correct_figure.py

# 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((770, len(LABELS)*2 + 2))  # 768 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