101 lines
2.8 KiB
Python
101 lines
2.8 KiB
Python
import sys
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import os
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from time import sleep, time
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import warnings
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import torch
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import argparse
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from kokoro import KPipeline
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import soundfile as sf
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import vlc
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from tqdm import tqdm
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# Disable all warnings
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warnings.filterwarnings("ignore")
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# See voices: https://huggingface.co/hexgrad/Kokoro-82M/blob/main/VOICES.md
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voices = {
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'pro': 'af_heart',
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'hot': 'af_bella',
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'asmr':'af_nicole',
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'brit': 'bf_emma'
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}
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def parse_args():
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parser = argparse.ArgumentParser(description="Simple TTS")
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parser.add_argument(
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"input_text",
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type=str,
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nargs='?',
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default="",
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help="Text to read",
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)
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parser.add_argument(
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"--voice",
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required=False,
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type=str,
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default="pro",
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help="Voice to use (pro, hot, asmr, brit)",
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)
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parser.add_argument(
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"--input_file",
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required=False,
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type=str,
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default="demo/tongue-twister.txt",
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help="Path to the input text file",
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)
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parser.add_argument(
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"--device",
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required=False,
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type=str,
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default=("cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else ("xpu" if torch.xpu.is_available() else "cpu"))),
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help="Device for inference: cuda | mps | cpu",
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)
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return parser.parse_args()
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def main():
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args=parse_args()
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pipeline = KPipeline(lang_code='a', device=args.device, repo_id='hexgrad/Kokoro-82M')
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if args.voice in voices:
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voice=voices[args.voice]
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else:
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voice=voices['pro'] if args.voice is None else args.voice
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# filename argument
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if args.input_text == "":
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file_path = args.input_file
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directory, file_name = os.path.split(file_path)
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name = '.'.join(file_name.split('.')[:-1])
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file = open(file_path, "r")
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text = file.read()
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else:
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name = "chat"
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text = args.input_text
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generator = pipeline(text, voice=voice)
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output_files = []
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length = 0
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start_time = time()
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print("Generating...")
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for i, (gs, ps, audio) in enumerate(generator):
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output_file_name=f'outputs/{name}-{voice}-{i}.wav'
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os.makedirs(os.path.dirname(output_file_name), exist_ok=True)
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output_files.append(output_file_name)
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sf.write(output_file_name, audio, 24000)
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length = length + 1
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generation_time = time() - start_time
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print(f"Done in {generation_time:.2f} seconds")
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for i, output in enumerate(output_files):
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full_path = os.path.abspath(output)
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media = vlc.MediaPlayer(f"file://{full_path}")
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media.play()
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sleep(0.1)
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duration=media.get_length() / 1000
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description = f"\u25B6 {i+1}/{length} ({'{0:0>5.2f}'.format(duration)}s)"
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for i in tqdm(range(100), desc=description):
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sleep(duration / 100)
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if __name__ == "__main__":
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main()
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