Heiko J Schick
99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
"""
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Text-to-speech synthesis (TTS)
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Sources:
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— https://huggingface.co/facebook/fastspeech2-en-ljspeech
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— https://github.com/AI-Guru/arxiv-reader
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"""
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import argparse
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from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
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from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
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import scipy
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import numpy as np
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def read_input_file(name: str):
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"""
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Read lines from input file
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"""
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with open(name, "r", encoding="utf-8") as file:
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lines = file.readlines()
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return lines
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def main():
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"""
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Defined starting point of source code.
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"""
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rate = 44100
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full_wave_file = []
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sentences = []
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# Parsing command line arguments
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parser = argparse.ArgumentParser(description='Convert teext to speech.')
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parser.add_argument('-i','--input', help='Input filename', required=True)
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parser.add_argument('-o','--output', help='Output filename', required=True)
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args = vars(parser.parse_args())
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models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
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"facebook/fastspeech2-en-ljspeech",
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arg_overrides={"vocoder": "hifigan", "fp16": False}
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)
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TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
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generator = task.build_generator(models, cfg)
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# Read input file
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lines = read_input_file(args['input'])
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# Convert to sentences
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for line in lines:
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# abbreviations (in alphabetical order)
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line = line.replace("%", "per cent")
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line = line.replace("5G", "5 G")
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line = line.replace("CO2", "C O 2")
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line = line.replace("EUR", "Euro")
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line = line.replace("II", "2")
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line = line.replace("IBM", "I B M")
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line = line.replace("IMF", "I M F")
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line = line.replace("OECD", "O E C D")
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line = line.replace("UN", "U N")
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line = line.replace("USB", "U S B")
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line = line.replace("WHO", "W H O")
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line = line.replace("WTO", "W T O")
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# compound words
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line = line.replace("biotechnology", "bio technology")
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line = line.replace("Coronavirus", "Corona virus")
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line = line.replace("immunocompetence", "immuno competence")
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# punctuation marks
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line = line.replace("-", " - ")
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line = line.replace("/", ", ")
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line = line.replace("—", ". ")
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line = line.replace(":", ". ")
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line = line.replace(";", ". ")
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line = line.replace("?", "?. ")
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line = line.replace("(", ". ")
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for sentence in line.split(". "):
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sentences.append(sentence.strip())
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sentences.append("<PAUSE>")
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# Synthesis text
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for text in sentences:
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if text == "":
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continue
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if text == "<PAUSE>":
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full_wave_file.extend(np.zeros(rate))
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continue
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sample = TTSHubInterface.get_model_input(task, text)
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wav, rate = TTSHubInterface.get_prediction(task, models[0], generator, sample)
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wav = wav.numpy()
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full_wave_file.extend(wav)
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full_wave_file = np.array(full_wave_file, dtype=np.float32)
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scipy.io.wavfile.write(args['output'], rate, full_wave_file)
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if __name__ == "__main__":
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main()
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