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from TTS.api import TTS
from pydub import AudioSegment
import os
import re
import ffmpeg
import shutil
import argparse
import torch
os.environ["COQUI_TOS_AGREED"] = "1"
def adjust_speed(input_file, speed_factor):
output_file = input_file.replace(".wav", "_adjusted.wav")
ffmpeg.input(input_file).filter('atempo', speed_factor).output(output_file, acodec='pcm_s16le').run()
return output_file
def generate_speech(text, speaker_voice_map, output_file):
combined_audio = AudioSegment.empty()
temp_files = []
if torch.cuda.is_available():
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to("cuda")
else:
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2")
for line in text.split("\n"):
if not line.strip():
continue
match = re.match(r"\[SPEAKER_(\d+)\] \[(\d+\.\d+)-(\d+\.\d+)\] (.+)", line)
if not match:
continue
speaker_id, start_time, end_time, sentence = match.groups()
start_time, end_time = float(start_time), float(end_time)
segment_duration = (end_time - start_time) * 1000 # Duration in milliseconds
speaker_wav = speaker_voice_map.get(f"SPEAKER_{speaker_id}")
if not speaker_wav:
continue
os.makedirs('./audio/temp', exist_ok=True)
temp_file_path = f"./audio/temp/temp_output_part_{len(temp_files)}.wav"
temp_files.append(temp_file_path)
tts_speed = 2.0 # original 1.0
tts.tts_to_file(text=sentence, file_path=temp_file_path, speaker_wav=speaker_wav, language="es", speed=tts_speed)
segment_audio = AudioSegment.from_wav(temp_file_path)
if segment_audio.duration_seconds * 1000 > segment_duration:
#while tts_speed < 2.0 and segment_audio.duration_seconds * 1000 > segment_duration:
# tts_speed += 0.5
# tts.tts_to_file(text=sentence, file_path=temp_file_path, speaker_wav=speaker_wav, language="es", speed=tts_speed)
# segment_audio = AudioSegment.from_wav(temp_file_path)
if segment_audio.duration_seconds * 1000 > segment_duration:
required_speed = segment_duration / (segment_audio.duration_seconds * 1000)
if required_speed < 1.0:
required_speed = 1.0 / required_speed
temp_file_path = adjust_speed(temp_file_path, required_speed)
segment_audio = AudioSegment.from_wav(temp_file_path)
if combined_audio.duration_seconds == 0 and start_time > 0:
combined_audio = AudioSegment.silent(duration=start_time * 1000) + combined_audio
if segment_audio.duration_seconds * 1000 > segment_duration:
segment_audio = segment_audio[:segment_duration]
else:
segment_audio = segment_audio + AudioSegment.silent(duration=segment_duration - len(segment_audio))
combined_audio += segment_audio
combined_audio.export(output_file, format="wav")
for temp_file in temp_files:
os.remove(temp_file)
def map_speaker_ids(directory):
speaker_voice_map = {}
for file in os.listdir(directory):
if file.endswith(".wav"):
speaker_id = file.replace(".wav", "")
speaker_voice_map[speaker_id] = os.path.join(directory, file)
return speaker_voice_map
def main(speaker_directory, aligned_text_file, output_audio_file):
speaker_voice_map = map_speaker_ids(speaker_directory)
with open(aligned_text_file, 'r') as file:
translated_text = file.read()
generate_speech(translated_text, speaker_voice_map, output_audio_file)
if os.path.exists('./audio/temp'):
shutil.rmtree('./audio/temp')
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate speech from translated text")
parser.add_argument("speaker_directory", help="Directory containing speaker voice clips")
parser.add_argument("aligned_text_file", help="Path to the translated and aligned text file")
parser.add_argument("output_audio_file", help="Path to save the generated speech audio file")
args = parser.parse_args()
main(args.speaker_directory, args.aligned_text_file, args.output_audio_file)
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