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# https://github.com/audeering/shift/tree/main -- RUN FROM THIS REPO
import shutil
import csv
import io
import os
import typing
import wave
import sys
import audresample
from mimic3_tts.__main__ import (CommandLineInterfaceState,
get_args,
initialize_args,
initialize_tts,
# print_voices,
# process_lines,
shutdown_tts,
OutputNaming,
process_line)
import msinference
import time
import json
import pandas as pd
import os
import numpy as np
import audonnx
import audb
from pathlib import Path
import transformers
import torch
import audmodel
import audinterface
import matplotlib.pyplot as plt
import audiofile
# ================================================ LIST OF VOICES
ROOT_DIR = '/data/dkounadis/mimic3-voices/'
foreign_voices = []
english_voices = []
for lang in os.listdir(ROOT_DIR + 'voices'):
for voice in os.listdir(ROOT_DIR + 'voices/' + lang):
if 'en_' in lang:
try:
with open(ROOT_DIR + 'voices/' + lang + '/' + voice + '/speakers.txt', 'r') as f:
for spk in f:
english_voices.append(lang + '/' + voice + '#' + spk.rstrip())
# voice_spk_string = lang + '/' + voice + '#' + spk.rstrip() for spk in f
except FileNotFoundError:
english_voices.append(lang + '/' + voice)
else:
try:
with open(ROOT_DIR + 'voices/' + lang + '/' + voice + '/speakers.txt', 'r') as f:
for spk in f:
foreign_voices.append(lang + '/' + voice + '#' + spk.rstrip())
except FileNotFoundError:
foreign_voices.append(lang + '/' + voice)
#
[print(i) for i in foreign_voices]
print('\n_______________________________\n')
[print(i) for i in english_voices]
# ====================================================== LIST Mimic-3 ALL VOICES
list_voices = [
'en_US/m-ailabs_low#mary_ann',
'en_UK/apope_low',
'de_DE/thorsten-emotion_low#neutral', # is the 4x really interesting we can just write it in Section
# 'ko_KO/kss_low',
'fr_FR/m-ailabs_low#gilles_g_le_blanc',
#'human',
] # special - for human we load specific style file - no Mimic3 is run
# ================================================== INTERFACE MODELS
LABELS = [
'arousal', 'dominance', 'valence',
# 'speech_synthesizer', 'synthetic_singing',
'Angry',
'Sad',
'Happy',
'Surprise',
'Fear',
'Disgust',
'Contempt',
'Neutral'
]
config = transformers.Wav2Vec2Config() #finetuning_task='spef2feat_reg')
config.dev = torch.device('cuda:0')
config.dev2 = torch.device('cuda:0')
def _softmax(x):
'''x : (batch, num_class)'''
x -= x.max(1, keepdims=True) # if all -400 then sum(exp(x)) = 0
x = np.maximum(-100, x)
x = np.exp(x)
x /= x.sum(1, keepdims=True)
return x
from transformers import AutoModelForAudioClassification
import types
def _infer(self, x):
'''x: (batch, audio-samples-16KHz)'''
x = (x + self.config.mean) / self.config.std # plus
x = self.ssl_model(x, attention_mask=None).last_hidden_state
# pool
h = self.pool_model.sap_linear(x).tanh()
w = torch.matmul(h, self.pool_model.attention)
w = w.softmax(1)
mu = (x * w).sum(1)
x = torch.cat(
[
mu,
((x * x * w).sum(1) - mu * mu).clamp(min=1e-7).sqrt()
], 1)
return self.ser_model(x)
teacher_cat = AutoModelForAudioClassification.from_pretrained(
'3loi/SER-Odyssey-Baseline-WavLM-Categorical-Attributes',
trust_remote_code=True # fun definitions see 3loi/SER-.. repo
).to(config.dev2).eval()
teacher_cat.forward = types.MethodType(_infer, teacher_cat)
# ===================[:]===================== Dawn
def _prenorm(x, attention_mask=None):
'''mean/var'''
if attention_mask is not None:
N = attention_mask.sum(1, keepdim=True) # here attn msk is unprocessed just the original input
x -= x.sum(1, keepdim=True) / N
var = (x * x).sum(1, keepdim=True) / N
else:
x -= x.mean(1, keepdim=True) # mean is an onnx operator reducemean saves some ops compared to casting integer N to float and the div
var = (x * x).mean(1, keepdim=True)
return x / torch.sqrt(var + 1e-7)
from torch import nn
from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2PreTrainedModel, Wav2Vec2Model
class RegressionHead(nn.Module):
r"""Classification head."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.final_dropout)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
class Dawn(Wav2Vec2PreTrainedModel):
r"""Speech emotion classifier."""
def __init__(self, config):
super().__init__(config)
self.config = config
self.wav2vec2 = Wav2Vec2Model(config)
self.classifier = RegressionHead(config)
self.init_weights()
def forward(
self,
input_values,
attention_mask=None,
):
x = _prenorm(input_values, attention_mask=attention_mask)
outputs = self.wav2vec2(x, attention_mask=attention_mask)
hidden_states = outputs[0]
hidden_states = torch.mean(hidden_states, dim=1)
logits = self.classifier(hidden_states)
return logits
# return {'hidden_states': hidden_states,
# 'logits': logits}
dawn = Dawn.from_pretrained('audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim').to(config.dev).eval()
# =======================================
def process_function(x, sampling_rate, idx):
'''run audioset ct, adv
USE onnx teachers
return [synth-speech, synth-singing, 7x, 3x adv] = 11
'''
# x = x[None , :] ASaHSuFDCN
#{0: 'Angry', 1: 'Sad', 2: 'Happy', 3: 'Surprise',
#4: 'Fear', 5: 'Disgust', 6: 'Contempt', 7: 'Neutral'}
#tensor([[0.0015, 0.3651, 0.0593, 0.0315, 0.0600, 0.0125, 0.0319, 0.4382]])
logits_cat = teacher_cat(torch.from_numpy(x).to(config.dev)).cpu().detach().numpy()
# USE ALL CATEGORIES
# --
# logits_audioset = audioset_model(x, 16000)['logits_sounds']
# logits_audioset = logits_audioset[:, [7, 35]] # speech synthesizer synthetic singing
# --
logits_adv = dawn(torch.from_numpy(x).to(config.dev)).cpu().detach().numpy() #['logits']
cat = np.concatenate([logits_adv,
# _sigmoid(logits_audioset),
_softmax(logits_cat)],
1)
print(cat)
return cat #logits_adv #model(signal, sampling_rate)['logits']
interface = audinterface.Feature(
feature_names=LABELS,
process_func=process_function,
# process_func_args={'outputs': 'logits_scene'},
process_func_applies_sliding_window=False,
win_dur=7.0,
hop_dur=40.0,
sampling_rate=16000,
resample=True,
verbose=True,
)
# ================================== ====== END INTERFACE
def process_lines(state: CommandLineInterfaceState, wav_path=None):
'''MIMIC3 INTERNAL CALL that yields the sigh sound'''
args = state.args
result_idx = 0
print(f'why waitings in the for loop LIN {state.texts=}\n')
for line in state.texts:
# print(f'LIN {line=}\n') # prints \n so is empty not getting the predifne text of state.texts
line_voice: typing.Optional[str] = None
line_id = ""
line = line.strip()
# if not line:
# continue
if args.output_naming == OutputNaming.ID:
# Line has the format id|text instead of just text
with io.StringIO(line) as line_io:
reader = csv.reader(line_io, delimiter=args.csv_delimiter)
row = next(reader)
line_id, line = row[0], row[-1]
if args.csv_voice:
line_voice = row[1]
process_line(line, state, line_id=line_id, line_voice=line_voice)
result_idx += 1
time.sleep(4)
# Write combined audio to stdout
if state.all_audio:
# _LOGGER.debug("Writing WAV audio to stdout")
if sys.stdout.isatty() and (not state.args.stdout):
with io.BytesIO() as wav_io:
wav_file_play: wave.Wave_write = wave.open(wav_io, "wb")
with wav_file_play:
wav_file_play.setframerate(state.sample_rate_hz)
wav_file_play.setsampwidth(state.sample_width_bytes)
wav_file_play.setnchannels(state.num_channels)
wav_file_play.writeframes(state.all_audio)
# play_wav_bytes(state.args, wav_io.getvalue())
# wav_path = '_direct_call_2.wav'
with open(wav_path, 'wb') as wav_file:
wav_file.write(wav_io.getvalue())
wav_file.seek(0)
print('\n\n5T', wav_path)
else:
print('\n\nDOES NOT TTSING --> ADD SOME time.sleep(4)', wav_path)
# -----------------------------------------------------------------------------
# cat _tmp_ssml.txt | mimic3 --cuda --ssml --noise-w 0.90001 --length-scale 0.91 --noise-scale 0.04 > noise_w=0.90_en_happy_2.wav
# ======================================================================
# END DEF
# https://huggingface.co/dkounadis/artificial-styletts2/tree/main/mimic3_foreign
# STYLES Already Made - HF
out_dir = 'out_dir/'
Path(out_dir).mkdir(parents=True, exist_ok=True)
for _id, _voice in enumerate(list_voices):
_str = _voice.replace('/', '_').replace('#', '_').replace('_low', '')
if 'cmu-arctic' in _str:
_str = _str.replace('cmu-arctic', 'cmu_arctic') #+ '.wav'
print('\n\n\n\nExecuting', _voice,'\n\n\n\n\n')
if (
not os.path.isfile(out_dir + 'mimic3__' + _str + '.wav') or
not os.path.isfile(out_dir + 'styletts2__' + _str + '.wav')
):
# Mimic3 GitHub Quota exceded:
# https://github.com/MycroftAI/mimic3-voices
# Above repo can exceed download quota of LFS
# Copy mimic-voices from local copies
# clone https://huggingface.co/mukowaty/mimic3-voices/tree/main/voices
# copy to ~/
#
#
if 'human' not in _voice:
# assure mimic-3 generator .onnx exists
home_voice_dir = f'/home/audeering.local/dkounadis/.local/share/mycroft/mimic3/voices/{_voice.split("#")[0]}/'
Path(home_voice_dir).mkdir(parents=True, exist_ok=True)
speaker_free_voice_name = _voice.split("#")[0] if '#' in _voice else _voice
if (
(not os.path.isfile(home_voice_dir + 'generator.onnx')) or
(os.path.getsize(home_voice_dir + 'generator.onnx') < 500) # .onnx - is just LFS header
):
# Copy
shutil.copyfile(
f'/data/dkounadis/mimic3-voices/voices/{speaker_free_voice_name}/generator.onnx',
home_voice_dir + 'generator.onnx')
# prompt_path = f'mimic3_{folder}_4x/' + _str + '.wav'
with open('harvard.json', 'r') as f:
harvard_individual_sentences = json.load(f)['sentences']
total_audio_mimic3 = []
total_audio_styletts2 = []
ix = 0
for list_of_10 in harvard_individual_sentences[:4]: # 77
text = ' '.join(list_of_10['sentences'])
print(ix, text)
ix += 1
# Synthesis Mimic-3 then use it as prompt for StyleTTS2
# MIMIC-3 if _voice is not HUMAN
if 'human' not in _voice:
rate = 1
_ssml = (
'<speak>'
'<prosody volume=\'64\'>'
f'<prosody rate=\'{rate}\'>'
f'<voice name=\'{_voice}\'>'
'<s>'
f'{text[:-1] + ", .. !!!"}'
'</s>'
'</voice>'
'</prosody>'
'</prosody>'
'</speak>'
)
with open('_tmp_ssml.txt', 'w') as f:
f.write(_ssml)
# ps = subprocess.Popen(f'cat _tmp_ssml.txt | mimic3 --ssml > {reference_wav}', shell=True)
# ps.wait() # using ps to call mimic3 because samples dont have time to be written in stdout buffer
args = get_args()
args.ssml = True
args.text = [_ssml] #['aa', 'bb'] #txt
args.interactive = False
# args.output_naming = OutputNaming.TIME
state = CommandLineInterfaceState(args=args)
initialize_args(state)
initialize_tts(state)
# args.texts = [txt] #['aa', 'bb'] #txt
# state.stdout = '.' #None #'makeme.wav'
# state.output_dir = '.noopy'
# state.interactive = False
# state.output_naming = OutputNaming.TIME
# # state.ssml = 1234546575
# state.stdout = True
# state.tts = True
style_path = 'tmp1.wav'
process_lines(state, wav_path=style_path)
shutdown_tts(state)
x, fs = audiofile.read(style_path)
# print(x.shape)
else:
# --
# MSP['valence.train.votes'].get().sort_values('7').index[-1]
# style_path = '/cache/audb/msppodcast/2.4.0/fe182b91/Audios/MSP-PODCAST_0235_0053.wav'
# --
# MSP['emotion.test-1'].get().sort_values('valence').index[-1]
# style_path = '/cache/audb/msppodcast/2.4.0/fe182b91/Audios/MSP-PODCAST_0220_0870.wav'
# --
style_path = '/cache/audb/librispeech/3.1.0/fe182b91/test-clean/3575/170457/3575-170457-0024.wav'
x, fs = audiofile.read(style_path) # assure is not very short - equl harvard sent len
print(x.shape,' human') # crop human to almost mimic-3 duration
total_audio_mimic3.append(x)
print(f'{len(total_audio_mimic3)=}')
print(fs, text, 'mimic3')
# MIMIC3 = = = = = = = = = = = = = = END
if 'en_US' in _str:
style_path = 'mimic3_english_4x/' + _str + '.wav'
elif ('de_DE' in _str) or ('fr_FR' in _str):
style_path = 'mimic3_foreign_4x/' + _str + '.wav'
else:
print(f'use human / generated style for {_str}')
style_vec = msinference.compute_style(style_path) # use mimic-3 as prompt
x = msinference.inference(text,
style_vec,
alpha=0.3,
beta=0.7,
diffusion_steps=7,
embedding_scale=1)
total_audio_styletts2.append(x)
# save styletts2 .wav
total_audio_styletts2 = np.concatenate(total_audio_styletts2) # -- concat 77x lists
total_audio_styletts2 = audresample.resample(total_audio_styletts2,
original_rate=24000,
target_rate=16000)[0]
print('RESAMPLEstyletts2', total_audio_styletts2.shape)
audiofile.write(out_dir + 'styletts2__' + _str + '.wav', total_audio_styletts2, 16000)
# print('Saving:', out_dir + 'styletts2__' + _str + '.wav')
# save mimic3 or human .wav
total_audio_mimic3 = np.concatenate(total_audio_mimic3) # -- concat 77x lists
if 'human' not in _str:
total_audio_mimic3 = audresample.resample(total_audio_mimic3,
original_rate=24000,
target_rate=16000)[0]
else:
print('human is already on 16kHz - MSPpodcst file')
print('RESAMPLEmimic3', total_audio_mimic3.shape)
audiofile.write(out_dir + 'mimic3__' + _str + '.wav', total_audio_mimic3, 16000)
print(total_audio_mimic3.shape, total_audio_styletts2.shape, 'LEN OF TOTAL\n')
# print('Saving:', out_dir + 'mimic3__' + _str + '.wav')
# AUD I N T E R F A C E
for engine in ['mimic3',
'styletts2']:
harvard_of_voice = f'{out_dir}{engine}__{_str}'
if not os.path.exists(harvard_of_voice + '.pkl'):
df = interface.process_file(harvard_of_voice + '.wav')
df.to_pickle(harvard_of_voice + '.pkl')
print('\n\n', harvard_of_voice, df,'\n___________________________\n')
print('\nVisuals\n')
# ===============================================================================
# V I S U A L S
#
# ===============================================================================
voice_pairs = [
[list_voices[0], list_voices[1]],
[list_voices[2], list_voices[3]]
] # special - for human we load specific style file - no Mimic3 is run
# PLot 1 list_voices[0] list_voices[1]
# Plot 2 list_voices[2] list_voices[2]
for vox1, vox2 in voice_pairs: # 1 figure pro pair
_str1 = vox1.replace('/', '_').replace('#', '_').replace('_low', '')
if 'cmu-arctic' in _str1:
_str1 = _str1.replace('cmu-arctic', 'cmu_arctic') #+ '.wav'
_str2 = vox2.replace('/', '_').replace('#', '_').replace('_low', '')
if 'cmu-arctic' in _str2:
_str2 = _str2.replace('cmu-arctic', 'cmu_arctic') #+ '.wav'
vis_df = {
f'mimic3_{_str1}' : pd.read_pickle(out_dir + 'mimic3__' + _str1 + '.pkl'),
f'mimic3_{_str2}' : pd.read_pickle(out_dir + 'mimic3__' + _str2 + '.pkl'),
f'styletts2_{_str1}' : pd.read_pickle(out_dir + 'styletts2__' + _str1 + '.pkl'),
f'styletts2_{_str2}' : pd.read_pickle(out_dir + 'styletts2__' + _str2 + '.pkl'),
}
SHORT_LEN = min([len(v) for k, v in vis_df.items()]) # different TTS durations per voic
for k,v in vis_df.items():
p = v[:SHORT_LEN] # TRuncate extra segments - human is slower than mimic3
print('\n\n\n\n',k, p)
p.reset_index(inplace= True)
p.drop(columns=['file','start'], inplace=True)
p.set_index('end', inplace=True)
# p = p.filter(scene_classes) #['transport', 'indoor', 'outdoor'])
p.index = p.index.map(mapper = (lambda x: x.total_seconds()))
vis_df[k] = p
preds = vis_df
fig, ax = plt.subplots(nrows=8, ncols=2, figsize=(24, 19.2), gridspec_kw={'hspace': 0, 'wspace': .04})
# ADV - subplots
time_stamp = preds[f'mimic3_{_str1}'].index.to_numpy()
for j, dim in enumerate(['arousal',
'dominance',
'valence']):
# MIMIC3
ax[j, 0].plot(time_stamp,
# np.ones_like(time_stamp) * .4, --> to find the line on the legend
preds[f'styletts2_{_str1}'][dim], # THIS IS THE BLUE LINE VERIFIED
color=(0,104/255,139/255),
label='mean_1',
linewidth=2)
# ax[j, 0].plot(time_stamp, preds[f'styletts2_{_str1}'][dim],
# color=(.2, .2, .2),
# label='mean_1',
# linewidth=2,
# marker='o')
ax[j, 0].fill_between(time_stamp,
preds[f'styletts2_{_str1}'][dim],
preds[f'mimic3_{_str1}'][dim],
color=(.5,.5,.5),
alpha=.4
)
if j == 0:
ax[j, 0].legend([f'StyleTTS2 using {_str1}',
f'mimic3_{_str1}'],
prop={'size': 10},
# loc='lower right'
)
ax[j, 0].set_ylabel(dim.lower(), color=(.4, .4, .4), fontsize=14)
# TICK
ax[j, 0].set_ylim([1e-7, .9999])
ax[j, 0].set_xticklabels(['' for _ in ax[j, 0].get_xticklabels()])
ax[j, 0].set_xlim([time_stamp[0], time_stamp[-1]])
# MIMIC3 4x speed
ax[j, 1].plot(time_stamp, preds[f'styletts2_{_str2}'][dim],
color=(0,104/255,139/255),
label='mean_1',
linewidth=2)
ax[j, 1].fill_between(time_stamp,
preds[f'mimic3_{_str2}'][dim],
preds[f'styletts2_{_str2}'][dim],
color=(.5,.5,.5),
alpha=.4)
if j == 0:
ax[j, 1].legend([
f'StyleTTS2 using {_str2}',
f'mimic3_{_str2}'],
prop={'size': 10},
# loc='lower right'
)
ax[j, 1].set_xlabel('767 Harvard Sentences (seconds)')
# TICK
ax[j, 1].set_ylim([1e-7, .9999])
# ax[j, 1].set_yticklabels(['' for _ in ax[j, 1].get_yticklabels()])
ax[j, 1].set_xticklabels(['' for _ in ax[j, 0].get_xticklabels()])
ax[j, 1].set_xlim([time_stamp[0], time_stamp[-1]])
ax[j, 0].grid()
ax[j, 1].grid()
# CATEGORIE
time_stamp = preds[f'mimic3_{_str1}'].index.to_numpy()
for j, dim in enumerate(['Angry',
'Sad',
'Happy',
# 'Surprise',
'Fear',
'Disgust',
# 'Contempt',
# 'Neutral'
]): # ASaHSuFDCN
j = j + 3 # skip A/D/V suplt
# MIMIC3
ax[j, 0].plot(time_stamp, preds[f'styletts2_{_str2}'][dim],
color=(0,104/255,139/255),
label='mean_1',
linewidth=2)
ax[j, 0].fill_between(time_stamp,
preds[f'styletts2_{_str2}'][dim],
preds[f'mimic3_{_str2}'][dim],
color=(.5,.5,.5),
alpha=.4)
ax[j, 0].set_ylabel(dim.lower(), color=(.4, .4, .4), fontsize=14)
# TICKS
ax[j, 0].set_ylim([1e-7, .9999])
ax[j, 0].set_xlim([time_stamp[0], time_stamp[-1]])
ax[j, 0].set_xticklabels(['' for _ in ax[j, 0].get_xticklabels()])
ax[j, 0].set_xlabel('767 Harvard Sentences (seconds)', fontsize=16, color=(.4,.4,.4))
# MIMIC3 4x speed
ax[j, 1].plot(time_stamp, preds[f'styletts2_{_str2}'][dim],
color=(0,104/255,139/255),
label='mean_1',
linewidth=2)
ax[j, 1].fill_between(time_stamp,
preds[f'mimic3_{_str2}'][dim],
preds[f'styletts2_{_str2}'][dim],
color=(.5,.5,.5),
alpha=.4)
# ax[j, 1].legend(['StyleTTS2 style mimic3 4x speed',
# 'StyleTTS2 style crema-d'],
# prop={'size': 10},
# # loc='upper left'
# )
ax[j, 1].set_xlabel('767 Harvard Sentences (seconds)', fontsize=16, color=(.4,.4,.4))
ax[j, 1].set_ylim([1e-7, .999])
# ax[j, 1].set_yticklabels(['' for _ in ax[j, 1].get_yticklabels()])
ax[j, 1].set_xticklabels(['' for _ in ax[j, 1].get_xticklabels()])
ax[j, 1].set_xlim([time_stamp[0], time_stamp[-1]])
ax[j, 0].grid()
ax[j, 1].grid()
plt.savefig(f'pair_{_str1}_{_str2}.png', bbox_inches='tight')
plt.close() |