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helpers.py
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helpers.py
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import json
from TTS.api import TTS
import os
from pydub import AudioSegment
import librosa
import soundfile as sf
import streamlit as st
import datetime
import zipfile
import requests
from tqdm import tqdm
clonner_file_path = "clonner_output/voice_clonning_audio.wav"
def download_audio_file(audio, file_name):
st.download_button(
label="Download The Audio File",
data=audio,
file_name="{}-{}.wav".format(file_name, datetime.datetime.now()),
mime="audio/wav")
class DownloadModels():
def __init__(self):
pass
def download_models(self):
# Set the URL of the ZIP file on Google Drive
url = "https://drive.google.com/uc?id=16cP_FBEejiQhGhhrPP7u9LZj-DM2L9aB&export=download&confirm=t&uuid=0cc5bfb7-8242-4558-988c-c05ed63daf9b&confirm=t&uuid=48657c6e-7d73-496b-9d39-6852de088676&confirm=t&uuid=42cd659c-7823-4151-be6e-1b9c0ff10814&at=ALgDtswGngwh3ZGdgQo1J0e6ra29:1677086779517"
temp_path = 'temp.zip'
model_path = "language_model"
dict_list = os.listdir(path=model_path)
if len(dict_list) == 1:
# Download the ZIP file to a temporary folder
response = requests.get(url, stream=True)
total_size_in_bytes = int(response.headers.get('content-length', 0))
block_size = 1024 # 1 Kibibyte
progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
with open(temp_path, 'wb') as f:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
f.write(data)
progress_bar.close()
print("Downloading Of Models Completed!")
print("Starting The Extraction Process to {} Folder...".format(model_path))
# Extract the contents of the ZIP file to a specific folder
extract_folder = model_path
with zipfile.ZipFile(temp_path, 'r') as zip_ref:
zip_ref.extractall(extract_folder)
# Delete the ZIP file
os.remove(temp_path)
print("Extraction Completed!")
else:
print("Models already exists, skipping download and extraction.")
class ProcessModelList():
def __init__(self) -> None:
pass
def read_file(self):
f = open("model_list.json", "r")
data = json.load(f)
return data
def get_langauge_labels(self):
data = self.read_file()
return data["labels"]
def get_popular_voice_list(self, model_name):
data = self.read_file()
if model_name in data["custom_voice"]:
return True
else:
return False
def get_popular_voice_path(self, selected_language, selected_model):
data = self.read_file()
model_names = data["data"][selected_language]
for i in range(len(model_names)):
model_name = model_names[i]["name"]
if model_name == selected_model:
model_voice = model_names[i]["model_voice"]
return model_voice
def get_model_name(self, selected_language):
model_name_list = []
data = self.read_file()
model_names = data["data"][selected_language]
for i in range(len(model_names)):
model_name = model_names[i]["name"]
model_name_list.append(model_name)
return model_name_list
def get_model_path(self, selected_language, selected_model):
data = self.read_file()
model_names = data["data"][selected_language]
for i in range(len(model_names)):
model_name = model_names[i]["name"]
if model_name == selected_model:
model_path = model_names[i]["model_path"]
return model_path
def multi_language_selected(self, model_path):
tts = TTS(model_path="language_model/{}/model_file.pth".format(str(model_path)), config_path="language_model/{}/config.json".format(str(model_path)))
return tts.speakers, tts.languages
def get_multi_speaker_model(self, model_path):
search_model_path = "language_model/{}".format(str(model_path))
subdirectories = os.listdir(path=search_model_path)
if "speaker_ids.json" in subdirectories:
tts = TTS(model_path="language_model/{}/model_file.pth".format(str(model_path)), config_path="language_model/{}/config.json".format(str(model_path)))
return True, tts.speakers
else:
return False, None
class ConvertTextToSpeech():
def __init__(self, model_name, model_path, text):
self.model_name = model_name
self.model_path = model_path
self.text = text
def convert_text_to_speech(self, speaker_id=None):
tts = TTS(model_path="language_model/{}/model_file.pth".format(str(self.model_path)), config_path="language_model/{}/config.json".format(str(self.model_path)))
if speaker_id == None:
tts.tts_to_file(text=self.text, file_path="output/output.wav")
else:
tts.tts_to_file(text=self.text, speaker=speaker_id, file_path="output/output.wav")
def convert_text_to_speech_multi_langauge(self, speaker, language, model_name, selected_langauge):
if ProcessModelList().get_popular_voice_list(model_name=model_name):
tts = TTS(model_path="language_model/{}/model_file.pth".format(str(self.model_path)), config_path="language_model/{}/config.json".format(str(self.model_path)))
tts.tts_to_file(text=self.text, speaker=tts.speakers[0], language=tts.languages[0], speaker_wav="language_model/{}".format(ProcessModelList().get_popular_voice_path(selected_model=model_name, selected_language=selected_langauge)), file_path="output/output.wav")
else:
tts = TTS(model_path="language_model/{}/model_file.pth".format(str(self.model_path)), config_path="language_model/{}/config.json".format(str(self.model_path)))
tts.tts_to_file(text=self.text, speaker=speaker, language=language, file_path="output/output.wav")
def read_audio_file(self):
audio_file = open("output/output.wav", 'rb')
audio_bytes = audio_file.read()
return audio_bytes
class AudioClonning():
def __init__(self, audio, audio_filename, text, emotion):
self.audio = audio
self.audio_filename = audio_filename
self.text = text
self.emotion = emotion
def convert_mp3_to_wav(self):
input_file = self.audio
# load the audio file using pydub
audio = AudioSegment.from_file(input_file)
# convert the audio to wav format
audio.export(clonner_file_path, format='wav')
def check_audio_file_format(self):
audio_file_name = str(self.audio_filename).split(".")[-1]
if audio_file_name == "mp3":
self.convert_mp3_to_wav()
return clonner_file_path
else:
audio = AudioSegment.from_file(self.audio)
audio.export(clonner_file_path, format='wav')
return clonner_file_path
def convert_text_to_speech(self):
clonner_audio = self.check_audio_file_format()
tts = TTS(model_path="language_model/tts_models--multilingual--multi-dataset--your_tts/model_file.pth", config_path="language_model/tts_models--multilingual--multi-dataset--your_tts/config.json")
tts.tts_to_file(text=self.text, speaker=tts.speakers[0], language=tts.languages[0], speaker_wav=clonner_audio, file_path="clonner_output/voice_clonning_audio.wav")
audio_file = open(clonner_file_path, 'rb')
return audio_file.read()
def emotion_modification(self):
emotion = str(self.emotion).lower()
# load audio file
y, sr = librosa.load(clonner_file_path)
# modify audio file
if emotion == 'happy':
y_pitch = librosa.effects.pitch_shift(y, sr, n_steps=2) # increase pitch by 2 semitones
y_energy = y * 1.5 # increase energy level by 50%
elif emotion == 'sad':
y_pitch = librosa.effects.pitch_shift(y, sr, n_steps=-2) # decrease pitch by 2 semitones
y_energy = y * 0.5 # reduce energy level by 50%
elif emotion == 'angry':
y_pitch = librosa.effects.pitch_shift(y, sr, n_steps=-1) # decrease pitch by 1 semitone
y_energy = y * 2.0 # increase energy level by 100%
elif emotion == 'surprise':
y_pitch = librosa.effects.pitch_shift(y, sr, n_steps=3) # increase pitch by 3 semitones
y_energy = y * 2.5 # increase energy level by 150%
elif emotion == 'neutral':
y_pitch = y
y_energy = y * 1.0 # no change in energy level
elif emotion == 'dull': # emotion is dull
y_pitch = librosa.effects.pitch_shift(y, sr, n_steps=-3) # decrease pitch by 3 semitones
y_energy = y * 0.5 # reduce energy level by 50%
# save modified audio file
sf.write('clonner_output/voice_clonning_audio1.wav', y_pitch * y_energy, sr)