File size: 13,659 Bytes
9cbdf67 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 |
# we have to evaluate emotion & cer per sentence -> not audinterface sliding window
import os
import audresample
import torch
import matplotlib.pyplot as plt
import soundfile
import json
import audb
from transformers import AutoModelForAudioClassification
from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2PreTrainedModel
import types
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import pandas as pd
import json
import numpy as np
from pathlib import Path
import transformers
import torch
import audmodel
import audiofile
import jiwer
# https://arxiv.org/pdf/2407.12229
# https://arxiv.org/pdf/2312.05187
# https://arxiv.org/abs/2407.05407
# https://arxiv.org/pdf/2408.06577
# https://arxiv.org/pdf/2309.07405
import msinference
import os
from random import shuffle
config = transformers.Wav2Vec2Config() #finetuning_task='spef2feat_reg')
config.dev = torch.device('cuda:0')
config.dev2 = torch.device('cuda:0')
LABELS = ['arousal', 'dominance', 'valence',
'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')
# https://arxiv.org/pdf/2407.12229
# https://arxiv.org/pdf/2312.05187
# https://arxiv.org/abs/2407.05407
# https://arxiv.org/pdf/2408.06577
# https://arxiv.org/pdf/2309.07405
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()
# =======================================
torch_dtype = torch.float16 #if torch.cuda.is_available() else torch.float32
model_id = "openai/whisper-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
).to(config.dev)
processor = AutoProcessor.from_pretrained(model_id)
_pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
chunk_length_s=30,
batch_size=16,
return_timestamps=True,
torch_dtype=torch_dtype,
device=config.dev,
)
def process_function(x, sampling_rate, idx):
# 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)).softmax(1)
logits_adv = dawn(torch.from_numpy(x).to(config.dev))
out = torch.cat([logits_adv,
logits_cat],
1).cpu().detach().numpy()
# print(out.shape)
return out[0, :]
def load_speech(split=None):
DB = [
# [dataset, version, table, has_timdeltas_or_is_full_wavfile]
# ['crema-d', '1.1.1', 'emotion.voice.test', False],
#['librispeech', '3.1.0', 'test-clean', False],
['emodb', '1.2.0', 'emotion.categories.train.gold_standard', False],
# ['entertain-playtestcloud', '1.1.0', 'emotion.categories.train.gold_standard', True],
# ['erik', '2.2.0', 'emotion.categories.train.gold_standard', True],
# ['meld', '1.3.1', 'emotion.categories.train.gold_standard', False],
# ['msppodcast', '5.0.0', 'emotion.categories.train.gold_standard', False], # tandalone bucket because it has gt labels?
# ['myai', '1.0.1', 'emotion.categories.train.gold_standard', False],
# ['casia', None, 'emotion.categories.gold_standard', False],
# ['switchboard-1', None, 'sentiment', True],
# ['swiss-parliament', None, 'segments', True],
# ['argentinian-parliament', None, 'segments', True],
# ['austrian-parliament', None, 'segments', True],
# #'german', --> bundestag
# ['brazilian-parliament', None, 'segments', True],
# ['mexican-parliament', None, 'segments', True],
# ['portuguese-parliament', None, 'segments', True],
# ['spanish-parliament', None, 'segments', True],
# ['chinese-vocal-emotions-liu-pell', None, 'emotion.categories.desired', False],
# peoples-speech slow
# ['peoples-speech', None, 'train-initial', False]
]
output_list = []
for database_name, ver, table, has_timedeltas in DB:
a = audb.load(database_name,
sampling_rate=16000,
format='wav',
mixdown=True,
version=ver,
cache_root='/cache/audb/')
a = a[table].get()
if has_timedeltas:
print(f'{has_timedeltas=}')
# a = a.reset_index()[['file', 'start', 'end']]
# output_list += [[*t] for t
# in zip(a.file.values, a.start.dt.total_seconds().values, a.end.dt.total_seconds().values)]
else:
output_list += [f for f in a.index] # use file (no timedeltas)
return output_list
natural_wav_paths = load_speech()
with open('harvard.json', 'r') as f:
harvard_individual_sentences = json.load(f)['sentences']
synthetic_wav_paths = ['./enslow/' + i for i in
os.listdir('./enslow/')]
synthetic_wav_paths_4x = ['./style_vector_v2/' + i for i in
os.listdir('./style_vector_v2/')]
synthetic_wav_paths_foreign = ['./mimic3_foreign/' + i for i in os.listdir('./mimic3_foreign/') if 'en_U' not in i]
synthetic_wav_paths_foreign_4x = ['./mimic3_foreign_4x/' + i for i in os.listdir('./mimic3_foreign_4x/') if 'en_U' not in i] # very short segments
# filter very short styles
synthetic_wav_paths_foreign = [i for i in synthetic_wav_paths_foreign if audiofile.duration(i) > 2]
synthetic_wav_paths_foreign_4x = [i for i in synthetic_wav_paths_foreign_4x if audiofile.duration(i) > 2]
synthetic_wav_paths = [i for i in synthetic_wav_paths if audiofile.duration(i) > 2]
synthetic_wav_pathsn_4x = [i for i in synthetic_wav_paths_4x if audiofile.duration(i) > 2]
shuffle(synthetic_wav_paths_foreign_4x)
shuffle(synthetic_wav_paths_foreign)
shuffle(synthetic_wav_paths)
shuffle(synthetic_wav_paths_4x)
print(len(synthetic_wav_paths_foreign_4x), len(synthetic_wav_paths_foreign),
len(synthetic_wav_paths), len(synthetic_wav_paths_4x)) # 134 204 134 204
for audio_prompt in ['english',
'english_4x',
'human',
'foreign',
'foreign_4x']: # each of these creates a separate pkl - so outer for
#
data = np.zeros((767, len(LABELS)*2 + 2)) # 720 x LABELS-prompt & LABELS-stts2 & cer-prompt & cer-stts2
#
OUT_FILE = f'{audio_prompt}_analytic.pkl'
if not os.path.isfile(OUT_FILE):
ix = 0
for list_of_10 in harvard_individual_sentences[:10004]:
# long_sentence = ' '.join(list_of_10['sentences'])
# harvard.append(long_sentence.replace('.', ' '))
for text in list_of_10['sentences']:
if audio_prompt == 'english':
_p = synthetic_wav_paths[ix % len(synthetic_wav_paths)]
# 134
style_vec = msinference.compute_style(_p)
elif audio_prompt == 'english_4x':
_p = synthetic_wav_paths_4x[ix % len(synthetic_wav_paths_4x)]
# 134]
style_vec = msinference.compute_style(_p)
elif audio_prompt == 'human':
_p = natural_wav_paths[ix % len(natural_wav_paths)]
# ?
style_vec = msinference.compute_style(_p)
elif audio_prompt == 'foreign':
_p = synthetic_wav_paths_foreign[ix % len(synthetic_wav_paths_foreign)]
# 204 some short styles are discarded ~ 1180
style_vec = msinference.compute_style(_p)
elif audio_prompt == 'foreign_4x':
_p = synthetic_wav_paths_foreign_4x[ix % len(synthetic_wav_paths_foreign_4x)]
# 174
style_vec = msinference.compute_style(_p)
else:
print('unknonw list of style vector')
x = msinference.inference(text,
style_vec,
alpha=0.3,
beta=0.7,
diffusion_steps=7,
embedding_scale=1)
x = audresample.resample(x, 24000, 16000)
_st, fsr = audiofile.read(_p)
_st = audresample.resample(_st, fsr, 16000)
print(_st.shape, x.shape)
emotion_of_prompt = process_function(_st, 16000, None)
emotion_of_out = process_function(x, 16000, None)
data[ix, :11] = emotion_of_prompt
data[ix, 11:22] = emotion_of_out
# 2 last columns is cer-prompt cer-styletts2
transcription_prompt = _pipe(_st[0])
transcription_styletts2 = _pipe(x[0]) # allow singleton for EMO process func
# print(len(emotion_of_prompt + emotion_of_out), ix, text)
print(transcription_prompt, transcription_styletts2)
data[ix, 22] = jiwer.cer('Sweet dreams are made of this. I travel the world and the seven seas.',
transcription_prompt['text'])
data[ix, 23] = jiwer.cer(text,
transcription_styletts2['text'])
print(data[ix, :])
ix += 1
df = pd.DataFrame(data, columns=['prompt-' + i for i in LABELS] + ['styletts2-' + i for i in LABELS] + ['cer-prompt', 'cer-styletts2'])
df.to_pickle(OUT_FILE)
else:
df = pd.read_pickle(OUT_FILE)
print('\nALREADY EXISTS\n{df}')
# From the pickle we should also run cer and whisper on every prompt
|