Heiko J Schick
66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
# rm test.wav; python tts.py; play test.wav
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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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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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import IPython.display as ipd
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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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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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full_wave_file = []
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rate = 44100
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sentences = []
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# Read input file
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with open(f"input.txt", "r") as f:
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lines = f.readlines()
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# Convert to sentences
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for line in lines:
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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 x in line.split(". "):
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sentences.append(x.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("test.wav", rate, full_wave_file)
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if __name__ == "__main__":
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main() |