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import re
import os
import json
import ffmpeg
import argparse
import torch
import torch.nn as nn
from python.fastpitch import models
from scipy.io.wavfile import write
from torch.nn.utils.rnn import pad_sequence
from python.common.text import text_to_sequence, sequence_to_text
class FastPitch(object):
def __init__(self, logger, PROD, device, models_manager):
super(FastPitch, self).__init__()
self.logger = logger
self.PROD = PROD
self.models_manager = models_manager
self.device = device
self.ckpt_path = None
torch.backends.cudnn.benchmark = True
self.init_model("english_basic")
self.isReady = True
def init_model (self, symbols_alphabet):
parser = argparse.ArgumentParser(description='PyTorch FastPitch Inference', allow_abbrev=False)
self.symbols_alphabet = symbols_alphabet
model_parser = models.parse_model_args("FastPitch", symbols_alphabet, parser, add_help=False)
model_args, model_unk_args = model_parser.parse_known_args()
model_config = models.get_model_config("FastPitch", model_args)
self.model = models.get_model("FastPitch", model_config, self.device, self.logger, forward_is_infer=True, jitable=False)
self.model.eval()
self.model.device = self.device
def load_state_dict (self, ckpt_path, ckpt, n_speakers, base_lang=None):
self.ckpt_path = ckpt_path
with open(ckpt_path.replace(".pt", ".json"), "r") as f:
data = json.load(f)
if "symbols_alphabet" in data.keys() and data["symbols_alphabet"]!=self.symbols_alphabet:
self.logger.info(f'Changing symbols_alphabet from {self.symbols_alphabet} to {data["symbols_alphabet"]}')
self.init_model(data["symbols_alphabet"])
if 'state_dict' in ckpt:
ckpt = ckpt['state_dict']
symbols_embedding_dim = 384
self.model.speaker_emb = nn.Embedding(1 if n_speakers is None else n_speakers, symbols_embedding_dim).to(self.device)
self.model.load_state_dict(ckpt, strict=False)
self.model = self.model.float()
self.model.eval()
def infer_batch(self, plugin_manager, linesBatch, outputJSON, vocoder, speaker_i, old_sequence=None, useSR=False, useCleanup=False):
print(f'Inferring batch of {len(linesBatch)} lines')
sigma_infer = 0.9
stft_hop_length = 256
sampling_rate = 22050
denoising_strength = 0.01
text_sequences = []
cleaned_text_sequences = []
for record in linesBatch:
text = record[0]
text = re.sub(r'[^a-zA-Z\s\(\)\[\]0-9\?\.\,\!\'\{\}]+', '', text)
sequence = text_to_sequence(text, "english_basic", ['english_cleaners'])
cleaned_text_sequences.append(sequence_to_text("english_basic", sequence))
text = torch.LongTensor(sequence)
text_sequences.append(text)
text_sequences = pad_sequence(text_sequences, batch_first=True).to(self.device)
with torch.no_grad():
pace = torch.tensor([record[3] for record in linesBatch]).unsqueeze(1).to(self.device)
pitch_amp = torch.tensor([record[7] for record in linesBatch]).unsqueeze(1).to(self.device)
pitch_data = None # Maybe in the future
mel, mel_lens, dur_pred, pitch_pred, start_index, end_index = self.model.infer_advanced(self.logger, plugin_manager, cleaned_text_sequences, text_sequences, speaker_i=speaker_i, pace=pace, pitch_data=pitch_data, old_sequence=None, pitch_amp=pitch_amp)
if "waveglow" in vocoder:
self.models_manager.init_model(vocoder)
audios = self.models_manager.models(vocoder).model.infer(mel, sigma=sigma_infer)
audios = self.models_manager.models(vocoder).denoiser(audios.float(), strength=denoising_strength).squeeze(1)
for i, audio in enumerate(audios):
audio = audio[:mel_lens[i].item() * stft_hop_length]
audio = audio/torch.max(torch.abs(audio))
output = linesBatch[i][4]
audio = audio.cpu().numpy()
if useCleanup:
ffmpeg_path = f'{"./resources/app" if self.PROD else "."}/python/ffmpeg.exe'
if useSR:
write(output.replace(".wav", "_preSR.wav"), sampling_rate, audio)
else:
write(output.replace(".wav", "_preCleanupPreFFmpeg.wav"), sampling_rate, audio)
stream = ffmpeg.input(output.replace(".wav", "_preCleanupPreFFmpeg.wav"))
ffmpeg_options = {"ar": 48000}
output_path = output.replace(".wav", "_preCleanup.wav")
stream = ffmpeg.output(stream, output_path, **ffmpeg_options)
out, err = (ffmpeg.run(stream, cmd=ffmpeg_path, capture_stdout=True, capture_stderr=True, overwrite_output=True))
os.remove(output.replace(".wav", "_preCleanupPreFFmpeg.wav"))
else:
write(output.replace(".wav", "_preSR.wav") if useSR else output, sampling_rate, audio)
if useSR:
self.models_manager.init_model("nuwave2")
self.models_manager.models("nuwave2").sr_audio(output.replace(".wav", "_preSR.wav"), output.replace(".wav", "_preCleanup.wav") if useCleanup else output)
os.remove(output.replace(".wav", "_preSR.wav"))
if useCleanup:
self.models_manager.init_model("deepfilternet2")
self.models_manager.models("deepfilternet2").cleanup_audio(output.replace(".wav", "_preCleanup.wav"), output)
os.remove(output.replace(".wav", "_preCleanup.wav"))
del audios
else:
self.models_manager.load_model("hifigan", f'{"./resources/app" if self.PROD else "."}/python/hifigan/hifi.pt' if vocoder=="qnd" else self.ckpt_path.replace(".pt", ".hg.pt"))
y_g_hat = self.models_manager.models("hifigan").model(mel)
audios = y_g_hat.view((y_g_hat.shape[0], y_g_hat.shape[2]))
# audio = audio * 2.3026 # This brings it to the same volume, but makes it clip in places
for i, audio in enumerate(audios):
audio = audio[:mel_lens[i].item() * stft_hop_length]
audio = audio.cpu().numpy()
audio = audio * 32768.0
audio = audio.astype('int16')
output = linesBatch[i][4]
if useCleanup:
ffmpeg_path = f'{"./resources/app" if self.PROD else "."}/python/ffmpeg.exe'
if useSR:
write(output.replace(".wav", "_preSR.wav"), sampling_rate, audio)
else:
write(output.replace(".wav", "_preCleanupPreFFmpeg.wav"), sampling_rate, audio)
stream = ffmpeg.input(output.replace(".wav", "_preCleanupPreFFmpeg.wav"))
ffmpeg_options = {"ar": 48000}
output_path = output.replace(".wav", "_preCleanup.wav")
stream = ffmpeg.output(stream, output_path, **ffmpeg_options)
out, err = (ffmpeg.run(stream, cmd=ffmpeg_path, capture_stdout=True, capture_stderr=True, overwrite_output=True))
os.remove(output.replace(".wav", "_preCleanupPreFFmpeg.wav"))
else:
write(output.replace(".wav", "_preSR.wav") if useSR else output, sampling_rate, audio)
if useSR:
self.models_manager.init_model("nuwave2")
self.models_manager.models("nuwave2").sr_audio(output.replace(".wav", "_preSR.wav"), output.replace(".wav", "_preCleanup.wav") if useCleanup else output)
os.remove(output.replace(".wav", "_preSR.wav"))
if useCleanup:
self.models_manager.init_model("deepfilternet2")
self.models_manager.models("deepfilternet2").cleanup_audio(output.replace(".wav", "_preCleanup.wav"), output)
os.remove(output.replace(".wav", "_preCleanup.wav"))
if outputJSON:
for ri, record in enumerate(linesBatch):
# linesBatch: sequence, pitch, duration, pace, tempFileLocation, outPath, outFolder
output_fname = linesBatch[ri][5].replace(".wav", ".json")
containing_folder = "/".join(output_fname.split("/")[:-1])
os.makedirs(containing_folder, exist_ok=True)
with open(output_fname, "w+") as f:
data = {}
data["inputSequence"] = str(linesBatch[ri][0])
data["pacing"] = float(linesBatch[ri][3])
data["letters"] = [char.replace("{", "").replace("}", "") for char in list(cleaned_text_sequences[ri].split("|"))]
data["currentVoice"] = self.ckpt_path.split("/")[-1].replace(".pt", "")
data["resetEnergy"] = []
data["resetPitch"] = [float(val) for val in list(pitch_pred[ri].cpu().detach().numpy())]
data["resetDurs"] = [float(val) for val in list(dur_pred[ri].cpu().detach().numpy())]
data["ampFlatCounter"] = 0
data["pitchNew"] = data["resetPitch"]
data["energyNew"] = data["resetEnergy"]
data["dursNew"] = data["resetDurs"]
f.write(json.dumps(data, indent=4))
del mel, mel_lens
return ""
def infer(self, plugin_manager, text, output, vocoder, speaker_i, pace=1.0, editor_data=None, old_sequence=None, globalAmplitudeModifier=None, base_lang=None, base_emb=None, useSR=False, useCleanup=False):
self.logger.info(f'Inferring: "{text}" ({len(text)})')
sigma_infer = 0.9
stft_hop_length = 256
sampling_rate = 22050
denoising_strength = 0.01
text = re.sub(r'[^a-zA-Z\s\(\)\[\]0-9\?\.\,\!\'\{\}]+', '', text)
sequence = text_to_sequence(text, "english_basic", ['english_cleaners'])
cleaned_text = sequence_to_text("english_basic", sequence)
text = torch.LongTensor(sequence)
text = pad_sequence([text], batch_first=True).to(self.models_manager.device)
with torch.no_grad():
if old_sequence is not None:
old_sequence = re.sub(r'[^a-zA-Z\s\(\)\[\]0-9\?\.\,\!\'\{\}]+', '', old_sequence)
old_sequence = text_to_sequence(old_sequence, "english_basic", ['english_cleaners'])
old_sequence = torch.LongTensor(old_sequence)
old_sequence = pad_sequence([old_sequence], batch_first=True).to(self.models_manager.device)
mel, mel_lens, dur_pred, pitch_pred, start_index, end_index = self.model.infer_advanced(self.logger, plugin_manager, [cleaned_text], text, speaker_i=speaker_i, pace=pace, pitch_data=editor_data, old_sequence=old_sequence)
if "waveglow" in vocoder:
self.models_manager.init_model(vocoder)
audios = self.models_manager.models(vocoder).model.infer(mel, sigma=sigma_infer)
audios = self.models_manager.models(vocoder).denoiser(audios.float(), strength=denoising_strength).squeeze(1)
for i, audio in enumerate(audios):
audio = audio[:mel_lens[i].item() * stft_hop_length]
audio = audio/torch.max(torch.abs(audio))
write(output, sampling_rate, audio.cpu().numpy())
del audios
else:
self.models_manager.load_model("hifigan", f'{"./resources/app" if self.PROD else "."}/python/hifigan/hifi.pt' if vocoder=="qnd" else self.ckpt_path.replace(".pt", ".hg.pt"))
y_g_hat = self.models_manager.models("hifigan").model(mel)
audio = y_g_hat.squeeze()
audio = audio * 32768.0
# audio = audio * 2.3026 # This brings it to the same volume, but makes it clip in places
audio = audio.cpu().numpy().astype('int16')
if useCleanup:
ffmpeg_path = f'{"./resources/app" if self.PROD else "."}/python/ffmpeg.exe'
if useSR:
write(output.replace(".wav", "_preSR.wav"), sampling_rate, audio)
else:
write(output.replace(".wav", "_preCleanupPreFFmpeg.wav"), sampling_rate, audio)
stream = ffmpeg.input(output.replace(".wav", "_preCleanupPreFFmpeg.wav"))
ffmpeg_options = {"ar": 48000}
output_path = output.replace(".wav", "_preCleanup.wav")
stream = ffmpeg.output(stream, output_path, **ffmpeg_options)
out, err = (ffmpeg.run(stream, cmd=ffmpeg_path, capture_stdout=True, capture_stderr=True, overwrite_output=True))
os.remove(output.replace(".wav", "_preCleanupPreFFmpeg.wav"))
else:
write(output.replace(".wav", "_preSR.wav") if useSR else output, sampling_rate, audio)
if useSR:
self.models_manager.init_model("nuwave2")
self.models_manager.models("nuwave2").sr_audio(output.replace(".wav", "_preSR.wav"), output.replace(".wav", "_preCleanup.wav") if useCleanup else output)
if useCleanup:
self.models_manager.init_model("deepfilternet2")
self.models_manager.models("deepfilternet2").cleanup_audio(output.replace(".wav", "_preCleanup.wav"), output)
del audio
del mel, mel_lens
[pitch, durations] = [pitch_pred.cpu().detach().numpy()[0], dur_pred.cpu().detach().numpy()[0]]
[energy, em_angry, em_happy, em_sad, em_surprise] = [[], [],[],[],[]]
pitch_durations_text = ",".join([str(v) for v in pitch]) + "\n" + \
",".join([str(v) for v in durations]) + "\n" + \
",".join([str(v) for v in energy]) + "\n" + \
",".join([str(v) for v in em_angry]) + "\n" + \
",".join([str(v) for v in em_happy]) + "\n" + \
",".join([str(v) for v in em_sad]) + "\n" + \
",".join([str(v) for v in em_surprise]) + "\n" + "{"+"}"
del pitch_pred, dur_pred, text, sequence
return pitch_durations_text +"\n"+cleaned_text+"\n" + f'{start_index}\n{end_index}'
def set_device (self, device):
self.device = device
self.model = self.model.to(device)
self.model.device = device
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