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import torch
from transformers import VitsModel, AutoTokenizer
import torchaudio
import os
from gtts import gTTS
def generate_audio(text, filename="output.mp3", gender="female", speed="normal"):
"""
Convert text to speech and save it as an audio file.
Parameters:
text (str): The text to convert.
filename (str): The output file name.
gender (str): "male" (use MMS-TTS) or "female" (use gTTS).
speed (str): "slow", "normal", or "fast" (only for gTTS).
"""
lang = "vi"
if gender.lower() == "female":
# gTTS chỉ có giọng nữ
speed_mapping = {"slow": True, "normal": False, "fast": False}
slow = speed_mapping.get(speed.lower(), False)
tts = gTTS(text=text, lang=lang, slow=slow)
tts.save(filename)
print(f"✅ Audio saved as {filename}")
elif gender.lower() == "male":
# MMS-TTS cho giọng nam
model = VitsModel.from_pretrained("facebook/mms-tts-vie")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-vie")
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
# Lưu file âm thanh
torchaudio.save(filename, output, 24000, backend="sox_io")
print(f"✅ Audio saved as {filename}")
else:
print("⚠️ Giọng không hợp lệ! Chỉ hỗ trợ 'male' hoặc 'female'.")
def text_to_speech(gender, speed):
text_folder = "./"
text_files = sorted([f for f in os.listdir(text_folder) if f.endswith('.txt') and f != "text.txt" and f != "requirements.txt"])
for text_file in text_files:
with open(f"{text_file}", "r", encoding="utf-8") as file:
content = file.read()
audio_file = text_file.replace("txt","mp3")
generate_audio(content, f"{audio_file}", gender=gender, speed=speed)
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