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import numpy as np |
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import torch |
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import sys |
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from encoder import inference as encoder |
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from synthesizer.inference import Synthesizer |
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from vocoder import inference as vocoder_wavrnn |
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from parallel_wavegan.utils import load_model as vocoder_hifigan |
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import soundfile as sf |
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import os |
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import argparse |
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main_path = os.getcwd() |
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models_path = os.path.join(main_path, 'saved_models/final_models/') |
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def wavRNN_infer(text, ref_wav_path, test_name): |
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encoder.load_model(os.path.join(models_path, 'encoder.pt')) |
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synthesizer = Synthesizer(os.path.join(models_path, 'synthesizer.pt')) |
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vocoder_wavrnn.load_model(os.path.join(models_path, 'vocoder_WavRNN.pt')) |
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ref_wav_path = os.path.join(main_path, 'dataset/persian_data/train_data/book-1/', ref_wav_path) |
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wav = Synthesizer.load_preprocess_wav(ref_wav_path) |
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encoder_wav = encoder.preprocess_wav(wav) |
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embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True) |
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texts = [text] |
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embeds = [embed] * len(texts) |
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specs = synthesizer.synthesize_spectrograms(texts, embeds) |
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breaks = [spec.shape[1] for spec in specs] |
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spec = np.concatenate(specs, axis=1) |
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wav = vocoder_wavrnn.infer_waveform(spec) |
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b_ends = np.cumsum(np.array(breaks) * Synthesizer.hparams.hop_size) |
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b_starts = np.concatenate(([0], b_ends[:-1])) |
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wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)] |
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breaks = [np.zeros(int(0.15 * Synthesizer.sample_rate))] * len(breaks) |
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wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)]) |
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wav = wav / np.abs(wav).max() * 0.97 |
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res_path = os.path.join(main_path, 'results/', test_name+".wav") |
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sf.write(res_path, wav, Synthesizer.sample_rate) |
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print('\nwav file is saved.') |
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def hifigan_infer(text, ref_wav_path, test_name): |
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encoder.load_model(os.path.join(models_path, 'encoder.pt')) |
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synthesizer = Synthesizer(os.path.join(models_path, 'synthesizer.pt')) |
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vocoder = vocoder_hifigan(os.path.join(models_path, 'vocoder_HiFiGAN.pkl')) |
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vocoder.remove_weight_norm() |
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vocoder = vocoder.eval().to('cpu') |
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wav = Synthesizer.load_preprocess_wav(ref_wav_path) |
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encoder_wav = encoder.preprocess_wav(wav) |
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embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True) |
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texts = [text] |
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embeds = [embed] * len(texts) |
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specs = synthesizer.synthesize_spectrograms(texts, embeds) |
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spec = np.concatenate(specs, axis=1) |
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x = torch.from_numpy(spec.T).to('cpu') |
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with torch.no_grad(): |
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wav = vocoder.inference(x) |
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wav = wav / np.abs(wav).max() * 0.97 |
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res_path = os.path.join(main_path, 'results/', test_name+".wav") |
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sf.write(res_path, wav, Synthesizer.sample_rate) |
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print('\nwav file is saved.') |
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def main(args): |
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if str(args.vocoder).lower() == "wavrnn": |
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wavRNN_infer(args.text, args.ref_wav_path, args.test_name) |
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elif str(args.vocoder).lower() == "hifigan": |
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hifigan_infer(args.text, args.ref_wav_path, args.test_name) |
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else: |
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print("--vocoder must be one of HiFiGAN or WavRNN") |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--vocoder", type=str, help= "vocoder name: HiFiGAN or WavRNN") |
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parser.add_argument("--text", type=str, help="input text") |
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parser.add_argument("--ref_wav_path", type=str, help="path to refrence wav to create speaker from that") |
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parser.add_argument("--test_name", type=str, default="test1", help="name of current test to save the result wav") |
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args = parser.parse_args() |
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main(args) |
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