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121 lines
4.3 KiB
Python
121 lines
4.3 KiB
Python
from models.synthesizer.inference import Synthesizer
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from models.encoder import inference as encoder
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from models.vocoder.hifigan import inference as gan_vocoder
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from pathlib import Path
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import numpy as np
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import soundfile as sf
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import torch
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import sys
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import os
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import re
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import cn2an
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vocoder = gan_vocoder
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def gen_one_wav(synthesizer, in_fpath, embed, texts, file_name, seq):
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embeds = [embed] * len(texts)
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# If you know what the attention layer alignments are, you can retrieve them here by
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# passing return_alignments=True
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specs = synthesizer.synthesize_spectrograms(texts, embeds, style_idx=-1, min_stop_token=4, steps=400)
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#spec = specs[0]
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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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# If seed is specified, reset torch seed and reload vocoder
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# Synthesizing the waveform is fairly straightforward. Remember that the longer the
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# spectrogram, the more time-efficient the vocoder.
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generated_wav, output_sample_rate = vocoder.infer_waveform(spec)
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# Add breaks
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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 = [generated_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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generated_wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])
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## Post-generation
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# There's a bug with sounddevice that makes the audio cut one second earlier, so we
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# pad it.
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# Trim excess silences to compensate for gaps in spectrograms (issue #53)
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generated_wav = encoder.preprocess_wav(generated_wav)
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generated_wav = generated_wav / np.abs(generated_wav).max() * 0.97
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# Save it on the disk
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model=os.path.basename(in_fpath)
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filename = "%s_%d_%s.wav" %(file_name, seq, model)
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sf.write(filename, generated_wav, synthesizer.sample_rate)
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print("\nSaved output as %s\n\n" % filename)
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def generate_wav(enc_model_fpath, syn_model_fpath, voc_model_fpath, in_fpath, input_txt, file_name):
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if torch.cuda.is_available():
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device_id = torch.cuda.current_device()
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gpu_properties = torch.cuda.get_device_properties(device_id)
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## Print some environment information (for debugging purposes)
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print("Found %d GPUs available. Using GPU %d (%s) of compute capability %d.%d with "
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"%.1fGb total memory.\n" %
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(torch.cuda.device_count(),
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device_id,
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gpu_properties.name,
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gpu_properties.major,
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gpu_properties.minor,
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gpu_properties.total_memory / 1e9))
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else:
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print("Using CPU for inference.\n")
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print("Preparing the encoder, the synthesizer and the vocoder...")
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encoder.load_model(enc_model_fpath)
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synthesizer = Synthesizer(syn_model_fpath)
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vocoder.load_model(voc_model_fpath)
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encoder_wav = synthesizer.load_preprocess_wav(in_fpath)
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embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
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texts = input_txt.split("\n")
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seq=0
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each_num=1500
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punctuation = '！，。、,' # punctuate and split/clean text
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processed_texts = []
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cur_num = 0
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for text in texts:
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for processed_text in re.sub(r'[{}]+'.format(punctuation), '\n', text).split('\n'):
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if processed_text:
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processed_texts.append(processed_text.strip())
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cur_num += len(processed_text.strip())
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if cur_num > each_num:
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seq = seq +1
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gen_one_wav(synthesizer, in_fpath, embed, processed_texts, file_name, seq)
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processed_texts = []
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cur_num = 0
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if len(processed_texts)>0:
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seq = seq +1
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gen_one_wav(synthesizer, in_fpath, embed, processed_texts, file_name, seq)
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if (len(sys.argv)>=3):
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my_txt = ""
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print("reading from :", sys.argv[1])
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with open(sys.argv[1], "r") as f:
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for line in f.readlines():
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#line = line.strip('\n')
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my_txt += line
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txt_file_name = sys.argv[1]
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wav_file_name = sys.argv[2]
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output = cn2an.transform(my_txt, "an2cn")
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print(output)
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generate_wav(
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Path("encoder/saved_models/pretrained.pt"),
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Path("synthesizer/saved_models/mandarin.pt"),
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Path("vocoder/saved_models/pretrained/g_hifigan.pt"), wav_file_name, output, txt_file_name
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)
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else:
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print("please input the file name")
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exit(1)
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