mirror of
https://github.com/liuhaozhe6788/voice-cloning-collab.git
synced 2025-12-16 19:58:01 +01:00
87 lines
2.6 KiB
Python
87 lines
2.6 KiB
Python
from vocoder.models.fatchord_version import WaveRNN
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from vocoder import hparams as hp
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from scipy.fft import rfft, rfftfreq
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import torch
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import numpy as np
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import noisereduce as nr
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_model = None # type: WaveRNN
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def load_model(weights_fpath, verbose=True):
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global _model, _device
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if verbose:
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print("Building Wave-RNN")
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_model = WaveRNN(
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rnn_dims=hp.voc_rnn_dims,
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fc_dims=hp.voc_fc_dims,
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bits=hp.bits,
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pad=hp.voc_pad,
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upsample_factors=hp.voc_upsample_factors,
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feat_dims=hp.num_mels,
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compute_dims=hp.voc_compute_dims,
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res_out_dims=hp.voc_res_out_dims,
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res_blocks=hp.voc_res_blocks,
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hop_length=hp.hop_length,
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sample_rate=hp.sample_rate,
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mode=hp.voc_mode
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)
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if torch.cuda.is_available():
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_model = _model.cuda()
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_device = torch.device('cuda')
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else:
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_device = torch.device('cpu')
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if verbose:
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print("Loading model weights at %s" % weights_fpath)
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checkpoint = torch.load(weights_fpath, _device)
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_model.load_state_dict(checkpoint['model_state'])
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_model.eval()
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def is_loaded():
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return _model is not None
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def infer_waveform(mel, normalize=True, batched=True, target=8000, overlap=800,
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progress_callback=None):
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"""
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Infers the waveform of a mel spectrogram output by the synthesizer (the format must match
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that of the synthesizer!)
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:param normalize:
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:param batched:
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:param target:
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:param overlap:
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:return:
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"""
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if _model is None:
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raise Exception("Please load Wave-RNN in memory before using it")
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if normalize:
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mel = mel / hp.mel_max_abs_value
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mel = torch.from_numpy(mel[None, ...])
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wav = _model.generate(mel, batched, target, overlap, hp.mu_law, progress_callback)
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return wav
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def waveform_denoising(wav):
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fft_max_freq = get_dominant_freq(wav)
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prop_decrease = hp.prop_decrease_low_freq if fft_max_freq < hp.split_freq else hp.prop_decrease_high_freq
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# prop_decrease = 0.6 for low freq audio
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# prop_decrease = 0.9 for high freq audio
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print(f"\nthe dominant frequency of output audio is {fft_max_freq}Hz")
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return nr.reduce_noise(wav, hp.sample_rate, prop_decrease=prop_decrease)
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def get_dominant_freq(wav):
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N = len(wav)
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fft_wav = rfft(wav)
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fft_freq = rfftfreq(N, 1 / hp.sample_rate)
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fft_max = max(fft_wav)
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fft_max_index = np.where(fft_wav == fft_max)[0][0]
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fft_max_freq = fft_freq[fft_max_index]
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# plt.plot(fft_freq, fft_wav)
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# plt.clf()
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# plt.savefig(f"{speaker_name}.png", dpi=300)
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return fft_max_freq |