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| from TTS.api import TTS | |
| from pydub import AudioSegment | |
| import os | |
| import re | |
| import ffmpeg | |
| import shutil | |
| import argparse | |
| 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 = [] | |
| tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to("cuda") | |
| 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 = 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) | |