mirror of https://github.com/coqui-ai/TTS.git
73 lines
2.9 KiB
Python
73 lines
2.9 KiB
Python
import io
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import os
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import librosa
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import torch
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import scipy
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import numpy as np
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import soundfile as sf
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from TTS.utils.text import text_to_sequence
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from TTS.utils.generic_utils import load_config
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from TTS.utils.audio import AudioProcessor
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from TTS.models.tacotron import Tacotron
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from matplotlib import pylab as plt
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class Synthesizer(object):
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def load_model(self, model_path, model_name, model_config, use_cuda):
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model_config = os.path.join(model_path, model_config)
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self.model_file = os.path.join(model_path, model_name)
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print(" > Loading model ...")
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print(" | > model config: ", model_config)
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print(" | > model file: ", self.model_file)
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config = load_config(model_config)
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self.config = config
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self.use_cuda = use_cuda
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self.model = Tacotron(config.embedding_size, config.num_freq, config.num_mels, config.r)
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self.ap = AudioProcessor(config.sample_rate, config.num_mels, config.min_level_db,
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config.frame_shift_ms, config.frame_length_ms, config.preemphasis,
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config.ref_level_db, config.num_freq, config.power, griffin_lim_iters=60)
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# load model state
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if use_cuda:
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cp = torch.load(self.model_file)
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else:
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cp = torch.load(self.model_file, map_location=lambda storage, loc: storage)
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# load the model
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self.model.load_state_dict(cp['model'])
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if use_cuda:
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self.model.cuda()
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self.model.eval()
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def save_wav(self, wav, path):
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wav *= 32767 / max(1e-8, np.max(np.abs(wav)))
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# sf.write(path, wav.astype(np.int32), self.config.sample_rate, format='wav')
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# wav = librosa.util.normalize(wav.astype(np.float), norm=np.inf, axis=None)
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# wav = wav / wav.max()
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# sf.write(path, wav.astype('float'), self.config.sample_rate, format='ogg')
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scipy.io.wavfile.write(path, self.config.sample_rate, wav.astype(np.int16))
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# librosa.output.write_wav(path, wav.astype(np.int16), self.config.sample_rate, norm=True)
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def tts(self, text):
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text_cleaner = [self.config.text_cleaner]
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wavs = []
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for sen in text.split('.'):
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if len(sen) < 3:
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continue
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sen = sen.strip()
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sen +='.'
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print(sen)
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sen = sen.strip()
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seq = np.array(text_to_sequence(text, text_cleaner))
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chars_var = torch.from_numpy(seq).unsqueeze(0)
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if self.use_cuda:
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chars_var = chars_var.cuda()
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mel_out, linear_out, alignments, stop_tokens = self.model.forward(chars_var)
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linear_out = linear_out[0].data.cpu().numpy()
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wav = self.ap.inv_spectrogram(linear_out.T)
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# wav = wav[:self.ap.find_endpoint(wav)]
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out = io.BytesIO()
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wavs.append(wav)
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wavs.append(np.zeros(10000))
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self.save_wav(wav, out)
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return out
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