MockingBird/encoder/inference.py
Vega b617a87ee4
Init ppg extractor and ppg2mel (#375)
* Init  ppg extractor and ppg2mel

* add preprocess and training

* FIx known issues

* Update __init__.py

Allow to gen audio

* Fix length issue

* Fix bug of preparing fid

* Fix sample issues

* Add UI usage of PPG-vc
2022-03-03 23:38:12 +08:00

196 lines
8.4 KiB
Python

from encoder.params_data import *
from encoder.model import SpeakerEncoder
from encoder.audio import preprocess_wav # We want to expose this function from here
from matplotlib import cm
from encoder import audio
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
_model = None # type: SpeakerEncoder
_device = None # type: torch.device
def load_model(weights_fpath: Path, device=None):
"""
Loads the model in memory. If this function is not explicitely called, it will be run on the
first call to embed_frames() with the default weights file.
:param weights_fpath: the path to saved model weights.
:param device: either a torch device or the name of a torch device (e.g. "cpu", "cuda"). The
model will be loaded and will run on this device. Outputs will however always be on the cpu.
If None, will default to your GPU if it"s available, otherwise your CPU.
"""
# TODO: I think the slow loading of the encoder might have something to do with the device it
# was saved on. Worth investigating.
global _model, _device
if device is None:
_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
elif isinstance(device, str):
_device = torch.device(device)
_model = SpeakerEncoder(_device, torch.device("cpu"))
checkpoint = torch.load(weights_fpath, _device)
_model.load_state_dict(checkpoint["model_state"])
_model.eval()
print("Loaded encoder \"%s\" trained to step %d" % (weights_fpath.name, checkpoint["step"]))
return _model
def set_model(model, device=None):
global _model, _device
_model = model
if device is None:
_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
_device = device
_model.to(device)
def is_loaded():
return _model is not None
def embed_frames_batch(frames_batch):
"""
Computes embeddings for a batch of mel spectrogram.
:param frames_batch: a batch mel of spectrogram as a numpy array of float32 of shape
(batch_size, n_frames, n_channels)
:return: the embeddings as a numpy array of float32 of shape (batch_size, model_embedding_size)
"""
if _model is None:
raise Exception("Model was not loaded. Call load_model() before inference.")
frames = torch.from_numpy(frames_batch).to(_device)
embed = _model.forward(frames).detach().cpu().numpy()
return embed
def compute_partial_slices(n_samples, partial_utterance_n_frames=partials_n_frames,
min_pad_coverage=0.75, overlap=0.5, rate=None):
"""
Computes where to split an utterance waveform and its corresponding mel spectrogram to obtain
partial utterances of <partial_utterance_n_frames> each. Both the waveform and the mel
spectrogram slices are returned, so as to make each partial utterance waveform correspond to
its spectrogram. This function assumes that the mel spectrogram parameters used are those
defined in params_data.py.
The returned ranges may be indexing further than the length of the waveform. It is
recommended that you pad the waveform with zeros up to wave_slices[-1].stop.
:param n_samples: the number of samples in the waveform
:param partial_utterance_n_frames: the number of mel spectrogram frames in each partial
utterance
:param min_pad_coverage: when reaching the last partial utterance, it may or may not have
enough frames. If at least <min_pad_coverage> of <partial_utterance_n_frames> are present,
then the last partial utterance will be considered, as if we padded the audio. Otherwise,
it will be discarded, as if we trimmed the audio. If there aren't enough frames for 1 partial
utterance, this parameter is ignored so that the function always returns at least 1 slice.
:param overlap: by how much the partial utterance should overlap. If set to 0, the partial
utterances are entirely disjoint.
:return: the waveform slices and mel spectrogram slices as lists of array slices. Index
respectively the waveform and the mel spectrogram with these slices to obtain the partial
utterances.
"""
assert 0 <= overlap < 1
assert 0 < min_pad_coverage <= 1
if rate != None:
samples_per_frame = int((sampling_rate * mel_window_step / 1000))
n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
frame_step = int(np.round((sampling_rate / rate) / samples_per_frame))
else:
samples_per_frame = int((sampling_rate * mel_window_step / 1000))
n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
frame_step = max(int(np.round(partial_utterance_n_frames * (1 - overlap))), 1)
assert 0 < frame_step, "The rate is too high"
assert frame_step <= partials_n_frames, "The rate is too low, it should be %f at least" % \
(sampling_rate / (samples_per_frame * partials_n_frames))
# Compute the slices
wav_slices, mel_slices = [], []
steps = max(1, n_frames - partial_utterance_n_frames + frame_step + 1)
for i in range(0, steps, frame_step):
mel_range = np.array([i, i + partial_utterance_n_frames])
wav_range = mel_range * samples_per_frame
mel_slices.append(slice(*mel_range))
wav_slices.append(slice(*wav_range))
# Evaluate whether extra padding is warranted or not
last_wav_range = wav_slices[-1]
coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start)
if coverage < min_pad_coverage and len(mel_slices) > 1:
mel_slices = mel_slices[:-1]
wav_slices = wav_slices[:-1]
return wav_slices, mel_slices
def embed_utterance(wav, using_partials=True, return_partials=False, **kwargs):
"""
Computes an embedding for a single utterance.
# TODO: handle multiple wavs to benefit from batching on GPU
:param wav: a preprocessed (see audio.py) utterance waveform as a numpy array of float32
:param using_partials: if True, then the utterance is split in partial utterances of
<partial_utterance_n_frames> frames and the utterance embedding is computed from their
normalized average. If False, the utterance is instead computed from feeding the entire
spectogram to the network.
:param return_partials: if True, the partial embeddings will also be returned along with the
wav slices that correspond to the partial embeddings.
:param kwargs: additional arguments to compute_partial_splits()
:return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If
<return_partials> is True, the partial utterances as a numpy array of float32 of shape
(n_partials, model_embedding_size) and the wav partials as a list of slices will also be
returned. If <using_partials> is simultaneously set to False, both these values will be None
instead.
"""
# Process the entire utterance if not using partials
if not using_partials:
frames = audio.wav_to_mel_spectrogram(wav)
embed = embed_frames_batch(frames[None, ...])[0]
if return_partials:
return embed, None, None
return embed
# Compute where to split the utterance into partials and pad if necessary
wave_slices, mel_slices = compute_partial_slices(len(wav), **kwargs)
max_wave_length = wave_slices[-1].stop
if max_wave_length >= len(wav):
wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
# Split the utterance into partials
frames = audio.wav_to_mel_spectrogram(wav)
frames_batch = np.array([frames[s] for s in mel_slices])
partial_embeds = embed_frames_batch(frames_batch)
# Compute the utterance embedding from the partial embeddings
raw_embed = np.mean(partial_embeds, axis=0)
embed = raw_embed / np.linalg.norm(raw_embed, 2)
if return_partials:
return embed, partial_embeds, wave_slices
return embed
def embed_speaker(wavs, **kwargs):
raise NotImplemented()
def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)):
if ax is None:
ax = plt.gca()
if shape is None:
height = int(np.sqrt(len(embed)))
shape = (height, -1)
embed = embed.reshape(shape)
cmap = cm.get_cmap()
mappable = ax.imshow(embed, cmap=cmap)
cbar = plt.colorbar(mappable, ax=ax, fraction=0.046, pad=0.04)
sm = cm.ScalarMappable(cmap=cmap)
sm.set_clim(*color_range)
ax.set_xticks([]), ax.set_yticks([])
ax.set_title(title)