mirror of https://github.com/coqui-ai/TTS.git
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
import logging
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import torch
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import torch.utils.data
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from librosa.filters import mel as librosa_mel_fn
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from TTS.utils.audio.torch_transforms import amp_to_db
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logger = logging.getLogger(__name__)
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MAX_WAV_VALUE = 32768.0
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mel_basis = {}
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hann_window = {}
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def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
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if torch.min(y) < -1.0:
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logger.info("Min value is: %.3f", torch.min(y))
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if torch.max(y) > 1.0:
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logger.info("Max value is: %.3f", torch.max(y))
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global mel_basis, hann_window
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dtype_device = str(y.dtype) + "_" + str(y.device)
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fmax_dtype_device = str(fmax) + "_" + dtype_device
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wnsize_dtype_device = str(win_size) + "_" + dtype_device
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if fmax_dtype_device not in mel_basis:
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mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
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mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
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if wnsize_dtype_device not in hann_window:
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hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
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y = torch.nn.functional.pad(
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y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect"
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)
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y = y.squeeze(1)
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spec = torch.view_as_real(
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torch.stft(
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y,
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n_fft,
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hop_length=hop_size,
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win_length=win_size,
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window=hann_window[wnsize_dtype_device],
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center=center,
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pad_mode="reflect",
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normalized=False,
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onesided=True,
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return_complex=True,
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)
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)
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spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
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spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
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spec = amp_to_db(spec)
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return spec
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