
+    "Galician": "gl",
+    "Gujarati": "gu",
+    "Hebrew": "he",
+    "Hindi": "hi",
+    "Croatian": "hr",
+    "Hungarian": "hu",
+    "Indonesian": "id",
+    "Icelandic": "is",
+    "Italian": "it",
+    "Japanese": "ja",
+    "Javanese": "jv",
+    "Georgian": "ka",
+    "Kazakh": "kk",
+    "Khmer": "km",
+    "Kannada": "kn",
+    "Korean": "ko",
+    "Lao": "lo",
+    "Lithuanian": "lt",
+    "Latvian": "lv",
+    "Macedonian": "mk",
+    "Malayalam": "ml",
+    "Mongolian": "mn",
+    "Marathi": "mr",
+    "Malay": "ms",
+    "Maltese": "mt",
+    "Burmese": "my",
+    "Norwegian Bokmål": "nb",
+    "Nepali": "ne",
+    "Dutch": "nl",
+    "Polish": "pl",
+    "Pashto": "ps",
+    "Portuguese": "pt",
+    "Romanian": "ro",
+    "Russian": "ru",
+    "Sinhala": "si",
+    "Slovak": "sk",
+    "Slovenian": "sl",
+    "Somali": "so",
+    "Albanian": "sq",
+    "Serbian": "sr",
+    "Sundanese": "su",
+    "Swedish": "sv",
+    "Swahili": "sw",
+    "Tamil": "ta",
+    "Telugu": "te",
+    "Thai": "th",
+    "Turkish": "tr",
+    "Ukrainian": "uk",
+    "Urdu": "ur",
+    "Uzbek": "uz",
+    "Vietnamese": "vi",
+    "Chinese": "zh",
+    "Zulu": "zu"
+}
+
+
+
+female_voice_list={'Vietnamese': 'vi-VN-HoaiMyNeural',
+ 'Bengali': 'bn-BD-NabanitaNeural',
+ 'Thai': 'th-TH-PremwadeeNeural',
+ 'English': "en-US-AvaMultilingualNeural", #'en-AU-NatashaNeural', #"en-IE-EmilyNeural"
+ 'Portuguese': 'pt-BR-FranciscaNeural',
+ 'Arabic': 'ar-AE-FatimaNeural',
+ 'Turkish': 'tr-TR-EmelNeural',
+ 'Spanish': 'es-AR-ElenaNeural',
+ 'Korean': 'ko-KR-SunHiNeural',
+ 'French': 'fr-BE-CharlineNeural',
+ 'Indonesian': 'id-ID-GadisNeural',
+ 'Russian': 'ru-RU-SvetlanaNeural',
+ 'Hindi': 'hi-IN-SwaraNeural',
+ 'Japanese': 'ja-JP-NanamiNeural',
+ 'Afrikaans': 'af-ZA-AdriNeural',
+ 'Amharic': 'am-ET-MekdesNeural',
+ 'Azerbaijani': 'az-AZ-BanuNeural',
+ 'Bulgarian': 'bg-BG-KalinaNeural',
+ 'Bosnian': 'bs-BA-VesnaNeural',
+ 'Catalan': 'ca-ES-JoanaNeural',
+ 'Czech': 'cs-CZ-VlastaNeural',
+ 'Welsh': 'cy-GB-NiaNeural',
+ 'Danish': 'da-DK-ChristelNeural',
+ 'German': 'de-AT-IngridNeural',
+ 'Greek': 'el-GR-AthinaNeural',
+ 'Irish': 'ga-IE-OrlaNeural',
+ 'Galician': 'gl-ES-SabelaNeural',
+ 'Gujarati': 'gu-IN-DhwaniNeural',
+ 'Hebrew': 'he-IL-HilaNeural',
+ 'Croatian': 'hr-HR-GabrijelaNeural',
+ 'Hungarian': 'hu-HU-NoemiNeural',
+ 'Icelandic': 'is-IS-GudrunNeural',
+ 'Italian': 'it-IT-ElsaNeural',
+ 'Javanese': 'jv-ID-SitiNeural',
+ 'Georgian': 'ka-GE-EkaNeural',
+ 'Kazakh': 'kk-KZ-AigulNeural',
+ 'Khmer': 'km-KH-SreymomNeural',
+ 'Kannada': 'kn-IN-SapnaNeural',
+ 'Lao': 'lo-LA-KeomanyNeural',
+ 'Lithuanian': 'lt-LT-OnaNeural',
+ 'Latvian': 'lv-LV-EveritaNeural',
+ 'Macedonian': 'mk-MK-MarijaNeural',
+ 'Malayalam': 'ml-IN-SobhanaNeural',
+ 'Mongolian': 'mn-MN-YesuiNeural',
+ 'Marathi': 'mr-IN-AarohiNeural',
+ 'Malay': 'ms-MY-YasminNeural',
+ 'Maltese': 'mt-MT-GraceNeural',
+ 'Burmese': 'my-MM-NilarNeural',
+ 'Norwegian Bokmål': 'nb-NO-PernilleNeural',
+ 'Nepali': 'ne-NP-HemkalaNeural',
+ 'Dutch': 'nl-BE-DenaNeural',
+ 'Polish': 'pl-PL-ZofiaNeural',
+ 'Pashto': 'ps-AF-LatifaNeural',
+ 'Romanian': 'ro-RO-AlinaNeural',
+ 'Sinhala': 'si-LK-ThiliniNeural',
+ 'Slovak': 'sk-SK-ViktoriaNeural',
+ 'Slovenian': 'sl-SI-PetraNeural',
+ 'Somali': 'so-SO-UbaxNeural',
+ 'Albanian': 'sq-AL-AnilaNeural',
+ 'Serbian': 'sr-RS-SophieNeural',
+ 'Sundanese': 'su-ID-TutiNeural',
+ 'Swedish': 'sv-SE-SofieNeural',
+ 'Swahili': 'sw-KE-ZuriNeural',
+ 'Tamil': 'ta-IN-PallaviNeural',
+ 'Telugu': 'te-IN-ShrutiNeural',
+ 'Chinese': 'zh-CN-XiaoxiaoNeural',
+ 'Ukrainian': 'uk-UA-PolinaNeural',
+ 'Urdu': 'ur-IN-GulNeural',
+ 'Uzbek': 'uz-UZ-MadinaNeural',
+ 'Zulu': 'zu-ZA-ThandoNeural'}
+male_voice_list= {'Vietnamese': 'vi-VN-NamMinhNeural',
+ 'Bengali': 'bn-BD-PradeepNeural',
+ 'Thai': 'th-TH-NiwatNeural',
+ 'English': 'en-US-BrianMultilingualNeural', #"en-US-BrianNeural"
+ 'Portuguese': 'pt-BR-AntonioNeural',
+ 'Arabic': 'ar-AE-HamdanNeural',
+ 'Turkish': 'tr-TR-AhmetNeural',
+ 'Spanish': 'es-AR-TomasNeural',
+ 'Korean': 'ko-KR-HyunsuNeural',
+ 'French': 'fr-BE-GerardNeural',
+ 'Indonesian': 'id-ID-ArdiNeural',
+ 'Russian': 'ru-RU-DmitryNeural',
+ 'Hindi': 'hi-IN-MadhurNeural',
+ 'Japanese': 'ja-JP-KeitaNeural',
+ 'Afrikaans': 'af-ZA-WillemNeural',
+ 'Amharic': 'am-ET-AmehaNeural',
+ 'Azerbaijani': 'az-AZ-BabekNeural',
+ 'Bulgarian': 'bg-BG-BorislavNeural',
+ 'Bosnian': 'bs-BA-GoranNeural',
+ 'Catalan': 'ca-ES-EnricNeural',
+ 'Czech': 'cs-CZ-AntoninNeural',
+ 'Welsh': 'cy-GB-AledNeural',
+ 'Danish': 'da-DK-JeppeNeural',
+ 'German': 'de-AT-JonasNeural',
+ 'Greek': 'el-GR-NestorasNeural',
+ 'Irish': 'ga-IE-ColmNeural',
+ 'Galician': 'gl-ES-RoiNeural',
+ 'Gujarati': 'gu-IN-NiranjanNeural',
+ 'Hebrew': 'he-IL-AvriNeural',
+ 'Croatian': 'hr-HR-SreckoNeural',
+ 'Hungarian': 'hu-HU-TamasNeural',
+ 'Icelandic': 'is-IS-GunnarNeural',
+ 'Italian': 'it-IT-DiegoNeural',
+ 'Javanese': 'jv-ID-DimasNeural',
+ 'Georgian': 'ka-GE-GiorgiNeural',
+ 'Kazakh': 'kk-KZ-DauletNeural',
+ 'Khmer': 'km-KH-PisethNeural',
+ 'Kannada': 'kn-IN-GaganNeural',
+ 'Lao': 'lo-LA-ChanthavongNeural',
+ 'Lithuanian': 'lt-LT-LeonasNeural',
+ 'Latvian': 'lv-LV-NilsNeural',
+ 'Macedonian': 'mk-MK-AleksandarNeural',
+ 'Malayalam': 'ml-IN-MidhunNeural',
+ 'Mongolian': 'mn-MN-BataaNeural',
+ 'Marathi': 'mr-IN-ManoharNeural',
+ 'Malay': 'ms-MY-OsmanNeural',
+ 'Maltese': 'mt-MT-JosephNeural',
+ 'Burmese': 'my-MM-ThihaNeural',
+ 'Norwegian Bokmål': 'nb-NO-FinnNeural',
+ 'Nepali': 'ne-NP-SagarNeural',
+ 'Dutch': 'nl-BE-ArnaudNeural',
+ 'Polish': 'pl-PL-MarekNeural',
+ 'Pashto': 'ps-AF-GulNawazNeural',
+ 'Romanian': 'ro-RO-EmilNeural',
+ 'Sinhala': 'si-LK-SameeraNeural',
+ 'Slovak': 'sk-SK-LukasNeural',
+ 'Slovenian': 'sl-SI-RokNeural',
+ 'Somali': 'so-SO-MuuseNeural',
+ 'Albanian': 'sq-AL-IlirNeural',
+ 'Serbian': 'sr-RS-NicholasNeural',
+ 'Sundanese': 'su-ID-JajangNeural',
+ 'Swedish': 'sv-SE-MattiasNeural',
+ 'Swahili': 'sw-KE-RafikiNeural',
+ 'Tamil': 'ta-IN-ValluvarNeural',
+ 'Telugu': 'te-IN-MohanNeural',
+ 'Chinese': 'zh-CN-YunjianNeural',
+ 'Ukrainian': 'uk-UA-OstapNeural',
+ 'Urdu': 'ur-IN-SalmanNeural',
+ 'Uzbek': 'uz-UZ-SardorNeural',
+ 'Zulu': 'zu-ZA-ThembaNeural'}

microsoft_tts.py

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+#@title <-- Just run the cell (config edge TTS)
+edge_folder="."
+import nltk
+nltk.download('punkt')
+from nltk.tokenize import sent_tokenize
+
+from deep_translator import GoogleTranslator
+
+from lang_data import languages,male_voice_list,female_voice_list
+
+def translate_text(text, Language):
+    # print("calling translate")
+    target_language=languages[Language]
+    if Language == "Chinese":
+          target_language='zh-CN'
+    translator = GoogleTranslator(target=target_language)
+    translation = translator.translate(text.strip())
+    t_text=str(translation)
+    # print(f"{t_text}---{Language}----{target_language}")
+    return t_text
+
+
+def chunks_sentences(paragraph, join_limit=2):
+    sentences = sent_tokenize(paragraph)
+    # Initialize an empty list to store the new sentences
+    new_sentences = []
+
+    # Iterate through the list of sentences in steps of 'join_limit'
+    for i in range(0, len(sentences), join_limit):
+        # Join the sentences with a space between them
+        new_sentence = ' '.join(sentences[i:i + join_limit])
+        new_sentences.append(new_sentence)
+    return new_sentences
+
+
+def calculate_rate_string(input_value):
+    rate = (input_value - 1) * 100
+    sign = '+' if input_value >= 1 else '-'
+    return f"{sign}{abs(int(rate))}"
+
+
+def make_chunks(input_text, language):
+    language="English"
+    if language == "English":
+      filtered_list=chunks_sentences(input_text, join_limit=2)
+      # temp_list = input_text.strip().split(".")
+      # filtered_list = [element.strip() + '.' for element in temp_list[:-1] if element.strip() and element.strip() != "'" and element.strip() != '"']
+      # if temp_list[-1].strip():
+      #     filtered_list.append(temp_list[-1].strip())
+      return filtered_list
+
+
+
+
+import re
+import uuid
+def tts_file_name(text):
+    if text.endswith("."):
+        text = text[:-1]
+    text = text.lower()
+    text = text.strip()
+    text = text.replace(" ","_")
+    truncated_text = text[:25] if len(text) > 25 else text if len(text) > 0 else "empty"
+    random_string = uuid.uuid4().hex[:8].upper()
+    file_name = f"{edge_folder}/edge_tts_voice/{truncated_text}_{random_string}.mp3"
+    return file_name
+
+
+from pydub import AudioSegment
+import shutil
+import os
+def merge_audio_files(audio_paths, output_path):
+    # Initialize an empty AudioSegment
+    merged_audio = AudioSegment.silent(duration=0)
+
+    # Iterate through each audio file path
+    for audio_path in audio_paths:
+        # Load the audio file using Pydub
+        audio = AudioSegment.from_file(audio_path)
+
+        # Append the current audio file to the merged_audio
+        merged_audio += audio
+
+    # Export the merged audio to the specified output path
+    merged_audio.export(output_path, format="mp3")
+
+def edge_free_tts(chunks_list,speed,voice_name,save_path,translate_text_flag,Language):
+  # print(voice_name)
+  # print(chunks_list)
+  store_text=""
+  if len(chunks_list)>1:
+    chunk_audio_list=[]
+    if os.path.exists(f"{edge_folder}/edge_tts_voice"):
+      shutil.rmtree(f"{edge_folder}/edge_tts_voice")
+    os.mkdir(f"{edge_folder}/edge_tts_voice")
+    k=1
+    for i in chunks_list:
+      # print(i)
+      if translate_text_flag:
+        text=translate_text(i, Language)
+      else:
+        text=i
+      store_text+=text+" "
+      text=text.replace('"',"")
+      edge_command=f'edge-tts  --rate={calculate_rate_string(speed)}% --voice {voice_name} --text "{text}" --write-media {edge_folder}/edge_tts_voice/{k}.mp3'
+      var1=os.system(edge_command)
+      if var1==0:
+        pass
+      else:
+        print(f"Failed: {i}")
+        print(edge_command)
+      chunk_audio_list.append(f"{edge_folder}/edge_tts_voice/{k}.mp3")
+      k+=1
+    # print(chunk_audio_list)
+    merge_audio_files(chunk_audio_list, save_path)
+  else:
+    if translate_text_flag:
+      text=translate_text(chunks_list[0], Language)
+    else:
+      text=chunks_list[0]
+    text=text.replace('"',"")
+    store_text+=text+" "
+    edge_command=f'edge-tts  --rate={calculate_rate_string(speed)}% --voice {voice_name} --text "{text}" --write-media {save_path}'
+    var2=os.system(edge_command)
+    if var2==0:
+      pass
+    else:
+      print(f"Failed: {chunks_list[0]}")
+      print(edge_command)
+  with open("./temp.txt", "w", encoding="utf-8") as text_file:
+    text_file.write(store_text)
+  return save_path
+
+
+# speed = 1  # @param {type: "number"}
+# translate_text_flag  = True # @param {type:"boolean"}
+# long_sentence = True # @param {type:"boolean"}
+
+
+
+
+
+
+
+
+# from IPython.display import clear_output
+# from IPython.display import Audio
+if not os.path.exists(f"{edge_folder}/audio"):
+    os.mkdir(f"{edge_folder}/audio")
+import uuid
+def random_audio_name_generate():
+  random_uuid = uuid.uuid4()
+  audio_extension = ".mp3"
+  random_audio_name = str(random_uuid)[:8] + audio_extension
+  return random_audio_name
+def edge_tts_pipeline(input_text,Language='English',voice_name=None,Gender='Male',translate_text_flag=True,no_silence=False,speed=1,tts_save_path="",long_sentence=True):
+  # print("calling gradio_talk")
+  # global long_sentence,translate_text_flag,Language,speed,voice_name,Gender
+  global male_voice_list,female_voice_list
+  # long_sentence=True
+  # translate_text_flag=True
+  # speed=1
+  if long_sentence==False:
+    if len(input_text)>500:
+      long_sentence=True
+  if voice_name==None:
+    if Gender=="Male":
+      voice_name=male_voice_list[Language]
+    if Gender=="Female":
+      voice_name=female_voice_list[Language]
+  if long_sentence==True and translate_text_flag==True:
+    chunks_list=make_chunks(input_text,Language)
+  elif long_sentence==True and translate_text_flag==False:
+    chunks_list=make_chunks(input_text,"English")
+  else:
+    chunks_list=[input_text]
+  temp_save_path=f"{edge_folder}/audio/"+random_audio_name_generate()
+  save_path=temp_save_path.lower().replace(".mp3",".wav")
+  # print(chunks_list,speed,voice_name,save_path,translate_text_flag,Language)
+  edge_save_path=edge_free_tts(chunks_list,speed,voice_name,temp_save_path,translate_text_flag,Language)
+  mp3_to_wav(edge_save_path, save_path)
+  audio_return_path=save_path
+  if no_silence:
+    clean_path=f"{edge_folder}/audio/"+random_audio_name_generate().replace(".mp3",".wav")
+    remove_silence(save_path,clean_path)
+    audio_return_path=clean_path
+    # return clean_path
+  if tts_save_path=="":
+    return audio_return_path
+  else:
+    shutil.copyfile(audio_return_path,tts_save_path)
+    return audio_return_path
+
+
+
+def talk(input_text):
+  # global long_sentence,translate_text_flag,Language,speed,voice_name,Gender
+  global Language, Gender,male_voice_list,female_voice_list
+  global no_silence
+  long_sentence=True
+  translate_text_flag=False
+  speed=1
+
+  if Gender=="Male":
+    voice_name=male_voice_list[Language]
+  if Gender=="Female":
+    voice_name=female_voice_list[Language]
+  if long_sentence==True and translate_text_flag==True:
+    chunks_list=make_chunks(input_text,Language)
+  elif long_sentence==True and translate_text_flag==False:
+    chunks_list=make_chunks(input_text,"English")
+  else:
+    chunks_list=[input_text]
+  
+  temp_save_path=f"{edge_folder}/audio/"+random_audio_name_generate()
+  # print(f"temp_save_path: {temp_save_path}")
+  save_path=temp_save_path.replace(".mp3",".wav")
+  # print(f"save_path: {save_path}")
+  edge_save_path=edge_free_tts(chunks_list,speed,voice_name,temp_save_path,translate_text_flag,Language)
+  
+  mp3_to_wav(edge_save_path, save_path)
+  if no_silence:
+    clean_path=f"{edge_folder}/audio/"+random_audio_name_generate().replace(".mp3",".wav")
+    remove_silence(save_path,clean_path)
+    return clean_path
+  return save_path
+
+from pydub import AudioSegment
+from pydub.silence import split_on_silence
+import os
+ 
+def remove_silence(file_path,output_path):
+    # Extract file name and format from the provided path
+    file_name = os.path.basename(file_path)
+    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=50)
+
+    # Putting the file back together
+    combined = AudioSegment.empty()
+    for chunk in audio_chunks:
+        combined += chunk
+
+
+    combined.export(output_path, format=audio_format)
+    print(f"Remove silence successfully: {output_path}")
+
+    return output_path
+
+
+from pydub import AudioSegment
+
+def mp3_to_wav(mp3_file, wav_file):
+    # Load the MP3 file
+    # print("calling mp3_to_wav")
+    # print(mp3_file,wav_file)
+    audio = AudioSegment.from_mp3(mp3_file)
+
+    # Export the audio to WAV format
+    audio.export(wav_file, format="wav")
+
+
+###use case
+
+# from microsoft_tts import edge_tts_pipeline
+# def tts(text, Language='English',voice_name=None, tts_save_path='', Gender='Male', translate_text_flag=False, no_silence=True, speed=1.0, long_sentence=True):          
+#     edge_save_path = edge_tts_pipeline(text, Language,voice_name, Gender, translate_text_flag=translate_text_flag, 
+#                                         no_silence=no_silence, speed=speed, tts_save_path=tts_save_path, 
+#                                         long_sentence=long_sentence)
+#     return edge_save_path
+
+# text="Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks."
+# save_path = tts(text, Language='English',Gender="Male")
+# print(save_path)
+# import simpleaudio as sa
+# def play_sound(filename):
+#     wave_obj = sa.WaveObject.from_wave_file(filename)
+#     play_obj = wave_obj.play()
+#     play_obj.wait_done()
+# play_sound(save_path)
+
+
+
+
+
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+
+
+
+# edge_save_path=talk(text)
+# print(f"Audio File Save at: {edge_save_path}")
+
+# text = "a quick brown fox jumps over the lazy dog and the dog barks loudly"
+# Language = "English" # @param ['English','Hindi','Bengali','Afrikaans', 'Amharic', 'Arabic', 'Azerbaijani', 'Bulgarian', 'Bosnian', 'Catalan', 'Czech', 'Welsh', 'Danish', 'German', 'Greek', 'Spanish', 'French', 'Irish', 'Galician', 'Gujarati', 'Hebrew', 'Croatian', 'Hungarian', 'Indonesian', 'Icelandic', 'Italian', 'Japanese', 'Javanese', 'Georgian', 'Kazakh', 'Khmer', 'Kannada', 'Korean', 'Lao', 'Lithuanian', 'Latvian', 'Macedonian', 'Malayalam', 'Mongolian', 'Marathi', 'Malay', 'Maltese', 'Burmese', 'Norwegian Bokmål', 'Nepali', 'Dutch', 'Polish', 'Pashto', 'Portuguese', 'Romanian', 'Russian', 'Sinhala', 'Slovak', 'Slovenian', 'Somali', 'Albanian', 'Serbian', 'Sundanese', 'Swedish', 'Swahili', 'Tamil', 'Telugu', 'Thai', 'Turkish', 'Ukrainian', 'Urdu', 'Uzbek', 'Vietnamese', 'Chinese', 'Zulu']
+# no_silence = False
+# Gender = "Male"# @param ['Male', 'Female']
+# translate_text_flag=True
+# no_silence=True
+# speed=1
+# tts_save_path='temp.wav'
+# edge_save_path=edge_tts_pipeline(text,Language,Gender,translate_text_flag=translate_text_flag,no_silence=no_silence,speed=speed,tts_save_path=tts_save_path)
+# print(f"Audio File Save at: {edge_save_path}")
+
+# from microsoft_tts import edge_tts_pipeline
+# def tts(text,tts_save_path=''):
+#     # text = "a quick brown fox jumps over the lazy dog and the dog barks loudly"
+#     Language = "English" # @param ['English','Hindi','Bengali','Afrikaans', 'Amharic', 'Arabic', 'Azerbaijani', 'Bulgarian', 'Bosnian', 'Catalan', 'Czech', 'Welsh', 'Danish', 'German', 'Greek', 'Spanish', 'French', 'Irish', 'Galician', 'Gujarati', 'Hebrew', 'Croatian', 'Hungarian', 'Indonesian', 'Icelandic', 'Italian', 'Japanese', 'Javanese', 'Georgian', 'Kazakh', 'Khmer', 'Kannada', 'Korean', 'Lao', 'Lithuanian', 'Latvian', 'Macedonian', 'Malayalam', 'Mongolian', 'Marathi', 'Malay', 'Maltese', 'Burmese', 'Norwegian Bokmål', 'Nepali', 'Dutch', 'Polish', 'Pashto', 'Portuguese', 'Romanian', 'Russian', 'Sinhala', 'Slovak', 'Slovenian', 'Somali', 'Albanian', 'Serbian', 'Sundanese', 'Swedish', 'Swahili', 'Tamil', 'Telugu', 'Thai', 'Turkish', 'Ukrainian', 'Urdu', 'Uzbek', 'Vietnamese', 'Chinese', 'Zulu']
+#     no_silence = False
+#     Gender = "Male"# @param ['Male', 'Female']
+#     translate_text_flag=True
+#     no_silence=True
+#     speed=1
+#     # tts_save_path='temp.wav'
+#     long_sentence=True
+#     edge_save_path=edge_tts_pipeline(text,Language,Gender,translate_text_flag=translate_text_flag,no_silence=no_silence,speed=speed,tts_save_path=tts_save_path,long_sentence=long_sentence)
+#     print(f"Audio File Save at: {edge_save_path}")
+#     return edge_save_path

requirements.txt

@@ -0,0 +1,6 @@
+edge-tts
+deep_translator==1.11.4
+nltk==3.8.1
+pydub==0.25.1
+gradio>=5.6.0
+click==8.1.7